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Davidson D001x - Medicinal Chemistry - The Molecular Basis of Drug Discovery Instructor - Erland Stevens - Davidson College Start Date - March 10, 2014 I. Learning Goals The overall goal of this course is to teach a student how to relate the chemical structure of a drug to its biological function. An outcome of this goal is that a student who completes this course will be able to attend lectures on drug discovery and reasonably understand the content of the lectures. II. Prerequisites Students should be able to be able to identify organic chemistry functional groups and read lineangle chemical structures. Students should also know the parts of a cell and be comfortable working with mathematical expressions containing exponents and logarithms. Students who lack the necessary organic chemistry experience may be able to supplement their knowledge through courses such of those offered by Khan Academy. III. Required Materials This course does not have a required textbook. All materials for the course will be provided through the edX platform. Students have access to a spreadsheet application. Examples include Microsoft Excel, Apache OpenOffice (a free, downloadable office suite), and Google Docs Spreadsheet (available free to anyone with a free Google account). The spreadsheet application will allow analysis of data that will be encountered throughout the course. IV. Course Schedule - course launch March 10, 2014 at 15:00 UTC

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Week 1 - release date March 10, 2014 at 15:00 UTC Chapter 1 – Pre-Regulatory Medicine Chapter 2 – Drug Discovery: From Concept to Marketplace All Week 1 graded content to be completed by March 24, 2014 at 15:00 UTC Week 2 - release date March 17, 2014 at 15:00 UTC Chapter 3 – Proteins Chapter 4 – Enzymes Chapter 5 – Receptors Examination 1 All Week 2 graded content, including Examination 1, to be completed by March 31, 2014 at 15:00 UTC Week 3 - release date March 24, 2014 at 15:00 UTC Chapter 6 – Blood and Drug Transport Chapter 7 – Pharmacokinetics All Week 3 graded content to be completed by April 7, 2014 at 15:00 UTC Week 4 - release date March 31, 2014 at 15:00 UTC Chapter 7 – Pharmacokinetics (con't) Chapter 8 – Metabolism Examination 2 All Week 4 graded content, including Examination 2, to be completed by April 14, 2014 at 15:00 UTC Week 5 - release date April 7, 2014 at 15:00 UTC Chapter 9 – Structure and Diversity Chapter 10 – Lead Discovery All Week 5 graded content to be completed by April 21, 2014 at 15:00 UTC


Week 6 - release date April 14, 2014 at 15:00 UTC Chapter 10 – Lead Discovery All Week 6 graded content to be completed by April 28, 2014 at 15:00 UTC Week 7 - release date April 21, 2014 at 15:00 UTC Chapter 11 – Lead Optimization Examination 3 All Week 7 graded content, including Examination 3, to be completed by May 5, 2014 at 15:00 UTC V. Grading The course grade is based on two types of assignments: in-chapter exercises (ICEs) and examinations. ICEs are found in course pages following almost each video. Some videos are followed by two pages with ICE questions. Each question is worth one point, and students can make two attempts at the correct answer. Examinations are found at the end of Weeks 2, 4, and 7. Each exam question is worth two points each, and only one attempt is allowed. Any student who scores 70% of the possible points in the course will have a passing grade. All graded content must be completed within two weeks of its release date within the course. VI. Honor Code In order to participate in this or any other edX course, a student must agree to abide by the edX Honor Code Pledge. Under the terms of the pledge, students may collaborate on the questions and exercises in each chapter. The examinations, however, must be completed independently by each student. VII. Discussion Board The course has a discussion board, or forum, for interaction between students, course teaching assistants, the instructor, and technical support staff. Students are encouraged to participate on the discussion board for assistance with course material and to converse with other students on course topics. Students are expected to follow the guidelines below, which are taken from the World Wide Web Consortium (W3C) web site.


Tone of messages must be maintained at the highest level of professionalism; flaming, sarcasm, or personal attacks will not be tolerated.

Don't attack a person. Disagree with an idea.

Respect the right of others to disagree.

Be polite and show respect. If you have nothing new, positive, informative or helpful to say, refrain from sharing it.

It's inappropriate to repeat the same argument over and over without adding new information.

Debate; Don't argue.

Listen; Don't shout.

Stay on topic.

Students who fail to adhere to these guidelines will experience one of more of the following consequences.

Private warning

Public warning

Temporary removal from the discussion board

Permanent removal from the discussion board

Students who demonstrate a consistent willingness and ability to help other students on the discussion board may be elevated to the status of Community Teaching Assistant and gain additional privileges on the discussion board. VIII. Hints and Tips

Print the materials for the course Each video clip has an available summary. Print and read the video summaries for additional insight into the course material.


Take notes on both the video and written content of the course Record your own notes on the printed materials so that you can make connections between the topics.

Use the discussion board There is no reason to be confused or frustrated. Reach out to your fellow students, the course teaching assistants, and instructor through the discussion board.

Protect your time When working on the course, minimize your distractions, and be prepared to focus on the material. The more time you put into the course, the more you will receive from it.


Welcome to Week 1 Starting week one video Please watch the online video (1 minutes 7 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 1 ‐ Pre‐Regulatory Medicine Introduction to Chapter 1 Chapter 1 contains three subsections.  

Natural Products 

Synthetic Drugs 

Need for Regulation 

At the conclusion of this chapter, you should have an appreciation for the types of drugs that have  been used throughout history and up to the late 1930s.  You should also understand the challenges  faced by drug regulatory agencies as they try to enforce the creation of a safe and effective drug  supply.  OPTIONAL‐Please participate in the online discussion forum. 

1.1 Natural Products Ephedrine video Please watch the online video (7 minutes, 6 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Pharmacophores in drugs Background: The phenethylamine compounds mentioned in the video are relatively small structures  with an easily identified pharmacophore.  Pharmacophores in drugs can be considerably more  complex. 


Instructions: Read the passage concerning compounds that contain the same pharmacophore  elements as morphine.  Use the information to answer the questions that follow the text.  Learning Goal: To examine a molecule's structure and determine whether a molecule contains a  specified pharmacophore.  Opiates, Opioids, and Their Pharmacophores  A notable natural product that can be traced to the early history of humankind is morphine  (1).  Morphine is found in opium, the residue that seeps from damaged poppy seed pods.  Morphine  is a complex alkaloid with very potent analgesic (pain relieving) properties.  Beyond offering pain  relief, morphine is also highly addictive. 

Morphine is not the only compound found in opium.  Opium contains approximately two dozen  other compounds including codeine (2).  Morphine and codeine are both known as opiates.  Opiates  are naturally occurring compounds that share the same activity as morphine. 

A number of compounds similar to morphine and codeine can be synthesized in a lab.  Some are  prepared by modifying morphine itself.  Examples of synthetic and semi‐synthetic morphine  analogues include heroin (3) and methadone (4).  These unnatural compounds with morphine‐like  activity are called opioids.  Methadone looks very little like morphine, but it is still considered an  opioid because of its biological activity. 

Almost all opiates and opioids share common structural features known as the morphine rule.  The  morphine rule stipulates the structural requirements for a compound to have morphine‐like  activity.  Specifically, the morphine requires a compound to have (1) a benzene ring (2) attached to a 


quaternary carbon connected by (3) a two‐carbon spacer to (4) a tertiary amine.  These structural  elements describe the pharmacophore of morphine. 

The pharmacophore cores of heroin and methadone are shown below traced in blue. 

Collectively the opiates and opioids play a key role in pain management.  Because of their addictive  properties, opiates and opioids are not suitable for long‐term use.  For certain situations, such as  post‐operative surgery pain, they are very effective and safe.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Regulation of herbal dietary supplements? Background: Many herbal medicines have been used for thousands of years and continue to be  used today.  The herbal medicine, or herbal supplement, industry is very loosely regulated in the  United States as well as most nations.  Instructions: Read the linked article below from BMC Medicine concerning the quality of herbal  supplements in the marketplace.  Use the information in the article to answer the questions that  follow.  Learning Goal:  understand purity issues within the herbal supplement industry.  A recent article in BMC Medicine reported that possibly a high percentage of herbal supplements  contain little or none of the supposed active plant material.  Please return to the online course and read the article to which the above paragraph refers.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


1.2 Synthetic Drugs Sulfa drugs video Please watch the online video (6 minutes, 23 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Early synthetic drugs Background: Around the middle to late 1800s, organic chemistry had advanced sufficiently to allow  the preparation of organic molecules as drugs.  Instructions: Read the passage of text below on three very early synthetic drugs.  Learning Goal: To gain exposure to the early important synthetic pharmaceuticals.  Below are three very early synthetic drugs.  

chloral hydrate  Chloral hydrate (1) is a solid formed by the reaction of trichloroacetaldehyde and water.  The  compound is a potent sedative.  Once its properties were discovered around 1870, chloral  hydrate was used widely in medicine.  Chloral hydrate was prone to abuse, and solutions of  chloral hydrate became called "knock‐out drops" or a "Mickey Finn".  The phrase "slip him a  Mickey" became synonymous with using chloral hydrate to incapacitate a person for ignoble  ends. 

aspirin Aspirin (2) was first prepared around 1900 by a scientist at Bayer.  It is an effective analgesic,  antipyretic (reduces fever), anti‐inflammatory, and anticoagulant (reduces blood clotting).  The  name Aspirin is still under trademark protection in some parts of the world.  In those  jurisdictions the generic form of the compound is referred to as acetylsalicylic acid, or ASA. 

phenobarbitol Phenobarbitol (3) is an anticonvulsant.  Phenobarbitol was discovered around 1900 during a 


flurry of early research in the area of barbiturates as anticonvulsants and  sedatives.  Phenobarbitol continues to be used widely today, especially in less developed  nations. 

OPTIONAL‐Please participate in the online discussion forum. 

Pharmacophore of sulfonamide antibiotics Background: Sulfanilamide is the parent molecule of the sulfa drug class.  The simple structure of  sulfanilamide very nearly describes the pharmacophore of the sulfa drugs.  Instructions: Read the text below and use the information to answer the questions on the  pharmacophore of sulfa drugs.  Learning Goal: To identify sulfonamide antibiotics based upon their structural features.  As an early class of drugs, sulfonamide antibiotics have been extensively explored.  Literally  thousands of different sulfonamides were prepared and tested in the 1930s and 1940s.  Through  these studies, the pattern of activity and pharmacophore for sulfonamide antibiotics became clear.  The pharmacophore for sulfonamides (1) consists of a 4‐aminosulfonamide core with tolerance for  aryl and acyl groups on the sulfonamide nitrogen.  Almost all sulfonamide antibiotics follow this  simple model. 

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

1.3 Need for Regulation Elixir Sulfanilamide tragedy video Please watch the online video (7 minutes, 33 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Continued issues with diethylene glycol Background: Since 1938 the US Food and Drug Administration has held increased powers of  oversight over the safety and effectiveness of pharmaceuticals in the United States.  The drug  regulatory agencies of other nations hold very similar roles and powers.  Instructions: Read the text below and the accompanying report from the World Health Organization  and answer the subsequent questions.  Learning Goal: To understand the complexities and risks that international trade creates for drug  regulatory agencies trying to maintain a safe drug supply.  Diethylene glycol played a deadly role in the Elixir Sulfanilamide tragedy.  Unfortunately diethylene  glycol continues to be found in medicines despite the efforts of drug regulatory agencies.  In 2006  many people, likely several hundred, died in Panama after taking cold medicine containing  diethylene glycol.  Read the linked bulletin from the World Health Organization and answer the questions below  Please return to the online course and read the bulletin to which the above paragraph refers.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Chapter 2 ‐ Drug Discovery: From Concept to Market Introduction to Chapter 2 Chapter 2 contains three subsections.  

Phenotype‐ vs. Target‐Based Drug Discovery 

Drug Development Outline 

Intellectual Property 

At the conclusion of this chapter, you should understand the different stages of development for a  drug.  You should also know the difference between generic and branded drugs as well as the role of  patents in drug discovery. 

2.1 Phenotype‐ and Target‐Based Drug Discovery Phenotype vs. target video Please watch the online video (6 minutes, 20 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Mixing target and phenotype Background: In their traditional forms, target‐based drug discovery tends to rely upon in vitro  testing for lead optimization, and phenotype‐based drug discovery leans upon in vivo testing.  Instructions: Read the passage below on how phenotype‐based drug discovery programs often  blend with target‐based techniques.  Learning Goal: To understand that different approaches to drug discovery can frequently blur  together.  A strength for phenotype‐based drug discovery is that the compounds of interest are known to be  active because their activity has already been observed in a living organism.  The reliance on in vivo  testing, however, is a significant hindrance for phenotype‐based drug discovery.  The in vivo tests  are more involved and longer than in vitro tests, and the lead optimization process is slower as a  result.  A more ideal situation would be to start with a compound with known in vivo activity and the ability  to optimize its potency with quick in vitro screens.  This ideal situation is a cross between 


phenotype‐ and target‐based drug discovery and most often begins with the phenotype model and  switches over to the target method.  The phenotype model begins with an observed effect in vivo.  The compound that causes the effect  is the lead molecule.  Instead of continuing the program with improving the lead with more in vivo  testing, the drug discovery group works to discover the protein to which the molecule binds in the  body.  In other words, the discovery group seeks out the target responsible for the biological  activity.  Once the target is known, a molecular biology group will attempt to develop a biochemical  binding assay to test the ability of a molecule to bind the target.  Newly prepared compounds are  then tested with the rapid in vitro binding assay.   In this blended model, the discovery group can be more confident that the final molecule will have  activity in animals.  Additionally, the improvement of the activity of the lead will be accelerated  because it is being accomplished with in vitro methods.  When performed properly (not easy!), the  blended approach can combine the best of both drug development methods.  Drug programs that  discover their leads through phenotype‐based observations, regardless of how they later optimize  the lead, are typically categorized as phenotype‐based discovery programs.  OPTIONAL‐Please participate in the online discussion forum. 

Support for phenotype‐based drug discovery Background: The topics of phenotype‐ and target‐based drug discovery can be very divisive.  Both  methods of drug development have very vocal supporters and detractors.  There has recently been  a push for a renewed emphasis on phenotype‐based methods.  Instructions: Read linked article from Science Business eXchange and answer the subsequent  questions.  Learning Goal: To gain an appreciation and understanding for phenotype‐based drug discovery in a  medicinal chemistry course that will emphasize target‐based drug discovery.  A recent article in Science Business eXchange discusses developments in phenotype‐based drug  discovery.  Please return to the online course and read the article to which the above paragraph refers.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


2.2 Drug Discovery Outline Drug discovery outline video Please watch the online video (8 minutes 22 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

The cost of doing business Background: The total cost of bringing a drug to market is highly debated, but the most reliable  estimates seem to place the figure at around US$1 billion or higher.  Instructions: Read the text below and use the information to answer questions in a subsequent unit.  Learning Goal: To learn about the cost of the different stages of drug development.  According to DiMasi et al.,1 the costs of a drug can be broken down as shown below. 

Note that the pre‐clinical stages of drug discovery, which can take years, only account for  approximately one quarter of the total costs. The expenses begin to accrue quickly with the clinical  trials. Phase III typically costs the most because of the very large number of patients involved. In  Phase I and Phase II, volunteers and patients are intensely monitored. The costs of Phase I and  Phase II might be lower than Phase III, but the per patient costs are higher in the early phases.  1. DiMasi, J. A.; Hansen, R. W.; Grabowski, H. G. The Price of Innovation: New Estimates of Drug  Development Costs. J. Health Econ. 2003, 22, 151‐185.  OPTIONAL‐Please participate in the online discussion forum. 


The risk of failure Background: Most attempts to develop a drug end in failure.  Since the costs of bringing a drug to  market are high, even failures in the early stages of a drug program add significant costs to a drug  company.  Instructions: Read the text below and use the information to answer questions in a subsequent unit.  Learning Goal: To learn about the different reasons for failures in drug development.  A widely mentioned statistic is that, for every 10,000 compounds that are analyzed in a drug  discovery program, only 5 will be tested in humans as clinical candidates, and only 1 will be  approved as a drug. The origin of these figures is hard to trace, but we can make an attempt. In a  target‐based drug discovery program, it would not be uncommon to screen a million or more  molecules in a library. Perhaps the top 1% in terms of binding to the target might be selected as hits  in the screen. That cut‐off would give 10,000 hits. Of these 10,000 compounds, 5 (following  optimization) might be advanced to the clinic with just 1 becoming a drug.  According to DiMasi et al.,1 the primary reasons for failure of a drug discovery effort in the United  States from 1981 to 1992 are listed below. These figures would be for leads that make it into animal  testing. 


DiMasi further gives the failure rates for compounds that make it into the different clinical trials.  Note that the failure rates are cumulative. Therefore, a clinical candidate has a 29% of failure in  Phase I. If it clears Phase I, it then has a 57% of failure in Phase II. 

One way to examine this bar graph is to consider the probably for success of a drug.  If a clinical  candidate has a 29% chance of failing in Phase I, then it has a 71% chance of success in Phase I.  Similarly, it will have a 43% and 76% chance of success in Phase II and Phase III, respectively.  With  this interpretation, a drug will have a 23% (0.71 × 0.43 × 0.76) chance of completing all the trials.  A  23% chance corresponds to somewhere between a 1‐in‐4 or 1‐in‐5 chance of success.  1. DiMasi, J. A. Risks in New Drug Development: Approval Success Rates for Investigational New  Drugs.Clin. Pharmacol. Ther. 2001, 69, 297‐307.  OPTIONAL‐Please participate in the online discussion forum. 

Questions on cost and failure Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

2.3 Intellectual Property Patents and branding video Please watch the online video (7 minutes 17 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Composition of matter in action Background: Patents are a type of intellectual property.  While they are invaluable to the drug  industry for the exclusive rights they afford, patents are legally very complex.  Instructions: Read the passage below for one example of how the composition of matter patent is  used in the drug industry.  Learning Goal: To learn about composition of matter patents and crystal polymorphs.  Crystalline Polymorphs of Ranitidine1  Drugs are normally protected with a composition of matter patent.  Surprisingly, some drugs require  multiple different composition of matter patents.  Multiple patents are needed when a drug exists in  multiple, different crystalline forms.  These different forms are called polymorphs.  In polymorphs the same molecule can pack together in different orientations and patterns.  Each  pattern is a different polymorph. A simple example of polymorphs can be found with bricks, which  can be stacked in various patterns as pavers.  A few patterns are shown below.  Molecules can stack  in different patterns in much the same fashion. 

Because polymorphs can have different physical properties (e.g., solubility and melting point), a  drug company must be able to control which polymorph of a drug is being synthesized.  Ranitidine (1) is a drug that treats acid reflux.  In 1978 Glaxo, the discoverer of ranitidine, obtained a  composition of matter patent on the compound.  At the time, the term of patents was 17 years from  the date of issue.  Therefore, Glaxo's patent on ranitidine was set to expire in 1995, or 17 years after  1978. 


As Glaxo continued to research ranitidine, a second polymorph was discovered.  Glaxo obtained a  composition of matter patent on the second polymorph, called Form 2, in 1985.  The Form 2 patent  would expire in 2002.  Glaxo ultimately marketed ranitidine as Form 2 under the name of Zantac.  During the 1980s Zantac  was a blockbuster drug, and its success continued into the 1990s.  Around 1990 a company called Novopharm, a generic drug manufacturer, started to develop a  generic form of ranitidine.  Novopharm hoped to capitalize on Form 1 of ranitidine because the  patent on Form 1 would expire in 1995.  During their research, chemists at Novopharm tried to  make the original, Form 1 polymorph of ranitidine based on the Glaxo procedures.  To their surprise,  the Novopharm chemists could only prepare Form 2.  Novopharm reasoned that if Form 2 was  known all the way back in 1978 in the first work on ranitidine, then the 1985 Form 2 patent was not  valid.  Novopharm pushed ahead and applied to the FDA to market generic ranitidine in 1995,  corresponding to the expiration of the Form 1 (perhaps Form 2) patent.  Glaxo then sued Novopharm for planning to market ranitidine's Form 2, on which Glaxo held a  patent until 2002.  A third‐party lab was called in to prepare Form 1 of ranitidine from Glaxo's 1978  Form 1 patent.  The lab successfully reproduced the procedure to make Form 1, and Glaxo won the  case.  Novopharm went back to work and managed to reproduce the Form 1 procedure.  Novopharm then  filed paperwork with the FDA to market generic ranitidine (Form 1) starting in 1995, the year of  expiration of Glaxo's Form 1 patent.  Glaxo again sued Novopharm.  This time, Glaxo claimed that Novopharm's Form 1 of ranitidine likely  contained impurities of Form 2.  If so, then marketing this mixture would violate Glaxo's exclusive  rights to Form 2.  Novopharm provided evidence that their ranitidine was free of Form 2, and  Novopharm won the case.  1. Bernstein, J. Polymorphism and Patents from a Chemist's Point of View. In Hilfiker, R.  (Ed.)Polymorphism: In the Pharmaceutical Industry. Weinheim, Germany: Wiley‐VCH, 2006,  Chapter 14.  OPTIONAL‐Please participate in the online discussion forum. 

Global complications with patents Background: The interpretation and implementation of patent law varies from one country to  another.  Instructions: Read the linked article from The Economist and answer the subsequent questions  related to patents. 


Learning Goals: To understand how patents affect drug profitability around the world.  A recent posting on the website of The Economist covers a key patent ruling in India.  Please return to the online course and read the posting to which the above paragraph refers.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Welcome to Week 2 Starting week two video Please watch the online video (1 minutes 24 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 3 – Protein Structure Introduction to Chapter 3 Chapter 3 contains two subsections.  

Intro to Structure Part 1  Amino Acids, Primary Structure, and Secondary Structure 

Intro to Structure Part 2  Tertiary Structure, Quaternary Structure, and X‐Ray Crystallography 

At the conclusion of this chapter, you should understand how the ordering of individual amino acids  in a protein can affect the localized and global folding and function of the entire protein.  You should  further have some appreciation of how x‐ray crystallographic data is used to determine the  structure of proteins.  OPTIONAL‐Please participate in the online discussion forum. 

3.1 Intro to Structure Pt 1 Amino acids to secondary structure video Please watch the online video (8 minutes, 15 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Working with Protein Data Bank entries Background: The structural information freely available in the Protein Data Bank is immense.  It  allows anyone to be able to study protein structure and function.  Instructions: Use the tools in the PDB to examine the structures mentioned below and answer the  assessment questions. 


Learning Goals: To learn how to manipulate proteins and identify their structural elements with the  tools in the PDB.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Finding PDB entry codes Background: Searching a specific entry in the Protein Data Bank requires one to know the  corresponding PDB code.  Instructions: Read the passage below concerning how PDB codes can be determined.  Learning Goals: To learn how to find specific, useful information within the vast PDB database.  The Protein Data Bank is only useful if one can find information of interest. As we work through this  chapter, some students might wonder how to find other proteins. In the videos, I start with a PDB  code. What if you do not already have a code?  Searching the PDB requires one to have an idea of what is being sought. For the different examples  in this course, I knew that I wanted to find proteins that are rich in one type of secondary structure. I  performed an online search using terms like "proteins rich in alpha‐helices" and found a few names  of proteins that seemed to match what I wanted. I then searched on those particular proteins within  the PDB. Any single protein may have multiple entries in the PDB. I then looked through the  different entries until I found a specific PDB entry that demonstrated the properties I want to  highlight.  Some websites on the internet include PDB codes. One example is SCOP: Structural Classification of  Proteins, an extensive site that can be searched by a number of keywords that correspond to  common traits of proteins. Most proteins in SCOP are linked to a PDB entry.  OPTIONAL‐Please participate in the online discussion forum. 

3.2 Intro to Structure Pt 2 Tertiary structure to X‐ray crystallography video Please watch the online video (7 minutes, 41 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Validating protein structures Background: Most protein structures are determined based on x‐ray crystallographic data.  The  primary sequence of the protein is matched with the electron density map, and the individual amino  acids are placed within the structure as closely as possible.  After all the amino acids are positioned,  the quality of the assigned structure can be measured with several tools.  One of the most common  tools is the Ramachandran plot.  Instructions: Read the passage below about the use of Ramachandran plots to validate protein  structural assignments.  Learning Goal: To learn how Ramachandran plots graphically represents dihedral angles of  individual amino acids to predict the validity of a proposed protein structure and folding.  The Ramachandran plot is one of the primary methods for validating proposed protein structures  based on x‐ray crystallographic data.  The plot compares selected dihedral angles in each amino acid  found within the proposed protein.  The key dihedral angles for each amino acid are located along  the backbone of the protein and are labeled φ (phi), ψ (psi), and ω (omega). To repeat, each amino  acid residue contributes three rotatable bonds and three distinct dihedral angles to the backbone of  a peptide chain. 


In theory, all dihedral angles can range in value from −180° to +180°. In practice, within a protein,  the dihedral angles tend to fall in well‐defined ranges.  Because of interactions between the  nitrogen and carbonyl, ω is either 0 or 180°, typically 180°. φ has a value near ‐50° in an α‐helix and  ranges between ‐50 and ‐160° in a β‐sheet. ψ also has a value near ‐50° in an α‐helix but ranges  from +100 and +180° in a β‐sheet. When ψ is plotted against φ for each amino acid, most amino  acids fall within tightly defined regions bounded by the angle ranges above and another small area  for a specific subtype of α‐helix. Such a graph is called a Ramachandran plot, shown below. 

While keeping track of dihedral angles in a protein may seem complex, the Ramachandran plot  makes the process very simple. Since most amino acid residues correspond to points that fall within  predicted ranges, those amino acids can be ignored. The important ones are those that fall beyond  the anticipated regions. Indeed, if a protein has over 5% of its amino acids as outliers, then the  structure of that protein may be reasonably suspected as being improperly assigned. For this  reason, the Ramachandran plot is a simple, visual tool for quickly checking the validity of an assigned  protein structure.  OPTIONAL‐Please participate in the online discussion forum. 


Evaluating Ramachandran plots Background: Ramachandran plots are a simple, visual tool for validating proposed protein  structures.  A website that allows users to generate Ramachandran plots from many PDB entries is  the Ramachandran Server at Uppsala University in Sweden.  Instructions: Compare the Ramachandran plots below to answer the questions.  Learning Goals: To learn how to read and compare data from Ramachandran plots.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 4 – Enzymes Introduction to Chapter 4 Chapter 4 contains three subsections.  

Michaelis‐Menten Kinetics 

Enzyme Inhibition 

Measuring Inhibition 

At the conclusion of this chapter, you should understand how enzyme kinetics data are presented  graphically.  You should also understand how different inhibitors affect enzymes, and how the  inhibition is quantified. 

4.1 Michaelis‐Menten Kinetics Theory of action video Please watch the online video (7 minutes, 8 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Working with concentrations Background: Data in medicinal chemistry, including enzyme kinetics data, rely upon numbers with  various concentration units.  Being comfortable with interconverting different concentration units is  a basic skill for one to have. 


Instructions: Read the passage below and use the information to answer the subsequent  assessment questions.  Learning Goal: To become comfortable working with the different types of units commonly  encountered in medicinal chemistry.  The goal of a drug discovery program is generally to find a molecule that binds a target protein at  very low concentrations.  As has been mentioned before (in Chapter 2), the binding is normally  determined in a biochemical assay, often in the form of a dissociation equilibrium constant (KD).  For  a drug, the values for KD are very small, indicating the drug and target bind very tightly and do not  readily dissociate.  Ideal KD values are in the nanomolar (nM) range, but during development  observed KD values are much higher.  Hits in an early screen might have KD values in the micromolar  (µM) range.  The table below shows the concentrations regularly encountered in a drug discovery  program.  name 

description

unit

relation to molarity 

molar

moles / liter 

M

1

millimolar

millimoles / liter 

mM

10‐3

micromolar

micromoles / liter 

µM

10‐6

nanomolar

nanomoles / liter 

nM

10‐9

picomolar

picomoles / liter 

pM

10‐12

Beyond the reporting of binding data (pharmacodynamics), the units in the table above are also  found throughout pharmacokinetics, especially in reports of the concentration of drugs in blood.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Graphing enzyme kinetics data Background: Interpreting enzyme kinetics data requires one to be able to graph the information.   The previous unit contained a video which provided instructions on how to generate graphs from  enzyme kinetics data as a saturation plot (Michaelis‐Menten equation) or in linear form  (Lineweaver‐Burk equation). 


Instructions: Use the videos on graphing kinetics data in Google Docs, Apache OpenOffice, and  Microsoft Excel to help you answer the assessment question below.  Note that a sample calculation  is available in the next component.  If the question gives you trouble, consider peeking ahead to see  one way to approach this problem.  Learning Goals: To learn how to graph kinetics data and use the resulting plot to understand the  activity of an enzyme. 

Linest in Google Docs spreadsheet video Please watch the online video (3 minutes 43 seconds). 

Linest in OpenOffice spreadsheet video Please watch the online video (4 minutes 9 seconds). 

Linest in Microsoft Excel video Please watch the online video (3 minutes 45 seconds).  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Sample calculation ‐ Lineweaver‐Burk plot Background: Lineweaver‐Burk plots depict 1/V vs. 1/[S]. These are very useful for determining the  nature of enzyme‐substrate interactions. 

Instructions: Review the sample calculation demonstrating the use of V‐[S] data points to generate a  Lineweaver‐Burk plot.  Learning Goal: To learn how to use V‐[S] data points to determine Vmax and Km of an enzyme‐ substrate system.  Task: Determine Vmax and Km based on the provided data points.  V (mmol/min) 

[S] (mmol) 

1.0

20

2.8

60

5.5

210

8

480


Solution: Start by loading the V‐[S] data points into a spreadsheet (Apache Open Office shown).  One column  is for [S] and the other for V. 

For the Lineweaver‐Burk plot, we need the reciprocal of both V and [S]. 

We can use LINEST to perform the regression of the 1/V‐1/[S] data points.  The format in OpenOffice  is LINEST(data_y;data_x;linear_type;stats).  For data_y, highlight the cells for 1/V.  For data_x,  highlight the cells for [S].  For linear_type, enter a 1.  For stats, enter a 0.  Remember to finish the  function properly based on your spreadsheet application.  In OpenOffice, press ctrl‐shift‐enter to  execute the function. 


With execution of the function, two cells are filled.  The one on the left is the slope, and the one on  the right is the y‐intercept.  I manually added the slope and Y‐intercept labels for clarity. 

From the y‐intercept, Vmax can be directly determined.  The slope can then be used to determine Km. 

Vmax is 12.3 mmol/min, and Km is 224 mmol.  A graph can be more satisfying than the LINEST function.  The graph below shows the data in a  Lineweaver‐Burk format.  Right click on a data point, select a Format Trend Line..., select a Linear  regression and Show Equation, and the graph will display the best‐fit equation for the data  points.  The slope and y‐intercept should match the output of the LINEST function. 


4.2 Enzyme Inhibition Reversible inhibitors video Please watch the online video (7 minutes 12 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Distinguishing types of enzyme inhibitors Background: Inhibitors can be readily distinguished by visually inspecting plots of V vs. [S] at varying  inhibitor concentrations ([I]) and noting the effect of the inhibitor upon Km and Vmax.  In the previous  video clip, we observed how an inhibitor changes the shape of a traditional Michaelis‐Menten‐type  plot.  Instructions: Use the Lineweaver‐Burk plots below to classify the type of inhibitor present in each  system.  Learning Goal: To learn how to interpret effects of an inhibitor upon Lineweaver‐Burk plots.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

4.3 Measuring Inhibition IC50 and Ki video Please watch the online video (6 minutes 41 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Determining inhibition Background: Measuring the binding of an inhibitor to an enzyme is a fundamental part of  preliminary screening in a drug discovery program.  The enzyme inhibition of molecule is normally  quantified as either a Ki or IC50 value.  Both are related to the potency of an inhibitor.  A  smaller Ki or IC50 value indicates a stronger inhibitor.  Instructions: Use what you know concerning Ki and IC50 values as well as the Cheng‐Prussoff  equation to answer the questions below.  Learning Goal: To learn how to work through enzyme inhibition data.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Chapter 5 – Receptors Introduction to Chapter 5 Chapter 5 contains three subsections.  

Types of Receptors 

Ligands

Occupancy Theory 

At the conclusion of this chapter, you should know the different types of receptors and what  structural features distinguish one from another.  You should also know the types of receptor  ligands and be able to identify each based on a response vs. log [L] graph.  Finally, you should  understand the fundamental assumptions of occupancy theory and situations in which the  assumptions fail.  OPTIONAL‐Please participate in the online discussion forum. 

5.1 Types of Receptors Receptor superfamilies video Please watch the online video (6 minutes 26 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

PDB example of an ion channel Background: Ligand‐gated ion channels are membrane‐bound receptors.  All membrane‐bound  receptors are characterized by a large number of parallel α‐helices. The α‐helices allow the protein  to criss‐cross the cell membrane and anchor itself in place.  Instructions: In a separate window, bring up the Protein Data Bank website and look up entry 4HFH.  Learning Goals: To appreciate the overall structure of ligand‐gated ion channels and gain more  experience examining proteins in the Protein Data Bank.  Please access the online Protein Data Bank website.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


PDB example of a G‐protein‐coupled receptor Background: G‐Protein‐coupled receptors are membrane‐bound receptors.  All membrane‐bound  receptors are characterized by a large number of parallel α‐helices. The α‐helices allow the protein to  criss‐cross the cell membrane and anchor itself in place.  Instructions: In a separate window, bring up the Protein Data Bank website and look up entry 2RH1.  Learning Goals: To appreciate the overall structure of G‐protein‐coupled receptors and gain more  experience examining proteins in the Protein Data Bank.  Please access the online Protein Data Bank website.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

5.2 Ligands Ligand types video Please watch the online video (7 minutes 40 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Digging deeper into antagonists Background: Antagonists are ligands that block the action of an agonist without causing a response  from a receptor.  Different subtypes of antagonists are known to exist.  Instructions: Read the passage below on competitive and noncompetitive antagonists.  Learning Goals: To learn about the two types of antagonists and understand how they are  distinguished in response curves. 


The two types of antagonist, competitive and noncompetitive, are distinguished by how the  antagonist binds the receptor.  A competitive antagonist binds at the same site as an agonist.  A  competitive antagonist increases the EC50 of any agonist present without decreasing the maximum  response.  By increasing the EC50 of the agonist, the antagonist decreases the agonist's potency. 

A noncompetitive antagonist binds at an allosteric site.  Such binding does not affect the EC50 value  (potency) agonist, but it does diminish the response caused by the agonist. 

Depending on the needs of the discovery program, one type of antagonist might be more effective  than the other.  Competitive antagonists, however, tend to be more common.  If the drug discovery  group knows the structure of a receptor's endogenous ligand, then the team often designs  antagonists to resemble the natural ligand.  The designed antagonist will likely the bind the same  site as the endogenous ligand.  OPTIONAL‐Please participate in the online discussion forum. 

Interpreting dose‐response graphs Background: Dose‐response relationships are commonly encountered in drug development  programs. The characteristic sigmoidal plots convey a large amount of information concisely and can  be readily interpreted. 


