Pharma Focus Asia Magazine - Issue 28 by Ochre Media Pvt. Ltd.

Issue 28 2017

Use of Real World Evidence Increasing throughout Asia Emerging Directions in R&D of Drug Discovery and Development The patient specificity paradigm

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Foreword Modelling & Simulation in Drug Development Pharma companies look to improve experimental drug success rate and accelerate clinical development while striving to improve efficiency cost-effectively. It is imperative for companies to be cautious in decision making to increase the probability of commercial success for developing the drug. Major pharmaceutical companies have been exploring use of computer-based simulation and modelling techniques for making informed decisions. Some of the strategies these companies focus on are Clinical Trial Simulation (CTS), Modelling and Simulation (M&S), Computer-Assisted Trial Design (CATD), Model-based Drug Development (MBDD), and model-informed drug discovery and development (MID3). Regulators have also taken note of such strategies to enable increased efficiencies in drug development. From the business perspective, M&S is being leveraged to showcase virtual bio-equivalence and receive bio waivers. M&S follows an in-vitro in-vivo correlation (IVIVC), which the US Food &Drug Association (FDA) defines as “a predictive mathematical model describing the relationship between an in-vitro property of a dosage form and an in-vivo response”. While developing and optimising a new drug formulation, there may be changes in composition of drug, manufacturing process, equipment used or the batch size. These changes can occur frequently prompting the need to conduct bioavailability studies for determining equivalence and IVIVC is a tool used to demonstrate this equivalence during the drug development and optimising formulation. Be it clinical trials or Research & Development (R&D), M&S in particular has significantly impacted both development and formulation of drugs. With clinical trials, M&S has shown to add value in optimising study design, while pursuing the objective of improving efficiency by choosing an accurate trial size and collecting relevant data at optimal times. A 2012 research paper highlights the positive impact of M&S in clinical trials on FDA approval and labelling decisions. From 2000 to 2008, Pharmacometric analysis have contributed to 64 per cent of drug

approval decisions and 67 per cent of labelling decisions. FDA is believed to have necessitated use of M&S in identifying and approving an experimental drug Peramivir to curb an influenza epidemic in 2009. Today, it is not surprising that around 90 per cent of FDA approvals are deemed to be around M&S. Such is the importance M&S has achieved over the years. A recent paper titled “Regulatory Experience with In Vivo In Vitro Correlations in New Drug Applications” published by the FDA, offered guidance in using IVIVC for pre-approval and post-approval changes, and thus minimise the need for in vivo bioequivalence studies. The paper also emphasised on how IVIVC has been aiding in drug development decisions in the wake of Quality by Design paradigm. Regulators including FDA, European Medical Agency, Japanese Pharmaceuticals and Medical Devices Agency and other regulatory agencies have been advocating the use of IVIVC as they believe it to be a key tool enhancing product and process understanding and ensuring steady performance through the product life cycle. IVIVC plays a strategic role enabling pharma manufacturers to replace expensive, time consuming bioavailability and bioequivalence studies with cost-effective and time efficient IVIVC analyses. For scientists, IVIVC offers better product insights enabling them to change a drug’s formulation to increase its in vivo performance as well as probability of regulatory success. In the cover story of this issue, Mikku Nagata of Certara GK emphasizes on how Modelling & Simulation can be a game changer in drug development saving time and money for companies, and also aiding in improved patient care. I am hopeful you will find the cover story and other articles equally interesting.

Prasanthi Sadhu Editor

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Contents Strategy

Changing the Game in Drug Development

06 Globalisation The right steps to successfully break into a new market

Modelling & Simulation

Kaarin Gordon SVP, Global Life Sciences, SDL

Mikku Nagata

10 Life Story The idea of product life cycle is more useful that you realize

Business Development Director, Certara GK

Brian D Smith, Principal Advisor, PragMedic

Clinical Trials 14 Use of Real World Evidence Increasing throughout Asia Zachary Peter Smith Tufts Center for the Study of Drug Development, Tufts University Medical School Christopher Paul Milne, Tufts Center for the Study of Drug Development, Tufts University Medical School

Research & Development 20 Knowing vs Hunting for Targets Prabhat Arya, Distinguished Research Professor in Chemistry and Chemical Biology Dr. Reddy's Institute of Life Sciences (DRILS), University of Hyderabad Campus

24 Single-Use Method Using Filter Aid Easy Removal of Midstream Cells Ralph Daumke, Market Manager Biologics

COVER STORY

Corinne Luechinger, Head of FILTROX Academy

28 Emerging Directions in R&D of Drug Discovery and Development The patient specificity paradigm

Subhadra Dravida, A Soorneedi Transcell Biologics

32 Mass Spectrometry in Research and Development of Protein Biologics Mallikarjun Dixit, VP, Accutest Biologics Private Limited Heramb M Kulkarni, Research Scientist, Accutest Biologics Private Limited

Manufacturing 44 Cultural Excellence as the Foundation For effectiveness of the quality system Thomas Friedli, Professor for Production Management University of St.Gallen Stephan Kรถhler, Research Associate, University of St.Gallen Paul Buess, Research Associate, University of St.Gallen

50 A Hard Pill to Swallow Measuring texture in novel oral dosage forms Jo Smewing, Applications Manager, Stable Micro Systems

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54 Real-Time in Line Monitoring For high shear wet granulation Jamie Clayton, Operations Director, Freeman Technology Ltd.