Instructions: Look at the dose‐response plots below and interpret which scenario is most likely for  each.  Learning Goals: To gain experience analyzing dose‐response relationships and determining how  different types of ligands affect dose‐response graphs.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

5.3 Occupancy Theory The pros and cons of the Clark model video Please watch the online video (8 minutes 42 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Determining an EC50 value Background: The EC50 value for an agonist occurs at the inflection point of a sigmoidal curve.  Instructions: Read the passage below on the easiest method for determining an EC50 from a set of  experimental data.  Then, use the data points to estimate the EC50 value of the ligand.  Learning Goals: To understand how to manipulate ligand‐response data and gain quantitative  information on the ligand.  Data points fitting a sigmoidal relationship are much more difficult to manipulate than linear  data.  Unfortunately, software packages that best handle sigmoidal data are not freely available.  In  the absence of a commercial data processing software package, linearizing ligand‐response data is a  viable option.  Because Clark's occupancy theory can be modeled with what is essentially the Michaelis‐Menten  equation, the ligand‐response data for many receptors can be linearized with what amounts to the  Lineweaver‐Burk equation.  The linearized, double‐reciprocal version of Clark's equation is shown  below. 


With this equation, any set of response (E) vs. [L] data points can be converted to their inverse  forms as 1/E vs. 1/[L], plotted, and matched to a best‐fit line with a spreadsheet function like  LINEST.  The y‐intercept can be used to determine Emax and then KD can be derived from the slope of  the line.  Remember that KD is equal to EC50, at least for agonists and partial agonists that follow the  Clark model.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

EC50 and Kd revisited Background: The inflection point of a log [L]‐response plot corresponds to the log EC50 value of the  ligand.  At the EC50 value, half of the total receptor concentration is bound in the receptor‐ligand  complex (R‐L) and the other half remains as free receptor, R.  At EC50, the ligand concentration is  equivalent to KD.  Instructions: Read the passage below that establishes the relationship between EC50 and KD.  Learning Goal: To more fully understand the equivalence of EC50 and KD in Clark's Occupancy Theory.  The graph below is a typical response vs. log [L] curve for a full agonist.  The point of inflection of  this curve occurs at 50% maximum response.  The ligand concentration required to achieve 50%  of Emax is called EC50, or the concentration required to achieve and effect of 50%. 


The rest of the discussion assumes that [L] is equal to EC50.  If we are at EC50, then the system is also  at 50% of Emax. 

According to Clark's theory, the only way to achieve 50% of Emax is for half of the receptors to be  bound by the ligand (a full agonist) as the receptor‐ligand complex (R‐L).  If half of the receptors are  bound, then an equal number (half) of the receptors are not bound and exist in the form of free  receptor (R). 

Remember that the binding of a ligand to a receptor is a reversible process.  This equilibrium binding  is quantified by the dissociation equilibrium constant (KD).  The equation for KD is shown below. 

As has been stated, at 50% Emax, [R‐L] = [R].  In the equation for the equilibrium dissociation  constant, two terms cancel and leave the relationship KD = [L]. 

At 50% E50, [L] also equals EC50.  Through a simple substitution, EC50 = KD. 

This relationship is important.  Experimentally, KD is not easy to measure directly, but through a log  [L]‐response plot, EC50 is easy fairly easy to determine.  Because EC50 and KD are equal, a log [L]‐ response plot is a relatively simple method for indirectly determining the equilibrium dissociation  constant (KD) of a receptor and its agonist or partial agonist.  OPTIONAL‐Please participate in the online discussion forum. 


Upregulation and downregulation Background: Clark's occupancy theory, while useful for modeling the behavior of many receptors,  fails to accommodate many observed qualities of ligand‐receptor interactions.  Examples already  covered include spare receptors and constituently active receptors.  Instructions: Read the passage below concerning the concepts of upregulation and downregulation,  two ideas that Clark's occupancy theory in its simple form cannot explain.  Learning Goals: To learn about the ideas of upregulation and downregulation, which are frequently  encountered in drug discovery and clinical medicine.  While Clark's occupancy has a very appealing (and useful) simplicity, the fact is that receptors are  not simple.  The different receptor superfamilies have completely different structures, and each  superfamily has its own unique behaviors and traits.  These behaviors make the task of developing a  single, unified ligand‐receptor theory very challenging.  One trait of some receptors is downregulation.  For some types of cells, if a receptor is continuously  stimulated to a high level by a ligand, the cell responds by decreasing its population of that  particular receptor.  In other words, the concentration of that receptor is decreased.  Because the  receptor concentration is lower, fewer receptors are available for stimulation and generation of a  response.  If the ligand concentration is left unchanged, that same ligand will affect a smaller  response because fewer receptors are available.  If the ligand is a drug, then the patient will receive  a smaller therapeutic effect from the same drug dosage.  The patient is said to be desensitized to  the drug.  Desensitization can often be observed in instances of downregulation.  In order to  experience the same therapeutic effect from a drug that has caused desensitization, a patient must  increase his dosing levels.  If a desensitized patient quits taking a medication, then it is generally true that the cells will slowly  return to their original receptor concentration in a process called upregulation.  Once the cells are  restored to their original state and original levels of receptor concentration, the patient will once  again be sensitized to the drug.  If the patient begins taking the medication again, he must return to  the original dosing levels.  If the patient takes elevated levels of medication, an overdose may be a  risk.  Desensitization is very common in patients who use opiates to manage pain.  Advanced patients  become desensitized and require a higher dose to achieve their original level of pain relief.  OPTIONAL‐Please participate in the online discussion forum. 


Cheng‐Prussoff for receptors Background: The Cheng‐Prussoff equation was introduced in Chapter 4 as a convenient method for  interconverting Ki and IC50 values of enzymes. 

Instructions: Read the passage below on the use of the Cheng‐Prussoff equation in the area of  receptors.  Learning Goals: To appreciate the generality of the Cheng‐Prussoff equation and more fully  understand the different variables used to describe the activity of antagonists.  Most drugs that bind to a receptor are antagonists, generally competitive antagonists.  The  competitive antagonist prevents the activation of the receptor by its endogenous ligand by binding  the receptor in the same position as the endogenous ligand.  A competitive antagonist is therefore  much like the competitive inhibitors that we discussed in Chapter 4.  Both the antagonist and  inhibitor prevent the action of a protein by blocking the protein's primary binding pocket.  The activity of competitive inhibitors can be quantified in two different ways: Ki, which is the  dissociation equilibrium constant of the enzyme‐inhibitor complex, or IC50, which is the  concentration of the inhibitor required to reduce the rate of an enzyme‐catalyzed reaction by  50%.  Ki is a constant and an inherent property of the enzyme‐inhibitor complex.  IC50, however,  varies depending on the concentration of the substrate used in the assay of the inhibitor's  activity.  If the substrate concentration in the assay is known along with the Km of the enzyme and  substrate, then the Cheng‐Prussoff equation can be used to interconvert Ki and IC50. 

Likewise, a competitive antagonist can be quantified in two different ways: Ki, which is the  dissociation equilibrium constant of the receptor‐antagonist complex, or IC50, which is the  concentration of the antagonist required to reduce the response caused by the action of an agonist  on a receptor by 50%.  Ki is a constant and an inherent property of the receptor‐antagonist  complex.  IC50 varies depending on the concentration of the agonist used in the assay of the  antagonist's activity.  Not surprisingly, the Cheng‐Prussoff can also be used to interconvert these  two values. 


When applied to receptors, the Cheng‐Prussoff equation requires the concentration of ligand, [L],  used in the assay.  Also required is the KD of the receptor‐ligand complex.  Specifically, that is the KD  of the receptor‐ligand complex without any antagonist present.  The receptor version of the Cheng‐ Prussoff equation is shown below. 

As with enzyme assays, studies of antagonists often report IC50 values for the antagonist.  IC50 values  generated by different research groups cannot be directly compared unless the agonist  concentration in the various assays are identical.  The Cheng‐Prussoff equation allows conversion  ofIC50 values to the directly comparable Ki values.  OPTIONAL‐Please participate in the online discussion forum. 

Examination 1 First Examination The exam is open book and open note.  All questions may be attempted only once, so be certain of  your answer before submitting it.  There are ten questions.  Each is its own component within the  Examination 1 subsection.  Remember that you are bound by the honor code.  No postings to the forum concerning the exam  are allowed.  Furthermore, you must work on the examination independently. 

Problems Please complete the online problems in Examination 1. 


Welcome to Week 3 Starting week three video Please watch the online video (58 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 6 ‐ Blood and Drug Transport Introduction to Chapter 6 Chapter 6 contains two subsections.  

Blood

ADME

At the conclusion of this chapter, you should understand the composition of blood and the role it  plays in transporting a drug to and from its site of action.  You should also be able to give the  meaning of the acronym ADME and understand the significance of each of the four letters.  OPTIONAL‐Please participate in the online discussion forum. 

6.1 Blood What is blood? video Please watch the online video (6 minutes, 4 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Fluids in the body Background: Blood is the medium by which drugs are normally distributed throughout the body.  A  70‐kg (154‐lbs) human has an approximate blood volume of 5 L (1.4 US gal).  Of those 5 L of whole  blood, approximately 46% consists of different cells.  The remaining 54% is the fluid portion, or  plasma, with a volume of 2.7 L.  Plasma contains water, electrolytes, small signal molecules, and  some proteins.  Serum is the fluid left behind after whole blood clots.  Serum is very similar to  plasma except serum is missing the clotting proteins (fibrinogens). 


Instructions: Read the passage below concerning the fluids within the body.  Learning Goals: To understand the fluids of the body into which a drug can be transported.  Drug concentrations are measured in plasma and reported as Cp.  Drugs, however, certainly travel in  other fluids in the body.  Below are descriptions of the most relevant fluids for drugs in a 70‐kg  patient.  whole blood ‐ 5 L  As has been mentioned, a 70‐kg human has 5 L of blood.  That works out to 0.071 L/kg, which is a  figure that is widely used to determine the blood volume of a specific patient.  Drugs do not  necessarily enter all parts of whole blood.  Drugs will freely move throughout the non‐cellular part  of blood, but they may or may not enter the red blood cells.  To test the concentration of a drug in  whole blood, the cells in a whole blood sample must be lysed (broken open), and then the resulting  solution may be analyzed for the drug.  Breaking open the cells releases drug that has entered the  cell as well as any drug that was bound within the cell membranes themselves.  Whole blood  concentrations are normally reported as Cb or Cwb.  plasma ‐ 2.7 L  Plasma is the fluid fraction of blood.  Plasma, because of its uniformity, is the medium of choice for  monitoring drug concentrations in the body.  Plasma concentration is reported as Cp.  interstitial fluid ‐ 10 L  Interstitial fluid is the liquid that sits between the cells of the body.  This fluid contains the nutrients  and waste of the cells.  Drugs reach the interstitial fluid from the capillaries.  The walls of the  capillaries have pores that allow passage of liquids and anything smaller than the pores.  Everything  in blood can pass except proteins and cells.  All orally‐delivered drugs are small enough to slip into  the interstitial fluid and reach the cells in the body.  intracellular fluid ‐ 25 L  The total water found within the cells of the body is 25 L.  Note that this volume includes the cellular  volume of whole blood.  Only drugs that can cross a cell membrane will have access to the  intracellular fluid.  total body water ‐ 38 L  Total body water includes plasma, interstitial fluid, and intracellular fluid. 


body volume ‐ 70 L  The volume of the body of a 70‐kg human is approximately 70 L.  This volume is not all water, but it  provides a comparison for the total body water value of 38 L.  OPTIONAL‐Please participate in the online discussion forum. 

Serum binding Background: Approximately 8 wt% of whole blood is protein dissolved in the fluid fraction.  Instructions: Read the passage below concerning the influence of blood proteins upon drugs.  Learning Goals: To understand the complicating factors of proteins and how they can affect the  behavior of drugs in the body.  Drugs are designed to target a specific protein in the body.  By binding that particular target, the  drug gives rise to a biological effect.  Just because a drug has a high affinity for one target does not  mean that it cannot bind to other proteins.  Proteins in the blood have a large effect on the drugs that bind them.  Because drugs are  transported by the blood, the blood proteins cannot be avoided by drugs.  Some of the blood  proteins have a very high concentration. Even if a drug has only a low affinity for a protein, if the  protein is present in a high enough concentration, the protein will bind a large fraction of the  drug.  The equilibrium below demonstrates this idea.  By Le Chatlier's principle, a higher  concentration of protein forces the reaction to shift to the left and form more protein‐drug complex  in order to reach equilibrium. 

The equilibrium expression below reinforces the concept.  A high protein concentration increases  the size of the numerator.  The value of KD is restored by shifting the equilibrium to the left.  This  shift raises the concentration of the protein‐drug complex (the denominator) while simultaneously  decreasing both the unbound protein and drug concentrations (the numerator). 

The interaction of a drug with proteins in the blood affects both how a drug is cleared from the  bloodstream as well as a drug's distribution.  A drug's clearance and distribution together determine  the half‐life of a drug.  Both concepts will be formally introduced in Chapter 7 ‐ Pharmacokinetics. 


The two blood proteins most relevant to drug action are discussed below.  serum albumin  Blood is up to 5 wt% serum albumin.  This is a remarkably high concentration of a single  protein.  Serum albumin has multiple potential binding sites.  Although serum albumin can affect  almost any drug, serum albumin tends to bind acid drugs most strongly.  Two acidic drugs that are  extensively bound by serum albumin are warfarin and ibuprofen, both shown below. 

globulins  Blood contains several different globulin proteins ‐ the α‐, β‐, and γ‐globulins.  These proteins can  comprise up to 2.5 wt% of blood.  One specific protein, α1‐acid glycoprotein, tends to bind basic  drugs, including disopyramide and lidocaine, which are shown below.  Since the concentration of α1‐ acid glycoprotein is lower than serum albumin, the degree of drug binding tends to be somewhat  lower. 

OPTIONAL‐Please participate in the online discussion forum. 

6.2 ADME ADME video Please watch the online video (7 minutes, 39 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Cell membranes Background: A high percentage of drugs are intended for oral delivery.  In order to be absorbed, a  drug must be able to cross a membrane to move from the intestinal tract to the bloodstream.  Instructions: Read the passage on the structure of biological membranes.  Learning Goal: To better understand why crossing membranes can be a challenge for a drug.  Membranes are a lipid bilayer that separate one volume from another.  A lipid bilayer consists of  phospholipids.  Phospholipids are a phosphorylated diglyceride.  The diglyceride, with its two fatty  acid chains, forms a non‐polar double tail.  The phosphate forms a charged group at the head of the  molecule. 

Phospholipids can stack together as sheets with their polar ends together on one side and the non‐ polar tails together on the other.  In an aqueous environment, two sheets can stack together with  their non‐polar ends face‐to‐face.  This stacking forms a bilayer that separates the aqueous layer on  one side of the bilayer from the other side. 

In order to enter the bloodstream from the digestive tract, a drug must cross at least two  bilayers.  This poses a challenge to a drug discovery group.  The drug must be soluble in the polar,  aqueous medium of the digestive system.  If the drug cannot mix with the digestive juices, then it  will never be absorbed.  A drug therefore must be somewhat polar.  If the drug is very polar,  however, it will likely never be able to cross the non‐polar bilayer.  A drug therefore must also be  somewhat non‐polar.  Designing a drug that falls within this Goldilocks zone of being a little polar and a little non‐polar is a  challenge.  Remember that drugs are typically organic molecules that are designed to bind  hydrophobic pockets on a protein target.  In general, as a lead is optimized, the molecule's 


lipophilicity tends to increase as its activity (target binding) increases.  The discovery team must  continually push against this tendency in order to preserve reasonable water solubility in the  lead.  This process requires a very delicate balance.  OPTIONAL‐Please participate in the online discussion forum. 

Lipinski's rules Background: Most major drugs are delivered orally.  Establishing how well a drug can be absorbed  from a pill form requires considerable experimentation.  Fortunately, simple methods have been  developed to visually inspect a molecule and make a crude prediction of a compound's potential to  be used as an oral drug.  Instructions: Read the passage below concerning the structural features of a molecule that help it to  be absorbed to the bloodstream from the digestive tract.  Use the information in the passage and  the DrugBank to answer the assessment questions that follow.  Learning Goals: To be introduced to molecular indices, which are often used as predictive tools in  drug discovery, and to be able to search for this information online.  In 1997 Chris Lipinski of Pfizer published a set of simple rules for predicting whether a molecule is  likely able to diffuse across membranes and therefore be absorbed from the digestive tract and  enter the bloodstream.1 The rules became known as the Rule of Five because each "rule" involves a  multiple of 5.  The rules are also commonly referred to as Lipinski's rules.  The rules are shown  below.  molecular property 

maximum value 

molecular weight (MW) 

500

lipophilicity (log P) 

5

hydrogen bond acceptors (HBA) 

10

hydrogen bond donors (HBD) 

5

Lipinski's rules involve four different qualities of a molecule.  The first is molecular  weight.  Molecules with a molecular weight of over 500 have more difficulty crossing a membrane.  Lipophilicity is a polarity measure for a molecule.  Less polar molecules are more lipophilic.  The use  of lipophilicity in Lipinski's rules emphasizes the fact that drugs must have reasonable solubility in  the aqueous, polar environment of the digestive juices.  If a drug cannot dissolve in this medium,  then it will likely pass straight through the patient and not be absorbed.  Although drugs are organic  molecules and typically interact with non‐polar binding sites on proteins, drugs must be somewhat  water soluble. 


2

Formalized by the late Corwin Hansch,  lipophilicity is measured as the logarithm of an equilibrium  constant (P) for the partitioning of a drug in a biphasic system of 1‐octanol (non‐polar) and water  (polar).  A molecule that too strongly favors the octanol layer (log P > 5) is likely too non‐polar to  adequately dissolve in digestive fluids.  Programs have been developed to predict  log P values.  These numbers are denoted as calculated log P or clog P (pronounced see‐log). 

The number of hydrogen bond acceptors (HBAs) is, at most, equal to the number of oxygen and  nitrogen atoms in the structure.  The lone pairs on nitrogen and oxygen atoms are normally able to  accept a hydrogen bond.  Exceptions occur if the lone pair is involved extensively in  resonance.  Examples include amide nitrogens and nitrogens in aromatic rings that require the lone  pair for aromaticity of the ring. 

The number of hydrogen bond donors (HBDs) is determined by the number of O‐H and N‐H bonds in  the structure. Keep in mind that O‐H and N‐H groups can be deprotonated depending on the pH of  the surrounding environment.  For example, a carboxylic acid contains an O‐H bond, but in most  parts of the body, the acid is deprotonated to a carboxylate.  Therefore, counting O‐H and N‐H  bonds in a structure may be somewhat inaccurate method for determining the exact number of  HBDs.  Through both HBAs and HBDs a molecule can very strongly interact with water.  While strong  interactions with water are good for water solubility, they are not good for diffusing across  membranes.  A drug must shed its shell of water molecules as it crosses from an aqueous medium to  the non‐polar lipids of a cell membrane.  The more strongly the water molecules interact with a  drug, the harder (less energetically favorable) shedding water molecules is. 


Collectively, Lipinski's rules attempt the balance a drug's need to interact both with an aqueous  environment as well as cell membranes.  Lipinski's rules are frequently adjusted and  criticized.  Lipinski's rules have regardless become a fixture in drug discovery discussions.  Indeed,  most online databases on drugs include values for the various Lipinski rules.  The DrugBank is an  example.  If one searches for a specific drug in DrugBank, the Information section (top of the page)  lists the Weight (molecular weight) of the drug.  The other Lipinski terms are shown further down  the page in the Properties section.  1. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and Computational  Approaches to Estimate Solubility and Permeability in Drug Discovery and Development  Settings. Adv. Drug Dev. Rev. 1997, 23, 3‐25.  2. Fujita, T.; Iwasa, J.; Hansch, C. A. A New Substituent Constant, π, Derived from Partition  Coefficients. J. Am. Chem. Soc. 1964, 86, 5175‐5180.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 7 ‐ Pharmacokinetics Introduction to Chapter 7 Chapter 7 contains eight subsections.  

IV Bolus ‐ Cp vs. time (begin during Week 3) 

Clearance I 

Clearance II 

Volume of Distribution I 

Volume of Distribution II 

Oral Delivery I 

Oral Delivery II (start of Week 4) 

Clearance and Volume of Distribution Revisited 

Upon completing this chapter, you should understand the mathematical relationships between drug  concentration in the plasma and time for both IV and oral drugs.  You will also understand the  concepts of clearance and volume of distribution, which together determine the half‐life of a 


drug.  With knowledge of clearance and distribution, you should be able to propose changes to a  drug's structure to shorten or lengthen the half‐life as desired. 

7.1 IV Bolus Cp vs. time video Please watch the online video (7 minutes, 52 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Sample calculation ‐ IV bolus Cp vs. time Background: Cp vs. time relationships for most drugs follow a predictable, first‐order relationship  and show a linear relationship of ln Cp vs. time.  Instructions: Review the sample calculation demonstrating the use of Cp vs. time data points to  determine the half‐life of a drug.  Learning Goal: To learn how to use concentration‐time data to determine a drug's properties.  Task: Determine the half‐life of the drug based on the provided data.  Cp (ng/L) 

time (h) 

90

2

82

4

67

8

55

12

45

16

30

24


Solution: Start by loading the Cp‐time data points into a spreadsheet (Apache Open Office shown).  One  column is for time and the other for Cp. 

The question explicitly asks for the half‐life, so we need a linear form of the data.  That means  making an ln Cp‐time plot.  The slope will give us kel.  From there we can determine half‐life. 

The next step is therefore to create and fill a column for ln Cp. 


It is now linear regression time.  LINEST is an easy option.  The format in OpenOffice is  LINEST(data_y;data_x;linear_type;stats).  For data_y, highlight the cells for ln Cp.  For data_x,  highlight the cells for time.  For linear_type, enter a 1.  For stats, enter a 0.  Remember to finish the  function by pressing ctrl‐shift‐enter to execute the function.  Just pressing enter is not sufficient. 

Upon pressing ctrl‐shift‐enter, two cells are filled.  The one on the left is the slope, and the one on  the right is the y‐intercept.  Formatting the cells to show more decimal places might be necessary to  see the number of significant figures that you want. 

At this point, just run the values through the equations. 

So, the half‐life of the drug is 13.9 h. 


This satisfies the question, but I prefer to see a graph and trend line to determine the slope and y‐ intercept rather than use LINEST.  Below is the graph.  Right click on a data point, select a Format  Trend Line..., select a Linear regression and Show Equation, and the graph will display the best‐fit  equation for the data points.  The slope and y‐intercept should match the output of the LINEST  function. 

OPTIONAL‐Please participate in the online discussion forum. 


Graphing practice Background: Most Cp vs. time relationships for drugs follow a predictable, first‐order relationship  and show a linear relationship of ln Cp vs. time.  Instructions: Answer the assessment questions below by graphing ln Cp data points.  Learning Goal: To gain practice graphing concentration‐time data points to determine drug  properties.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

7.2 Clearance I Clearance demo video Please watch the online video (7 minutes 50 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Blood flow and extraction Background: Clearance is the process of removing drug from the bloodstream.  As blood circulates  through various tissues and organs, drug is removed.  Instructions: Read the passage below concerning how total clearance is the cumulative effect of  clearance by the liver and kidneys.  Learning Goal: To understand better how drugs are cleared from the body, especially with regard to  the role of the liver and kidneys, and how the contributions from each can be determined.  In an ln Cp vs. time plot, the slope of line is equal to −kel, the elimination rate constant.  Back in  Chapter 6.2, we mentioned elimination within the larger picture of ADME.  Elimination was linked to  metabolism and excretion, which are dominated by the liver and kidneys, respectively.  By breaking  down drugs and filtering drugs from the blood, the liver and kidneys play a key role in clearance.  The elimination rate constant is directly related to the overall process of clearance, called total  clearance (CLT), in the body.  Total clearance, which has units of volume over time, describes the  volume of blood that is scrubbed of drug per unit time.  Most drugs are cleared almost exclusively 


by the liver and kidneys.  Therefore, total clearance is the sum of the clearance caused by the liver  (hepatic clearance, CLH) and the kidneys (renal clearance, CLR). 

Clearance at an individual organ is a function of two factors, blood flow (Q) and extraction  ratio (E).  Blood flow to an organ is simply the volume of blood that passes through an organ per  unit time.  Extraction ratio is a bit more complicated.  If an organ clears a drug, the plasma  concentration of a drug that enters the organ (Cpin) is higher than the plasma concentration of the  drug that leaves the organ (Cpout).  The difference between these two concentrations divided  by Cpin is the extraction ratio.  Extraction ratio is a dimensionless number that falls within the range  of 0 to 1.   

With these ideas in mind, one can re‐express CLT as a combination of the blood flow and extraction  ratio of both the liver and kidneys. 

Values for Q are easy to handle.  For a 70‐kg human, blood flow to the liver is approximately 1,500  mL/min (QH = 1,500 mL/min).  Blood flow to the kidneys is around 1,100 mL/min, but the kidneys are  only able to filter approximately 220 mL/min (QR = 220 mL/min).   The extraction ratios for the kidneys and liver are more challenging.  Assuming we can determine CLT  (to be covered in the next chapter section), we can estimate EH and ER.  To approach these two  variables, we need to put a drug into one of three categories: cleared by kidneys only, cleared by  liver only, or cleared by both the liver and kidneys. 


Category #1 ‐ cleared by liver only  If a drug is not cleared by the kidneys (i.e., no drug is found in the urine because ER = 0), then the  contribution of renal clearance to CLT can be ignored.  CLT reduces to just hepatic clearance, CLH.    If CLT is known and QH = 1,500 mL/min, then we can calculate the hepatic extraction ratio, EH.  In  general, the hepatic extraction ratio is equivalent to the bioavailability (F) of an oral drug.  For a drug  that is well absorbed from the digestive system (assume 100% absorption), then the only barrier to  a drug's reaching the bloodstream is liver metabolism.  Bioavailability (F) will be reduced from 100%  by the percent extraction of the liver (EH).    So, if a drug is only cleared by the liver, then the hepatic extraction ratio can generally be  determined through the drug's bioavailability.  The key assumption is that the drug is well absorbed  when administered orally.  Category #2 ‐ cleared by kidneys only  If a drug is not metabolized by the liver (i.e., bioavailability, F, is very high because EH = 0), then the  contribution of hepatic clearance to CLT can be ignored.  CLT reduces to just renal clearance, CLR. 

If CLT is known and QR = 220 mL/min, then we can calculate the renal extraction ratio, ER.  The value  we get for ER may not make sense.  The underlying problem is that the kidneys filter 220 mL/min but  they receive 1,100 mL/min.  The two volumes of blood are not completely separate because of  processes of reabsorption and secretion that can occur between the filtered and unfiltered blood in  the kidneys.  For this and other reasons, renal clearance is typically not broken down to QR and ER.  It  is just reported as CLR.  Category #3 ‐ cleared by both the liver and kidneys  If a drug is cleared by both the liver and kidneys, then some outside information is required to tease  apart CLR and CLH.  One useful piece of information is oral bioavailability, F.  Since EH = 1 − F, EH can  be calculated.  With QH known (1,500 mL/min), CLR may be determined as the product  of EH and QH.  CLR is then just the difference between CLT and CLH.  This entire discussion assumes that one can determine CLT.  Calculation of CLT will be covered in the  next section of Chapter 7. 


OPTIONAL‐Please participate in the online discussion forum. 

Illustrating clearance Background: Clearance is the process of removing drug from the bloodstream.  Clearance is one of  the fundamental properties of a drug that contributes to a drug's observed half‐life.  Instructions: Read the passage below, which outlines two approaches for visualizing the effect of  hepatic clearance on a drug.  Learning Goal: To appreciate exactly how drug is cleared by the liver and the relationships between  the different pharmacokinetic variables.  Diphenhydramine is an antihistamine that is frequently used to treat allergy symptoms such as  watery eyes, nasal congestion, etc.  According to Goodman and Gilman's The Pharmacological Basis  of Therapeutics, diphenhydramine has clearance of 6.2 mL/min/kg.  For a 70‐kg patient, that  equates to a clearance of approximately 430 mL/min as CLT.  The urinary excretion of  diphenhydramine is approximately 0, so renal clearance is negligible (CLR ≈ 0) and total clearance ≈  hepatic clearance (CLT≈ CLH).  Therefore, CLH = 430 mL/min. 

Hepatic clearance is 430 mL/min.  We can work with number in two different ways.  One is a literal  interpretation, and the other is more realistic.  While the literal interpretation is less accurate, both  methods of interpretation give us the same result.  literal interpretation of hepatic clearance  Hepatic clearance is 430 mL/min.  In other words, every minute, of all the blood that passed through  the liver (QH = 1,500 mL/min), 430 mL of that blood is completely cleared of  diphenhydramine.  If Cp = 50 ng/mL, a typical concentration of diphenhydramine, then the liver  receives blood with a concentration of 50 ng/mL (Cpin = 50 ng/mL).  In one minute, the volume  received is 1,500 mL.   In the literal picture of clearance, what exits the liver in one minute would be 430 mL of blood that is  completely cleared of diphenhydramine (CLH = 430 mL/min).  That 430 mL of blood has a  concentration of 0 (Cpout = 0 ng/mL).  The balance of the blood exiting the liver (1,070 mL) still has  the original concentration of 50 ng/mL.   


A picture of this literal interpretation is shown below.  Note that blood that exits the liver is shown  as having a drug concentration of either 50 ng/mL or 0 ng/mL. 

Perhaps the best way to evaluate any picture of an organ's clearance is to consider how much drug,  by mass, enters and leaves the organ.  For the picture above, the amount of drug entering the liver  is equal to the concentration of the drug multiplied by the fluid volume.  The amount of drug exiting  the liver can be calculated in a similar manner.  Note that we must use a correction factor of 0.54 in  these calculations to convert the volume of whole blood to a volume of plasma because our  concentration term references plasma. 

Keep these numbers ‐ 40,500 ng into the liver and 28,890 ng out of the liver ‐ in mind as we look at  the more realistic interpretation of clearance.  realistic interpretation of hepatic clearance  A more realistic picture for clearance is based around the extraction ratio of the liver (EH).  How do  we determine EH?  EH can be calculated from the provided bioavailability (F).  Assuming that  diphenhydramine is fully absorbed from the gastrointestinal system, we know that F is the fraction  of drug that manages to pass from the hepatic portal system to the general circulatory system by  way of the liver.  For diphenhydramine, F = 0.72, so 72% of the dose survives the first‐pass  effect.  Therefore, 28% is broken down in the first‐pass effect.  This value, 28%, is the basis  of EH.  EH = 0.28. 


Continuing our assumption that Cpin = 50 ng/mL, we can determine Cpout with the equation  below. Cpout = 36 ng/mL. 

In this picture of clearance, in the period of minute, 1,500 mL of blood enters the liver at a plasma  concentration 50 ng/mL.  The same blood volume exits the liver with a uniform plasma  concentration of 36 ng/mL. 

We can calculate the mass of drug that enters and leaves the liver in this more realistic picture.  

conclusion  Under the literal interpretation of clearance, the mass of drug leaving the liver was 28,890  ng/min.  Under the more realistic interpretation of clearance based on use of the hepatic extraction  ratio, the mass of drug leaving the liver was 29,160 ng/min.  These are essentially the same number  and equivalent when significant figures are considered. 


Through the discussion above, hopefully you understand two different yet equal methods to  consider the clearance of a drug from the bloodstream.  OPTIONAL‐Please participate in the online discussion forum. 

7.3 Clearance II Area under curve (AUC) video Please watch the online video (6 minutes 55 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Practicing AUC estimation Background: The two ways for calculating area under curve (AUC) of a Cp‐time plot are integration  of the Cp‐time curve and using the trapezoid approximation.  Both methods provide a value for  determining AUC, which can then be used to calculate total clearance.  Instructions: Read the passage below concerning the advantages and disadvantages of both  methods.  Use this information to answer the assessment questions below.  Learning Goal: To understand both the different approaches for calculating AUC and how to use  both methods.  Integration  Integration is an easy method for determining the AUC of a particular drug dose.  Pharmacokinetic  data, whether from animals or humans, are found in the form of Cp‐time data points.  As we have  seen in the assessment questions of Chapter 7.1, we can plot these points, especially in a linear  ln Cp vs. time form, and quickly determine kel and the hypothetical Cpo.  AUC is simply Cpo/kel.   

Although integration is easy, it is not always accurate.  The problem is that a series of ln Cp‐time data  points can be forced to fit to a linear equation without the data actually being linear.  If the data do  not fit, then the simplicity of integration is of little gain because the AUC value will be inaccurate. 


Trapezoid Approximation  Using the trapezoid rule or approximation sounds less than desirable.  We want an exact number,  not an approximation.  Despite being an approximation, the trapezoid method does provide a very  useful estimate of AUC.  The trapezoid rule is applied to Cp‐time data by calculating the area between each Cp‐time data  point and then adding all the areas for an AUC estimate.  The area between two adjacent data  points can be approximated as a trapezoid.  The area is equal to the average of the two Cp values  multiplied by the time interval between the two points. 

The area for all the time intervals can be determined except for two special regions.  One, the  interval between time=0 and the first data point.  Two, the area after the last data point.  For the initial trapezoid one needs a value for Cpo, which is a hypothetical value and must be  estimated.  One method is to extrapolate the ln Cp data points of #1 and #2 backwards to Cpo.  Once  a value is chosen for Cpo, calculation of the area of the first trapezoid is the same as the others.  With  all the areas in hand, they can be added together to provide an AUC estimate.  The area after the last data point also requires an estimation.  In this case one needs to estimate kel  for the last data points.  Using the last two Cp‐time points, one can generate a line which has a slope  of ‐kel.  The AUC of the unplotted area can be estimated as Cplast/kel. 

The advantage of the trapezoid rule is that the areas are based solely on the experimental data.  The  data points are not forced into a model that may not fit the data at hand. 


Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

7.4 Volume of Distribution I One‐compartment model video Please watch the online video (5 minutes 33 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Detailing the one‐compartment model Background: The one‐compartment model is a pharmacokinetic model that describes the fluid that  contains a drug in the body as a single volume of plasma.  Although simple, the one‐compartment  model can be interpreted usefully to understand a drug's behavior.  Instructions: Read the passage below concerning the Vd of ranitidine.  Learning Goal: To understand how to make sense of the numerical value of a drug's Vd based on the  one‐compartment model.  Ranitidine is a drug that can be used to treat heartburn, acid reflux, and occasionally  ulcers.  The Vd of ranitidine is 1.4 L/kg, or 98 L for a 70‐kg patient. To be clear, that is 98 L  hypothetical volume of plasma.  The volume is hypothetical because a 70‐kg patient only has an  actual plasma volume of 2.7 L (54% of the volume of whole blood). 

While ranitidine is normally administered orally, IV dosages of 25 mg are available.  When  administered by IV, Do = 25 mg.  We know Vd is 98 L.  From this information we can calculate Cpo to  be 0.255 mg/L. 


While the hypothetical Vd may be 98 L, we know for certain that the actual volume of plasma in a  70‐kg patient is only 2.7 L.  We can therefore calculate how much of the original 25‐mg dose is  contained in the plasma at time=0. 

Wow!  Of the 25‐mg IV bolus dose, only 0.69 mg resides in the plasma.  That means over 24 mg of  the drug is NOT in the plasma.  Where is it?  It is elsewhere in the body ‐ in the interstitial fluid,  perhaps in muscles, even in fatty tissues.  The drug is mostly outside the central compartment.  We  can model the drug as if it all resides in the plasma, but the fact is the drug, based on its properties,  distributes into many parts of the body.  Although Vd is only a hypothetical number, it certainly provides a sense of how extensively a drug  distributes out of the plasma and into other parts of the body.  The more a drug leaves the plasma,  the less it is subject to hepatic and renal clearance because those organs only act on a drug that is  contained in the plasma.  Drugs with a higher Vd take longer to be cleared from the body and have a  longer half‐life. 

Calculating Vd from Cp‐time data Background: Volume of distribution is one of the key pharmacokinetic parameters for a drug.  It  describes the theoretical volume of plasma that is required to contain a drug in the body.  Instructions: Use the Cp‐time data points below with the initial dose mass to determine the Vd of a  drug.  Learning Goal: To practice how to calculate the Vd of a drug from Cp‐time data points.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Parameters by the kilo? Background: Unlike other pharmacokinetic parameters, both CL and Vd are reported on a per mass  basis.  Instructions: Read the passage below on the units of the most common pharmacokinetic  parameters.  Learning Goal: To understand why clearance and volume of distribution are listed as per unit mass. 


When drugs are reported in the literature and on the web, values for both CL and Vd are listed per  kilogram mass of the patient.  CL is reported as mL/min/kg, and Vd is listed as L/kg.  These two  variables are reported in this fashion because they both scale with a patient's body mass.  A larger  patient has a larger liver and kidneys and a higher rate of clearance.  A larger patient also has a  larger volume of distribution.  What is the net effect of body mass on parameters like kel and t1/2?  Nothing.  Because kel is defined  as the ratio of CL and Vd.  The kg term cancels out, and both kel and t1/2 are unaffected by patient  mass. 

Rarely one will encounter a drug with a Vd that is reported not based on patient mass but based on  body surface area (BSA).  The use of BSA in place of body mass is most commonly seen in certain  types of cancer drugs.  Almost all major oral medications list Vd values based on body mass.  OPTIONAL‐Please participate in the online discussion forum. 

7.5 Volume of Distribution II Two‐compartment model video Please watch the online video (6 minutes 48 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Calculating half‐life Background: Together Vd and CL determine kel and t1/2 of a drug according to the relationship below. 

Instructions: Use the reported Vd and CL values to calculate the t1/2 of a drug.  Learning Goal: To become comfortable working with pharmacokinetic parameters and carefully  watching the units on different values.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Volumes of fluids in the body Background: Drugs are not confined to just the plasma.  They can potentially distribute anywhere in  the body.  Instructions: Read the short section below for examples of different fluids in the body and their  approximate volumes.  Learning Goal: To learn how to interpret Vd values of drugs and make reasonable predictions on  how specific drugs distribute.  Information very similar to this was presented back in section 1 of Chapter 6.  It is worth repeating  here since we have a better understanding of volume of distribution.  plasma  A 70‐kg patient has 5 L of whole blood.  Plasma is the non‐cellular fraction of blood.  At 54% of the  volume of whole blood, the volume of plasma in a 70‐kg patient is approximately 2.7 L.  Because not  everyone has a mass of 70 kg, the volume of plasma is very frequently reported as 0.039 L/kg.  If a  drug has a Vd of only 0.039 L/kg (which would be uncommon), then the drug would most likely be  confined to the plasma.  interstitial fluid  Interstitial fluid is the liquid that sits between the cells of the body.  The fluid contains the nutrients  and waste of the cells.  Drugs reach the interstitial fluid from the capillaries.  The walls of the  capillaries have pores that allow passage of liquids and anything smaller than the pores.  Everything  in blood can pass except proteins and cells.  All orally‐delivered drugs are small enough to slip into  the interstitial fluid and reach the cells in the body.  A 70‐kg patient has 10 L of interstitial fluid.  That is 0.14 L/kg.  Oral drugs have full access to both the  plasma and interstitial fluid and should therefore have a Vd of at least 0.18 L/kg (sum of volume of  plasma and interstitial fluid).  intracellular fluid  The total water found within the cells of the body is 25 L for a 70‐kg person.  That is about 0.36  L/kg.  For a drug to enter cells, it must be able to cross cell membranes.  Therefore, a drug that has  access to cell water will also have access to the volume of all the cell membranes in the  body.  The Vd of such a drug is hard to predict, but it could certainly be much higher than 0.54 L/kg ‐  the sum of the plasma, interstitial fluid, and intracellular fluid. 


body volume  The volume of the body of a 70‐kg human is approximately 70 L, or 1 L/kg.  It may be surprising, but  drugs regularly have Vd values in excess of 1 L/kg.  Remember that Vd is a hypothetical value and not  limited to a real physical space like the volume of the body.  Drugs with very high Vd values are likely  very hydrophobic and bury themselves into cell membranes and fatty tissues.  OPTIONAL‐Please participate in the online discussion forum. 

Plasma proteins and Vd Background: Back in the video for section 2 of Chapter 6, a statement was made that plasma  proteins can affect the pharmacokinetics of a drug.  This statement has been left without  justification until now.  Instructions: Read the passage below on drug‐plasma protein binding.  Learning Goal: To understand how plasma proteins can affect both the Vd and CL of a drug.  Plasma can be a challenging medium for drugs.  Drugs are designed to bind a protein ‐ typically a  receptor or enzyme ‐ but drugs often bind the proteins in plasma.  Because the concentration of  protein in the blood is high, even a low affinity between a plasma protein and a drug can  dramatically decrease the amount of unbound drug in the plasma.  The concentration of a drug in  plasma (Cp) includes both bound and unbound drug.  The fraction of drug that is not bound to a  protein (fu) is simply the ratio of the free drug to Cp. 

The binding of proteins in the plasma has a direct impact on both clearance and volume of  distribution.  impact on clearance  Drug clearance is primarily attributed to the action of the liver and kidneys.  Enzymes in the liver  metabolize drugs. Liver enzymes only interact with drugs that are not bound to plasma  proteins.  Protein binding therefore protects a drug from clearance in the liver.  Similarly, the  kidneys do not filter proteins from the blood because the proteins are too large to be  removed.  Drugs that are bound to the blood proteins are also not filtered by the kidneys.  Protein 


binding therefore protects a drug from clearance by both the liver and kidneys.  Drugs with a higher  plasma protein binding, or lower fu, show a lower rate of clearance.  impact on volume of distribution  Plasma proteins are only found in the circulatory system.  If a drug is highly bound to plasma  proteins, then that drug will be largely confined to the volume of the plasma.  This binding can  greatly reduce a drug's Vd.  overall impact  The overall impact of plasma protein binding must be interpreted on a case‐by‐case basis.  Both Vd  and CL are decreased by protein binding, so the overall effect on kel and t1/2 cannot be immediately  predicted.  Regardless, protein binding does affect the pharmacokinetics of drugs and is monitored  by drug discovery groups within a pharmaceutical company. 

7.6 Oral Delivery I Oral bioavailability video Please watch the online video (7 minutes 9 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Estimating AUC for an oral drug Background: The calculation of area under curve (AUC) for a given dose of a drug is the first step for  determining CL for a drug.  Instructions: Use the Cp‐time data points for an oral drug to determine the AUC of the dose.  Learning Goal: To understand how to use the trapezoid rule to approximate AUC for an oral drug.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Welcome to Week 4 Starting week four video Please watch the online video (46 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

7.7 Oral Delivery II Multiple oral doses video Please watch the online video (7 minutes, 34 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Calculating Cp for an oral drug Background: The Cp of an oral drug is equal to the sum of the remaining drug from all the previous  doses administered to a patient.  Instructions: Read the passage below on calculating Cp for a drug and use the information to answer  the assessment questions that follow.  Learning Goals: To understand better how to determine Cp values for oral drugs at different time  points.  The Cp for an oral drug dose can be calculated at any time with the formula below if all the variables  are known. 


Any reference with pharmacokinetic parameters on drugs will list bioavailabilty (F), volume of  distribution (Vd), half‐life (t1/2), and tmax, among others.  kel can be calculated from t1/2.  kab can be  estimated from tmax and kel (with some trial and error).  With these parameters in hand, one can  calculate Cp at any time. 

In order to determine Cp from many doses, one must calculate the Cp from each dose and add them  together.  An important idea to remember is that the time of each dose is different.  For example, if  a drug is dosed every 4 hours, then at 10 hours, Cpdose 1 would be calculated at the full time of 10  hours, but Cpdose 2 would be calculated at only 6 hours, and Cpdose 3 would be calculated at just 2  hours. 

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Complexities of dosing Background: A successful dosing regimen usually involves administering a drug so that the  concentration of the drug remains within a window that provides a therapeutic effect without  demonstrating toxic effects.  Instructions: Read the case study on daptomycin, a drug that poses particularly difficult dosing  challenges.  Learning Goal: To gain exposure to more subtle issues surrounding the dosing of drugs.  Daptomycin is a large antibiotic.  Daptomycin violates Lipinski's rules in just about every way  possible.  Its molecular weight is 1,620 g/mol, well above the 500 g/mol limit of  Lipinski.  Daptomycin also has far more than 5 hydrogen bond donors and 10 hydrogen bond  acceptors.  Based on these traits, one might expect that daptomycin has a very low oral  bioavailability.  That expectation is correct.  Daptomycin cannot be formulated as an oral drug and is  instead administered intravenously. (See structure on following page.) 


The antibacterial activity of daptomycin was originally discovered by scientists at Eli Lilly in the  1980s.  The therapeutic effect of daptomycin is accompanied with toxicity to muscle  tissues.  Unfortunately, the concentrations required for the antibacterial activity of daptomycin are  very similar to those that cause muscle toxicity.  Researchers at Eli Lilly explored several options for administering daptomycin and ultimately  focused upon a twice daily IV regimen.  The hope was that a twice daily dosing would provide a  narrower range for Cp in the patient.  By hitting a narrow Cp range, the drug may be able to hit  concentrations ideal for antibacterial activity and yet avoid or minimize the toxic effects.  A representation of Cp vs. time for both once daily (black line) and twice daily (red line) dosing is  shown below.  Note that the twice daily schedule does keep Cp of daptomycin within a tighter range. 


Unfortunately, researchers at Eli Lilly were unable to separate satisfactorily the antibacterial and  toxic effects of daptomycin.  Eli Lilly closed their daptomycin research program.  Another company,  Cubist, expressed interest in reviving the project.  Eli Lilly then entered an agreement with Cubist,  and Cubist started its own research program in daptomycin in 1997.  This type of arrangement is common between drug companies.  Large companies often have more  potential projects than available resources.  Projects that are not making progress will be shelved to  free resources for more promising ones.  Partnering with another company allows more projects to  be in development.  If the shared project is successful, then both companies will share in the profits.  The drug discovery group at Cubist pursued a once daily dosing regimen for daptomycin.  Instead of  minimizing the peak Cp values of daptomycin with twice daily dosing, Cubist's approach emphasized  the deeper Cp troughs of a once daily dosing.  Cubist found that the muscle toxicity effects could be  minimized without sacrificing antibacterial activity.  One possible interpretation is that the deeper  dips in Cp in the once daily regimen allow the muscles to recover periodically from the toxic effects  of the drug.  Cubist obtained a patent on their specific and novel dosing schedule which was counter to the  prevailing logic on daptomycin.  Even though the composition of matter patent on daptomycin has  expired, generic manufacturers have been excluded from the market because of Cubist's dosing  patent.  The case of daptomycin demonstrates how each drug brings its unique challenges to drug  discovery.  The therapeutic and toxic levels of each drug create new issues that must be addressed  by medicinal chemists.  OPTIONAL‐Please participate in the online discussion forum. 

7.8 CL and Vd Revisited CL vs. Vd plots video Please watch the online video (7 minutes, 27 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Modifying structures to improve PK Background: A CL vs. Vd plot is an excellent tool for providing insight into how a drug's structure  might be modified in order to optimize its pharmacokinetics.  Instructions: Answer the questions below.  Learning Goal: To learn how to modify a drug or lead in order to achieve desired pharmacokinetic  properties.  Daptomycin is a large antibiotic.  Daptomycin violates Lipinski's rules in just about every way  possible.  Its molecular weight is 1,620 g/mol, well above the 500 g/mol limit of  Lipinski.  Daptomycin also has far more than 5 hydrogen bond donors and 10 hydrogen bond  acceptors.  Based on these traits, one might expect that daptomycin has a very low oral  bioavailability.  That expectation is correct.  Daptomycin cannot be formulated as an oral drug and is  instead administered intravenously. (See structure on following page.)  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 8 ‐ Metabolism Introduction to Chapter 8 Chapter 8 contains six subsections.  

Introduction

Phase I Pt 1 

Phase I Pt 2 

Phase II 

Metabolite Issues 

Prodrugs


Upon completing this chapter, you should understand the common types of drug metabolism  reactions that occur in the body and how to recognize and predict those reactions.  You should also  recognize the complications that metabolites introduce to drug discovery based on demographic  difference and biological activity of the metabolites.  Finally, you will see drug metabolism as an  exploitable process to improve the bioavailability of certain drugs.  OPTIONAL‐Please participate in the online discussion forum. 

8.1 Introduction Metabolism and Vd video Please watch the online video (7 minutes, 9 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Comparing metabolites Background: When drugs are metabolized, the resulting metabolites are typically (but not always)  more polar than the original drug.  One simple method for comparing the polarity of two  compounds is through lipophilicity, log P.  In general, compounds with a lower lipophilicity (lower  log P), have a lower Vd and a shorter half‐life.  Instructions: Use data from the DrugBank (http://www.drugbank.ca/)to answer the questions  below.  Learning Goal:  To understand more fully how metabolism can affect a drug and its metabolites in  terms of polarity and Vd.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

8.2 Phase I Pt I Oxidation video Please watch the online video (6 minutes, 52 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Omega oxidation Background: Most oxidations of sp3 hybridized carbons occur adjacent to a nitrogen or oxygen atom  and cause dealkylation of the nitrogen or oxygen.  Instructions: Read the passage below for an example of another type of sp3 hybridized carbon  oxidation.  Learning Goal:  To see another somewhat common type of Phase I oxidation.  Oxidations of simple sp3 hybridized carbon atoms are less rapid because the oxidation is not  activated by an adjacent oxygen or nitrogen atom.  Simple alkyl chains can regardless undergo  metabolism in the body.  These oxidations are called ω‐oxidations (omega oxidations).  One classic example of an ω‐oxidation was seen in the previous subsection.  Terfenadine, an  antihistamine, undergoes ω‐oxidation on one of the tert‐butyl carbons.  The C‐H bond is presumably  oxidized to an alcohol, which is rapidly oxidized to an aldehyde and then the acid.  Interestingly, the  acid metabolite is itself also an antihistamine drug. 


Another example of an ω‐oxidation can be found in the antidiabetic chlorpropamide.  The propyl  chain in chlorpropamide actually undergoes two different ω‐oxidations.  One is on the end of the  propyl chain.  This is a true ω‐oxidation as it occurs on the end (omega) of the chain.  The other is an  oxidation on the second‐to‐last carbon.  This is formally called an ω‐1‐oxidation because it occurs  one carbon removed from the end of the chain. 

While ω‐oxidations are less common than other types of Phase I metabolic reactions, they are  encountered with some regularity.  OPTIONAL‐Please participate in the online discussion forum. 

Predicting phase I metabolites Background: Based on the functional groups in a molecule, the most likely metabolites of a drug can  often be predicted.  Instructions: In the questions below, predict likely structures of metabolites of mepyramine, one of  the early antihistamine drugs.  Drawing the metabolite structures will require you to watch a video  on the use of JSDraw, a chemistry drawing program.  Learning Goals:  To learn how to use JSDraw and gain experience predicting metabolites. 

JSDraw tutorial video Please watch the online video (4 minutes, 33 seconds).  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


8.3 Phase I Pt II Reduction and hydrolysis video Please watch the online video (3 minutes, 55 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Predicting phase I metabolites Background: One can often predict the metabolites of a drug based on the drug's functional groups.  Instructions: In the questions below, predict likely phase I structures of metabolites of the indicated  drugs.  Learning Goal:  To gain experience predicting phase I metabolites of a drug.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

8.4 Phase II Conjugation video Please watch the online video (6 minutes, 41 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Role of glutathione Background: Glutathione is a nucleophilic molecule that reactions with electrophilic molecules in  the liver.  Instructions: Read the passage below concerning how glutathione protects the liver and body from  being damaged by certain drugs.  Learning Goals:  To understand better how the liver metabolizes drugs and the limits of the liver in  detoxification.  The liver breaks down drugs, primarily through its arsenal of oxidative CYP‐450 enzymes.  The  process of oxidation involves the loss of electrons.  As a molecule loses electrons, it becomes  electron poor and therefore more electrophilic.  Strong electrophiles, which are often formed 


through metabolic reactions, can be very damaging to cells.  This raises a question...  How can the  liver continuously perform oxidative reactions on drugs and other molecules without being  extensively damaged by electrophilic metabolites?  The answer is that the liver is protected by processes such as glutathione conjugation.  Glutathione,  a natural nucleophile through its thiol group, reacts with strong electrophiles and sacrifices itself  before the liver tissue is damaged.  The protection of the liver is only limited by the liver's stores of  glutathione.  The concentration of glutathione in the liver is around 5 mM.  If a person ingests too  much of a compound that reacts with glutathione, then once the glutathione reserves are  consumed, the liver will be damaged.  It is for this reason that drug overdoses often lead to  extensive liver damage 

Predicting phase II metabolites Background: Based on the functional groups in a molecule, the metabolites of a drug can often be  predicted.  Instructions: In the questions below, predict likely structures of phase II metabolites of the drugs  that are shown.  Learning Goal:  To gain experience predicting phase II metabolites.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

8.5 Metabolism Issues Metabolism inhibition video Please watch the online video (7 minutes, 24 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Case study ‐ cimetidine Background: Drugs that inhibit any of the CYP enzymes have the potential to interfere with the  metabolism of other drugs.  This type of drug interaction can cause safety problems and affect the  marketability of a drug.  Instructions: Read the passage below about CYP inhibition of cimetidine.  Learning Goals:  To learn about the implications of CYP inhibition of a drug.  Cimetidine is a histamine H2‐receptor antagonist and represented a first‐in‐class drug for the  treatment of acid reflux.  Cimetidine was marketed in the United States in 1979 under the brand  name Tagamet.  Cimetidine met great success and enjoyed high sales. 

In 1983 another histamine H2‐receptor antagonist, ranitidine, reached the market in the United  States.  Ranitidine, sold under the name of Zantac, has a structure that is strikingly similar to  cimeditine.  Ranitidine is an example of a me‐too drug.  Me‐too drugs tend to follow quickly behind  a first‐in‐class drug and have similar structures.  The composition of matter patents surrounding  cimetidine emphasized the importance of the imidazole ring for the activity of histamine H2‐ receptor antagonists.  The researchers who discovered ranitidine were able to use a furan ring in  place of imidazole and still maintain activity. 

A me‐too drug rarely matches the profitability of the first drug in a class.  The first drug dominates  the market, and the me‐too drug lags behind.  Ranitidine, however, surpassed the market share of  cimetidine in a just a few years.  The reason behind the success of ranitidine was CYP inhibition by  cimetidine.  Cimetidine inhibits several forms of CYP, including CYP1A2, CYP2D6, and, most  importantly, CYP3A4.  Cimetidine therefore has a long list of drug interactions.  While ranitidine also  inhibits several CYP forms, ranitidine is a less potent inhibitor than cimetidine.  Many patients who  experienced complications with cimetidine switched to ranitidine 


Factors in metabolism Background: Based on the functional groups in a molecule, the metabolites of a drug can often be  predicted.  Instructions: Genetic differences can play a significant role in how a drug is metabolized in one  patient or another.  Other factors also are important.  Learning Goal:  To learn how health and age affect drug metabolism.  While genetic differences can affect drug metabolism, metabolic differences between people of  different races are typically very small, even negligible.  Gender is yet another factor that is normally  of little importance when discussing metabolism.  Other factors, including age and health, are far  more influential with regard to drug metabolism.  The compound theophylline, a drug sometimes  used to treat asthma, highlights these differences.1 

Age can greatly influence drug metabolism.  Below is a table showing how half‐life can vary with age  for theophylline.  Bear in mind that theophylline is cleared primarily by the liver, so changes in half‐ life reflect changes in liver activity.  age 

half‐life (h) 

infants (1‐2 days) 

25.7

infants (3‐30 weeks) 

11.0

children (1‐4 years) 

3.4

children (6‐17 years) 

3.7

adults (18‐60 years) 

8.7

elderly (>60 years) 

9.8


The variability of the half‐life of theophylline shows how metabolism changes with age.  Very young  infants are not completely developed metabolically, so the half‐life is long.  By an age of 12 months,  children are metabolically extremely active and the half‐life of theophylline drops below 4  hours.  Once a person matures physically, the half‐life lengthens to just under 9 hours.  Late in life,  as a person's metabolism gradually slows, the half‐life of theophylline also lengthens slightly.  Physical health can also affect the half‐life of a drug.  Below is a table showing how half‐life can vary  with health or physical condition for theophylline.  condition 

half‐life (h) 

liver cirrhosis 

32.0

hepatitis

19.2

pregnancy (1st trimester) 

8.5

pregnancy (2nd trimester) 

8.8

pregnancy (3rd trimester) 

13.0

sepsis

18.8

hypothyroid

11.6

hyperthyroid

4.5

Conditions that negatively affect or place stress on the liver (cirrhosis, hepatitis, late pregnancy,  blood infection, and hypothyroid) lengthen the half‐life of theophylline.  Hyperthyroidism, a  condition in which a patient has an accelerated metabolism (among other things), causes a dramatic  decrease in half‐life.  Another condition that frequently affects a drug's half‐life is diabetes.  Advanced diabetic patients  often have limited kidney function.  Renal clearance is diminished, and drugs that rely on the  kidneys for clearance have lengthened half‐lives.  1. Murray, L., Sr. (Ed.). Physician's Desk Reference (58th ed.) Montvale, NJ: Thomson PDR, 2004.  OPTIONAL‐Please participate in the online discussion forum. 


Pharmacokinetic scatter Background: Drugs are listed with distinct pharmacokinetic parameters.  These parameters provide  a sense of precision for a drug and exactly how it behaves.  Instructions: Read the short passage below and review the figures in the linked article  (http://aac.asm.org/content/50/7/2281.full) in Antimicrobial Agents and Chemotherapy and answer  the questions that follow.  Learning Goal:  To appreciate the broad variability in the action of drugs across different patients.  One of the drugs used to prevent and treat malaria is mefloquine.  Mefloquine is administered orally  in its racemic form and is well absorbed.  The drug is mostly excreted through the bile, meaning it is  absorbed, collected in the gall bladder, excreted in bile, and exits the body in feces.  Compounds  found in the feces are often not absorbed, but in this case, the drug is indeed absorbed from the  gastrointestinal tract. 

In a study on mefloquine dosing, 50 patients received 8 mg mefloquine per kg of body mass (8  mg/kg) on Day 0, Day 1, and Day 2 of the study.  Patients were then monitored for approximately  one month for their Cp levels of mefloquine.   From this data, the pharmacokinetic parameters of  mefloquine could be determined.  The ideal Cp‐time curve (http://aac.asm.org/content/50/7/2281/F2.expansion.html) shows a  smooth, predictable relationship between Cp and time.  The scatter plot (http://aac.asm.org/content/50/7/2281/F1.expansion.html) of the experimental  data gives a much less convincing relationship based on a visual inspection.  The scatter seen in the  experimental graph is representative of experimental pharmacokinetic data.  The behavior of a drug  can vary wildly among different patients.  It is for this reason, in part, that drug must have a wide  therapeutic window.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


8.6 Prodrugs Prodrugs video Please watch the online video (6 minutes, 43 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Case study ‐ antiplatelet drugs Background: Prodrugs are compounds that are converted in the body to the biologically active form  of the molecule.  Many prodrugs are administered in an inactive form because the active form is not  well absorbed.  Instructions: Read the passage below about two antiplatelet drugs, clopidogrel and prosugrel.  Learning Goal:  To understand that metabolic activation of a prodrug can have problematic genetic  variability across a broad patient population.  Antiplatelet drugs are compounds that inhibit clot formation and therefore reduce the incidence of  stroke, heart attacks, and other clot‐dependent conditions.  Antiplatelet drugs can fall into several  different classes, one of which is the adenosine diphosphate receptor inhibitors.  Two examples of  adenosine diphosphate receptor inhibitors are clopidogrel and prasugrel.  Both clopidogrel and  prasugrel are prodrugs. 

Clopidogrel was approved by the US FDA in 1997.  The compound is activated in the body by phase I  oxidation of the thiophene ring.  One of the sp2 hybridized carbons in the ring is oxidized by  CYP2C19.  The resulting hydroxythiophene is unstable and hydrolyzes to give the ring‐opened, active  form of the drug. 


Around 5‐10% of the US population have a genetic variation in CYP2C19 that renders the enzyme  less active in the metabolism of clopidogrel.  For these patients clopidogrel is not activated properly  in the body, and the patients do not have therapeutically effective levels of the metabolite.  Because  of the potential lack of effect of prosugrel, the FDA added labeling in 2010 to clopidogrel packing to  indicate the possible lack of effectiveness of the drug.  This so‐called black box warning raises safety  awareness and concerns for both prescribing physicians and patients.  Prasugrel is in the same drug class as clopidogrel and was approved in 2009.  Prasugrel is nearly  identical in structure to clopidogrel.  The major difference is that the thiophene ring already bears  an oxygen.  The ring is essentially oxidized in its administered form.  In the body the drug is activated  by hydrolysis of the acetate ester to form the hydroxythiophene metabolite, which forms the active  form of the drug. 

Prasugrel, which is activated by plasma lipases instead of CYP2C19, shows fewer variations in how it  is metabolized across broad populations of patients.  For this reason, prasugrel is not packaged with  black box warnings.  Prasugrel is an example of how understanding metabolic issues of a drug  allowed the clever design of a safer version of what is otherwise a nearly identical drug.  OPTIONAL‐Please participate in the online discussion forum. 

Drawing prodrugs Background: Prodrugs are compounds that are converted in the body to the biologically active form  of the molecule.  Instructions: Do the problems below by predicting the structure of the prodrug based on the  structure of its active metabolite.  Learning Goal:  To recognize how particular functional groups can be formed in the body through  metabolic reactions.  Please complete the online exercise. 


OPTIONAL‐Please participate in the online discussion forum. 

Survey Week 4 Survey Please complete the very brief online survey, which should take less than one minute. 

Examination 2 Second Examination The exam is open book and open notes.  All questions may be attempted once, so be certain of your  answer before submitting it.  There are ten questions.  Each is its own unit within the Examination 2  subsection.  Remember that you are bound by the honor code.  No postings to the forum concerning the exam  are allowed.  Furthermore, you must work on the examination independently 

Problems Please complete the online problems in Examination 2. 


Welcome to Week 5 Starting week five video Please watch the online video (49 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 9 ‐ Binding, Structure, and Diversity Introduction to Chapter 9 Chapter 9 contains six subsections.  

Intermolecular Forces 

Case Study ‐ Stromelysin 

Drug‐Target Complementarity 

Molecular Diversity 

Molecular Libraries 

Building Libraries 

Upon completing this chapter, you should understand how drugs bind a target and how to  determine the energy of binding.  You should gain a preliminary idea of how to control the shape of  a molecule in order to maximize available drug‐target interactions.  You should realize the challenge  of discovering active molecules within the immense number of possible drug molecules and what  tools are available to drug companies to explore drug space  OPTIONAL‐Please participate in the online discussion forum. 

9.1 Intermolecular Forces Binding energy video Please watch the online video (10 minutes, 26 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Henderson‐Hasselbalch equation Background: Hydrogen and ionic bonds are very important for drug‐target binding.  These bonds can  be highly dependent upon pH.  Instructions: Read the passage below concerning the Henderson‐Hasselbalch equation and the pKa  of common functional groups.  Use the information to answer the questions that follow.  Learning Goals: To review usage of the Henderson‐Hasselbalch equation and understand how pH  influences the availability of hydrogen and ionic bonds between a target and a drug.  An acid (H‐A) can react reversibly with water to form a conjugate base (A‐) and hydronium ion  (H3O+).  The equilibrium constant (K) for this reaction can be expressed in the standard way ‐ the  product of the concentrations of the products divided by the product of the concentrations of the  reagents.  Because the concentration of water is virtually constant at around 55 M, the term [H2O] is  combined with K to define a new constant, Ka. 

While Ka is a constant, the position of the acid‐base equilibrium can be affected by the pH of the  medium.  A lower pH, with a raised [H3O+], shifts the equilibrium to the left to favor H‐A.  A higher  pH, with a lowered [H3O+], pulls the equilibrium to the right to favor A‐.  The Henderson‐Hasselbalch  equation quantitatively relates how pH affects the equilibrium ratio of A‐ and H‐A for an acid with a  known Ka.  Remember that pKa = −log Ka. 

  Henderson‐Hasselbalch equation  To use the Henderson‐Hasselbalch equation, one needs to know the pKa of different functional  groups.  pKa values for a handful of common functional groups encountered in drugs are shown in  the table below.  A much more comprehensive list can be found here. 


acid (name) 

conjugate base (name) 

pKa

H3O+ (hydronium) 

H2O (water) 

−1.7

PhCO2H (benzoic acid) 

PhCO2− (benzoate) 

4.2

PhNH3+ (anilinium) 

PhNH2 (aniline) 

4.6

CH3CO2H (acetic acid) 

CH3CO2− (acetate) 

4.8

(pyridine) 

5.1

(pyridinium) 

(imidazolium) 

(imidazole) 

7.0

NH4+ (ammonium) 

NH3 (ammonia) 

9.2

PhOH (phenol) 

PhO− (phenolate) 

10.0

H2O (water) 

HO− (hydroxide) 

15.7

For reference, the pH of some different regions in the body are listed below.  

blood ‐ 7.4 

stomach ‐ as low as 1 

small intestine ‐ 7‐8 

extracellular fluid ‐ 7.4 

intracellular fluid ‐ 6.8 

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Calculating binding energies Background: The binding energy of a drug‐target complex can be calculated with the equation  below.  If 0.00199 kcal*K/mol is used for R, then the energy is calculated with units of  kcal/mol.  Temperature (T) is usually 298 K. 

Instructions: Use the equation above to answer the questions that follow.  Learning Goal: To practice calculating binding energies between a drug and target.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

9.2 Case Study ‐ Stromelysin Stromelysin video Please watch the online video (8 minutes, 30 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

More stromelysin inhibitors Background: The binding energy of a drug‐target complex can be calculated with the equation  below.  If 0.00199 kcal*K/mol is used for R, then the energy is calculated with units of  kcal/mol.  Temperature (T) is usually 298 K. 

Instructions: Use the equation above to answer the questions that follow concerning stromelysin  inhibitors.  Learning Goal: To practice calculating binding energies between an enzyme and its inhibitor.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


9.3 Drug‐Target Complementarity Pharmacophores revisited video Please watch the online video (7 minutes, 32 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Rotatable bonds Background: Rotatable bonds increase the conformational flexibility of a molecule and minimize the  probability that the functional groups in a molecule match the desired pharmacophore.  Instructions: Read the passage below on identifying rotatable bonds.  Use the information to answer  the questions that follow.  Learning Goal: To learn how to identify which bonds in a molecule qualify as rotatable.  No universally accepted rules exist on defining rotatable bonds.  Each set of definitions has its loop  holes and problems.  Regardless, most methods include the rules shown below.  Bonds that are not rotatable...  

non single bonds 

bonds to hydrogen and other monovalent atoms (halogens) 

ring bonds 

bonds to terminal atoms, including CH3, NH2, and OH 

the C‐N bond between a carbonyl and amide nitrogen (also goes for the C‐N bond in thioamides  and the S‐N bond in sulfonamides) 

bonds connecting two aromatic rings with collectively three or more ortho substituents 

bonds connected to terminal triple bonds, including bonds to cyano groups 


Under these rules, naproxen has only three rotatable bonds (bolded below).  Duloxetine has six  (bolded below). 

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

9.4 Molecular Diversity Numbers game video Please watch the online video (7 minutes, 55 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Behind the numbers Background: Molecular space for molecules with the proper size for oral drugs has been estimated to  contain 1063 molecules.  Instructions: Answer the questions below concerning probabilities in potential drug space.  Learning Goal: To appreciate the vast numbers of different molecules that could possibly be  candidates as oral drugs.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Privileged structures Background: Molecular space for potential drug molecules is indescribably diverse.  Instructions: Read the passage below about certain types of structures and substructures that  repeatedly appear in many different types of drug.  Learning Goal: To understand the concept of privileged structures. 


While drug space is incredibly diverse, a handful of common structures and molecular fragments can  be found regularly among active sets of molecules.  These compounds and fragments have become  known as privileged structures because of their seemingly universal ability to bind protein targets.  One such privileged structure is the diphenylmethane subunit.  The subunit can be seen in  numerous compounds that bind a variety of different targets. 

Privileged structures are both good and bad for drug discovery.  On the good side, privileged  structures help researchers focus on molecular scaffolds that are more likely to show activity.  The  drug discovery team can focus on promising compounds and hopefully avoid the less interesting  structures.  On the bad side, single compounds that contain privileged structures may show activity  against multiple targets.  Compounds that bind multiple targets often cause side effects.  Such  compounds are sometimes labeled as promiscuous.  Another issue with privileged structures is that  they have received considerable research attention.  Patenting a compound with a privileged  structure can be a challenge because so many similar structures have already been patented  OPTIONAL‐Please participate in the online discussion forum. 

9.5 Molecular Libraries Molecule collections video Please watch the online video (6 minutes 56 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


High‐throughput screening Background: Potential drug space is immense with an estimated number of molecules of  1063.  Compound libraries, although nowhere near the same size as drug space, are regardless very  large and may include 1,000,000 or more compounds.  Instructions: Read the text below and the accompanying Wikipedia entry concerning the rapid  testing of molecular libraries.  Learning Goal: To understand how the large number of molecules in compound libraries can be  quickly and inexpensively tested for biological activity.  For molecular libraries to be useful to a drug company, there must exist a method for quickly and  inexpensively testing the activity of each compound in the library.  Fortunately there is.  That  method is called high‐throughput screening or HTS.  HTS involves the automated testing of molecules in a quick, inexpensive, in vitro assay.  The process  relies upon robotic equipment to perform the screen reproducibly.  With such a method, a  pharmaceutical company can screen an entire large library, which may include a million or more  compounds, in around a week in a particular screen.  Therefore, in a fairly short period of time, hits  for a specific target can be identified.  The entry for high‐throughput screening in Wikipedia provides some more details on precisely how  the process is automated.  The statistical and emerging technology discussions in the article are  beyond the scope of this course, but they do reveal interesting facets of HTS.  OPTIONAL‐Please participate in the online discussion forum. 

HTS and academia Background: High‐throughput screening (HTS) is an automated, quick method for gaining  preliminary activity information on molecules in a compound library.  HTS has traditionally been a  technique only available to pharmaceutical companies.  Instructions: Read the linked article from the journal Nature Methods concerning a growing  movement of academic laboratories screening their own molecular libraries.  Use the information in  the article to answer the questions that follow.  Learning Goal: To understand how the drug discovery process is becoming increasingly available to  groups outside the pharmaceutical industry.  Please return to the online course and read the article to which the above instructions refer.  Please complete the online exercise. 


OPTIONAL‐Please participate in the online discussion forum. 

9.6 Building Libraries Combinatorial chemistry video Please watch the online video (7 minutes 10 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Ugi reaction Background: Reactions that can quickly and easily assemble large numbers of diverse molecules are  highly sought after in combinatorial chemistry.  Instructions: Read passage below on the Ugi reaction and answer the question that follows.  Learning Goal: To learn about a reaction that is commonly exploited in combinatorial chemistry.  The Ugi reaction involves the reaction of four different starting materials in a single reaction  vessel.  The starting materials are a carboxylic acid (1), primary amine (2), aldehyde (3), and  isocyanide (4). 

The simplicity of the Ugi reaction and the widespread availability of the starting materials make the  reaction very popular starting point in preparing molecular scaffolds in combinatorial chemistry.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Combichem challenges Background: In the early 1990s combinatorial chemistry was hoped to accelerate greatly the rate at  which new drugs could be discovered.  The benefits of combinatorial chemistry did not however  materialize as expected.  Instructions: Read the Chemistry and Engineering News article linked below.  Use the information to  answer the questions that follow. 


Learning Goal: To learn about a reaction that is commonly exploited in combinatorial chemistry.  Although over 15 years old, an article from Chemistry and Engineering News, a trade magazine  published by the American Chemical Society, captures the early sentiments of a booming field called  combinatorial chemistry.  Even in 1998 in the face of great enthusiasm, the promises of  combinatorial chemistry were already being questioned.  The article nicely captures both sides of  the story.  Please return to the online course and read the article to which the above paragraph refers.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Welcome to Week 6 Starting week six video Please watch the online video (52 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 10 ‐ Lead Discovery Introduction to Chapter 10 Chapter 10 contains seven subsections.  

In Vitro Screening 

Fragment Based Screening 

Filtering Hits Pt 1 

Filtering Hits Pt 2 

Filtering Hits Pt 3 

Selective Optimization of Side Activities 

Natural Products 

Upon completing this chapter, you understand the different methods for screening libraries of  molecules for hits.  You should also understand how to prioritize the hits as leads for their potential  for ultimately becoming drugs.  Finally, you should realize that some leads are discovered through  non‐screening approaches.  OPTIONAL‐Please participate in the online discussion forum. 

10.1 In Vitro Screening Finding hits video Please watch the online video (6 minutes, 34 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


In silico screening Background: The most common method for finding hits involves searching through libraries of  molecules using high‐throughput screening to reveal compounds with promising activity.  Instructions: Read the passage below on screening molecules through computer modeling.  Learning Goals: To understand the advantages and limitations of screening molecules through  computer simulations.  The rise in computing power, especially in the area of protein modeling, allows library compounds  to be “flown” into a binding pocket that is modeled on a computer. Binding energies can then be  estimated to approximate the Ki of each library member. The process of matching a compound to a  binding site is called docking. Estimating the binding energy is called scoring. The overall process of  testing for biological activity using a computer simulation is called in silico screening or virtual  screening.  A very attractive aspect of in silico screening is that the library does not to be real. As long as one  can draw the molecules in a computer, the computer will handle docking the molecule to the target  protein. The logistics of obtaining, maintaining, and dispensing compounds for testing are  unnecessary. The library can potentially be far larger than the one or two million compound libraries  held by major pharmaceutical companies.  While there are many attractive aspects to in silico screening, the method is still developing. One  problematic aspect is scoring. Current scoring methods are somewhat inaccurate, and many  compounds that are not strong binders are predicted to be hits. The number of false hits, or false  positives, can be reduced by using multiple different scoring methods. Only compounds that are  predicted to be active through multiple methods are selected as hits. This approach is called  consensus scoring. The activity of any virtual hits must be confirmed by synthesizing a sample of the  molecule and testing the compound in an in vitro screen.  OPTIONAL‐Please participate in the online discussion forum. 

10.2 Fragment Based Screening Fragment‐based screening video Please watch the online video (7 minutes, 58 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Revisiting stromelysin inhibitors Background: Fragment based drug discovery involves screening for small, weakly active compounds  in an assay and then tethering them together to form hit‐level compound.  Instructions: Read the passage below concerning a fragment based search for inhibitors of  stromelysin, a topic that was first presented in Chapter 9.  Use the ideas in the passage to answer  the questions that follow.  Learning Goal: To understand better the types of molecules used as fragments and how hits are  generated from the fragments.  Back in Chapter 9 we discussed the development of stromelysin inhibitors to highlight the  relationship between binding energies and the structure of a molecule.  As it so happens, the  stromelysin study is also an example of fragment based drug discovery.  The study started with two fragments that were found to bind to stromelysin.  One was  acetohydroxamic acid (1) and the other was 4‐hydroxybiphenyl (2).  Note that both are small,  fragment‐sized molecules and have weak Ki values close to 1 mM and binding energies of −2.4 and  −4.8 kcal/mol, respectively. 

The fragments were then joined together.  In this particular study, the binding sites for 1 and 2 were  known to be close together and the approximate orientations of the two fragments were also  known.  These two details greatly helped the research team in designing tethers to connect the two  fragments.  The team reported four different tethers through the addition of between one and four  CH2 units. 

The discussion becomes more complicated because the activities are reported as IC50 values instead  of Ki values.  Through the Cheng‐Prussoff equation, if we know the concentration of the substrate 


([S]) and Km of the substrate for stromelysin, we can convert the IC50 values to Ki values, which can in  turn be used to calculate ΔGobind of the tethered fragments. 

The original stromelysin report gives [S] as 200 µM.  Km is not provided, but a very similar enzyme  has a Km of 4,000 µM for stromelysin.  Using these values, we can determine Ki and ΔGobind for the  best hit formed by tethering fragments.  The most potent hit is compound 4. 

An interesting thing about fragment binding is that Ki and ΔGobind of hit can be determined based on  the fragments that were combined.  Specifically, if fragments 1 and 2 are correctly combined to  form a new hit, then Ki of the hit should be equal to the product of the Ki values of the two  fragments.  Ki (hit) = Ki (fragment 1) × Ki (fragment 2)  Similarly, ΔGobind of the hit should be equal to the sum of the ΔGobind of the two fragments.  ΔGobind (hit) = ΔGobind (fragment 1) + ΔGobind (fragment 2)  Under this logic, Ki of compound 4 should be 4.8×10−6 M (17×10−3 × 0.28×10−3 = the product of the  two fragment Ki  values).  Instead, the actual value is 0.30×10−6 M.  ΔGobind of compound 4 should be  −7.2 kcal/mol (−2.4 + −4.8).  Instead, the actual value is −8.9 kcal/mol.  Compound 4 (the hit) binds  more strongly than we would predict based on its fragments.  Why do the predictions (which are theoretically sound) differ from the experimental value?  The  discrepancy is the tether.  Compound 4 has two more CH2 groups than the individual  fragments.  These CH2 groups lie within a channel in the protein and generate binding energy  through the hydrophobic effect.  In Chapter 9, the binding energy of a CH2 group through the  hydrophobic effect was listed as 0.8 kcal/mol.  The energy difference between 4 and the fragments 


is 1.7 kcal/mol, essentially equal to 2 × 0.8.  Once we consider the effect of the additional  CH2 groups, the strong binding of hit 4 is more reasonable.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

10.3 Filtering Hits Pt 1 Visual inspection video Please watch the online video (8 minutes, 33 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Another structural alert Background: Compounds that contain functionality that may cause toxicity problems raise a  structural alert.  Anilines are perhaps the most common functional group that causes structural  alert.  Instructions: Read the passage below about arylacetic acids, which also trigger a structural alert.  Learning Goal: To gain exposure to another functional group that forms reactive metabolites and is  associated with structural alerts. 

Please continue on the following page… 


In addition to anilines, arylacetic acids (1) frequently form reactive metabolites.  Like most carboxylic  acids, arylacetic acids often undergo phase II metabolism and are conjugated with glucuronic acid  (2).  Glucuronides of arylacetic acids can rearrange from the 1‐glucuronide (3) to the 3‐ glucuronide (4).  The 3‐glucuronide exists in equilibrium with its open‐chain form (5).  The open‐ chain form is important because it can react with the NH2 groups on lysine residues of proteins, and  ultimately the glucuronide becomes covalently bound to the protein through a multistep  process.  Modified proteins can trigger an immune response and cause tissue damage.  Tissue  damage in the liver is particularly common because glucuronidation occurs primarily in the  liver.  Skin rashes can also indicate immune response problems. 

OPTIONAL‐Please participate in the online discussion forum. 

Picking out problem compounds Background: Functional groups in a hit can cause the hit to be less attractive to a drug discovery  program.  Being able to visually identify problematic compounds can help a medicinal chemistry  group to advance the correct compounds to the lead optimization stage.  Instructions: Look at the structures below and answer the questions that follow.  Learning Goal: To practice identifying compounds that contain less desirable functional groups. 


Below are six hit‐like compounds. 

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

10.4 Filtering Hits Pt 2 Molecular indexes video Please watch the online video (5 minutes 15 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Ligand efficiency calculations Background: Of the many molecular indexes, ligand efficiency (LE) is among the most widely used. 

Instructions: Use the equation above to calculate the typical LE values of hits and  drugs.  Furthermore, use the equation to calculate the LE value of two drugs, duloxetine and  sildenafil.  Learning Goal: To gain a sense of typical values for LE for different types of compounds relevant to  drug discovery. 


Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Ligand lipophilicity efficiency Background: Molecular indexes and metrics allow members of a drug discovery team to quickly  prioritize one hit above another.  Instructions: Read the passage below on another metric, ligand lipophilicity efficiency, by which hits  may be prioritized.  Use the information on ligand lipophilicity efficiency to answer the questions  that follow.  Learning Goal: To learn another molecular index by which the quality of a hit can be gauged.  A frequently encountered molecular index is ligand lipophilicity efficiency, or LLE.1  (Do not confuse  LLE with LE, the visually similar but very different ligand efficiency.)  LLE is the difference between a  drug's activity in the form of −log IC50 or −log Ki and a drug's lipophilicity in the form of log P.  A more  highly positive value for LLE is more favorable.    LLE acknowledges the reality that additional activity (a larger value for −log IC50.) is obtained by  adding molecular weight and likely increasing lipophilicity (a larger value for log P).  According to  Lipinski's rule, log P has a maximum value of 5.  Therefore, hits with a value for log P closer to 5 and  only hit‐like activity have low values for LLE leave little room in terms of lipophilicity for growth of  the molecule. LLE creates a direct relationship between activity and lipophilicity.  A more active  molecule will have a larger, positive value for −log IC50.  1. Edwards, M. P.; Price, D. A. Role of Physiochemical Properties and Ligand Lipophilicity Efficiency  in Addressing Drug Safety Risks. Annu. Rep. Med. Chem. 2010, 45, 2615‐2623.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

10.5 Filtering Hits Pt 3 Other lead selection criteria video Please watch the online video (7 minutes 59 seconds).  A condensed summary of this video can be found in the Video summary page. 


OPTIONAL‐Please participate in the online discussion forum. 

Leads are better than hits Background: Hits are often defined as compounds with moderate activity (Ki = 1 µM) against a  target.  The most promising hits will be advanced as leads.  Instructions: Read the passage below on how a hit is structurally modified and improved in activity  during the lead selection process.  Learning Goal: To make clear that a hit is typically tweaked and improved before it becomes a lead.  Back in the 1970s, Saturday morning cartoons for children on ABC (a United States television  network) were broadcast with occasional educational programming called Schoolhouse Rock.  The  short Schoolhouse Rock cartoons taught about topics like grammar and history.  One particular  cartoon was called I'm Just a Bill and followed a congressional bill through the long and winding  approval process of becoming a federal law.  Only after significant modification and editing can a bill  be approved.  At the close of the cartoon, the US President approves the bill.  The bill responds by  shouting, "Oh yes!"  A hit is very much like a congressional bill.  A hit merits investigation because it shows activity  against a target, but that credential alone does not make the hit worthy of being a lead.  The hit is  put through a battery of tests ‐ the filtering process that we discussed in sections 10.3, 10.4, and  10.5 ‐ to make sure that only the most promising compounds are advanced as leads.  Once a lead, a  molecule will require more and more resources from the pharmaceutical company.  One aspect of lead discovery that we have not discussed is how the structure of a hit is explored  during the lead discovery stage.  Chemists are not idle during this process, and simple structural  modifications to the hit will be made to confirm that there is room for improving the binding of the  molecule to its intended target.  The outcome of these modifications is a series of molecules very  similar to the original hit.  It is likely one of these modified hits that will ultimately be advanced as  the lead compound.  Therefore, it is common for a lead molecule to bind more strongly to the target  than the original hit.  Leads will often have Ki values of 100 nM or even lower ‐ a marked  improvement over the typical value of 1 µM of a hit.  Just as a bill, if it becomes a law, is modified and crafted as it is marched through the approval  process, so also a hit is incrementally improved as it is becoming judged to be a lead.  (Oh yes!)  OPTIONAL‐Please participate in the online discussion forum. 


10.6 Selective Optimization of Side Activities SOSA video Please watch the online video (6 minutes 41 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Case study: viloxazine Background: SOSA ‐ selective optimization of side activities ‐ is an alternative to traditional lead  discovery techniques.  In SOSA, a compound that is intended to bind to one target is modified so  that the compound instead binds to a different target.  Instructions: Read the passage below about viloxazine, a drug that was discovered through the  SOSA approach.  Learning Goal: To gain exposure to another SOSA drug.  Many treatments for high blood pressure have been developed.  β‐Blockers are a class of blood  pressure medication developed in the 1960s and antagonize (block) β‐receptors, which play a role in  regulating heart rate and blood vessel dilation.  Propranolol (1) is among the most frequently  prescribed β‐blockers.  β‐Blockers also display sedative and anticonvulsant activity. 

In order to exploit the secondary sedative effects of β‐blockers, researchers modified the  pharmacophore of β‐blockers (2) by forming a new ring (3) with the flexible side chain.  The  rationale was that restricting the conformation of the molecule would enhance binding to secondary  targets will minimizing binding to β‐receptors.  The formation of rings is a standard approach for  controlling the conformation of a molecule.  The ultimate outcome of the research program was  viloxazine (4), an antidepressant that blocks the uptake of certain neurotransmitters from synaptic  junctions. 

Image credit: Pearson Education 


Viloxazine is therefore an example of SOSA drug.  Viloxazine was designed by the modification of an  existing compound.  The modification were specifically intended to improve side activities and  diminish binding to the original target.  OPTIONAL‐Please participate in the online discussion forum. 

SOSA and molecular indexes Background: SOSA is potentially excellent method for discovering leads by screening established  drugs against new targets.  SOSA hits are typically safe and show good cell permeability.  Instructions: Answer the questions below about how SOSA hits might fare when evaluated against  the molecular indexes discussed in this chapter.  Learning Goal: To apply the ideas of filtering hits to the active molecules identified in a SOSA based  screen.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

10.7 Natural Products Natural products video Please watch the online video (6 minutes 50 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

More natural products Background: Natural products are an established source for hits, leads, and drugs in a drug  discovery program.  Instructions: Read the passage below on three drugs that were developed as a result of insights  gained from related natural products.  Learning Goal: To recognize the structural similarities between certain drugs and the natural  products that resulted in their discovery. 


atorvastatin The synthesis of cholesterol by the body requires conversion of 3‐hydroxy‐3‐methylglutaryl‐CoA (1,  HMG‐CoA) to mevalonic acid (2) by HMG‐CoA reductase. This fact was known in the early 1970s.  During the 1970s, scientists at several pharmaceutical companies investigated different fungi for  compounds that might inhibit HMG‐CoA reductase and therefore shut down cholesterol  biosynthesis. 

The first marketed compound from this search was lovastatin (3), which is found in oyster  mushrooms. Once hydrolyzed in the body, the resulting acid (4) bears a clear resemblance to  mevalonic acid (2) (see boxed part of molecule). Armed with the idea of attaching a non‐polar group  to mevalonic acid as a lead compound (5), drug companies sought to develop synthetic versions of  lovastatin. The first synthetic version was atorvastatin (6), which is better known as Lipitor. 

zidovudine  Just as amino acids are linked together to make proteins, nucleosides are connected to make the  biological molecules DNA and RNA.  The enzymes that make DNA or RNA are called nucleotide  polymerases.  Nucleosides consist of two parts.  One part is a sugar, either ribose (7) or 2‐


deoxyribose (8).  The other part is a nucleobase.  Nucleobases can vary in structure but consist of  either a bicyclic purine core (9) or a monocyclic pyrimidine core (10).  Specific examples of  nucleosides are shown below. 

Nucleosides play a vital role in certain diseases, including viral infections.  A cell infected by a virus is  converted into a virus factory.  One thing the cell produces is the DNA or RNA required to make a  new virus.  Naturally, to make viral DNA or RNA, a cell needs a steady supply of nucleosides.  A  method for treating viral infections is to dose a patient with nucleoside analogues.  Nucleoside  analogues are molecules that are similar enough to natural nucleosides to serve as a substrate for  nucleotide polymerases but lack complete functionality to form active DNA or RNA.  The overall  effect of nucleoside analogues is that they slow the rate of virus production by infected cells.  The first nucleoside analogue developed to treat HIV infections was zidovidine (13).  Zidovudine very  closely resembles the structure of 2'‐deoxythymidine (12), a naturally occurring nucleoside in the  body.  Other antiviral nucleoside analogues include aciclovir (14) and telbivudine (15).  Nucleoside  analogues walk a very fine line for their activity.  The compounds must resemble natural nucleosides  closely enough to bind the active site of nucleotide polymerases.  The compounds must also be  different enough not to engage in the chain elongation process of making DNA or RNA. 


propranolol β‐adrenergic receptors affect a person's heart rate and vasoconstriction.  Epinephrine (16) is an  endogenous ligand for the β‐adrenergic receptors.  When pharmaceuticals started researching β‐ adrenergic receptor antagonists as potential blood pressure therapies, epinephrine was a natural  starting point. 

The first β‐adrenergic receptor antagonist, called a β‐blocker, was dichloroisoprenaline  (17).  Dichloroisoprenaline was not sufficiently active in humans, but continued research in the field  afforded propranolol (18), the first successful β‐blocker.  Both dichloroisoprenaline and propranolol  have obvious structural similarities to epinephrine. 

OPTIONAL‐Please participate in the online discussion forum. 


Welcome to Week 7 Starting week seven video Please watch the online video (1 minutes, 18 seconds).  OPTIONAL‐Please participate in the online discussion forum. 

Chapter 11 ‐ Lead Optimization Introduction to Chapter 11 Chapter 11 contains six subsections.  

Introduction

Functional Group Replacements 

Alkyl Group Replacements 

Directed Combinatorial Libraries 

Isosteres

Peptidomimetics

Upon completing this chapter, you will understand how medicinal chemists decide to make changes  to a lead to improve its properties.  You should be able to distinguish molecular changes that affect  target binding and pharmacodynamics from changes that affect the lead's pharmacokinetics.  OPTIONAL‐Please participate in the online discussion forum. 

11.1 Introduction Feedback cycle video Please watch the online video (6 minutes, 57 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Pharmacophores ‐ one last time Background: Breaking down a lead's structure and determining its pharmacophore is a key step  toward understanding how new functional groups can be introduced to affect both the binding of  the lead as well as its pharmacokinetic properties.  Instructions: Read the passage below on a familiar topic ‐ the pharmacophore of morphine.  Learning Goal: To appreciate how molecules can be minimized to their core elements that are  required for activity.  The idea of a pharmacophore was introduced back in Chapter 1.  The classic example of a  pharmacophore can be found in morphine.  Morphine (1), a natural product isolated from the poppy  seed pods, is a very complex structure. 

While morphine is indeed a complex compound, the structural elements that are required for  analgesic activity are much more modest.  By testing pieces of the skeleton of morphine,  researchers have found the pharmacophore of morphine to be a relatively simple collection of  molecular fragments.  Representation 2 shows the pharmacophore in manner to show its directly  relationship to morphine.  Representation 3 shows the pharmacophore in a more easily viewed  form. 


By reducing a lead to its pharmacophore, the lead optimization group accomplishes two tasks.  First,  the group understands what part of the lead is responsible for target binding and what part might  be modified to improve pharmacokinetics.  Second, the simpler pharmacophore may be easier to  prepare synthetically.  A more easily made structure will accelerate the rate at which new analogues  of the lead can be prepared.  For example, two synthetic and approved analogues of morphine are  meperidine (4) and ketobemidone (5).  These structures are far simpler than morphine and were  prepared directly from knowledge of the morphine pharmacophore. 

OPTIONAL‐Please participate in the online discussion forum. 

11.2 Functional Group Replacements Structure‐activity relationships video Please watch the online video (5 minutes, 49 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Single point modifications Background: Lead optimization requires the synthesis of analogues of a lead and testing of the new  analogues.  Through repeated modification and testing, the structure‐activity relationships of the  lead can be discovered and an optimized structure can be prepared.  Instructions: Read the passage below about an approach that can minimize the number of new  analogues required to optimize a lead.  Use this information to answer the questions that follow.  Learning Goal: To learn time‐saving approaches for lead optimization.  Lead analogues are prepared individually during the lead optimization phase of drug discovery.  This  synthetic effort can require a considerable amount of time.  Shortening the lead optimization  process can accelerate the advancement of new leads into the clinic.  One method for accelerating  the lead optimization process is by breaking a molecule into parts and optimizing each part  individually.  Once the optimal substituents for each part have been determined, all the optimal  parts are combined into a (hopefully) optimized lead.  This approach is called single point  modification. 


An example of the use of single point modifications to optimize a lead is shown below.  The lead  shown is a compound with two clear halves ‐ the two benzene rings.  Let's assume that the plan is to  test four different substituents (CH3, Cl, F, and CN) on each of the two rings.  To try all possible  combinations would require the synthesis of 16 (42) compounds.  With single point modifications,  the task can be accomplished with the synthesis of just 9 compounds. 

Four analogues would be made with the left half of the lead being varied.  Four analogues would be  made with the right half being varied.  The best R‐group from the left half would be combined with  the best R'‐group from the right half to give the final compound with hopefully the highest activity. 

An assumption in single point modification is that changes on one part of a molecule can be made  independently of changes on another part of a molecule.  This assumption does not always hold  true.  A seemingly small change on one side of a molecule may alter how the compound fits into the  target binding site and therefore affect how all other parts of the molecule interact with the  target.  Normally, however, the assumptions do hold up with the types of analogues that are  prepared in lead optimization studies.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Compass point nomenclature Background: Leads are often subdivided into parts or positions in the single point modification  approach of lead optimization.  Instructions: Read the passage below concerning how parts of a molecule or even molecules  themselves are sometimes named during lead discovery.  Learning Goal: To see how chemists have a little fun in the laboratory.  When leads are divided into parts for individual exploration in lead optimization, the parts and leads  normally take on a name of some sort.  Titles like "compound 10" or "C3 of the indole" might be  functional, but normally the names develop a little more flair.  An example is ezetimibe (1), a drug that decreases the absorption of cholesterol from ingested  food.  On first glance, ezetimibe has a passing resemblance to the outline of the continental United  States. 

For this reason, key compounds and functional groups in the development of ezetimibe came to be  named by the positioning of groups within the molecule.  Two examples are Florida phenol (2)  [Florida is a state in the southeast corner of the United States] and Western ketone (3). 

Naming molecules after compass points or places on a map is a small thing, but it is representative  of how chemists, who expend much energy working with series of molecules, bring some levity into  the laboratory.  OPTIONAL‐Please participate in the online discussion forum. 


11.3 Alkyl Group Replacements Homologous series video Please watch the online video (7 minutes, 8 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Ring‐chain interconversion Background: Manipulations of alkyl chains are a common part of lead optimization.  Instructions: Read the passage about converting alkyl chains on leads into rings.  Learning Goal: To learn anther use of alkyl chains in lead optimization.  Alkyl chains on leads are not just incrementally extended to generate a homologous series.1  Alkyl  chains are also frequently tied into rings with the same number of carbon atoms.  When a chain is  restrained into a ring, the lipophilicity of the lead is not greatly changed, but the steric bulk can be  reduced.  The ring can also affect how the steric bulk of the chain is presented off the lead.  Ring‐ chain analogues allow the medicinal chemistry group to probe different conformations of the alkyl  group.  Compound 1 was prepared in a study of anticholinergics.  Anticholinergics prevent the binding of  acetylcholine, a neurotransmitter, to receptors.  Anticholinergics are found in a range of drugs,  including those that treat nasal congestion, motion sickness, and muscle spasms.  Compound 1, with  its hexyl side chain, has no anticholinergic activity.  The hexyl chain was replaced with a cyclohexyl  group, the new analogue (2) was an anticholinergic.  Another valid ring‐chain analogue, which was  not reported, would be compound 3.  The side chain of 3 contains six carbons, just like  compounds 1 and2. 

Image credit: Pearson Education  1. Sastry, B. V. R. Anticholinergics: Antispasmodic and Antiulcer Drugs. In M. E. Wolff (Ed.), Burger's  Medicinal Chemistry (4th ed., Chapter 44). New York: Wiley & Sons, 1981.  OPTIONAL‐Please participate in the online discussion forum.   


Case study: oseltamivir Background: Manipulations of alkyl chains are an important part of most drug discovery programs.  Instructions: Read the passage below on a chain homologation and ring‐chain interconversion study  in the development of oseltamivir.  Learning Goal: To appreciate the extensive degree to which a single alkyl group modification in a  lead can be explored.  Oseltamivir (1) is an inhibitor that blocks neuraminidase, an enzyme that plays a role in the spread  of influenza A and B viruses.  Inhibition of neuraminidase can shorten the duration and severity of a  viral infection. 

The five‐carbon side chain on oseltamivir was discovered through a long and thorough study of  different alkyl groups.1  The table below shows the different alkyl groups that were studied on the  core structure of oseltamivir (2).  Entries in the table are sorted by the number of carbons in each  chain.  The activity of each compound against neuraminidase in both influenza A and B is shown (ND  = not determined).  An ideal influenza medication should be active (low IC50) against both viruses. 

carbons

neuraminidase IC50 (nM) 

entry

in R‐group 

R‐group

influenza A 

influenza B 

1

0

H

6,300

ND

2

1

CH3

3,700

ND

3

2

CH2CH3

2,000

185

4

3

CH2CH2CH3

180

ND


carbons

neuraminidase IC50 (nM)

entry

in R‐group 

R‐group

influenza A 

influenza B

5

4

CH2CH2CH2CH3

300

215

6

4

CH2CH(CH3)2

200

ND

7

4

10

7

9

2

8

4  

9

5

CH2CH2CH2CH2CH3

200

ND

10

5

CH(CH2CH3)2 (oseltamivir)

1

3

11

6

CH2CH2CH2CH2CH2CH3

150

1,450

12

6

60

120

13

7

CH2CH2CH2CH2CH2CH2CH3

270

ND

14

8

CH2CH2CH2CH2CH2CH2CH2CH3

180

3,000

15

9

CH2CH2CH2CH2CH2CH2CH2CH2CH3

210

ND

16

10

CH2CH2CH2CH2CH2CH2CH2CH2CH2CH3

600

ND

17

10

1

4


carbons

neuraminidase IC50 (nM) 

entry

in R‐group 

R‐group

influenza A 

influenza B 

18

10

16

6,500

19

11

1

2,150

Note that the number of different R‐groups allowed the study of not just chain homologation and  ring‐chain interconversions, but also the stereochemical configuration of side chains off the main  alkyl chain.  With such an extensive study, the lead optimization group was able to discover the best  alkyl chain for binding in the enzyme pocket of both influenza A and B.  1. Kim, C. U.; Lew, W.; Williams, M. A.; Wu. H.; Zhang, L.; Chen, X.; Escarpe, P. A.; Mendel, D. B.;  Laver, W. G.; Stevens, R. C. Structure‐Activity Relationship Studies of Novel Carboxylic Influenza  Neuraminidase Inhibitors. J. Med. Chem. 1998, 41, 2451‐2460.  OPTIONAL‐Please participate in the online discussion forum. 

11.4 Isosteres PK‐focused changes video Please watch the online video (8 minutes, 11 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 

Lists of isosteres Background: Isosteres are groups that can be frequently interchanged for one another on a lead  without dramatically affecting target binding.  Instructions: Read over the list of classical and nonclassical isosteres below.  Use the isosteres to  answer the questions that follow.  Learning Goals: To gain exposure to different isosteres and practice using them to modify leads. 


Below are two tables of isosteres.  Remember that classical isosteres emphasize maintaining the  same steric size between different groups.  Nonclassical isosteres emphasize maintaining the same  electronic and hydrogen bonding interactions.  All the groups listed across a row are potential  isosteres of one another.  Therefore, replacement of a −CH2− (a divalent group) with an −O−  (another divalent group) is a valid classical isosteric substitution.  Replacement of hydrogen atom  with a fluorine is a valid nonclassical isosteric substitution.  By no means are these tables a complete  listing of all isosteres.    Classical isosteres  description 

isosters

equivalent univalent  groups  (by size) 

small groups: CH3, NH2, Cl  intermediate groups: Br, CH(CH3)2  large groups: I, C(CH3)3 

equivalent divalent groups  equivalent  aromatic  ring groups 

−CH2−, −NH−, −O− 

1.

2.


Nonclassical isosteres (or bioisosteres)  description 

isosters

hydrogen equivalents 

H, D (deuterium), F 

carboxylic acid equivalent   

hydroxy equivalants 

OH, CH2OH, CH(CN)2, NH(CN) 

thiourea equivalent   

Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

Case study: venlafaxine Background: Isosteres are groups that can be frequently interchanged for one another on a lead  without dramatically affecting target binding.  Instructions: Read the following passage about the development of improved forms of venlafaxine.  Learning Goals: To see an application of isosteres in compounds that are currently in clinical trials.  In the drawing on the following page, Venlafaxine (1) is an approved and marketed  antidepressant.  Venlafaxine is metabolized primarily by phase I oxidation of the O‐methyl group off  the benzene ring.  This demethylation is performed by CYP2D6.  The activity of CYP2D6 can vary  significantly across different patient populations.  Because of this metabolic variability, the Cp‐time  profile for venlafaxine can also vary from one patient to another.  Inconsistent Cp values have been  implied to be associated with some of the side effects of venlafaxine.1 


In order to make venlafaxine more predictable, researchers made numerous bioisosteric  replacements to the drug.  All the hydrogens on the methyl groups ‐ nine hydrogens in all ‐ were  replaced with deuterium atoms (2H).  The resulting compound (3) is shown below. 

Deuterium, because it is identical in size and electronic character to hydrogen, should have no effect  on the reversible binding of the molecule to its protein target.  Deuterium, because it has a higher  mass than hydrogen, can differ in how it participates in chemical reactions.  The deuterium‐carbon  bond vibrates at a lower frequency than the hydrogen‐carbon bond.  With the lower frequency  comes lower reactivity ‐ potentially a slower rate of metabolism by the population‐dependent  CYP2D6.  Compound 3 has completed phase I trials and reportedly shows a more consistent Cp‐time  profile than venlafaxine.  1. Auspex Pharmaceuticals. Pipeline. http://www.auspexpharma.com/pipeline/ (accessed January  2014).  OPTIONAL‐Please participate in the online discussion forum. 

11.5 Directed Combinatorial Libraries Covering all possibilities video Please watch the online video (5 minutes, 51 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Case study: sorafenib Background: Directed combinatorial libraries are useful for quickly searching for optimized leads  and can help discover compounds that might be missed by traditional SAR techniques.  Instructions: Read the passage below about sorafenib, a drug that was discovered in part through  use of a directed combinatorial library.  Learning Goal: To see a specific example of how directed combinatorial libraries can play an  important role in drug discovery.  Many kinases (enzymes that phosphorylate other molecules) are very important targets in cellular  pathways that are associated with cancer.  One such kinase is called raf kinase.  Compound 1 was found in a lead discovery program that was focused upon raf kinase inhibitors.1  As  part of the lead optimization process, researchers generated a directed combinatorial library around  the scaffold of lead 1. 

The library initially included around 1,500 analogues of 1, but only about 1,000 were determined to  be adequately pure for reliable testing.  The best molecule in the library was found to be  structure 2.2 

The discovery of 2 was a surprise to the lead optimization team.  As with all leads, the structure  of 1 had been explored somewhat before 1 had been elevated to lead status.  In that early SAR  work, the replacement of the thiophene ring with an isoxazole ring was found to decrease the  activity of the molecule.  The inclusion of groups larger than a CH3 group C4 of the benzene ring also  caused activity to decrease.  For these two reasons, the activity of compound 2 was unexpected.  In  a traditional, single‐point modification SAR study, it is very unlikely that these two non‐promising  functional groups would be pursued for further study.  An advantage to a directed combinatorial  library is that activity assumptions are ignored and all feasible analogues are prepared and tested. 


Lead 2 was further optimized and sorafenib (3), a potent inhibitor of raf kinase and an effective  treatment for certain forms of cancer.  Without the preparation of a combinatorial library, sorafenib  may not have been discovered. 

1. Smith, R. A.; Barbosa, J.; Blum, C. L.; Bobko, M. A.; Caringal, Y. V.; Daly, R.; Johnson, J. S.; Katz, M.  E.; Kennure, N.; Kingery‐Wood, J.; Lee, W.; Lowinger, T. B.; Lyons, J.; Marsh, V.; Rogers, D. H.;  Swartz, S.; Walling, T.; Wild, H. Discovery of Heterocyclic Ureas as a New Class of Raf Kinase  Inhibitors: Identification of a Second Generation Lead by a Combinatorial Chemistry  Approach. Bioorg. Med. Chem. Lett. 2001, 11, 2775‐2778.  2. Lowinger, T. B.; Riedl, B.; Dumas, J.; Smith, R. A. Design and Discovery of Small Molecules  Targeting Raf‐1 Kinase. Curr. Pharm. Des. 2002, 8, 2269‐2278.  OPTIONAL‐Please participate in the online discussion forum. 

Back to bioisosteres Background: Bioisosteres were introduced in the previous section, Chapter 11 Section 4.  Instructions: Read the following passage about the bioisosteres of a specific, common organic  function group ‐ the carboxylic acid.  Use the JSDraw application to draw valid bioisosteric analogues  of ibuprofen.  Learning Goals: To gain more exposure to bioisosteres and become more comfortable with the new  JSDraw interface.  The carboxylic acid is a functional group that has been explored extensively in terms of bioisosteric  substitutions.  On the following page is a small listing of the number of different groups that have  demonstrated potential as replacements for a carboxylic acid in a drug candidate.  To some degree,  each group preserves the electronic and hydrogen bonding ability of a carboxylic acid. 


Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 

11.6 Peptidomimetics Peptides as hits and leads video Please watch the online video (8 minutes, 9 seconds).  A condensed summary of this video can be found in the Video summary page.  OPTIONAL‐Please participate in the online discussion forum. 


Case study: HIV‐1 protease inhibitors Background: Peptidomimetics describes the design of a compound that imitates the activity and  appearance of a peptide while improving its bioavailability for oral delivery.  Instructions: Read the passage below about the development of HIV‐1 protease inhibitors.  Learning Goal: To see the challenges of developing an oral drug based on a peptide lead.  HIV‐1 protease cleaves peptide chains, so its natural substrate is a peptide.  Early x‐ray  crystallographic data on HIV‐1 protease showed a handful of important hydrophobic and hydrogen  bonding interactions between the substrate and enzyme.  Based off this information, researchers at  Abbott reported in 1994 a pseudopeptide, compound A74704 (1), with low nanomolar inhibitory  activity.  Compound 1 preserved the key interactions of the native substrate in part by allowing the  inclusion of a water molecule in the binding pocket.1,2 

Within five years three HIV‐1 protease inhibitors were approved by the US FDA.  Ritonavir (2) from  Abbott and saquinavir (3) from Roche were approved first, and amprenavir (4) from GlaxoSmithKline  appeared a few years later.  Both ritonavir and saquinavir retain some peptide character and even  contain an amino acid residue.  Amprenavir, on the other hand, is a true peptidomimetic with no  amide linkages or amino acid residues.  All three compounds have visual similarities, especially their  common reliance upon CH2Ph and isopropyl side chains. 


Not all research efforts into HIV‐1 protease inhibitors took the peptidomimetic route.  A group at  DuPont Merck use computer‐based screening techniques and brought forward a non‐peptide  lead (5) which evolved into DMP323 (6).  Similarly, a group at Abbott developed a nearly identical  compound (7), A98881.  Abbott's inclusion of an extra nitrogen in the ring was to avoid patents that  DuPont Merck held around their own related compounds.  Although it may not be obvious,  both 6 and 7 contain the required number of hydrophobic and hydrogen bonding groups to interact  the exact same binding positions as were revealed with Abbott original pseudopeptide lead (1),  A74704. 

Compounds 6 and 7 both failed in clinical trials.  Compound 6 showed poorly reproducible  bioavailability.  Compound 7 was poorly bioavailable.  The poor bioavailability was linked to the third  nitrogen in the ring.  In this example of HIV‐1 protease inhibitors, the peptidomimetic route won out over the small  molecule, traditional lead discovery route.  Sometimes, when a peptide is the primary lead, the  small molecule approach ends up being the better path to a drug.  Regardless, designing a drug from  a peptide lead is a challenging problem.  1. Lam, P. Y. S.; Jadhav, P. K.; Eyermann, C. J.; Hodge, C. H.; Ru, Y. Rational Design of Potent,  Bioavailable, Nonpeptide Cyclic Ureas as HIV Protease Inhibitors. Science 1994, 263, 380‐384.  2. Greer, J. Molecular Modeling. Presented at Residential School on Medicinal Chemistry, Drew  University, Madison, NJ, June 2002. Seminar I.  OPTIONAL‐Please participate in the online discussion forum. 

Identifying peptide isosteres Background: Peptide isosteres are often used to improve the bioavailability of peptide leads.  Instructions: Examine the compounds below and answer the questions that follow.  Learning Goal: To practice identifying peptide isosteres.  Please complete the online exercise.  OPTIONAL‐Please participate in the online discussion forum. 


Examination 3 Third Examination The exam is open book and open notes.  All questions may be attempted once, so be certain of your  answer before submitting it.  There are ten questions.  Each is its own unit within the Examination 3  subsection.  Remember that you are bound by the honor code.  No postings to the forum concerning the exam  are allowed.  Furthermore, you must work on the examination independently. 

Problems Please complete the online problems in Examination 3.   


DavidsonX – D001x – Medicinal Chemistry Chapter 1 – Pre-Regulatory Medicine Part 1 – Natural Products Video Clip – Ephedrine One of the first medical plants referenced in the historical record is Ma Huang, a shrub indigenous to China. When the dried stems of Ma Huang are steeped in hot water, the resulting extract contains therapeutic levels of ephedrine (1). The early Chinese noted the ability of Ma Huang to alleviate cold symptoms. The effect of Ma Huang is attributable to ephedrine, which is a vasoconstrictor and stimulant. OH

H N

CH3

CH3

ephedrine (isolated from Ma Huang) 1

Compounds that (1) are extracted from natural sources, (2) contain a basic nitrogen atom, and (3) have a degree of structural complexity are called alkaloids. Ephedrine is a member of a large class of molecules called phenethylamines. The parent compound of this class, phenethylamine (2). All compounds in this class contain phenyl and amino groups linked by a two-carbon tether. In the case of phenethylamines, these three structural elements (phenyl, amino, and tether) define the pharmacophore of the class. A pharmacophore is the minimal functionality for the compounds to be biologically active. Understanding which parts of a molecule are crucial for activity is an important concept in medicinal chemistry. H N

H

phenethylamine (a neurotransmitter) 2

Two other members of the phenethylamine class are pseudoephedrine (3) and methamphetamine (4). Pseudoephedrine is a diastereomer of ephedrine and is a common ingredient in over-the-counter cold medications. The popular cold medicine brand Sudafed partially derives its name from pseudoephedrine. The availability of pseudoephedrine in pharmacies makes it a favorite starting material for the illegal production of methamphetamine. Methamphetamine, often simply called "meth", is a powerful, addictive stimulant. OH

H N

CH3

CH3

pseudoephedrine decongestant 3

H N CH3

CH3

methamphetamine stimulant 4

The stimulant properties of phenethylamines makes the molecules excellent candidates as diet drugs. Indeed, a number of diet drugs are built upon the phenethylamine scaffold. Two examples are phentermine (5) and fenfluramine (6). In the late 1990s some physicians began prescribing phentermine and fenfluramine together. The co-use of the two drugs was called "fen-phen". Fen-phen was a very potent diet medication and quickly rose in popularity in diet centers. Unfortunately, some patients taking fen-phen suffered heart valve damage, and a few patients even died from heart issues.


H N

CH3

H3C CH3

phentermine anti-obesity 5

F3C

H N CH3

CH3

fenfluramine anti-obesity 6

Undesirable effects of a drug are called adverse effects, adverse events, or side effects . Examples of adverse effects can range in severity from severe, including major organ damage or death, to mild, such as drowsiness or dry mouth. [The video is misleading on this point. There is no difference technical difference between adverse effects, adverse events, or side effects. The terms adverse effects and adverse events are more commonly encountered in the pharmaceutical and healthcare industries. Side effect is a term used most often by the general public.] The co-administration of phentermine and fenfluramine was not an approved or tested use of the medications. The practice of prescribing medications in an unapproved fashion is called an off-label use. Off-label use is common and frequently very beneficial for patients, but the practice does carry increased risks. Early investigations into fen-phen focused on off-label use with the idea that the combination of drugs caused the adverse effect. Continued research revealed that some patients taking fenfluramine alone also develop heart valve problems. Even 15 years after the discovery of fen-phen's problems, lawsuits continue to be filed against fen-phen's manufacturer. The phenethylamine drug class includes compounds with a number of potent effects. The history of the class can be traced by 5,000 years to ancient China and the Ma Huang plant.


DavidsonX – D001x – Medicinal Chemistry Chapter 1 – Pre-Regulatory Medicine Part 2 – Synthetic Drugs Video Clip – Sulfa Drugs Some of the earliest synthetic organic molecules were dyes. The dyes were of interest not only in industry but also in biology. Dyes were found to be useful in biology for staining certain types of cells. Paul Ehrlich, an immunologist in the late 1800s, realized if dyes can selectively stain certain types of cells, then maybe toxic dyes can selectively kill certain types of cells. Ehrlich called this idea of a selective drug a magic bullet. After receiving a Nobel Prize in Medicine in 1908 for his work in immunology, Ehrlich developed a new arsenical drug with activity against the bacteria that cause syphillis. Arsenicals are a drug class in which each member contains an arsenic atom. The arsenic atom makes the drug toxic, but Ehrlich's drug was much less toxic, and still effective, than other arsenicals. Georg Domagk of Germany followed in the footsteps of Ehrlich's work and explored the use of dyes as antibiotics. Domagk discovered a particularly effective red dye, which was later called Prontosil Rubrum (1). Prontosil Rubrum was active against multiple different types of bacteria and was an immediate sensation in medicine. Domagk later received the Nobel Prize in Medicine in 1939 for his work on Prontosil Rubrum. O O S NH2

NH2 N

N

Prontosil Rubrum antibiotic 1

H2N

When Prontosil Rubrum reached the market in 1935, a group of French researchers almost immediately discovered that Prontosil Rubrum has little or no antibacterial effect. Instead, Prontosil Rubrum is metabolized in the body to form sulfanilamide (2), which is the active form of the drug. Prontosil Rubrum is therefore classified as a prodrug. Prodrugs are inactive compounds that react in the body to form an active drug. O O S NH2

sulfanilamide antibiotic 2

H2N

Sulfanilamide was soon used in place of the more complex and expensive Prontosil Rubrum as an antibiotic. Sulfanilamide itself is the parent structure of an entire class of molecules, the sulfonamide antibiotics or just sulfa drugs. The entire structure of sulfanilamide is very nearly equivalent to the pharmacophore of the sulfa drugs in general (see a following exercise for more details). While sulfa drugs were a very successful class of antibiotics, the compounds are not in widespread use today. As with many antibiotics, after prolonged use, sulfa drugs have become less effective against most bacteria. This mutational resistance involves genetically mutated bacteria that develop immunity to a drug. One exception is sulfamethoxazole (3). Sulfamethoxazole is occasionally prescribed for inner ear infections.


CH3 O O S N H

N

O

sulfamethoxazole antibiotic 3

H2N

Although the sulfa drugs are no longer in general use, the drug class is very important as one of the first examples of wholly synthetic medicines.


DavidsonX – D001x – Medicinal Chemistry Chapter 2 – Drug Discovery: From Concept to Market Part 1 – Phenotype- and Target-Based Drug Discovery Video Clip – Phenotype vs. Target The second chapter of this course gives an overview of the drug discovery process. In a broad sense, there are two different approaches to drug discovery. One is phenotype-based, and the other is target-based. A target-based discovery program begins with an intensive study of the biochemical pathways that are believed to be involved in a disease. Controlling these pathways are proteins. A target-based program selects one of these proteins as a point of intervention to affect the disease state. The selected protein is called the drug target. A biochemical test or assay (in vitro test) is created to test the ability of a drug to bind to the target. With a reproducible test in hand, a drug discovery group can then modify compounds and improve their ability to bind to the target. Once a compounds has been sufficiently optimized, it may become a drug. Optimization in target-based screens tends to be very fast because in vitro tests are typically automated and very rapid to perform. What is being optimized, however, is not biological effect. It is instead the binding of the drug to its target. A compound that binds well to its target, however, may not have a significant biological effect. Effects may require interaction with multiple pathways. Therefore, good binding does not always translate into an effective molecule. A phenotype is an external trait of an organism. That trait can be anything from height or hair color to blood pressure or a skin rash. From a drug standpoint, traits that can be affected by medication are of greatest interest. Phenotype-based drug discovery begins with an observation of a compound with properties that can change an organism's phenotype. Tests in animals (in vivo tests) are designed to observe and measure the degree of change caused by a molecule. With a reproducible test in hand, a drug discovery group can then modify the original compound to improve the ability of the drug to affect the phenotype. In the phenotype method, new compounds will almost certainly have a biological effect because they were derived from an original compound that was also effective. On the other hand, improvements in the original compound tend to be very slow because of the complicated nature of in vivo testing. In summary, because in vitro testing tends to be rapid, a target-based drug discovery program can procede quickly. The effectiveness of discovered molecules is, however, not ultimately confirmed until the compound is tested in vivo, especially in humans. Phenotype-base drug discovery, because it starts with an active molecule, may be more successful in developing an active molecule. The phenotype route, however, requires an active compound as a starting point, and optimization can be slow if the target is unknown and all testing must involve in vivo screens. While this course emphasizes the target-based approach to drug discovery, it is important to understand that other methods are used to develop drugs. The phenotype method is often favored when the biochemistry of the disease state is not well understood. The target approach is frequently preferred when the pathways are known. Both approaches have an ardent group of supporters.


DavidsonX – D001x – Medicinal Chemistry Chapter 2 – Drug Discovery: From Concept to Market Part 2 – Drug Discovery Outline Video Clip – Drug Discovery Outline The process of bringing a drug to the market is dominated by the total cost of the endeavor. Cost estimates vary considerably and can be found below US$100 million and all the way up to US$5 billion. The most common estimates hover around US$1 billion or a little higher. This figure both is easy to remember and emphasizes the point that drug discovery is enormously expensive. With such costs, it may be little surprise that drug discovery programs tend to focus upon diseases that affect a large percentage of the population or have a high probability of allowing a company to recoup its expenses. Conditions like hypertension, diabetes, cancer, and Alzheimer's frequently attract the attention of pharmaceutical companies. The first step in drug discovery program is to understand the biochemical pathways involved in the disease. This task is performed by the molecular biology group. The pathways are controlled by proteins. The molecular biology group ultimately selects a protein that plays a key role as the target of the drug program. By binding a drug to the target protein, the pathway will be affected along with the disease state in the patient. The molecular biology group then creates an assay, a test that allows measurement of the protein-drug binding. Drug-target binding involves an equilibrium between the drug and target. The equilibrium is typically quantified as the dissociation equilibrium constant, K. A smaller value for K represents a more tightly bound drug-target complex. K

[drug] [target] K=

+

drug-target complex

target

[drug-target]

drug

At this stage, the program is handed off to the medicinal chemistry group. The med chem group starts with the task of lead discovery. Large collections of 1,000,000 or even more different molecules are tested in the assay. The most potent molecules in this preliminary screen for activity are called hits. Hits typically have a value for K of around 1 μM (micromolar). The hits are filtered for properties other than target binding. Other properties include ability to diffuse across membranes, interaction with metabolic processes, and patentability. The most promising hits are promoted to lead status. Leads, which have likely been modified and somewhat improved over the original hits, may have an initial K value of 100 nM (nanomolar). The program then shifts to lead optimization, and the medicinal chemistry group aims to improve both drug-target binding and drug effectiveness (pharmacodynamics) as well as the in vivo transport properties (pharmacokinetics). Both goals are accomplished by modifying the chemical structure of a lead. Repeated structural changes with feedback from binding results and testing in animals ultimately provides a fully optimized lead with a K of 10 nM or lower. While leads are routinely tested in animals, only late in lead optimization will the lead be put


through formalized animal toxicology studies. These animal tests involve standardized protocols to determine the safety and fate of the drug in a living organism. If favorable, the full results of the laboratory and animal tests are compiled. The sponsoring company submits a investigational new drug (IND) application to the US Food and Drug Administration (FDA). If the IND is granted, the lead is becomes an investigational new drug, more commonly called a clinical candidate. Clinical trials begin. Phase I trials involve a small number of patients, often 10 to 20 or slightly more. Participants in Phase I trials are healthy volunteers, and the purpose of Phase I trials is to determine the safety of the drug along with preliminary pharmacokinetic information (e.g., the half-life of the compound). Phase II trials involve 100 to 200 patients who are diseased. Phase II continues to test safety, but the effectiveness of the drug becomes more important. Dosing also becomes a focus. Phase III trials can involve 1,000 or more (even many, many more) patients, and efforts are made to include a diverse population in the studies. Safety and factors for specific subpopulations (e.g., diabetics or juvenile patients) are the focus of Phase III trials. The full results of the project are sent to the FDA in the form of a new drug application (NDA). If the NDA is granted, the clinical candidate will become a drug and be marketed. Safety monitoring will continue for the drug, and these post-approval studies are sometimes called Phase IV trials.


DavidsonX – D001x – Medicinal Chemistry Chapter 2 – Drug Discovery: From Concept to Market Part 3 – Intellectual Property Video Clip – Patents and Branding Intellectual property is a legal description for certain types of intangible assets. The owner of intellectual property has the exclusive ability to operate within an area defined by those assets. Examples of intellectual property include patents, trademarks, trade secrets, and registered designs. In medicinal chemistry, patents and trademarks are the most important. An example of a trademark is the brand name of a drug. Acetominophen (1) is an over-thecounter medication. It is available in its generic form under the names of acetominophen and paracetamol as well as the brand name Tylenol. Tylenol is a trademark. The owner of the trademark, McNeil Consumer Healthcare, pays to preserve its rights to control the name. H N

CH3 O

HO

1

Tylenol

trade name

acetaminophen paracetamol

non-proprietary or generic names

Patents are another type of intellectual property. Patents come in many different types, and the composition of matter form is likely most important in the pharmaceutical industry. A composition of matter patent protects a particular chemical substance. Like all patents, a composition of matter patent is valid for 20 years from the date of patent filing. This 20-year window is extremely important and defines the time period during which the drug company will be able to realize the maximum profits from the drug. After the patent expires, generic manufacturers will be able to sell the same drug. Competition will drive down prices, and profitability for the original company will fall. A full 20 years may seem like a generous amount of time for a drug to recover its development expenses and turn a profit. It is not quite that simple. Drug companies patent interesting leads around the stage of animal studies. Before the lead can be sold as an approved drug, it must progress through all necessary animal studies, the IND application, clinical trials, and the NDA. The time period for each of these steps is estimated below, and the total can easily add up to ten years or more. Estimated Time of Development animal studies

2 years

IND application

1 month

clinical trials NDA

~7 years 1 year

The actual time for a drug manufacturer to recover its research costs is around ten years. The exact number depends on how smoothly the drug discovery process goes for a specific drug. The finite length of a patent's lifetime places significant pressure on the drug discovery group to work as efficiently as possible to move a lead through the clinical and regulatory steps. After a composition of matter patent expires, generic manufacturers are free to create their own versions of the same drug and seek approval from the FDA to market their drugs. The


approval process for a generic drug involves bioequivalence testing. The generic manufacturer must test its drug in humans and show that blood levels for the generic drug are similar to the branded drug. These trials are fairly short and must less expensive than traditional clinical trials for new drugs. Therefore, generic manufacturers incur much smaller developmental costs and can charge less for their drugs. An example of the financial impact of an expired patent can be seen in Pfizer's Lipitor. In 2011 Lipitor had global sales of US$9.6 billion. At the end of 2011 the composition of matter patent expired and generic drugs entered the market. In 2012 the global sales for Lipitor had dropped to US$3.9 billion – a drop of around 60%. The 2013 sales numbers will continue to drop. Because so much money is at stake, lawsuits between generic and branded drug manufacturers are very common. The role of patents in the marketplace is two-fold. First, by granting market exclusivity, patents encourage the introduction of new drugs. Patents reward innovation. Second, with a 20-year lifetime, patents ensure that new drugs eventually become widely available at a lower price. Patents assure that innovations are made available to the public over time.


DavidsonX – D001x – Medicinal Chemistry Chapter 3 – Protein Structure Part 1 – Intro to Structure Part 1 Video Clip – Amino Acids to Secondary Structure Proteins consist of many individual α-amino acids. Amino acids are carboxylic acids that have an amine group off the α-carbon relative to the acid. There are 20 standard amino acids. Each differs based upon the R-group that branches off the α-carbon. The 20 different groups provide structural and functional variability in proteins. H R HO

an alpha amino acid

 NH2 O

The amino acids can be sorted in different ways. One common method involves distinguishing amino acids by polarity and charge. By this method, half of the 20 amino acids can be considered to have non-polar R-groups, and the other half has polar R-groups. Of the polar R-groups, half are neutral at physiological pH (~7.4) and half may be charged in the body. Numerous online sources, such as Wikipedia, contain full listings of the structures and properties of the amino acids. Amino acids are strung together to form proteins. The connections between amino acids are called amide linkages. Short polymers of amino acids are called oligopeptides. Longer polymers are proteins. The amide linkages and α-carbons represent the backbone of proteins. This backbone (shown in blue below) serves as a scaffold to display hydrogen bond acceptors (the amide carbonyl), hydrogen bond donors (the amide N-H group), and the various R-groups one each amino acid. Collectively, the various functional groups allow the formation of all the diverse proteins from just 20 different amino acids. R H2N O

R2

O

H N 1

R

N H

O

R4

O

H N 3

R

N H

OH O

The order of amino acids represents a protein's primary structure. This is the simplest level of protein structure. The primary structure of angiotensin I (PDB: 1N9U), a small yet key oligopeptide in the regulation of blood pressure, is shown below. Two views are shown. The image on the left shows just the backbone, and the one on the right shows the protein with its full complement of R-groups.


As proteins get longer, regions of the protein assume patterns in folding. These regions are examples of secondary structure. Common types of secondary structure are the α-helix, βsheet, and random coil. Yes, the absence of an organized structure (random coil) is considered to be a type of secondary structure. In the Protein Data Bank, the secondary structures in proteins are color-coded. Right handed α-helices are pink. Left handed αhelices are purple. β-Sheets are yellow. Random coils are shown as white lines. Two protein examples are shown below. The image on the left is pilin (PDB: 3SOK), a bacterial protein that helps join the cell membranes of two bacteria. Pilin is characterized by a long α-helix (in pink) that penetrates the cell membrane. The image on the right is crystallin (PDB: 2WJ7), a protein in the eye lens. Crystallin is mostly β-sheets.

Together, primary and secondary structure only partially describe proteins. Tertiary and quaternary structure complete the picture and are the subject of the next section.


DavidsonX – D001x – Medicinal Chemistry Chapter 3 – Protein Structure Part 2 – Intro to Structure Part 2 Video Clip – Tertiary Structure to X-Ray Crystallography As a review, we have already seen amino acids, which are the building blocks of proteins. Amino acids link together to form the functionalized backbone of a protein. The order of the amino acids determines the primary structure of the protein. Sections of the protein assume folding patterns called secondary structure. Examples of secondary structure include the αhelix and β-sheet. Within a complete protein are several different secondary structures. The three-dimensional, overall arrangement of these secondary structures defines the overall shape, or tertiary structure, of the protein. An example of a complete protein is shown below. The protein is the enzyme adenosine deaminase (PDB: 3EWC).

Within the different secondary structures of 3EWC are pockets and cavities. These cavities create opportunities for smaller molecules, including drugs, to interact tightly with the protein affect the protein's function. Remember that proteins are frequent targets for drugs. In the very middle of the structure of 3EWC is a bound molecule, visible in the center of the structure. This bound molecule blocks of the function of adenosine deaminase and inhibits its function. Since tertiary structure describes the three-dimensional shape of a protein, another level of protein structure is not obvious. The final level, quaternary structure, accounts for the fact that some proteins aggregate with other proteins in order to function properly. Protein-protein complexes are common in cellular processes, so the concept of quaternary structure is needed to understand fully how proteins operate. An example of quaternary structure can be seen in alcohol dehydrogenase (PDB: 1AGN). Upon first glance, the structure of alcohol dehydrogenase appears unremarkable, but the protein is actually a dimer of two identical proteins (a homodimer). The Protein Data Bank


visualization software allows one to color the two proteins differently by selecting the Subunit button. The left image below is the original view of the dimer while the right image reflects different coloring for each protein subunit – one red and the other green-gray.

Protein structures are determined primarily through the interpretation of X-ray crystallographic data. X-ray data is obtained by passing X-rays through crystallized protein. The X-rays are scattered by the electons in the protein and create a diffraction pattern. The pattern can ultimately be interpreted to discover the structure of the protein.

While structures in data depositories like the Protein Data Bank seem very authoritative and inspire confidence, interpreting X-ray crystallographic data is not easy. The diffraction data is converted to an electron density map, which shows the location of different atoms within the protein. The clarity of the atom positions depends on the quality of the density map. A high quality map is said to have higher resolution. Resolution is listed as a number with angstrom units (Å). A smaller number denotes higher resolution. Crystal structures with a resolution of 2.0 Å or less are considered to be high resolution structures. Examples of electron density maps of the same structure at different resolutions are shown below. Up to a resolution of 2.0 Å each atom is fairly distinct. At 3.0 Å and above, structural details become much less clear, and accurately assigning the structure of the protein becomes more difficult. (Images of electron density maps used with permission of Paul Emsley, Cambridge University.)


0.62 Å

1.2 Å

3.0 Å

4.0 Å

2.0 Å

5.0 Å

Although correctly assigning a structure to a protein can be a challenge, knowing the structure of a protein target is invaluable for medicinal chemists. Understanding the folding of a protein and its potential binding sites can guide the design of drugs that will effectively target the protein.


DavidsonX – D001x – Medicinal Chemistry Chapter 4 – Enzymes Part 1 – Michaelis-Menten Kinetics Video Clip – Theory of Action Enzymes are catalysts that facilitate the conversion of a substrate (S) to a product (P). The action of many enzymes can be reduced to the very simple reaction scheme shown below. This scheme involves the reversible binding of an enzyme (E) and substrate to form an enzyme-substrate complex (E-S). The complex can either dissociate or the substrate can be converted to a product and immediately unbind from the enzyme. E+S

E-S

E+P

unbound enzyme & substrate

enzymesubstrate complex

unbound enzyme & product

Enzymes that fit this simplified model show a hyperbolic relationship between the rate of the enzymatic reaction (V) and the concentration of the substrate. In a plot of V vs. [S], the rate of reaction increases with higher substrate concentration. At low substrate concentrations, V and [S] are nearly linearly related. As [S] rises, V slowly plateaus to a maximum level, called Vmax. Vmax is only achieved at infinite [S] and is therefore a theoretical value. The substrate concentration required to achieve ½Vmax is a special concentration called the Michaelis constant, Km. Km is a reflection of the affinity of a substrate for an enzyme. A lower value for Km indicates a higher affinity.

Michaelis-Menten Kinetics

V

Vmax

1/2 Vmax

Km

[S]

The graph above of V vs. [S] is an example of Michaelis-Menten kinetics and follows the mathematical relationship, the Michaelis-Menten equation, shown below. V=

Vmax [S] Km + [S]

The development of the Michaelis-Menten equation was a wonderful advancement in enzyme kinetics in the early 1900s. The hyperbolic equation was, however, a challenging relationship to fit in an age without computers. The Lineweaver-Burk equation was developed in the 1930s as a linearized form of the Michaelis-Menten equation. Linear relationships were much


easier to fit to experimental data. 1 V

=

Km

1

Vmax

[S]

+

1 Vmax

The Lineweaver-Burk equation is plotted as 1/V vs. 1/[S]. The slope of the line is Km/Vmax, and the y-intercept is 1/Vmax. The x-intercept occurs at x = −1/Km.

1/V

Lineweaver-Burk plot

1/Vmax

slope =

-1/Km

Km Vmax

1/[S]


DavidsonX – D001x – Medicinal Chemistry Chapter 4 – Enzymes Part 2 – Enzyme Inhibition Video Clip – Reversible Inhibitors From a drug discovery standpoint, enzymes are very interesting, but only in as much as they can serve as drug targets. Drugs that interfere in an enzyme's function are called inhibitors. Inhibitors are classified as being reversible or irreversible. Among the reversible inhibitors are three different types: competitive, noncompetitive, and uncompetitive. All inhibitors, when bound to an enzyme, prevent the conversion of substrate to a product. Competitive inhibitors bind an enzyme just like a substrate does – at the active site. The active site is the binding pocket in which a substrate is converted to a product. By trying to occupy the active site, the inhibitor competes with the substrate for the enzyme. E-S + I

E+P+I

E+S+I E-I + S

Mathematically, the effect of a competitive inhibitor on an enzyme is to decrease the affinity of an enzyme for a substrate. The affinity is measured by Km, the Michaelis constant, and a lower affinity appears as a higher value for Km in the presence of the inhibitor. Vmax, however, is unchanged, but it does require a higher substrate concentration to approach Vmax if an inhibitor is present. Visually, these changes are apparent in the graph below.

Reversible Competitive Inhibitor

V

Vmax

1/2 Vmax

Kmuninh

Kminh

Kminh

[S]

Noncompetitive inhibitors can bind both the enzyme and the enzyme-substrate complex. The inhibitor binds at a site other than the active site. The other site is called an allosteric site. Noncompetitive inhibitors reduce Vmax but give no change in Km. E+S+I

E-S + I

E-I + S

I-E-S

E+P+I


Reversible Noncompetitive Inhibitor

V

Vmaxuninh

Vmaxinh

Vmaxinh

Km

[S]

Uncompetitive inhibitors bind only the enzyme-substrate complex. This binding decreases Vmax and decreases Km. The net effect is that the inhibited V vs. [S] lines tightly follow the uninhibited line and then rapidly plateau toward Vmax. In extreme cases, the inhibited lines can actually shift left of the uninhibited line before quickly moving to the right with a lower Vmax value. This “crossing� of the lines is found with some, but not all, uncompetitive inhibitors. E+S+I

E-S + I

E+P+I

I-E-S

Reversible Uncompetitive Inhibitor

V

Vmaxuninh

Vmaxinh

Vmaxinh

[S]

Of the three types of reversible inhibitors, we will focus most on competitive inhibitors. Competitive inhibitors are very common in medicinal chemistry. The reason is because if you know the natural substrate of an enzyme, then you also know the same of a molecule that binds the enzyme's active site. That knowledge is very helpful in the design of other molecules, such as competitive inhibitors, that will also bind the active site. We have avoided irreversible inhibitors because drug programs also generally avoid irreversible inhibitors. Irreversible inhibitors act by chemically reacting with the enzyme. The altered enzyme is then ineffective for converting a substrate to a product. Irreversible


inhibitors therefore are somewhat chemically unstable and at risk for reacting elsewhere in the body and not just at the desired enzyme. In the past five years, however, medicinal chemistry has enjoyed a resurgence in irreversible inhibitors. This new interest is irreversible inhibitors is most common in cancer treatments, an area in which the potential benefits outweigh the serious risks. Regardless, competitive reversible inhibitors are still more common than all other types of enyzme inhibitors.


DavidsonX – D001x – Medicinal Chemistry Chapter 4 – Enzymes Part 3 – Measuring Inhibition Video Clip – IC50 and Ki Understanding how to distinguish types of inhibitors is important, but much of the drug discovery process focuses instead upon determining whether one inhibitor is more effective than another. This determination requires quantification of an inhibitor's potency. The two most common values for quantifying an inhibitor's effect are IC50 and Ki. While these values can be determined for different types of inhibitors, we will be discussing specifically reversible competitive inhibitors. IC50 is the concentration of an inhibitor required to reduce the rate of an enzymatic reaction by 50%. IC50 values are determined through a series of experiments. For all experiments, a high, constant concentration of substrate is present so that the enzyme can react at an appreciable rate. In each experiment, the amount of inhibitor (log [I]) is steadily increased, and the observed rate of the reaction (V) decreases accordingly. The various experiments are plotted at V vs. log [I] to generate a sigmoidal curve. The point of inflection of this curve corresponds to the logarithm of the inhibitor concentration that decreases V by 50%.

V

IC50 Determination

log IC50

log [I]

Ki is the dissociation equilibrium constant of the enzyme-inhibitor complex (E-I). E-I

Ki

E+I [E] [I]

Ki =

[E-I]

Ki values are determined through a series of experiments with varying amounts of inhibitor present. Each experiment allows a Km (Kmobs) for that particular concentration of inhibitor ([I]). If it seems odd to think that Km (the Michaelis constant) can vary, remember that in the presence of a competitive inhibitor, the affinity of an enzyme for a substrate decreases. That is to say, Km increases. Plotting Kmobs values against [I] generates a line with a slope of Km/Ki and a y-intercept of Km.


Kmobs =

Km

[I] + Km

Ki

Km obs

Ki Determination

Km slope =

Ki

Km Ki

[I]

[Note that the Ki determination plot very closely resembles Lineweaver-Burk plot in terms of its general shape and the intercepts. Be careful to remember that these are completely different relationships.] While IC50 and Ki are both measures of an inhibitor's ability to block the action of an enzyme, they are not equivalent. Ki values, which are a true equilibrium constant, are considered a more pure measure since the Ki of an enzyme-inhibitor complex is a constant. IC50 values, in contrast, can vary since they depend on the substrate concentration used in the IC50 determination. Comparing IC50 values of the same inhibitor between different research laboratories can be problematic. Fortunately, the Cheng-Prussoff equation allows conversion of IC50 values to a more universal Ki for direct comparison of data. One needs to know the Km of the uninhibited enzyme for its substrate, the IC50 value of the inhibitor, and the substrate concentration at which the IC50 value was determined. Ki =

IC50 [S] 1+

Km


Chapter 5 – Receptors Part 1 – Types of Receptors Video Clip – Receptor Superfamilies Receptors are protein switches that control biochemical processes in a cell. The switches are turned on through the binding of molecules called ligands. Much as a substrate binds an enzyme to trigger a reaction, a ligand reversibly attaches to a binding site on a receptor protein to activate a process. Ligands themselves can be divided into two categories: endogenous and exogenous. Endogenous ligands are the natural ligands found in the body that bind a receptor. Exogenous are unnatural compounds, often drugs, that bind a receptor. Receptors are classed into superfamilies, of which there are four: ligand-gated ion channels, G-protein-coupled receptors, tyrosine kinase-linked receptors, and nuclear receptors. Ligand-gated ion channels (LGICs) are large receptor proteins that span a cell membrane. When the proper ligand binds an ion channel, the channel opens and ions of the type specified by the channel are allowed to pass across the cell membrane and the cell membrane undergoes depolarization. Ligands that bind an ion channel and cause depolarization are often neurotransmitters and act upon neurons. In the case of ion channels, the ligand-binding event leads directly to depolarization. Neurotransmitters that act on ion channels are therefore called fast neurotransmitters. G-Protein-coupled receptors (GPCRs) are also cell membrane-spanning receptor proteins, but GPCRs do not form a channel across the membrane. GPCRs instead provide a means of communication across the membrane. When a ligand binds on the extracellular side of a GPCR, the conformation of the receptor protein changes on the intracellular side of the GPCR. The conformation change causes the release of bound proteins into the cytosol. These released proteins transmit the signal of the ligand and ultimate cause a cellular change through a series of steps. Because the ligand binding event so distant from the eventual cellular response, neurotransmitters that act on GPCRs are called slow neurotransmitters. Tyrosine kinase-linked receptors (TKLRs) also span the cell membrane. The binding of a ligand on the extracellular side of a TKLR causes two TLKRs to combine into a supramolecular complex. Key amino acids (tyrosines) in the complex are phosphorylated, and the entire complex becomes able to bind key proteins on the intracellular side of the receptor. The key proteins then propagate the extracellular ligand signal into the cell. TKLRs are associated with many growth factors and therefore with many cancer pathways when cellular regulation fails. Nuclear receptors are not bound to the cell membrane and instead are found free in the cell nucleus. The binding a nuclear receptors impacts many aspects of DNA regulation and gene expression. The binding of a nuclear receptor by a drug can have broad and difficult to control consequences within a cell. Nuclear receptor drugs often have a long list of potential side effects.


DavidsonX – D001x – Medicinal Chemistry Chapter 5 – Receptors Part 2 – Ligands Video Clip – Ligand Types Ligands can have different effects on a receptor. Each type of ligand can be readily classified according to its behavior. A type of ligand is the full agonist. The term agonist refers to a compound that binds a receptor and elicits a response (E). Full agonists elicit the same level of full response (E = Emax = 100%) as the endogenous ligand of the receptor. Graphically, a receptor-ligand interaction is plotted as response (E/Emax) vs. log [L]. The relationship is sigmoidal. A full agonist approaches full response (E/Emax = 1.0) as log [L] reaches relatively high levels.

E / Emax

response vs. log [L] - full agonist 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 log [L]

Two ligands can achieve a full response without being equivalent. Ligands can differ with respect to the concentration required to trigger a response. A ligand that affects a response at a lower concentration has a higher potency. Potencies are measured as the effective ligand concentration required to reach a 50% response – EC50 or, in these graphs, log [EC50]. A more potent ligand has a lower EC50 value.


E / Emax

full agonist comparison 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

full agonist 1 (more potent)

full agonist 2 (less potent) log EC50 log EC50

log [L]

Partial agonists also cause a response, but they cannot reach the same, 100% response level of the endogenous ligand. Partial agonists also show a sigmoidal relationship between response and log [L]. The potency of a partial agonist is still reported as an EC50 value, but it does not occur at 50% response. The EC50 value instead occurs at 50% of the maximum response possible for that partial agonist. For example, if a partial agonist can only achieve a maximum response of 60%, then it's log EC50 would be measured at just 30% response. Partial agonists and full agonists typically bind at the same site. This similarity in binding gives rise to an interesting effect. If a full agonist is at levels sufficient to cause a full reponse and a partial agonist is added, then the response will decreases. If partial agonist levels are further elevated, the response will eventually decrease to the maximum response of the partial agonist. The partial agonist can displace the full agonist from the binding site and decrease the response.

E / Emax

response vs. log [L] - partial agonist (Emax = 60%) 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

full agonist with added partial agonist

partial agonist alone log EC50 (50% of max response)

log [L]

Antagonists bind a receptor, do not cause a response, and block the response caused by an agonist. Based on this description an antagonist by itself has no effect the response of a receptor. An antagonist does, however, decrease an agonist's response.


E / Emax

effect of an antagonist on a full agonist 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 log [antagonist]

Inverse agonists are very interesting ligands because they decrease the response of a receptor. Many receptors can cause a response without being bound to a ligand. Such receptors are said to have constituent activity. The level of constituent activity is typically very low and may only be a few percent of Emax. When an inverse agonist is added to a constituently active receptor, the response of the receptor approaches zero.

E / Emax

effect of an inverse agonist on a constituently active receptor 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 log [inverse agonist]


DavidsonX – D001x – Medicinal Chemistry Chapter 5 – Receptors Part 3 – Occupancy Theory Video Clip – The Pros and Cons of the Clark Model The dominant model for discussing receptor-ligand interactions was developed by Clark in the 1920s and 30s and arose directly from the enzyme work of Michaelis and Menten. Clark's occupancy theory is based on the idea that the fraction of total receptors bound by a ligand is directly proportional to the response. If 0, 50, or 100% of the receptors in a cell are bound by a ligand, then the level of response will be 0, 50, or 100% of the maximum, respectively.

Clark's equation relating ligand concentration and response is shown below. It bears more than a passing resemblance to the Michaelis-Menten equation. Clark's equation includes a term, KD, which is the dissociation equilibrium constant for the receptor-ligand complex.

KD R-L

R+L

Clark's equation is the relationship that generated the sigmoidal response vs. log [L] plots in the previous section. The point of inflection of the curve corresponds to the log [L] at which the response is 50% of the ligand's maximum. Based on Clark's theory, 50% maximum occurs when 50% of the receptors are bound. When 50% of the receptors are bound as R-L, the 50% of the receptors are also unbound as R. At this point, [R] = [R-L], and [L] = EC50. For the equation immediately above, if the system is at equilibrium and [R] = [R-L], then [L] = KD. Therefore, EC50 = KD. So, in working with receptors, medicinal chemists will normally compare ligands based on their EC50 values. These are actually KD values. Relating activity to KD, a measure of binding, reinforces the idea that drugs target proteins. Clark's occupancy theory is far from perfect, and its key assumption – binding is directly proportional to response – does not always hold true. Exceptions abound. One is the idea of constituent activity. Some receptors, without being bound to a ligand, generate a response, albeit small. According to Clark's theory, a receptor without a ligand should not afford a response. Another exception is that, in some instances, the full response is reached without having all receptors bound. The receptors that are not needed in order to give a full response are called spare receptors. The weakness of occupancy theory is that it is based on the Michaelis-Menten model, a model developed for enzymes. Enzymes are much simpler than receptors. Many receptors involve long and complicated pathways between the ligand binding event and the eventual


response. With the extra layers of complexities, the occupancy model frequently breaks down. Elaborations on occupancy theory have been developed, as have competing models predicting ligand-response relationships. One of the newer theories involves an idea called drug-target residence time. In the drug-target residence time model, the response is linked to the length of time a ligand binds a receptor. A longer binding time gives a stronger response. An example of the relevance of drug-target residence time can be found in anti-cancer epidermal growth factor receptor (EGFR) ligands. Three ligands, all of which are FDAapproved drugs, are shown below with their IC50 values. Their IC50 values are very similar, but lapatinib (1) is considerably more active than the other two. When the compounds were instead compared based upon their residence times, lapatinib shows a far longer residence time than the other two compounds. Therefore, when occupancy theory and EC50 values fail to explain trends in observed biological activity, other receptor theories must be investigated. N N HN

N H

H3CO2S

Ki (nM)

residence time (min)

3.0

300

gefitinib (Iressa) 2

0.4

<10

erlotinib (Tarceva) 3

0.7

<10

lapatinib (Tykerb) 1 Cl F

O

N N

N

O

O

HN

Cl F

H3CO H3CO

O

N N

O HN

One final note is necessary. In the example of lapatinib, ligand for a receptor, was measured in terms of its IC50 value. IC50 values are normally only used to describe enzyme inhibitors. The receptor bound by lapatinib is EFGR, a tyrosine kinase-linked receptor. Tyrosine kinaselinked receptors have enzymatic activity once they dimerize to their active form. Therefore, it is completely appropriate to refer to lapatinib as both a ligand, specifically as an antagonist, as well as an inhibitor.


DavidsonX – D001x – Medicinal Chemistry Chapter 6 – Blood and Drug Transport Part 1 – Blood Video Clip – What Is Blood? Blood is the medium that transports drugs, and it is also the medium from which drug concentrations are measured. One usually cannot monitor a drug at its target, such as tissue from a joint for an arthritis drug or tissue from the brain for a headache reliever. One can, however, easily draw blood from a vein and check the concentration of a drug present in the bloodstream. Presumably, the concentration of the drug is somehow proportional to the concentration of the drug at the target. If that assumption is true (and it generally is), knowing the amount of drug in the blood is just as good as knowing the concentration at the target. Blood, more specifically whole blood, is a complex mixture of water, electrolytes, small organic molecules (e.g., hormones), proteins, and cells. An overall breakdown in shown in the figure below.

red blood cells white blood cells and platelets water, salts, small molecules proteins

When whole blood is sampled from a patient, it is centrifuged so that all the cells can be removed from the fluid fraction of the blood. The fluid fraction of the blood is called plasma. Plasma is approximately 54% of the volume of whole blood. Plasma includes water, salts, small molecules, and proteins. Closely related to plasma is serum. Serum is the residual fluid left behind after whole blood clots. Serum is approximately equivalent to plasma without the proteins responsible for clotting. Proteins in the blood can significantly impact a drug. One way is by affecting how a drug is transported and removed from the bloodstream. That aspect of blood proteins will be covered in a web component. The other way blood proteins affect a drug is by making the drug less effective in terms of the drug-target interaction. Drugs are designed to bind to targets, and sometimes a drug has a difficult time distinguishing a different protein from its intended target. Blood proteins are never far from a drug because the drug hitches a ride on the bloodstream to get to and from its site of action. If a drug is hindered by binding a blood protein, then the concentration of free, unbound drug is lower and less is available to act on the intended target. Therefore, the drug will be less effective.


Most assays for preliminary activity for a molecule are in vitro assays. These biochemical tests are idealized to determine drug-target binding. As a pool of hits is being filtered to determine which will become leads, the same assays may be performed in the presence of 10 to 50% human serum or plasma. The intent of using serum or plasma is to introduce the type of proteins that will be encountered by the lead in a living organism. The activity (as KD, IC50, Ki, or EC50) of a hit is almost always lower when determined in the presence of blood proteins. The activity can even be dramatically lower. Hits that are greatly affected by blood proteins may be downgraded relative to other hits.


DavidsonX – D001x – Medicinal Chemistry Chapter 6 – Blood and Drug Transport Part 2 – ADME Video Clip – ADME ADME is a universally used acronym in medicinal chemistry. The four letters stand for absorption, distribution, metabolism, and excretion. Collectively, these four topics cover the major areas of the field pharmacokinetics. In covering these topics we will prepare for the next chapter, which is a quantitative treatment of pharmacokinetics. Absorption describes the movement of a drug from its site of administration to the bloodstream. The US Food and Drug Administration recognizes over 100 different routes of administration, but we will focus upon just two – intravenous (IV) and oral. IV administration bypasses the absorption step altogether because the drug is administered directly into the circulatory system through a vein. In oral administration, however, a drug is absorbed by crossing membranes from the digestive tract to the bloodstream. The digestive tract, especially the small intestine, is designed to absorb nutrients from food. Properly designed drugs can readily exploit this fact and gain access to the bloodstream via the digestive system. Distribution covers the travel of a drug from the bloodstream to the various tissues and organs of the body. Each drug has its own unique properties, and those properties determine the degree to which the drug spreads from the circulatory system to the muscles, fatty tissue, or the even the brain. Metabolism refers to the chemical modification of a drug by the body. While a drug may be metabolized almost anywhere that the drug has been distributed, most metabolism occurs in the liver. The liver contains a host of a enzymes that can perform chemical reactions on the different functional groups found in drugs. The net effect of metabolism is that the concentration of the original, unchanged drug in the bloodstream is decreased, and therefore the effect of the drug is diminished. Processes that decrease the amount of drug in the body fall under the larger category of elimination. We will expand upon metabolism in Chapter 8. Metabolism by the liver has a significant influence upon drugs. All oral drugs, once they are absorbed from the bloodstream, enter a specific part of the circulatory system called the hepatic portal system. All blood and its contents in the hepatic portal system must pass through the liver before reaching the general circulatory system and accessing the body as a whole. By coursing through the liver before reaching the body, the drug's concentration is likely reduced by the metabolic action of the liver. This is called the first pass effect. A drug's bioavailability is the fraction of an administered dose of the drug that reaches the general circulatory system. The variable for bioavailability is F. IV drugs, which are directly placed in the bloodstream, have 100% bioavailability (F = 1.00). The bioavailability of oral drugs depends upon their absorption from the gastrointestinal tract as well as their resistance to being metabolized by the liver. If a drug has a low bioavailability, researchers can find clues to the problem. If a drug is poorly absorbed, it will likely be found in the feces. If a drug is absent from the feces and still shows a low bioavailability, then the drug is likely broken down extensively by the liver. Excretion refers to the removal of waste and unwanted materials by an organism. Excretion can be performed by many organs through a variety of processes (e.g, sweating from the skin


and exhalation of CO2 by the lungs), but the major method of excretion for drugs is through filtration of drug from the blood by the kidneys. Like metabolism, excretion reduces the concentration of a drug in the blood and therefore falls under the broader category of elimination. The kidneys initially filter whole blood. A fraction of the particles smaller than the blood proteins are temporarily removed from whole blood. The list of affected molecules includes water, electrolytes, small molecules (including almost all drugs), and waste. The filtered fluid, or filtrate, is essentially protein-free plasma. Through the process of reabsorption, the kidneys draw valuable water, electrolytes, and sugars from the filtered fluid to be returned to the blood. Some filtered drugs also are largely returned to the blood. Similarly, through a process called secretion, small molecules that were missed in the initial filtration have a second chance to enter the filtrate by crossing a membrane separating the filtrate and whole blood. The end result is urine â&#x20AC;&#x201C; an aqueous fluid that is highly concentrated in waste molecules and possibly drug and its metabolites.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 1 – IV Bolus Video Clip – Cp vs. time We are beginning a math-intensive chapter. During this chapter, pay particular attention to the dimensions of each variable, and make sure the units cancel properly through the calculations. Do not, however, be overly enticed by the math. Sometimes, when learning the quantitative side of pharmacokinetics, one can focus too much on making sure that equations balance and reduce to the proper terms. The equations are only models – estimates – for the behavior of a drug. Remember that we are learning pharmacokinetics so that we may better understand a drug. We need to be able to see through the math and relate the numbers to molecular structure. The concentration of a drug in the body is tracked through plasma. Drug concentration is almost always related with the variable Cp, or plasma concentration with units of either mass or moles over volume. In its simplest treatment (our starting point), the relationship between Cp and time (t) is first-order and shown in the equation below. The variables in the equation include Cp, t, and kel, the elimination rate constant. kel has units of inverse time. Cpo is Cp at time=0 or to.

Graphically, the equation above generates a plot of Cp vs. time shown below. This plot approximates the behavior of a drug that administered by an intravenous (IV) bolus. In an IV bolus, the entirety of a drug dose is quickly injected into a patient's vein. Note that the curve is estimated back to Cpo. Cpo is a theoretical value because a drug's Cp value cannot be accurately measured at the instant of the injection. A drug needs time to distribute throughout the blood as well as other parts of the body. Although Cpo is a theoretical value and can only be determined by extrapolation back to to, the number is still useful for us in other calculations.

Cp vs. time

Cp

Cpo

time

Note that the slope of the Cp vs. time line is variable. The slope, which is the first derivative of


the equation with respect to time, is equal to the product of Cp and the elimination rate constant. Because Cp is constantly changing, so is the slope.

While the slope of the line may not be constant, the time required for Cp to decrease by 50% is a constant. This time period is called a drug's half-life and labeled t1/2. The half-life a drug can be calculated directly from the elimination rate constant, kel.

Because the half-life is derived from a constant number (kel), half-life itself is a constant. Following the injection of an IV bolus, after one half-life has elapsed, Cp drops from Cpo to 0.5Cpo [or (0.5)1Cpo]. During the second half-life, Cp drops from 0.5Cpo to 0.25Cpo [or (0.5)2Cpo]. After the third half-life, Cp drops from 0.25Cpo to 0.125Cpo [or (0.5)3Cpo]. As this trend continues, Cp drops but theoretically never drops to 0. Although a Cpo never reaches 0, it certainly does decrease to levels that are not therapeutically effective. A general rule of thumb is that a drug is fully eliminated after five half-lives, (0.5) 5Cpo or approximately 0.03Cpo.

Cp vs. time

Cp

Cpo

0.5Cpo 0.25Cpo 0.125Cpo 1t1/2

2t1/2

time

3t1/2

Cp-time plots are very informative, but a linear relationship would be easier to model and interpret. Fortunately, ln Cp vs. time provides a nice line. The equation of the line is shown below. The relationship, in the form of y = mx + b, has a slope of â&#x20AC;&#x201C;kel, and the y-intercept is ln Cpo.


ln Cp vs. time

ln Cp

ln Cpo

time

The elimination rate constant deserves special discussion. The elimination rate constant determines the half-life of a drug. While kel may seem to be an important value (and it is), two more important values are clearance (CL) and apparent volume of distribution (Vd). Clearance and apparent volume of distribution (or simply volume of distribution) are two properties of a drug that together determine kel.

Both clearance and volume of distribution return the discussion to the topic of blood. Clearance describes the volume of blood per unit time (typically L/min or mL/min) from which drug is removed. Volume of distribution describes a hypothetical volume of blood that is required to contain the drug within the body. These two topics will be covered in the following several sections of Chapter 7.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 2 – Clearance I Video Clip – Poolside Demonstration The water in a pool is very much like blood within a body. The water in a pool is circulated by a pump and particulates are removed by a filter. In the same way, the blood in a body is circulated by the heart and wastes are removed by the kidneys. The kidneys clear the blood, and the filter clears the pool water. We can simulate the effect of clearance with a large beaker of water (2 L), 400 plastic beads, and 1 cup scoop with small holes to remove beads but not water. A US cup has a volume of approximately 240 mL, so one scoop of the cup should clear a little more than 10% (240 mL / 2,000 mL) of the volume of the water. A common misconception is to think if one cup clears 10% of the beads, then five scoops should remove 50% of the beads and ten scoops should remove 100% of the beads. A key detail is that the beads are removed but water is not. The remaining beads are now fewer in number but in the same volume of water. In other words, their concentration has decreased, so the next scoop will remove fewer beads. Over time, the concentration continues to drop more and more slowly. This observation follows the trend in drug elimation. The slope of a Cp-time curve slowly becomes flatter and flatter as Cp decreases.

Below is the experimental data for scooping beads from the beaker. Scoop number

Beads removed

Beads remaining

0

0

400

1

35

365

2

18

347

3

17

330

4

28

302

5

37

267

6

24

241

7

25

216

8

17

199

9

17

182

10

11

171

Plotting ln (beads remaining) againsts scoop number simulates a Cp-time plot. ln (beads remaining)-scoop number simulates a ln Cp-time plot.


beads remaining vs. scoop number

beads remaining

400 300 200 100 0 0

1

2

3

4

5

scoop number

6

7

8

9

10


ln (beads remaining) vs. scoop number

ln (beads remaining)

6.5 6 5.5 f(x) = -0.09x + 6.02 R² = 0.99

5 4.5 0

1

2

3

4

5

6

7

8

9

10

scoop number

The slope of the line in the logarithmic plot is â&#x2C6;&#x2019;0.088. Ideally, the slope should be around â&#x2C6;&#x2019;0.125. One potential source of error is the density of the beads. The heavy beads do not evenly mix in the water and tend to be more concentrated at the bottom. Because I remove water from the top, where the beads are less concentrated, my simulated rate of elimination is low. Another source of error is my scooping motion. If I do not get a completely full scoop each time, my experimental rate of elimination will be low. Despite some of the flaws in the experiment, the data do represent the first-order nature of elimination and clearance that is observed for most drugs.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 3 – Clearance II Video Clip – Area Under Curve Drug's with a lower clearance persist for a longer time in the body. Drug's with a higher clearance persist for a shorter time in the body. The longer a drug resides in the body, then the greater the exposure a patient has to a drug. A measure of drug exposure is the area under curve (AUC). The units on AUC are a very non-intuitive concentration∙time. (As with any area, the units are simply the units for the x-axis multiplied by the units of the y-axis.) For this discussion, concentration is specifically mass/vol and not molarity. Molarity is convenient for the coverage of in vitro results and binding studies, but pharmacokinetic data originate from living systems – either animals or humans. Cp data in animals and humans are normally in mass/vol. AUC and clearance are intimately related. In fact, it is through AUC that clearance can be calculated. So, how can one determine AUC for a drug? AUC can be determined in two ways. Method one involves integrating the Cp-time plot of a drug. An idealized Cp-time plot for an IV bolus is shown below. The AUC for this plot, when evaluated from t=0 to t=∞, is Cpo/kel. Of course, one would first need to know both Cpo and kel. These values could be determined from a ln Cp-time plot from the same data.

Cp vs. time for an IV bolus Cpo

Cp

AUC = Cpo/k el

time

Method two for determining AUC for a drug is to crudely estimate the value with the trusty trapezoid rule. Each data point defines a trapezoid shaped region in the curve. The sum of the areas created by each data point gives an approximate AUC. While crude, this method is fairly effective and simple. Because Cp-time data points do not stretch to infinity, one needs a method to estimate the AUC from the last Cp data point to infinite time. The remaining area can be estimated as the value of the final Cp point divided by the kel value of the drug.


Cp vs. time for an IV bolus estimate area of each trapezoid

Cp

estimate unplotted area as Cplast/k el

time

Regardless of how AUC is estimated, dividing the amount of drug that the animal or human was dosed by AUC gives CL. This calculation is the most common method for determining CL for a drug.

Note the units on CL. If dose is a drug mass and AUC uses mass instead of moles, then the mass units cancel and CL comes out with the correct units of volume/time.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 4 – Volume of Distribution I Video Clip – One-Compartment Model In the first section of this chapter, we set a goal of understanding kel, which determines a drugs half-life and is a function of two variables – clearance and volume of distribution. We have thoroughly discussed clearance, and now we need to focus upon volume of distribution.

Volume of distribution is the hypothetical volume of blood – or, more precisely, a volume of plasma – that contains a drug in the body. How we describe this volume of distribution depends on the which compartment model we choose. To keep things simple, we will start with the one-compartment model, and we will stick with an IV bolus. In the one-compartment model with an IV bolus, we assume that the drug is instantaneously administered into the blood, which defines the central compartment, the only compartment in the one-compartment model. From the central compartment, the drug is slowly cleared by the kidneys and/or the liver. The effect of drug clearance is captured by the elimination rate constant, kel. The central compartment defines the volume of distribution for our model, but we cannot directly measure the volume. We need to determine the volume indirectly. IV bolus

central compartment

kel

At time=0, we know quite a bit about our system. Since no time has elapsed, no drug has been cleared. That means the entire mass of the drug dose (Do) resides in the central compartment at time=0. Furthermore, we can determine Cp at time=0, Cpo, by plotting ln Cp vs. time and extrapolating the line back to the y-intercept, which is ln Cpo.

ln Cp vs. time for an IV bolus

ln Cp

ln Cpo

time

If we know the mass (Do) and the concentration (Cpo) in the compartment, then we can


calculate the volume of the compartment (Vd).

It is important to note that because we are using the plasma concentration of the drug (Cp), then the calculated volume of distribution is a volume of plasma, not a volume of blood. It is very important to remember (and yet easy to forget), that Vd is not a real number. The amount of plasma in a 70-kg human is about 2.7 L (54% of the blood volume of 5 L). It would seem that the Vd for all drugs in a 70-kg patient must be 2.7 L. In fact, 2.7 L is the minimum value for Vd. If Vd is greater than 2.7 L, then the drug must have left the central compartment. The simple assumptions of the one-compartment model are showing their limitations. Despite its shortcomings, the one-compartment model is by far the most widely used method for describing a drug's volume of distribution. To come full circle, we engaged this discussion to finish the puzzle of understanding CL, Vd, and kel. A consequence of a large Vd is that kel will be smaller, and the half-life of the drug will be longer.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 5 – Volume of Distribution II Video Clip – Two-Compartment Model The one-compartment model for volume of distribution has utility, but only poorly describes the distribution of a drug in the body. Our next option is to consider the two-compartment model. The two-compartment model adds a peripheral compartment to the one-compartment model. The peripheral compartment represents the opportunity for a drug to leave the plasma and enter other tissues. Drug can equilibrate between the two compartments, and elimination (by clearance through the liver and/or kidneys) only occurs from the central compartment. IV bolus

central compartment

kel

peripheral compartment

An example that demonstrates the two-compartment model is the very early time period after an IV bolus is first injected. Remember that a blood is not drawn from a patient until around 15 minutes have elapsed so that the drug has time to distribute through the entire blood volume and the tissues. Mathematically, an IV bolus can be more accurately described by the equation below. This equation is little more than our original equation for Cp with an extra exponential term.

Graphically, the equation gives the Cp-time relationship shown below. One term mostly describes the distribution phase of the IV bolus while the other covers the elimination phase.


two-compartment model Cpo from two-compartment model

ln Cp

distribution phase

elimination phase

Cpo from one-compartment model time

The very early data points are dominated by the first term in the equation (Ae−αt) and form a line with a slope of −α. This line can be extrapolated back to the y-axis to give a twocompartment model Cpo. While any value for Cpo is hypothetical, the two-compartment model is closer to reality than the one-compartment model estimate. While the term Ae−αt dominates early, quickly approaches a value of 0 as time increases, the value of Cp simplifies to just Be−βt, just like our one-compartment model. The slope decreases to −β, and we can extrapolate the line back to our familiar, inaccurate Cpo. Drugs are not limited to two compartments. Models can be much more complicated. Cp-time plots can have multiple regions dominated by different elimination and distribution processes. Late in the ln Cp-time plot of almost any drug, however, a linear region emerges and continues throughout the rest of the drug's elimination. The slope of this linear region is labeled −kel and is called the terminal elimination rate constant. It is from this terminal elimination rate constant that the pharmacokinetic parameters (t1/2, CL, and Vd) are based. Treatment of a drug in this fashion is equivalent to forcing all drugs into a one-compartment model. Drugs are treated this way for the sake of simplicity. If a drug is reported with multiple elimination rate constants, and therefore multiple half-lives, depending on the time that has elapsed from the IV bolus, then confusion will result. Instead, drugs are reported with a single half-life, which corresponds to the slowest elimination process.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 6 – Oral Delivery I Video Clip – Bioavailability We have now laid enough foundation to move from an IV bolus to oral delivery. Oral delivery is more complicated because the drug must be absorbed from the gastrointestinal tract and withstand the metabolic before reaching general circulation. A typical Cp-time curve for an oral drug is shown below. The curve demonstrates how the rise in concentration as the drug is absorbed followed by the eventual drop in concentration as the drug is cleared. The peak in concentration, Cpmax, occurs at tmax, which is a standard pharmacokinetic parameter that is listed with oral drugs.

Cp vs. time - oral dose

Cp

Cpmax

tmax

time

The formula for Cp is more complicated for an oral drug and adds two new variables – bioavailability (F) and the absorption rate constant (kab). We have already talked about bioavailability, the fraction of an administered dose that actually reaches general circulation. kab, with units of inverse time, is a rate constant and almost always larger than kel.

Before a drug is tested in an oral form, it will have already been dosed as an IV bolus. That means the variables kel and Vd will already be known. That leaves F and kab to be determined. kab can be calculated from tmax, which can be estimated from the curve. The calculation of kab is not exact, but it can be estimated by trial-and-error. Keep in mind that kab is virtually always larger than kel.

Calculating F requires more work. It is possible that F might be known from the IV bolus. If renal clearance is zero, then total clearance can be used to determine the hepatic extraction


ratio (EH) and then bioavailability (F). If F is not known, then it will need to be estimated from AUC data and the trapezoidal rule. The trapezoidal rule is actually easier to apply to oral drug data because one does not need to estimate Cpo. Cpo for an oral drug is 0. For a specific drug, the AUC of the oral form can be compared to the AUC of the IV bolus to determine F. If the two formulations are administered in different doses, then the dose amounts can be used to normalize the data in the calculation.

The calculated F is specific to oral administration and is often reported as oral bioavailability (Foral). Other non-IV routes of administration (e.g., intramuscular injection, nasal, transdermal, or inhalation) have their own distinct bioavailability. Each is calculated by comparing the AUC of a dose from one administrative route to AUCIV.


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 7 – Oral Delivery II Video Clip – Multiple Oral Doses The requirement for any drug discovery program is to design a drug that can be dosed in a manner that allows the drug to maintain safe and effective levels in the body. An effective level is defined as at least the minimum effective concentration. A safe level is any concentration below the maximum tolerated concentration, or the dose below which severe adverse effects are not observed. The concentration range between the minimum effective concentration and maximum tolerated concentration defines the therapeutic window of the drug. A drug dosing regimen must maintain Cp within the therapeutic window.

Cp

Multiple doses of oral drugs give a characteristic sawtooth shape. As soon as the drug is taken, the plasma concentration slowly rises until the absorption phase reaches tmax. The elimination phase then dominates, and the concentration drops until the next dose is administered. The maximum occurs at tmax after a dose, and the minimum occurs at to of the next dose. The observed Cp in the patient corresponds to the sum of the concentrations of the different oral doses that have been administered to a patient. In the graph below, individual doses are shown as dotted lines, and the sum of all doses is the solid line.

time

A successful orally dosed drug will maintain the Cp of the drug within the therapeutic window. Administering a drug more frequently with a smaller dose will maintain Cp within a tighter range, but it inconveniences a patient with taking medication more often. The gold standard for oral dosing is a once-per-day regimen. Taking a drug once a day allows a patient to establish a convenient routine without being overly burdened.


Cp

larger, less frequent dose

smaller, more frequent dose

time


DavidsonX – D001x – Medicinal Chemistry Chapter 7 – Pharmacokinetics Part 8 – CL and Vd Revisited Video Clip – CL vs. Vd Plots In this chapter on pharmacokinetics, we have covered a number of topics and equations. While we have been faithful to the topic of pharmacokinetics, we have perhaps lost sight of medicinal chemistry. We need to bring the discussion back to the relationship between the molecular structure of a drug and its properties (pharmacokinetics). Half-life (t1/2) and the elimination rate constant (kel) are determined by clearance (CL) and volume of distribution (Vd).

If a drug is only cleared by the liver (i.e., renal clearance is zero), then an interesting relationship between CL and Vd can be established. The slope of such a line is 0.693 / t1/2. Remember that blood flow to the liver is at most approximately 25 mL/min/kg, and hepatic clearance is related to oral bioavailability (F).

A plot of CLH vs. Vd is shown below. Each half-life corresponds to a different slope on the graph. The point of this graph is not that we can generate it. The point is that we can plot a molecule, based on its CLH and Vd, on this graph and make reasonable decisions on how to change the structure in order to change its half-life.

CL (hepatic) vs. Vd

hepatic CL (mL/min/kg)

25

t1/2 = 4 h

t1/2 = 8 h

t1/2 = 16 h

20

F = 0.20

15

F = 0.40

10

F = 0.60

5

F = 0.80

0 0

5

10

15

20

25

30

35

Vd (L/kg)

The target half-life for an oral drug is often around 8 hours. This tends to give a drug that can be dosed once per day. If a lead compound is in development and shows a CLH of 5 mL/min/kg and a Vd of 7 L/kg, then the compound will fall along the 16 h half-life line. This


value is rather high, and the drug discovery group may seek to decrease the half-life. Decreasing the lead's half-life can be accomplished in a combination of two ways. One, the Vd of the lead might be decreased to 4 L/kg. A CLH of 5 mL/min/kg and a Vd of 4 L/kg falls on the 8 h half-life line. Two, the CLH of the lead might be increased to 10 or 11 mL/min/kg. A CLH of 11 mL/min/kg and a Vd of 7 L/kg falls on the 8 h half-life line. Alternatively, a combination of increasing clearance and decreasing volume of distribution might be explored. These are fine options, but they are only helpful if we understand how to modify a lead in order to affect clearance and volume of distribution. Increasing clearance can be accomplished by adding functional groups that increase the metabolic activity of the molecule. Similarly, decreasing clearance requires reactive functional groups to be minimized. Metabolism is the topic of Chapter 8, and reactive functional groups will be covered in that chapter. Decreasing volume of distribution can often be achieved by making a lead more polar. A more polar lead may be more confined to the central compartment than a highly non-polar compound. Likewise, increasing the non-polarity of a drug tends to raise volume of distribution. One of the parameters encountered in Lipinski's rules, lipophilicity or log P, is a common measure of lead polarity. Log P is often tracked during a discovery program to gain a qualitative sense of how a drug might distribute. By guiding the discovery team on how a lead might be modified to improve it's pharmacokinetics, a CL vs. Vd graph can be a very useful tool in drug development.


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 1 – Introduction Video Clip – Metabolism and Vd Metabolism is general biological term that may refer to different processes. Two of those processes are listed below. •

Catabolism – converting food into useful energy (e.g., glycolysis or the citric acid cycle)

Anabolism – using energy to synthesize proteins and other necessary molecules

In the context of drug discovery, metabolism refers to body's ability to break down foreign molecules, called xenobiotics. Almost all drugs are examples of xenobiotics. Drugs are not metabolized for their energy or utility; they are metabolized so that the molecules and their effects may be eliminated from the body. Metabolic reactions are typically performed by liver enzymes. Once a drug has entered the body, the drug can undergo a number of different metabolic processes. A drug's metabolism may be irreversible, reversible with both the forward and backward reactions being performed by the same enzyme, and reversible with the forward and backward reactions being performed by different enzymes. The metabolism products, called metabolites, are generally excreted by the kidneys. Metabolites may themselves be further metabolized before being removed by the kidneys. Sometimes drugs are excreted in unchanged form by the kidneys. This fate of a drug falls outside the category of metabolism. reversible metabolism elimination

irreversible metabolism

metabolite

drug

metabolite

elimination

elimination

elimination of unchanged drug by kidneys

enz A elimination

metabolite metabolism reversed by a second enzyme

enz B

metabolite

metabolite

elimination

metabolism of a metabolite

Figure credit: Pearson Education

While drugs are carefully designed to distribute in the body in a manner that is ideal for the action of a drug. Metabolites, however, have been chemically modified from the original drug and therefore can have very different properties. In general, metabolic reactions on a drug cause the metabolite to be more polar than the original drug. As polarity increases, Vd normally decreases. Metabolized compounds also tend to be cleared more quickly by the kidneys (CLR increased). Because metabolites concentrate in the central compartment (plasma) and clear more quickly, most metabolites have a shorter half-life than the original drug. Metabolic reactions, therefore, do help remove a drug from the body more quickly. Metabolic reations are normally divided into two categories – phase I and phase II. (Do not


confuse these terms with the different stages of clinical trials.) Phase I metabolism includes oxidations, reductions, and hydrolyses. Phase II metabolism involves the connection or conjugation of small polar molecules to a compound. These two processes often work together. A drug will undergo phase I metabolism to form a new metabolite. The new metabolite then undergoes phase II metabolism and is linked to a polar group. The final metabolite is highly polar, concentrated in the plasma (low Vd), and readily excreted by the kidneys (high CLR). According to our pharmacokinetic equations in the previous chapter, the end result is a metabolite with a short half-life.


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 2 – Phase I – Part 1 Video Clip – Oxidation Phase I metabolism covers oxidations, reductions, and hydrolytic reactions. In this section we are focusing on oxidations, but many oxidations are coupled with a hydrolysis. We will treat the downplay the hydrolysis stage and emphasize on the oxidation step. The cytochrome P-450 (CYP-450) family of enzymes performs many of the oxidations in the body. Oxidations are often divided into three categories: •

sp3 hybridized carbons

sp2 hybridized carbons

heteroatoms

These few reactions do not cover the entire spectrum of possible drug metabolisms, but they do indeed include a surprisingly high percentage of the commonly encountered metabolic reactions in drugs. sp3 hybridized carbon oxidations Oxidations of sp3 hybridized carbons are often observed as dealkylations, especially demethylations of amines and ethers. Amines that lose an N-methyl group often take on the prefix “nor”. Ethers that lose an O-methyl group take on the prefix “desmethyl”. F3C

F 3C O

O NHMe

NH2

fluoxetine (Prozac) antidepressant

norfluoxetine

MeO O

HO H

H

O

H

NMe HO

H NMe

HO codeine analgesic

morphine analgesic

Alcohol oxidations are also common. Secondary alcohols tend to be oxidized to ketones. Primary alcohols initially are oxidized to aldehydes, when tend to rapidly oxidized to carboxylic acids. In the example of losartan, the acid is considered the only metabolite since the aldehyde is so short-lived.


Cl

Cl N

OH N N N NH

N

Cl

N

O N N N NH

N alcohol oxidation

losartan (Cozaar) antihypertensive

N

OH

N

O

alcohol oxidation

aldehyde metabolite

N N N NH

acid metabolite

sp2 hybridized carbon oxidations Oxidations of sp2 hybridized carbons normally involve aromatic rings. The aromatic ring is initially epoxized, and the epoxide rearranges to restore aromaticity and form a phenol. The rearrangement is frequently not regioselective, and isomeric metabolites may be observed. Me

Me

H N O Me

H

HO

N oxidation

3

O Me

ropivacaine (Naropin) local anesthetic

Me

H N

N

H

HO

3-hydroxy metabolite

H N O Me

4

H

N

4-hydroxy metabolite

heteroatom oxidations Heteroatom oxidations occur on sulfur atoms and nitrogens, especially sp 2 hybridized nitrogens. Me N

Me N

N

O N

N Cl

Cl N H clozapine (Clozaril) antipsychotic

Me HN

N

S

H N

NHMe N

N cimetidine (Tagamet) acid reflux

N H clozapine N-oxide

Me HN

CN

N

S O

H N

NHMe N

cimetidine sulfoxide

CN


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 3 – Phase I – Part 2 Video Clip – Reduction and Hydrolysis Phase I metabolism covers oxidations, reductions, and hydrolytic reactions. In this section we are focusing on reductions and hydrolyses. Oxidations are mostly associated with enzymes in the liver, but reductive and hydrolytic enzymes are found more broadly throughout the body. reductions Reductions can take many forms and affect a number of different functional groups. Selected examples include reductions of ketones to alcohols, reductive cleavage of azo linkages (especially important for the early azo dye antibiotics), and conversion of nitro groups to amines. O

O

O

CH3

OH

O

O

OH

warfarin (Coumadin) anticoagulant

alcohol metabolite

O O S NH2 N H2N

H2N

NH2

NH2 sulfamidochrysoidine (Protosil Rubrum) antibiotic

H2N

metabolites

H N O OH

O2N

O O S NH2

NH2

N

OH

CH3

OH

chloramphenicol antibiotic

Cl

OH Cl

H N

Cl Cl

O OH

H2N

amino metabolite

hydrolyses Hydrolyses normally involve esters and amides. Esters are hydrolyzed by esterases that are found in the livery and the plasma. Enzymes that cleave amides include proteases and fatty acid amidases. O

O OH

OH

O aspirin

O

OH

CH3

salicylic acid


O CH3

N H

O

H N CH3

prilocaine local anesthetic

NH2 CH3 amine

HO

H N CH3 acid


Cl

Cl N

OH N N N NH

N

Cl

N

O N N N NH

N alcohol oxidation

losartan (Cozaar) antihypertensive

N

OH

N

O

alcohol oxidation

aldehyde metabolite

N N N NH

acid metabolite

sp2 hybridized carbon oxidations Oxidations of sp2 hybridized carbons normally involve aromatic rings. The aromatic ring is initially epoxized, and the epoxide rearranges to restore aromaticity and form a phenol. The rearrangement is frequently not regioselective, and isomeric metabolites may be observed. Me

Me

H N O Me

H

HO

N oxidation

3

O Me

ropivacaine (Naropin) local anesthetic

Me

H N

N

H

HO

3-hydroxy metabolite

H N O Me

4

H

N

4-hydroxy metabolite

heteroatom oxidations Heteroatom oxidations occur on sulfur atoms and nitrogens, especially sp 2 hybridized nitrogens. Me N

Me N

N

O N

N Cl

Cl N H clozapine (Clozaril) antipsychotic

Me HN

N

S

H N

NHMe N

N cimetidine (Tagamet) acid reflux

N H clozapine N-oxide

Me HN

CN

N

S O

H N

NHMe N

cimetidine sulfoxide

CN


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 4 – Phase II Video Clip – Conjugation Phase II metabolism involves a process called conjugation. In conjugation, a molecule is combined with another, typically more polar structure. Conjugated compounds tend to be considerably more polar and have a smaller Vd than the original molecule. Interestingly, the types of functional groups that are formed by Phase I metabolism – acids, amines, and alcohols – are often highly reactive toward the conjugation reactions of Phase II metabolism. Several types of conjugations are known. •

Acylation

Sulfonylation

Glucuronidation

Conjugation with glutathione

acetylation Acetylation most commonly occurs on anilines and other aromatic amines. Unlike most metabolic reactions, acetylation does not increase the polarity of the conjugated metabolite relative to the starting material. Acetylation does, however, decrease the toxicity of aromatic amines. O O S H2N

O O S H2N NH2

O

N H acetylated metabolite

sulfanilamide antibiotic

CH3

sulfonation Sulfonation is performed by sulfotransferases and generally affects phenols. The resulting sulfates are very polar due to the negative charge on the sulfate. H N

H3C O

acetaminophen (Tylenol) analgesic

H N

H3C OH

O

O O S O O

acetaminophen O-sulfate

glucuronidation Glucuronidation involves the combination of a molecule with glucuronic acid. Functional groups that mostly frequently serve as the site of glucuronidation include alcohols, phenols, and carboxylic acids.


HO

HO OH OH

O HO2C

OH

N

OH

H

O

H

O

CH3

N O

HO

morphine analgesic

glucuronic acid

morphine 6-O-glucuronide

OH

O HO2C

CH3

OH OH

conjugation with glutathione Glutathione is a tripeptide that contains a cysteine residue. The thiol group of the cysteine residue attacks electrophilic functional groups in molecules, especially drug metabolites. Once the cysteine has bound to the electrophile, two residues have been cleaved, and an acetyl group has been added, the final conjugated metabolite has been formed. O H3N CO2

N H

CO2

El

CO2

S-attack

O glutathione El S

O H3N

N H

SH H N

modifications H N

CO2

El S

O H3C

O

O

N H

O

final metabolite

glutathione conjugate

(GS-El)

The classic example for glutathione conjugation is N-acetyl-p-benzoquinone imine (NAPQI), a toxic metabolite of acetaminophen. Glutathione attacks NAPQI. The resulting conjugate is less toxic and more polar, which facilitates removal from the body. H N

CH3 oxidation O

HO

CH3 O

O

acetaminophen analgesic

N-acetyl-p-benzoquinone imine (NAPQI) toxic metabolite

H N O

N

H N

CH3 O

SG initial product

CH3 O

HO SG

glutathione conjugate

GS-H


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 5 – Metabolism Issues Video Clip – CYP Inhibition and Genetics Drug metabolism comes with many consequences. Before we can appreciate those consequences, we need to cover the oxidative liver enzymes – the cytochrome P-450 (CYP) enzyme superfamily. The CYP superfamily in humans 57 different enzymes. Each CYP enzyme is classified through a code that includes a number (gene family), a letter (subfamily), and another number (the gene number). Specific examples include CYP26C1 and CYP4F12. These enzymes are involved in a number of processes in the body. The most relevant enzymes for drug metabolism are 3A4, 3A5, 1A2, 2C9, 2C19, and 2D6. This set of CYP enzymes metabolizes the majority of drugs. Since so few enzymes carry the burden of drug metabolism, the proper function of these enzymes is essential for the predictable behavior of drugs. Important factors to consider include both inhibition of one or more of these enzymes and genetic factors. Drugs are often substrates for the various CYP enzymes, but they can also serve as inhibitors of these enzymes. A drug that inhibits a CYP enzyme has the potential to slow dramatically the metabolism of another drug. For example, a patient is taking Drug A. Drug A is metabolized primarily by CYP1A2. The patient later begins taking Drug B, which is an inhibitor of CYP1A2. With Drug B in the patient's system, the action of CYP1A2 will be diminished. Hepatic clearance, CLH, for Drug A will decrease. As clearance for Drug A decreases, remember from the previous chapter that as clearance decreases AUC increases.

Since AUC is related to a patient's exposure to a drug, in our example, inhibition of CYP1A2 will result in a higher Cp of Drug A. It may be possible that the standard dosing regimen of Drug A will exceed the drug's therapeutic window. The effect of one drug upon another is a drug interaction. Drug interactions pose potential safety concerns and are disclosed in the prescribing information of drugs. Because drugs that inhibit CYP enzymes can have such a dramatic impact on the behavior of other drugs, the ability of a molecule to inhibit CYP enzymes is studied very early in drug discovery – at the lead discovery stage. Hits are studied for their properties as inhibitors before being promoted to lead status. In a similar manner, genetic factors can also play a key role in drug metabolism. Some CYP enzymes show high variability among different populations. Population differences of CYP enzymes can fall somewhat along racial lines, but the correlations are often poor and only weakly relevant. CYP2C9 is an example of a CYP enzyme that shows genetic variability. Around 10% of the population has a less active form of CYP2C9. If those people with a less active form of CYP2C9 take a drug that is metabolized primarily by 2C9, then they must follow a lower dosage regimen or else risk have excessive levels of drug in their systems. Early in lead discovery, lead candidates can be screened for which forms of CYP are responsible for metabolism a given molecule. Hits that are exclusively metabolized by CYP forms that carry high genetic variability are less likely to be promoted to leads.


DavidsonX – D001x – Medicinal Chemistry Chapter 8 – Metabolism Part 6 – Prodrugs Video Clip – Metabolism Exploitation Metabolism may seem to pose a serious challenge for drug discovery. Metabolism destroys drugs. Metabolism introduces variables such as genetics and inhibition. As it turns out, drug metabolism can also be exploited to the advantage of a drug discovery program. Specifically, metabolism can be used to improve the oral availability of the active form of a drug. The general idea is that the drug is administered in an original, inactive form that is absorbed well. The inactive form is called a prodrug. After absorption, the prodrug is metabolized to reveal the active form of the drug, which can then bind of the target of interest. Two examples of prodrugs are enalapril and valaciclovir. Enalapril is metabolized through a phase I hydrolysis to enalaprilat. Valaciclovir is metabolized through a phase I hydrolysis to acyclovir. The stories behind the two compounds are somewhat different. O

O

HO

CH3 N

N H

CO2H

O

O O

O

N

N

O enalaprilat

O H2N

CH3

N H

phase I hydrolysis

enalapril

N

O

esterases

CO2H

O esterases

NH N

NH2

phase I hydrolysis

N HO

O

valaciclovir

N

NH N

NH2

acyclovir

Enalaprilat inhibits angiotensin-converting enzyme (ACE), an key enzyme in the renin pathway that partially regulates blood pressure. Enalaprilat is an effective inhibitor, but it has no oral bioavailability (F = 0.00). The problem with enalaprilat is that it is extensively charged. At a physiological pH of 7.4, the compound carries three charges, two on the carboxylates and one on the secondary amine. The presence of these three charges greatly hinders the ability of the molecule to cross the intestinal wall and enter the hepatic portal system. Enalapril itself only has one charged functional group, the acid, at pH 7.4. With just one charge, enalapril much more easily crosses membranes for oral absorption. The oral bioavailability of enalapril is a respectable 60% (F = 0.60). O

O N H2

O

CH3

O enalaprilat at pH 7.4

N CO2

O N H

CH3

O enalapril at pH 7.4

N CO2

Acyclovir is an antiviral compound used to treat different herpes infections. The problem with acyclovir is that its oral bioavailability is low at only 10-20%. The problem is not first-pass metabolism. Acyclovir simply is not absorbed well from the digestive tract. To improve the absorption of acyclovir, researchers had the idea to acylate the alcohol with valine, an amino acid. The presence of the valine residue does not improve the ability of the new molecule, valaciclovir, to diffuse across a membrane. Instead, the valine residue causes valaciclovir to


resemble an oligopeptide closely enough to fool a peptide transporter into transporting valaciclovir from the intestines and into the hepatic portal system. The valine residue is then removed by esterases in the liver, and the active form of the drug, acyclovir, is free to act in the patient. The oral bioavailability of valaciclovir is around 55%, a considerable improvement over the 10-20% of acyclovir.


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 1 – Intermolecular Forces Video Clip – Binding Energy Drug-target binding is often measured through dissociation equilibrium constants, such as Ki for an enzyme inhibitor or KD for a receptor ligand. K

target-drug complex

target + drug

Equilibrium constants can be easily converted into a binding energy.

The calculated value for ΔGobind is the free energy of binding. If a value of 0.00199 kcal/mol•K is used for R with a temperature (T) of 298 K, then the binding energy has units of kcal/mol. More negative values for ΔGobind indicate a stronger binding energy. Free energy changes (ΔG) are a combination of both changes in enthalpy (ΔH) and entropy (ΔS), and both enthalpy and entropy must be considered in binding. One interaction that controlled by enthalpy changes is hydrogen bonding. An interaction that is dominated by changes in entropy is the hydrophobic effect. hydrogen bonding For our purposes, hydrogen bonds are generally limited to the interaction of an N-H or O-H bond (the hydrogen bond donor – HBD) with a nitrogen or oxygen lone pair (the hydrogen bond acceptor – HBA). Both a donor and acceptor are required for a hydrogen bond. Hydrogen bonds can be divided into three types. •

Both the HBD and HBA are neutral. This type of hydrogen bond can contribute up to 1.5 kcal/mol to binding energy. hydrogen bond donor (amide N-H)

O R

N R'

H

H

hydrogen bond acceptor (oxygen lone pair)

Either the HBD or HBA has a charge. This type of hydrogen bond is somewhat stronger with a strength of up to 3.0 kcal/mol. hydrogen bond acceptor (carboxylate)

R" O

O R

H O

O

R'

hydrogen bond donor (alcohol O-H)

Both the HBD and HBA have a charge. This type of hydrogen bond is strongest with a strength of up to 4.0 kcal/mol. In fairness, the strength of this interaction is a combination of both hydrogen bonding and an electrostatic attraction between the two opposite charges.


hydrogen bond acceptor (carboxylate)

H H N H R'

O R

O

hydrogen bond donor (ammonium O-H)

The pH of the environment has a significant impact on any hydrogen bond that involves a charged species. As the pH changes, some functional groups can gain or lose a proton and change in their ability to serve as a HBD or HBA. An example is imidazole, which is part of the side chain of the amino acid histidine. Above a pH of around 6, the ring is neutral and can act as either a HBD or HBA. Below 6, the ring is protonated and can only act as a HBD. above pH 6 H N

below pH 6 H N

N

N H

R

R HBA (N lone pair) or HBD (N-H)

HBD only

hydrophobic effect Most drugs, as organic molecules, are somewhat non-polar. In an aqueous medium, the nonpolar drug interacts poorly with the surrounding water molecules, which form an ordered solvent shell around the drug. After the drug binds its target, most of the water molecules are released and free to go their separate ways in solution. The disorder of the system increases (ΔS > 0) with binding of the drug, and this entropy increase makes ΔG for the binding process more negative (more favorable). drug

association (binding) dissociation

target &

drug target

= water molecules

Image credit: Pearson Education

The greater the surface area of a drug that is freed of water molecules during binding equals a greater hydrophobic effect. For this reason, energy changes for the hydrophobic effect are often measured by surface area. The approximate contribution of the hydrophobic effect to binding energy is 0.03 kcal/mol/Å2. A single CH2 group in a binding pocket provides about 0.8 kcal/mol. A phenyl ring contributes about 2.0 kcal/mol.


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 2 – Case Study – Stromelysin Video Clip – Stromelysin Stromelysin is a zinc-dependent protease. The enzyme breaks down and reforms collagen, and its action has been implicated in both arthritis and some forms of cancer. Stromelysin contains a zinc ion that acts as a Lewis acid and catalyzes the cleavage of proteins. Back in the mid-1990s, researchers at Abbott sought inhibitors of stromelysin. At the time two key facts were available about the active site in stromelysin. (1) Hydroxamic acids (1) bind well to the zinc ion in the active site. (2) The active site also contains a hydrophobic binding pocket. R H

O N

Zn+2 O H 1

Zn+2 bound by a hydroxamic acid

Based on these two ideas, the group chose acetohydroxamic acid (2) as one starting point for a stromelysin inhibitor. For a second starting point, the team tested a collection of small, nonpolar molecules for binding to stromelysin. Several compounds, including 4-hydroxybiphenyl (3), were identified. The dissociation equilibrium constants for both acetohydroxamic acid and 4-hydroxybiphenyl are shown. HO

O HO

N H

CH3

acetohydroxamic acid 2 KD = 17 mM

4-hydroxybiphenyl 3 KD = 0.28 mM

From a drug discovery standpoint, neither 2 nor 3 is a strong inhibitor of stromelysin. The KD values are high and correspond to weak binding energies. Acetohydroxamic acid has a ΔGobind of just −2.4 kcal/mol. 4-Hydroxybiphenyl is slightly stronger at −4.8 kcal/mol.

With two distinct binding compounds in hand, the Abbott group tried to link the compounds together and create a new, more potent inhibitor. Because of the methods being used to study the molecules, both compound 2 and 3 bound were known to bind stromelysin in nearby positions. Compounds tried different linkers between the CH 3 of 2 and the OH of 3. Linkers of zero, one, two, and three additional carbons were tested, and the compound with one additional carbon proved to be the most active of the group.


O HO

N H

O ( )n

n 1 2 3 4

IC50 (ď ­M) 3.9 0.31 110 100

IC50 values are not equilibrium constants, but they are related to equilibrium constants throught the Cheng-Prussoff equation, which was first introduced back in Chapter 4. One can, however, compare IC50 values of two compounds to determine relative binding energies. The only precaution is that the IC50 values must be determined using the same conditions in the same assay â&#x20AC;&#x201C; same enzyme substrate at constant concentration.

The difference in binding energy (BE) between the linked compounds for n = 1 and n = 2 can be determined to be 1.5 kcal/mol.

Original reference: Hajduk, P. J.; Sheppard, G.; Nettesheim, D. G.; Olejniczak, E. T.; Shuker, S. B.; Meadows, R. P.; Stelnman, D. H.; Carrera, Jr., G. M.; Marcotte, P. A.; Severin, J.; Walter, K.; Smith, H.; Gubbins, E.; Simmer, R.; Holzman, T. F.; Morgan, D. W.; Davidsen, S. K.; Summers, J. B.; Fesik, S. W. Discovery of Potent Nonpeptide Inhibitors of Stromelysin Using SAR by NMR. J. Am. Chem. Soc. 1997, 119, 5818-5827.


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 3 – Drug-Target Complementarity Video Clip – Pharmacophores Revisited Intermolecular forces hold a drug to its target. The intermolecular forces will not form, however, unless the drug's functional groups are oriented to interact strongly the the functional groups in the target. In other words, a drug needs the right functional groups with the correct geometry in order to bind strongly to a given target. Proper functionality and geometry describe a drug's pharmacophore. The idea of pharmacophores were introduced back in Chapter 1. The pharmacophore of morphine and the sulfa drugs were discussed in terms of very molecular scaffolds. (1) benzene ring

(3) two carbon linker

morphine pharmacophore

O O S R N H

H2N N

(4) tertiary amine (2) quaternary carbon

sulfonamide antibiotic pharmacophore

R = H, acyl, aryl, heteroaryl

Pharmacophores are more often described in terms of functional groups and their ideal spacing. A hypothetical example is shown below. The pharmacophore consists of a hydrogen bond donor, a negatively charged group, and a large non-polar group each separated by a certain distance. non-polar group

hydrogen bond donor

negatively charged group

To satisfy these functional groups, one might select an alcohol (hydrogen bond donor), carboxylate (negatively charged group), and a phenyl ring (non-polar group). Furthermore, these might be connected as shown in compound 1 to give the appropriate spacing. H alcohol O

H O

phenyl O O carboxylate

O

molecule satisfying pharmacophore 1

O

A problem with structure 1 is its conformational flexibility. While the structure is drawn to show an ideal orientation of the key functional groups, the molecule has a huge number of conformations that would improperly space the functionality. Indeed, some medicinal chemists recommend downgrading hits that have a high number of freely rotating bonds. The most common method for restricting flexibility of a molecule is the incorporation of rings or double bonds. Several possible restricted structures are shown below. Compounds 2


through 4 incorporate alkenes. Alkenes restrict conformations somewhat. Compounds 4 through 7 incorporate rings at different positions. Rings can greatly restrict flexibility and hold functional groups in a desired relative orientation. A problem with introducing rings is that they normally add extra carbons to the molecule. New carbon might add steric bulk to the structure and prevent binding. H O

H O

2

H O

4 O

O

6 O

O

H O

O

H O

H O

3

7

5 O

O

O

O

O

O

O

Pharmacophores often include many key functional groups, certainly more than just three. With four functional groups, unless they happen to lie within a plane, the pharmacophore will occupy a three-dimensional space. With three-dimensional space comes the importance of stereochemistry. While many drugs do not contain stereocenters, some do. Those that do contain stereocenters are often approved exclusively in a single enantiomer form. In these cases, the correct stereochemical configuration is normally required to achieve proper threedimensional orientation of the drugs functional groups for proper activity. Two examples of drugs that are marketed as single enantiomers are naproxen (8) and duloxetine (9). CH3

S

CO2H O

CH3O naproxen (Aleve) analgesic

8

duloxetine (Cymbalta) antidepressant

N H

CH3 9


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 4 – Molecular Diversity Video Clip – Numbers Game As the search for a drug begins, a natural question is “What kind of molecules can become drugs?” In 1996 Wayne Guida et al. published a review with an estimate for the number of potential molecules that could be considered as oral drug candidates. This number of molecules defines something called a molecular space. A molecular space is any set of molecules that fits defined criteria. What were our Guida's criteria for this molecular space? Guida defined the molecular space to include oral drugs. In general, drugs of any type need sufficient functionality to achieve a target binding energy of at least −11 kcal/mol. A binding energy of that magnitude corresponds to an equilibrium dissociation constant (KD or Ki) of 10 nM or lower. This level of drug potency minimizes the mass of the dose that a patient receives and can also minimize side effects. A specific concern for most oral drugs is that they must be small enough to passively diffuse across membranes in the digestive system and demonstrate reasonable oral bioavailability. Lipinski put this size limit at a MW of 500. Guida put the size limit at a molecule containing no more than 30 nonhydrogen atoms, specifically C, N, O, F, P, S, Cl, Br, and I. [Side discussion: The above paragraph describes a potential conflict. An oral drug must be large enough to generate strong binding, but the drug cannot be too big for absorption. This conflict – big enough but not too big – is a concern through most drug discovery projects.] Guida estimated the number of possible molecules as oral drugs to be 10 63. This number defines a molecular space, shown as an Euler diagram. This space is undoubtedly filled with mostly very poorly active molecules. Some regions contain compounds with low activity – hit spaces. Within some hit spaces are compounds with moderate activity – lead spaces. Finally, some lead spaces include regions of molecules that are not only highly active but also display excellent pharmacokinetic properties and low toxicity – drug spaces. Note that the hit, lead, and drug spaces are drawn larger than they actually are. molecular space (possible drug space)

hit space low activity poor properties lead space moderate activity improved properties

drug space high activity ideal properties

Image credit: Pearson Education

Potential drug space is vast. Finding a drug within this space is a needle-in-a-haystack problem. There may be some comfort, however, in the fact that for any target exist perhaps millions or billions of molecules that could be approved as drugs. The idea that a drug group must find the one best molecule is almost certainly false. The job of discovery team is to find a molecule that is good enough, not necessary the best.


Original reference: Bohacek, R. S.; McMartin, C.; Guida, W. C. The Art of Practice of Structure-Based Drug Design: A Molecular Modeling Perspective. Med. Res. Rev. 1996, 16, 3-50.


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 5 – Molecular Libraries Video Clip – Molecule Collections Major pharmaceutical companies have a weapon to search for the drug needle in the molecular space haystack. That weapon is a compound library. Compound libraries come in two forms – the in-house library of a pharmaceutical company and an out-sourced library held by another company. An in-house library reflects the past research efforts of a company. Each time a chemist in the company synthesizes a new molecule, a small sample of that molecule is deposited in the library for future use in searching for molecules that might bind to a protein target. A company with a history in researching blood pressure medications would have a library rich in compounds that bind targets that regulate blood pressure. Compounds libraries can also include materials such as natural product extracts. Furthermore, if one company purchases another company, an asset of the second company is its compound library, which will be incorporated into the library of the first company. The resulting merged library will be larger and more diverse than the original library of either company alone. A large compound library for a major pharmaceutical company might be one or two million compounds. Out-sourced libraries are held by companies that seek to partner with a drug firm. The library holder promotes the unique properties of its particular collection. The library holder and drug company work together to develop a drug. If a compound from the out-sourced library happens to result in the discovery of a drug, the library company and drug firm will share in the profits. Out-sourced libraries can have many sources. Some companies purchase compounds from academic libraries. Other companies may have a unique source. One company holds a large collection of fungi, which are an incredibly diverse class of organism capable of synthesizing a range of unique molecules. Extracts from the growth media of fungi may plausibly contain molecules that could lead to the discovery of new drugs. A good library provides a diverse collection of molecules that can be searched for desirable biological activity. The figure below shows drug space with possible hits, leads, and drugs shown in blue. The black dots represent individual molecules in a library. The compounds in this library are uniformly dispersed throughout drug space. This situation is ideal because it maximizes the probability that a library molecule will happen to overlap with a hit space. The greater the number of discovered hits means a greater chance of discovering a drug. No library achieves ideal scatter throughout drug space. Clustering of compounds to some degree is the norm. For example, a company with a strong history in antiviral research will have a library that is likely clustered mostly in one part of drug space. Ideal scatter, regardless, is a goal. potential drug space


DavidsonX – D001x – Medicinal Chemistry Chapter 9 – Binding, Structure, and Diversity Part 6 – Creating Libraries Video Clip – Combinatorial Chemistry Compound libraries tend to grow slowly because chemists have traditionally made compounds one at a time. Into the 1980s, the slow rate of chemical synthesis was not a problem because biological screening methods were also very slow. With biochemical advances and the advent of HTS techniques, however, screening capacity in drug companies increased greatly. Demand for an increase in chemical synthesis followed. The answer was found in combinatorial chemistry, or just combichem. Combinatorial chemistry is a method of preparing new molecules using small building blocks in simple, often automated, steps. An example of a combinatorial library synthesis is shown below. In very simple reactions, one can react an amine (1) with an acid chloride (2) to make a secondary amide (3). Deprotonation followed by alkylation of the nitrogen with an alkyl halide (4) forms a tertiary amide (5). All the reaction building blocks – amine 1, acid chloride 2, and alkyl halide 3 – are readily available in a number of different forms. If a chemist had five different amines, five acid chlorides, and five alkyl halides, conceivably 125 different tertiary amides could be made. That is 125 (53) new molecules from just 15 (5 × 3) buidling blocks. With ten of each building block, 1,000 new molecules could be made. R NH2

amines 1

O

O

+ Cl

R R'

acid chlorides 2

- HCl

N H

O

1. base R'

secondary amides 3

2. R"

R X

4

N

R'

R" tertiary amides 5

Image credit: Pearson Education

The potential value of this type of synthetic approach was immediately recognized. In the early 1990s, combinatorial chemistry grew from being little more than an idea to becoming its own industry. Drug companies created their own combinatorial chemistry teams to prepare molecules. Independent companies created their own libraries to sell or rent to drug companies. Drug discovery seemed poised to enter a new phase of productivity because HTS in tandem with combinatorial chemistry would allow the quick exploration of molecular space for the discovery of new drugs. Around 20 years later the growth in productivity has yet to materialize as expected. The failure of HTS and combinatorial chemistry is a subject of great debate. Some blame HTS because drug discovery groups relied too heavily on the simple binding data that HTS methods generate. Others blame combinatorial chemistry because the synthetic approach focused upon the wrong types of molecules. Whatever the reason, the major pharmaceutical companies invested deeply into HTS and combinatorial chemistry for questionable returns. In spite of debates that may surround the recent decisions in major pharma, HTS and combinatorial chemistry remain as very valuable tools to quickly sample the depths of drug space and search for biologically active molecules. The challenge for the pharmaceutical industry is to learn how these tools can be best used to drive the discovery of new drugs.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 1 – In Vitro and In Silico Screening Video Clip – Finding Hits We have discussed that oral drugs have generally accepted limits in terms of a maximum ideal molecular weight. Despite this limit, potential drug space is vast – so vast that the space can only be very partially sampled by the over million compound libraries of drug companies. Molecules in these libraries are tested for activity against a target by high-throughput screening techniques. Active molecules in this screening process are called hits and show an activity level (Ki) or binding (KD) of around 1 μM or lower. Before being officially named a hit, the activity of the compound must be confirmed with additional testing. A problem that can arise in screening a library is that individual compounds in a library may be of low purity. Confirmatory testing ensures that the activity of the compound in question is not an artifact of an impurity and is reproducible. The number of hits discovered varies with each screen. Sometimes hits will be defined as compounds having an activity that is stronger than a molecule with a known affinity for the target. Other times hits will be defined as being the compounds that exceed the average binding of the entire library by two or three standard deviations. A two standard deviation cutoff gives a hit rate of 2.1%. That is, the top 2.1% of the library would be considered as hits. A three standard deviation cut-off gives a hit rate of just 0.1%. Depending on the methods for selecting hits, hit rates might range from as high as 10% to as low as 0.1%. At a hit rate of only 0.1%, a million member library will generate 10,000 hits. That figure may sound excessive, but remember the depiction of potential drug space. Interspersed throughout drug space are hit spaces. The goal is to find molecules in as many different hit spaces as possible. This outcome gives the drug discovery group a broad selection of molecules from which leads can be chosen.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 2 – Fragment Based Screening Video Clip – Fragment Based Screening The size of molecular space varies with the size of the molecules in question. It has been estimated that drugs with 30 non-hydrogen atoms comprises a space of 10 63 molecules. Since hits and leads tend to be smaller than drugs with approximate 22 non-hydrogen atoms, hit-lead space is much smaller than 1063 molecules. Therefore, the challenge of sampling hitlead space is less formidable than sampling drug space. # non-H atoms 30

drug space

type of compound drug

22

hits/leads

14

fragments

1 Following this logic, if we focus instead upon even smaller molecules with even fewer nonhydrogen atoms, then sampling molecular space for these small molecules will be even more manageable. Drug researchers have followed through upon this idea. The smaller molecules, typically around 10-15 non-hydrogen atoms and molecular weights in the 150 to 250 g/mol range, are called fragments. Fragments, because they are smaller molecules with less functionality for intermolecular forces, have Ki values lower than drugs, hits, and leads. The Ki of a fragment might only be 1 mM. A molecule with an activity in the 1 mM range is not very interesting, but the idea behind fragments is that they can potentially be connected to create a larger molecule with a stronger, hit-like affinity of 1 μM or better. This approach is called fragment based drug discovery (FBDD). In FBDD a library of just a few thousand fragment-sized molecules is screened against a target protein. Fragments that likely bind different binding sites are then connected by tethers of carbon atoms. Two questions arise: (1) how should the tether be connected to each fragment and (2) how long should the tether be? These can be challenging questions, but the small size of the fragments restricts the possible solutions to a more manageable number. A hypothetical example of the process of FBDD is shown below. Fragments are screened. The strongly binding fragments are connected in various ways. The activity of the properly connected fragments are confirmed.


incorrect connection

target

fragment library

Step 1

Step 2

screen

combine fragments

tether too long

fragment hits Step 3

correct connection & tether

confirm hit activity tight-binding hit

Image credit: Pearson Education

FBDD has gained considerable momentum over the past 15 years. A drug company need not possess a massive compound library as with traditional HTS screening. A library of just 2,000 fragments may be sufficient for searching for hits.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 3 – Filtering Hits Pt 1 Video Clip – Visual Inspection HTS can generate many hits. These hits need to be filtered and ranked. The most promising hits can then be advanced in the drug discovery process. The least promising hits will be discarded. One method for sifting through hits is by visual inspection of the structures. Visual inspection of the hits allows medicinal chemists to identify functional groups that are notorious for causing problems at various stages of the drug discovery process. One problem is that some compounds are known to show activity in many different assays. These compounds have been labeled as PAINS, or pan assay interference compounds.1 PAINS do not interact in a specific manner with any one target. Instead, these compound have a general reactivity. Most often, the reactivity is that PAINS are strongly electrophilic and covalently react with proteins in the assay. Covalent interactions are irreversible. Remember that most drugs bind protein targets reversibly through intermolecular forces, not covalent bonds. Molecules that non-specifically and permanently react with the first protein they encounter tend to make very poor drugs. Functional groups that are frequently found in PAINS include electron-poor alkenes (alkenes bearing electron-withdrawing groups – EWGs) that are readily attacked by nucleophilic Rgroups of proteins. Selected examples are shown below. Unfortunately, these types of functional groups are easily made through combinatorial techniques. These functional groups are therefore frequently found in many combinatorial libraries and undermine the utility of the libraries for drug discovery.1 EWG EWG

EWG

Nu Nu

then protonate

O

EWG O

R amide N H O R ketone

O N O nitro

O O

O CH3

HN N

common EWGs

O

O

Cl

R ester

CH3

O Br

C N cyano

Br

O

O O O

A different problem with some hits and leads is that they contain functional groups that are known to frequently cause problems in drug metabolism. Problematic functional groups trigger a structural alert, or an awareness that the presence of a particular functional group in a lead can result in toxicity issues. One functional group that regularly triggers a structural alert is an aniline. Anilines undergo phase I oxidation and often form quinone imines, which are highly electrophilic and can damage the liver, the site of the phase I oxidation. Arylacetic acids undergo glucuronidation. The resulting acyl glucuronides can react with proteins and cause either tissue damage or an immune response. 2


phase I HO oxidation NH2

R aniline

phase I oxidation R

NH2

O R quinone imine

NH

potential liver damage

Some drugs contain structural elements found in PAINS. Some drugs contain functional groups associated with structural alerts. Regardless, when comparing hits and trying to determine which to advance as leads, a lead discovery team will favor compounds that do not have these structural issues. 1. Baell, J. B.; Holloway, G. A. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays. J. Med. Chem. 2010, 53, 2719-2740. 2. Stepan, A. F.; Walker, D. P.; Bauman, J.; Price, D. A.; Baillie, T. A.; Kalgutkar, A. S.; Aleo, M. D. Structural Alert/Reactive Metabolite Concept as Applied in Medicinal Chemistry to Mitigate the Risk of Idiosyncratic Drug Toxicity: A Perspective Based on the Critical Examination of Trends in the Top 200 Drugs Marketed in the United States. Chem. Res. Toxicol. 2011, 24, 1345-1410.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 4 – Filtering Hits Part 2 Video Clip – Molecular Indexes Medicinal chemists have developed a handful of simple, structure-based molecular indexes that can be used to prioritize hits identified by high-throughput screening. The metrics covered in this discussion in some way emphasize the size of the hit. One early set of criteria is Lipinski's rules.Remember that Lipinski's rules apply to ideal properties of oral drugs. Hits are not drugs. Hits have a typically affinity (Ki) of around 1 μM. Drugs have an affinity of 10 nM or lower. In order for hits to gain greater binding affinity to their targets, hits typically grow in molecular weight as more functional groups are added to exploit more drug-target intermolecular forces. A hit with a MW close to 500 may balloon to an excessive value of 600 or 650 as its activity is increased to drug level. Lipinski's rules 1. Moleulcar weight ≤ 500 g/mol 2. Lipophilicity (log P) ≤ 5 3. Hydrogen bond donors ≤ 5 4. Hydrogen bond acceptors ≤ 10 With this idea in mind, Simon Teague of AstraZeneca in 1999 published a paper that formalized the distinction between Lipinski's “drug-like” criteria and a new set of “lead-like” criteria. Leads and hits fall within a similar binding affinity range of 0.1 to 1.0 μM. Teague's criteria recognize that hits and leads tend to grow in both MW and lipophilicity as they are developed into drugs. Teague's lead-like criteria 1. Moleulcar weight ≤ 350 g/mol 2. Lipophilicity (log P) ≤ 3 Another hit selection metric is ligand efficiency (LE). LE is defined as the free energy of binding of a molecule (ΔGobind) divided by the number of non-hydrogen atoms in the molecule (n). Smaller, lead-like molecules (smaller n) with hit- or lead-level binding have a larger magnitude for LE than larger, drug-like molecules with hit- or lead-level binding.

None of these criteria are infallible, but they collectively can help the med chem group prioritize one hit over another.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 5 – Filtering Hits Pt 3 Video Clip – Other Lead Selection Criteria In the category of filtering hits, we have already discussed the presence of functional groups and the application of molecular indexes. These are both simple tests that can be performed by visual inspection of the hits. Other hit prioritization criteria, however, require experimentation. One experimental test is metabolism. The hit can be subjected to the different CYP enzymes to determine which, if any, of the CYP enzymes metabolize the hit. Remember from section 5 of Chapter 8 that some CYP enzymes, e.g. CYP2C9, show variability in activity across a patient population. If a hit is metabolized exclusively by one of these variably active forms of CYP, the hit may be deemed less attractive. A related experimental test is metabolism inhibition. The hit can be tested to determine whether it inhibits the most relevant forms of CYP for drug metabolism. Again, recall from Chapter 8 that drugs that inhibit metabolism run a high risk for interacting with other drugs. The prospect of significant drug interactions can downgrade one hit relative to another. Hits are also screened for their on-target activity in the presence of human serum, which contains significant amount of proteins, especially albumin. Almost all drugs and hit bind to serum proteins. Serum protein binding by a drug decreases the amount of drug that is available to bind the target. Screening a molecule in the presence of serum, therefore, decreases the apparent activity of the drugs. Hits that are strongly affected by serum proteins are often deprioritized in the lead discovery stage. Cell permeability is another experimental test for hits. Cell permeability tests use Caco-2 cells, cancer cells that can form monolayers of cells that replicate the epithelial lining of the small intestines. Through cell permeability tests, the absorption of a hit can tested. Hits that are only poorly able to passively diffuse across cell membranes will not likely be advanced as leads. The last test we will cover is not experimental in nature. It is instead a legal matter. For a hit to be considered a lead, the hit must be patentable. The legal team in a drug company searches the international patent literature to determine which of the hits can be patented. It is not uncommon for the simple structure of a hit to be elaborated with the addition of an extra ring to make the hit distinct from compounds that are already known in the patent literature. In this way, chemists can gain access to new intellectual property space and allow a hit to be patentable. Because there are so many hurdles for a hit to clear, having a large number of hits is ideal. The few hits that can clear all these hurdles may be advanced as leads. Hits that satisfy the various criteria will likely have been modified somewhat from their original structure. It is common for a hit with a Ki of 1 μM to be modified and improved to an activity of 100 nM by the time it is named as a lead.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 6 – Selective Optimization of Side Activities Video Clip – SOSA Not all hits are found through random screening. One alternative to HTS of a molecular library is called selective optimization of side activities or SOSA.1 The idea of taking a molecule with a primary activity in humans and then enhancing a secondary effect through structural changes describes the most common implementation of SOSA. An advantage to starting a drug discovery program with molecules that have already been tested in humans is that those molecules have already satisfied many safety criteria. With so many drugs failing because of toxic effects, it is natural for a premium to be placed upon safety at the lead discovery stage. Molecules that have been tested in humans also likely have favorable pharmacokinetic profiles. The primary approach for SOSA involves creating a molecular library populated with compounds that have been tested in humans and possibly approved as drugs. The included molecules should affect a broad range of protein targets. The drug library is then used as a starting point and screened against new protein targets. Any hits will presumably have a high potential for clearing the typical hurdles (membrane permeability, metabolism inhibition, etc.) that keep hits from becoming leads.1 A different SOSA approach, which is less deliberate and more opportunistic, takes a molecule with a known side effect and attempts to enhance the side effect activity. Perhaps the most famous example of this SOSA approach is sildenafil. O O O

HN

N H3C

N

CH3 N N

sildenafil (Viagra) erectile dysfunction

N O

CH3

CH3

Sildenafil was a drug in development for the treatment of angina, a condition in which heart muscle does not receive enough oxygen. In early phase trials, sildenafil was found to be poorly effective in the treatment of angina. When the trial was canceled, patients were slow to return their unused medication. It was soon discovered that sildenafil held promise as a treatment of erectile dysfunction. Sildenafil, the angina clinical candidate, become sildenafil, a lead for the treatment of erectile dysfunction. Sildenafil was later approved and became one of the most financially successful drugs in the history of pharmaceuticals. 2 Sildenafil is an atypical example of a SOSA drug. Sildenafil was ineffective in its original use and yet was effective and approved without a single molecular change for a completely different use. It is generally assumed that a SOSA program will require structural modifications to optimize the desired effect of the lead. 1. Wermuth, C. Selective Optimization of Side Activities: Another Way for Drug Discovery. J. Med. Chem. 2004, 47, 1303-1314. 2. Kling, J. From Hypertension to Angina to Viagra. Mod. Drug. Disc. 1998, 1, 31-38.


DavidsonX – D001x – Medicinal Chemistry Chapter 10 – Lead Discovery Part 7 – Natural Products Video Clip – Nature as a Source of Leads Biological organisms are full of molecules that are designed to interact with a protein target. Biological molecules are therefore a natural place to seek out inspiration for potential drugs. Numerous drugs, arguably most drugs, share some aspect of their origin or design to nature. 1 As we have already covered back in Chapter 3, knowing the structure of a protein target can give invaluable insight into the structure of molecules that bind the target. Insight can include the shape of an enzyme's active site or the polar or non-polar nature of the binding site of a ligand to a receptor. These are invaluable details for the medicinal chemistry team. Furthermore, some proteins can be crystallized with a weak inhibitor or ligand. An X-ray structure of such a complex allows the drug discovery group to examine precisely how a molecule fits a binding site. Other molecules might fit the exact same way. Cimetidine is an example of a drug that is clearly derived from natural substrates or ligands in the body. In the 1960s researchers learned the ability of histamine to stimulate stomach acid production by binding the H2 histamine receptor. 2 Little else was known. Therefore, when a discovery program for an anti-acid reflux drug was initiated, the research team started with compounds that resembled histamine (1). Nα-guanylhistamine (2) was the first lead with an ability to inhibit gastric acid secretion at high doses in rats (ID50 = 800 μmol/kg, ID = inhibitory dose). The program culminated in 1979 with the approval of cimetidine (3) (ID50 = 1.4 μmol/kg) by the US FDA. The similarities between histamine (1) and cimetidine (3) are apparent. H N

NH2 HN

N histamine 1

HN

N

NH2 NH

N-guanylhistamine 2

H3C HN

S

H N

N

NH2 N

CN

cimetidine (Tagamet) anti-acid reflux 3

1. Newman, D. J.; Cragg, G. M. Natural Products As Sources of New Drugs over the 30 Years from 1981 to 2010. J. Nat. Prod. 2012, 75, 311-335. 2. Ganellin, G. R.; Durant, G. J. Histamine H2-Receptor Agonists and Antagonists, In M. E. Wolff (Ed.), Burger's Medicinal Chemistry (4th ed., Chapter 48). New York: Wiley & Sons, 1981.


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 1 – Introduction Video Clip – Feedback Cycle Recall the picture of molecular space, specifically potential drug space. Most of the space is devoid of activity. Islands of hit-level activity are interspersed throughout the entirety of molecular space. Within hit spaces are possibly regions that contain leads, and the lead spaces may contain even smaller pockets of drug-quality molecules. Also remember that the lead discovery process starts with a search for hits, which are generally modified somewhat to afford promising leads. With a lead in hand, the lead optimization process can begin. Around the start of lead optimization, the lead is extensively analyzed. New versions of the lead with missing parts are synthesized in order to determine whether each part is critical to the target binding of the lead. In this fashion, the pharmacophore of the lead can be discovered. Knowing the pharmacophore provides two key pieces of information. First, the medicinal chemistry group knows where on the lead changes have the maximum impact on target binding. Second, the med chem group knows less sensitive parts of the lead. At these less sensitive areas modifications can be made to affect non-binding aspects, namely pharmacokinetics. The goal of lead optimization is to improve the properties of the lead to the extent that a marketable drug may be discovered. The lead optimization process is generally an iterative process. New compounds, called analogues, are made. The analogues are at least somewhat closely related structurally to the original lead. New analogues are tested for target binding as well as other properties, including cell permeability and liver metabolism. New analogues with less favorable properties are abandoned, and compounds with more favorable properties undergo further modification in an effort find a subsequent analogue with even better properties. The overall appearance of this process is a fractured, meandering walk through molecular space. In the scheme below, dots toward the left have less desirable properties. Dots on the right are more promising. The black dots represent synthesized compounds that were not pursued. The red dots and blue arrows show the progress of the lead as it starts from a hit and advances ultimately to a drug. A diagram of this sort for a real drug discovery program would have thousands of dots. inactive molecular space

hit space

lead space

drug space

final drug hit from screen improving drug properties

initial lead


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 2 – Functional Group Replacements Video Clip – Structure-Activity Relationships A major facet of lead optimization involves the replacement of one functional group with a new functional group. The newly prepared analogue is then tested to determine the effect of the structural change. Over time, as many different analogues have been synthesized and tested, certain structure-activity relationships (SARs) of the lead become apparent. An SAR is a link between a chemical structure and its physiological activity. Understanding the SAR of a molecule is a foundation of medicinal chemistry. Keep in mind that “activity” is not limited to target binding but also applies to pharmacokinetics. A very simple scheme showing a preliminary SAR study is shown below. Compound 1, a hypothetical lead, contains a monosubstituted benzene ring. A medicinal chemist would likely take compound 1 and “do some SAR” around the benzene ring. A simple change would be to place a CH3 group on the 4-position of the ring to form analogue 2. If compound 2 were more active than 1, then a reasonable interpretation may be that the methyl group is filling a hydrophobic pocket in the target binding site. If one carbon is good, then maybe two carbons are better. Analogues 3 and 4 would perhaps be prepared to test the hypothesis of a hydrophobic binding pocket. If compound 2 were less active than 1, then a reasonable interpretation may be that the binding site has no room for additional substitution at the 4position. Analogues 5 and 6 might test this idea. Analogue 5 replaces a ring C-H with a N, a hydrogen-bond acceptor, so the potential for a hydrogen bond with the target becomes a possibility. Analogue 6 replaces a hydrogen with a fluorine. Fluorine has an atomic radius of 1.5 Å compared to hydrogen's radius of 1.2 Å. While not identical, they are very similar. Fluorine, a very electronegative atom, can be introduced into a molecule to test electronic effects without adding steric bulk to the molecule. if activity 2>1

CH3

CH3

CH3 4

lead 1

H modify

4

additional promising analogues 3 4

CH3 screen

analogue 2

if activity 1>2

F

N

additional promising analogues 5 6

Image credit: Pearson Education


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 3 – Alkyl Group Replacements Video Clip – Homologous series Alkyl groups are very frequently explored as different R-groups during drug discovery. The different alkyl groups tend to be easy to prepare and often provide valuable information. Analogues are made by repeatedly adding an extra carbon to a alkyl chain. These analogues differ by one carbon and are often called homologues. If the homologues have a longer and longer chain with no branching, the set of homologues are sometimes called a homologous series. Two simple, hypothetical homologous series are shown below. CH3

lead (0 extra carbons)

NH2 lead (1 carbon between ring and amine)

CH3

CH3

homologous series (1, 2, and 3 extra carbons)

NH2

NH2

NH2

homologous series (2, 3, and 4 carbons between ring and amine)

Within a homologous series, activity often increases to a maximum as a side chain is lengthened. As the chain continues to be lengthened, the activity then drops from the maximum. This trend is often seen in both biochemical and cell-based assays.

activity

activity vs. carbon chain length

carbon chain length

In a biochemical assay, activity is related to binding. Extending an alkyl chain allows the chain to fill a binding pocket and increase hydrophobic interactions between the lead and target. Once binding is maximized, further extension of the chain causes the lead to no longer fit in the pocket, and activity drops quickly. Cell-based assays test the ability of the lead to interact with its target in a more complex


medium. Cell-based assays are particularly useful if the target is contained within a cell. Most enzyme targets lie within a cell. Changing the length of an alkyl chain alters the lipophilicity of a lead and its ability to cross the cell membrane. In many homologous series, there is an optimal value for lipophilicity. If lipophilicity is too low, the compound does not readily cross membranes. If the lipophilicity is too high, the compound enters the membrane easily but does not want to exit the membrane. Testing a homologous series can help discover the optimal lipophilicity of a lead. This activity trend has less to do with target binding, which can be determined through a biochemical assay. Instead, the trend is related to the ability of the lead to reach the target.


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 4 – Isosteres Video Clip – PK-Focused Changes At times during the lead optimization process, a lead may be found to have drug-like binding to the target but suboptimal pharmacokinetics. Problems in pharmacokinetics can be linked to any of the four aspects of ADME – absorption, distribution, metabolism, and excretion. Some functional group replacements have been found to preserve target binding and yet affect pharmacokinetics. These functional groups are known as isosteres. Isosteres are restrictively replacements. One specific group must be replaced with another specific group. Isosteres are often divided into two different categories, classical isosteres and nonclassical isosteres. Classical isosteres emphasize the preservation of steric effects within a molecule. Classical isosteres, therefore, are groups that tend to have approximately the same size. A methyl group and a chlorine atom are similarly sized and are isosteres of one another. A number of classical isosteres are included in the web content associated with this subsection. A simple example of using an isostere in drug discovery is shown below. Compound 1 is a lead with excellent activity on the intended target, but its half-life is shorter than desired. The main metabolite of 1 is carboxylic acid 2, which arises from ω-oxidation of the methyl group followed by oxidation of the resulting alcohol. In order to minimize the rate of clearance of 1 from the plasma by metabolism, one might replace the methyl group with a chlorine atom to make analogue 3. Since both the methyl and chlorine are similar in size, binding should be minimally affected, but the half-life of the compound should be lengthened. O

CH3 phase I

N

Cl OH

N

N

oxidation

N H

N H

N H

strong binding short half-life 1

strong binding? longer half-life? 3

inactive metabolite 2

Non-classical isosteres, often called bioisosteres, are isosteres that preserve electronic and hydrogen bonding properties of groups. One example is a tetrazole ring (6), which can be used in place of a carboxylic acid (5). Carboxylic acids in drugs provide a site for conjugation reactions. Tetrazoles, however, do not undergo conjugation in the body. While avoiding metabolism, tetrazoles have a very similar pKa to carboxylic acids – 4.5 for the carboxylic acid and 4.8 for tetrazole. The tetrazole ring therefore preserves any electronic interactions of the acid while avoiding metabolism issues. CN O

CN O

O

O

OH Cl

acid subject to conjugation pKa ~ 4.5 5

O Cl

N N N N H

tetrazole not subject to conjugation pKa ~ 4.8 6


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 5 – Directed Combinatorial Libraries Video Clip – Covering All Possibilities In this chapter, we have discussed SAR in the context of single-point modifications. Structural changes are made to a lead. The structural changes are evaluated based on their resulting property changes. New analogues of the lead are then synthesized for another round of testing. This process can be very linear. It seeks to improve the activity of the lead. The process marches the lead up the contours of activity until it reaches a maximum, which is hopefully of high enough quality to be a drug. A weakness in this approach is that the maximum discovered may be a local maximum. The global maximum, a molecular space with the most potent drugs, may be missed because areas of lower activity might lie between the lead and the global maximum. In single point modification approaches to lead optimization, areas of low activity are typically shunned. It is therefore possible to miss out on finding structures related to the lead with a high potency and favorable properties. hit space drug space

most potent drug space

lead space

An answer to this problem involves combinatorial chemistry. Generation of a small library of lead analogues can densely cover a small area of molecular space. With such dense coverage, compounds with high activity around other maxima with also be discovered. Libraries that are used in the lead optimization phase of drug discovery are called directed combinatorial libraries. Directed combinatorial libraries are far smaller than the massive compound libraries used in lead discovery. Directed combinatorial libraries may have a few hundred or a thousand members. By providing many similar compounds and avoiding the incremental improvements, directed combinatorial libraries certain contain many compounds with poor properties, but they also open the possibility of discovering new, potent compounds that would not otherwise have been made.


DavidsonX – D001x – Medicinal Chemistry Chapter 11 – Lead Optimization Part 6 – Peptidomimetics Video Clip – Peptides as Hits and Leads This chapter has focused upon synthetic molecules, but a rich source of leads is natural products. Natural products include all the compounds in the body that trigger biological responses, and proteins often serve in this role. So, pharmaceutical companies are often in a position having knowledge of very tight-binding, potent proteins. Proteins, unfortunately, are very problematic leads. They may have remarkable target binding and low Ki values, but their other properties, especially bioavailability, are very poor. The human digestive system is after all designed to break down proteins. They are not absorbed intact. A drug discovery group has two options when considering a peptide lead. Neither is easy. The group can abandon the peptide lead and start the lead discovery process from scratch by screening large compound libraries against the desired target. Abandoning a potent lead is hard to do especially because not all searches of compound libraries generate promising leads. The alternative option is to try to modify the peptide lead and improve its properties. This option requires peptidomimetics, which is the study of discovering non-peptide structures with peptide-like activity. Improving a molecule's pharmacokinetic properties while retaining its favorable binding closely resembles an idea that we have already discussed – isosteres. Indeed, there are specific types of isosteres that have been developed. These specific isosteres are peptide bond isosteres and strive to alter the amide linkage. Structure 1 is a simple peptide segment. The amide linkage is highlighted. One peptide isostere (2) involves R-groups from the α-carbon to the adjacent backbone nitrogen. This modification maintains the amide group, but the N-H is no longer present. Isostere 2 is called a peptoid. Another peptide isostere (3) is the retroinverso isostere. The position of the carbonyl and nitrogen are transposed, a change that inverts all the stereocenters along the peptide backbone. Other peptide isosteres are shown in structures 4 and 5. Through introduction of different peptide isosteres and other structural changes, the discovery team may be able to improve the pharmacokinetics of the lead while still retaining sufficient target binding for potency. R N H

N R

R' N

O

O

peptoid (shifted R-groups along peptide backbone) 2

O R

O

O

H N R' O N H

H N

R' retroinverso (swaps position of C=O and NH) 3

standard pepide structure 1 R N H

O O X

R'

X = O, ester 4 X = H2, methyleneoxy 5


Glossary absorption - the movement of a drug from its site of administration to the bloodstream active pharmaceutical ingredient (API) - the chemical in an administered drug that is responsible for its biological activity adverse effect - an undesired effect of a drug alkaloid - a molecule found in a natural source with a basic nitrogen and a level of structural complexity allosteric site - a site on an enzyme or receptor that is not bound by a substrate or response-causing ligand. Noncompetitive inhibitors (enzymes) and noncompetitive antagonists (receptors) bind at allosteric sites. alpha-helix (Îą-helix) - a type of secondary structure in which the protein backbone assumes a spiral conformation amide linkage - the amide bond formed between individual amino acid residues in a protein backbone analgesic - pain killer analogues - compounds related to a lead and prepared in an attempt to optimize the desired properties of the lead API - see active pharmaceutical ingredient apparent volume of distribution (Vd) - a hypothetical volume of plasma that is required to contain a specified drug dose


area under curve (AUC) - the area beneath a Cp-time curve. AUC is a measure of drug exposure. arsenicals - an early synthetic drug class that was used to treat syphillis and certainly protozoan infections assay - a general term for testing the biological activity of a molecule. An assay may be performed either in vivo or in vitro. AUC- see area under curve beta-sheet (β-sheet) - a type of secondary structure in which the protein backbone assumes a fairly flat shape formed by a back-andforth flow of the chain binding energy - the free energy of binding between a drug and its target based on the dissociation equilibrium constant between the drug and target (K)

bioavailability (F) - the fraction of an drug dose that actually reaches the bloodstream from its site of administration bioequivalence testing - an abbreviated clinical trial used by generic manufacturers to show that a generic product is biologically similar to an existing branded drug bioisosteres - isosteres that specifically preserve electronic and hydrogen bonding characteristics when one group is exchanged with another bolus - an amount of drug that is administered, typically intravenously, in a single burst Caco-2 cells - a cell that is used in cell permeability assays cell permeability - the ability of a molecule to passively cross cell membranes. High cell permeability indicates that a molecule will likely be well absorbed from the digestive system.


central compartment - blood plasma Cheng-Prussoff equation - an equation that can convert an IC50 value to a Ki value

classical isosteres - isosteres that specifically preserve steric bulk when one group is exchanged with another clearance (CL) - the removal of a drug from the bloodstream, normally by either excretion or metabolism. The variable CL has units of either mL/min or mL/min/kg. clinical candidate - see investigational new drug combinatorial chemistry - a method, often automated, for making large collections of molecules using varied building blocks around a molecular scaffold compartment model - a method for describing how a drug distributes into the various tissues of an organism competitive inhibitor - an enzyme inhibitor that binds at the active site of an enzyme. Competitive inhibitors decrease the affinity of an enzyme for its substrate, and therefore increases Km. composition of matter - a type of patent that covers new chemical substances, especially drugs compound library - a collection of molecules that can be used to test for biological activity against a protein target concensus scoring - the use of multiple scoring methods in an in silico screen to increase the relability of the resulting hits Cp - see plasma concentration CYP - see cytochrome P-450


cytochrome P-450 (CYP) - a superfamily of enzymes, mostly associated with the liver, that perform many oxidative metabolic reations on drugs depolarization - the flow of ions across a cell membrane from the side with high concentration to the side with low concentration desensitization - an abnormally low response to a drug, often because of downregulation of a receptor directed combinatorial chemistry - the use of combinatorial chemistry to generate libraries focused upon the SAR around a lead distribution - the transport of a drug to and from its site of action by the bloodstream distribution phase - the time period during which an absorbed drug reaches its full volume of distribution docking - the computer simulation of a molecule's binding to a target protein downregulation - a decrease in receptor expression by a cell in response to a continuous, high-level stimulation of the receptor drug-like - a description of a compound with a molecular weight between 400 and 500 g/mol and a lipophilicity (log P) of near 5 drug product - the entire administered drug. For orally delivered drugs, the drug product includes the drug substance and all the binders, dyes, and fillers in the pill. drug substance - the active material within a drug product drug-target residence time - the half-life of a drug-receptor complex as it equilibrates between its bound and unbound state elimination - any process that causes a decrease in the concentration of a drug in the bloodstream. Both metabolism and excretion are elimination processes.


elimination phase - the period of time after a drug has reached its full volume of distribution and is being cleared from the plasma elimination rate constant (kel) - a rate constant that describes rate of elimination for a drug. Elimination rate constants normally correspond to first-order processes and have units of inverse time. endogenous ligand - a ligand that is found naturally in the body enzyme (E) - usually a protein, a biological catalyst that converts a substrate to a product enzyme-substrate complex (E-S) - the aggregate substance formed by binding between an enzyme and its substrate excretion - the removal of waste from the body. For drugs, excretion is normally associated with the generation of urine by the kidneys through the filtration of blood. exogenous ligand - a ligand that is not naturally found in the body. Synthetic drugs that bind receptors are exogenous ligands. extraction ratio - the fraction of a drug that is removed by an organ based on the plasma concentration of a drug that enters and leaves the organ F - see bioavailability false negatives - compounds that fail to appear active in a screen despite the fact that the compounds do possess strong binding to the target of interest false positives - compounds that indicate activity in a screen but are actually not active fast neurotransmitter - a neurotransmitter that acts as a ligand for a ligand-gated ion channel first pass effect - the tendency for a significant fraction of an oral drug to be broken down the liver immediately after absorption from the digestive tract


fragment - a molecular library compound with a lower molecular weight (150-250 g/mol), fewer non-hydrogen atoms (10-15), and weaker target binding (Ki ~ 1 mM). Fragments are connected to form hits. fragment-based drug discovery (FBDD) - a method of discovering hits by linking smaller, weaker binding molecules (fragments) together to make molecules with hit-like activity G-protein-coupled receptor (GPCR) - a receptor superfamily that is a very common drug target and affects many metabolic functions glucuronic acid - a highly polar molecule that is conjugated with molecules, normally carboxylic acids, to facilitate excretion by the kidneys glutathione - a tripeptide that is added to molecules, often phase I metabolites, to detoxify the compound half-life (t1/2) - the time required for the concentration of a drug to decrease by 50% Henderson-Hasselbalch equation - an equation that determines the ratio of a conjugate base to its acid based upon the pKa of the functional group at the pH of the environment

hepatic clearance (CLH) - the elimination of a drug that is attributable to the liver hepatic portal system - a collection of blood vessels that gathers nutrient-rich blood from the gastrointestinal tract and transports it to the liver high-throughput screening (HTS) - a quick, automated method of in vitro screening for determining the activity of a molecule against a target


hit - a molecule found through screening with a binding affinity of around 1 ÂľM homologous series - a collection of analogues in which each compound differs by the incremental addition of a carbon, usually characterized by the lengthening of an alkyl chain homologue - a specific type of analogue which differs from the lead compound by a single carbon, generally a CH2 group hydrogen bonding - an intermolecular force based upon the interaction of a hydrogen attached to an oxygen or nitrogen with a nitrogen or oxygen lone pair hydrophobic effect - an entropy-driven force that favors the binding of a hydrophobic drug to a target based upon solvation changes between the bound and unbound drug IC50 - the concentration of an inhibitor required to reduce the rate of an enzymatic reaction by 50% in vitro - Latin for "in glass". In drug discovery in vitro refers to tests that are performed within a test tube or other artificial container. in vivo - Latin for "in the living". In drug discovery in vivo refers to the activity of molecule upon a living organism. IND - see investigational new drug inhibitor - a molecule that slows the reaction between an enzyme and a substrate intellectual property space - figurative room around a molecular structure that allows the original molecule and related compounds to be protected through patents because no other patents have been filed on the compounds intermolecular force - one of several non-covalent interactions that help bind a drug to its target interstitial fluid - the liquid that fills the tiny spaces between cells


intravenous (IV) - a method of administration that involves injection of a drug directly into the bloodstream by way of a vein investigational new drug - a classification for a molecule that has been approved to be tested in humans but has not been yet been approved to be marketed. An investigational new drug is also known as a clinical candidate. ionic bond - an intermolecular force based upon the electrostatic attraction between two oppositely charged ions in silico screening - the process of estimating a molecule's biological activity through a computer simulation. The screen involves docking a molecule into a target's binding pocket and then scoring the quality of the molecule-target interaction. isosteres - functional groups that can be interchanged with one another with minimal impact upon drug-target binding but significant impact on pharmacokinetics IV - see intravenous kel - see elimination rate constant Ki - the dissociation equilibrium constant of an enzyme-inhibitor complex lead - a molecule found through screening with a binding affinity of around 1 ÂľM. As the lead is modified and optimized, its binding will increase to the 1-10 nM level. lead discovery - a stage in the drug discovery process. Lead discovery involves the screening of molecules to discovery hits and then selecting the most promising hits as leads. lead-like - a description of a compound with a molecular weight between 250 and 350 g/mol and a lipophilicity (log P) of 3 or less lead optimization - a stage in the drug discovery process. Lead optimization improves the pharmacodynamics and pharmacokinetics of the lead until they are potentially good enough for the lead to act as a drug.


library - see compound library ligand - a molecule that binds a receptor ligand-gated ion channel (LGIC) - a receptor superfamily that controls ion flow across a cell membrane ligand efficiency (LE) - a calculated molecular descriptor that estimates the amount of binding energy (Î&#x201D;Gobind) contributed by each non-hydrogen atom (n) in a molecule. Drugs, hits, and leads typical have a LE value of â&#x2C6;&#x2019;0.30 kcal/mol/non-hydrogen atom or smaller.

ligand lipophilicity efficiency (LLE) - a calculated molecular descriptor that balances a molecule's target binding against its lipophilicity. LLE values of 3 or higher for a drug, hit, or lead are ideal.

Lineweaver-Burk equation - a linearized form of the MichaelisMenten equation that gives the relationship between 1/V and 1/[S]

Lipinski's rules - a set of molecular properties that are simple to determine and useful for predicting whether a drug will readily diffuse across a cell membrane lipophilicity (P) - the equilibrium constant that measures the ratio of the concentration of a drug in a mixture of 1-octanol and water. Lipophilicity is often used in a logarithmic form, log P. magic bullet - a term created by Paul Ehrlich to describe drugs that are able to destroy an invading organism without affecting the host


maximum tolerated concentration - the maximum concentration (or dose) of a drug that gives a therapeutic effect without causing excessive adverse effects. The maximum tolerated concentration is at the top of the therapeutic window. me-too drug - a drug that is very similar in structure and activity to a molecule that has already been approved and marketed metabolism - the chemical breakdown of a drug, generally caused by enzymes in the liver metabolite - the product of a metabolic reaction upon a drug Michaelis constant (Km) - a measure of the affinity between an enzyme and substrate. Michaelis constants carry a concentration unit. The Michaelis constant is used in the Michaelis-Menten equation as well as other enzyme kinetics relationships. Michaelis-Menten equation - an equation that models the relationship between the rate of an enzymatic reaction (V) and the concentration of the substrate ([S])

minimum effective concentration - the minimum concentration (or dose) require to observe a therapeutic effect of a drug. The minumum effective concentration defines the bottom of the therapeutic window of a drug. molecular library - see compound library molecular space - a hypothetical collection of molecules that fall within a defined set of properties or characteristics. morphine rule - a set a structural requirements that is followed by most opiates and opioids. The morphine rule requires a molecule to contain a benzene connected to a quaternary carbon, then a twocarbon tether, and finally a tertiary amine.


mutational resistance - the ability of a genetically altered organism to withstand a previously effective drug. Mutational resistance is frequently encounted in bacteria, viruses, and cancer. NDA - see new drug application new drug application (NDA) - a regulatory step in which the FDA reviews clinical data to determine whether a molecule is safe and effective enough to be approved as a drug non-classical isosteres - see bioisosteres noncompetitive inhibitor - an enzyme inhibitor that binds both an enzyme and enzyme-substrate complex. A noncompetitive inhibitor decreases Vmax without affecting Km. nuclear receptor - a receptor superfamily that regulates DNA replication and gene expression. Steroids generally target nuclear receptors. occupancy theory - a theory in ligand-response relationships that equates the fraction of receptors bound by a ligand to the fraction of response generated by the receptor off-label use - the use of a drug in a fashion for which the drug has not been formally tested or approved oligopeptide - a short string of amino acids with a length too short to be called a proper protein. Oligopeptides are normally no longer than 20 amino acids in length. Many signal peptides in the body are oligopeptides. one-compartment model - a simple compartment model in which the drug is assumed to be distributed into only the plasma oral bioavailability - the fraction of an oral drug that reaches the bloodstream relative to an IV bolus form of the drug pan assay interference compounds (PAINS) - molecules that, because of either a high degree of chemical reactivity or solubility problems, show up as false positives in screens for activity against a broad range of protein targets


patent - a form of intellectual property that grants to the holder exclusive rights to an invention for 20 years from the date of filing peptidomimetics - a form a lead optimization that attempts to develop a drug with good bioavailability from a typically poorly available peptide lead peptoid - a specific type of peptide isostere that is used to develop a peptidomimetic drug pharmacodynamics - the branch of medicinal chemistry that focuses the action of a drug at its site of action pharmacokinetics - the branch of medicinal chemistry that focuses on the movement of a drug from its site of administration, throughout the body, to its site of action, and out of the body pharmacophore - the core parts of a molecule that are required for a threshold level of activity phase I metabolism - oxidative, reductive, and hydrolytic chemical reactions on a drug phase II metabolism - reactions in which a drug or metabolite is connected to a group (generally very polar) to either detoxify the compound or assist excretion of the molecule phenotype - an observable trait of an organism. Symptoms of a disease are an example of a phenotype, and drugs can modify the symptoms. plasma - the non-cellular portion of whole blood. Plasma consists of water, electrolytes, signal molecules, and proteins. plasma concentration (Cp) - the concentration of a drug within the blood plasma polymorphism - the ability for most molecules, including drugs, to pack together in multiple, different arrangements. Each polymorph of a drug potential has different physical properties and is legally considered a different composition of matter.


pre-clinical trials - a series of standardized toxicity studies in animals to establish the safety of a drug and provide data for an IND application primary structure - the simplest level of protein structure. Primary structure describes the order of the amino acids in the peptide backbone. privileged structure - a molecular scaffold or part of a scaffold that appears in molecules that have activity against a range of different targets prodrug - a drug that is administered in an inactive form and is broken down in the body to reveal the active form product - in the context of enzyme kinetics, the material formed from the action of an enzyme on a substrate promiscuous - a description for a compound that binds to multiple different targets quaternary structure - the relative orientation of individual proteins within a multi-protein complex random coil - a type of secondary structure in which the protein backbone has no well-defined conformation receptor - a protein that acts as a switch for controlling cellular processes renal clearance (CLR) - the elimination of a drug caused by the kidneys resolution - a measure of the clarity of an electron density map in Xray crystallography. Resolution is measured in angstroms (Ă&#x2026;) (1 Ă&#x2026; = 10-10 m). retroinverso - a specific type of peptide isostere that is used to develop a peptidomimetic drug Rule of Five - see Lipinski's rules


SAR - see structure-activity relationship screen - a general term for testing the activity of a drug secondary structure - localized regions of folding within a protein. Common examples of secondary structure include the ιhelix, β-sheet, and random coil. sensitization - restoration of a normal response to a ligand, often because of upregulation of a receptor serum - the fluid left behind after whole blood is allowed to clot. Serum is closely related to plasma, but serum lacks some of the proteins responsible for clotting. serum albumin - a protein that tends to bind acidic drugs and makes up between 3 and 5% of the weight of whole blood scoring - the use of a computer algorithm to estimate the quality of binding between a molecule and a target protein selective optimization of side activities (SOSA) - the practice of discovering new drugs by the modification of old drugs. Normally, SOSA begins with the screening of a library of varied drugs as a search for hits. side effect - see adverse effect slow neurotransmitter - a neurotransmitter that acts as a ligand for a G-protein-coupled receptor spare receptors - extra receptors in a cell or tissue that need not be bound by a ligand in order to achieve a full response structural alert - an awareness that a molecule contains functional groups that frequently lead to drug toxicity. Two common problematic functional groups are anilines and arylacetic acids. structure-activity relationship (SAR) - the link between a compound's molecular structure and its physiological function


substrate (S) - the starting material for an enzymatic reaction. Substrates bind an enzyme at the enzyme's active site. sulfa drug - see sulfonamide antibiotics sulfonamide antibiotics - an early class of antibiotic drugs containing a sulfonamide (SO2N) group superfamily - the top level of classification for a receptor. The four superfamilies are ligand-gated ion channels, g-protein-coupled receptors, tyrosine kinase-linked receptors, and nuclear receptors. t1/2 - see half-life target - typically a protein that plays a key role in a biological pathway of a disease. Binding of a drug to the protein target often influences the pathway and affects the diseased condition. terminal elimination rate constant - the elimination rate constant than can be observed for a drug that has reached its full volume of distribution tertiary structure - the three-dimensional arrangement of secondary structures within a single protein therapeutic window - a drug's ideal concentration range, which lies between minimum effective concentration and maximum tolerated concentration total clearance (CLT) - the sum of all the drug clearing processes in the body trademark - a form of intellectual property that normally applies to the name brand of a drug. Only a company who owns a trademark may use the name to market its product. two-compartment model - a compartment model in which a drug is presumed to equilibrate between the plasma and a peripheral compartment tyrosine kinase-linked receptor (TKLR) - a receptor superfamily that is commonly targeted to affect cancer


uncompetitive inhibitor - an enzyme inhibitor that binds the enzyme-substrate complex. An uncompetitive inhibitor decreases both Vmax andKm of an enzyme-substrate system. upregulation - an increased expression of a receptor in a cell in response to a lack of stimulation by the receptor virtual screening - see in silico screening Vmax - the maximum rate of conversion that a particular enzymesubstrate system can attain volume of distribution (Vd) - see apparent volume of distribution whole blood - the entire contents of blood including water, electrolytes, proteins, signaling molecules, and cells xenobiotic - an unnatural compound in the body. Most drugs are xenobiotics. Â

MOOC Medicinal Chemistry  
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