58 A Novel Continuous Pharmaceutical Manufacturing Pilot-Plant Advanced model predictive control Ravendra Singh, C-SOPS, Department of Chemical and Biochemical Engineering Rutgers, The State University of New Jersey

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Advisory Board

Editor Prasanthi Sadhu Alan S Louie Research Director, Health Industry Insights an IDC Company, USA

Christopher-Paul Milne Director of Research, Tufts Center for the Study of Drug Development, Tufts University, USA

Douglas Meyer Senior Director, Aptuit Informatics Inc., USA

Frank Jaeger Regional Sales Manager, Metabolics, AbbVie, USA

Georg C Terstappen Director and Head of Biology, Neuroscience Discovery AbbVie Deutschland GmbH und Co. KG, Germany

Editorial Team Debi Jones Grace Jones Art Director M Abdul Hannan Product Manager Jeff Kenney Senior Product Associates David Nelson Peter Thomas Sussane Vincent Circulation Team Naveen M Nash Jones Sam Smith Subscriptions In-charge Vijay Kumar Gaddam Head-Operations S V Nageswara Rao

Kenneth I Kaitin Director and Professor of Medicine, Tufts Center for the Study of Drug Development, Tufts University, USA

Laurence Flint Head Clinical Research Cough, Cold & Respiratory Disease Novartis Consumer Health, Inc., USA

Neil J Campbell President & CEO, Helomics Corporation HealthCare Royalty Partners University of Liverpool, UK

Pharma Focus Asia is published by

In Association with

A member of

Phil Kaminsky Chair, Department of Industrial Engineering and Operations Research University of California, Berkeley, USA

Rustom Mody Senior Vice President and R&D Head Lupin Ltd., (Biotech Division), India

Sanjoy Ray Director, Strategic Alliances & Health Innovation Merck, US

Confederation of Indian Industry

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Strategy

Globalisation

The right steps to successfully break into a new market

As the pharmaceutical industry squares off against the challenge of globalisation, there is a greater need for content than ever. Driven by forces from both the clinical and commercial sides, pharmaceutical companies are experiencing rising costs and competition. Ultimately, to enable businesses to compete on a global scale, technology needs to keep pace with the volume and types of content needed. Kaarin Gordon SVP, Global Life Sciences, SDL

riving Forces for Globalisation. As the pharmaceutical industry squares off against the challenge of globalisation, there is a greater need for content than ever. Driven by forces from both the clinical and commercial sides, pharmaceutical companies are experiencing rising costs and competitionâ&#x20AC;&#x201D;when conducting clinical trials and commercialising existing medicines into new markets. This has led to an increased need to move fast in global markets to keep up with competitors. As a, result, pharmaceutical companies are faced with an enormous, language-dependent content management challenge. The increasing regulatory complexities for launching products and conducting clinical trials on a global scale has become a burden to life sciences organisations. There are diverse sets of regulatory challenges and cultural nuances that come along with each new geography that need to be taken into consideration when conducting a clinical trial.

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Strategy

As pharmaceutical global content needs expand, labelling and packaging is one area to pay close attention to, as indications must incorporate the appropriate language. Education is another area—both for patient recruitment and for clinical investigators —ensuring regulatory compliance across various markets. Asia in particular is a growing sector where it is increasingly important for pharma companies to be armed with the right content to educate patients and physicians, keeping in mind the local and cultural nuances. As pharma’s global content demand rises, the process of creating and localising this information must be managed efficiently and cost effectively.

language, recruitment timelines will expand. When a form is inaccurate, the patient will need to resign. Kits sent out for randomised studies must be labeled correctly too. When a trial spans several patients, years and countries, there is a significant amount of complexity involved. As trials change with protocol amendments—perhaps altering the parameters or dosage – this requires new labeling. Considering that it costs US$2.56 billon to bring a drug to market, according to Tufts Center for the Study of Drug Development, each day a clinical trial is delayed has the potential to cost millions in revenue.

The impact of globalisation on clinical development

The growing need for global content is much more complex than it may sound: content needs to be compliant with all local regulatory agencies, which differ from country to country. As a result, content must be laid out in a structured format and comply with specific requirements and timelines. Managing this full process—translation from a single source that is done accurately and complies with regulatory standards and then managing and maintaining updates and changes in multiple languages—is no small endeavour. However, making sure clinical trials meet timelines and budgets is business critical. Language can be a barrier in conducting a clinical trial. Supporting documentation not only has to be accurately translated to support global trials but also accurately reflect the content and nuances of the local in which the trial is conducted. This can impact investigator and clinical staff training, site monitors and patient recruitment. There is no room for error, as inaccuracies and delays can impact the medication a patient takes. Or, if a physician is not trained in the right

With powerful translation memories, translation management systems and customisable workflows, organsations can ensure quality and consistency while lowering translation volumes and costs.

The impact of globalisation on commercialisation

From clinical development to commercialisation, all content must meet regulatory standards. Today, a launch can happen across global markets simultaneously, instead of rolling out in each market individually. Due in part to this increase in speed and reach, the world prescription drug market is expected to grow to over US$810 billion in 2017, up from US$780 billion in 2016. Simultaneous market introduction into multiple countries adds enormous complexity and pressure for local affiliates to ensure localisation and accurate

document control and translations. There is a short window where an enormous amount of content needs to be produced in a language and formatted to meet local regulatory agency guidelines, all while adhering to strict timelines. Life sciences companies will continue to look to emerging market regions for new sources of revenue. Localising for new markets demands rigour and brings with it the added risks of legal and regulatory penalties if done poorly. Today, however, website localisation, mobile localisation and the support for local language social networks are now a necessity. Health professionals and patients have come to expect life sciences organisations to support their native language. After all, we are all consumers and have become accustomed to the on-demand experiences we have with brands. In a digital age, we can often receive the information we need instantaneously, via the channel we prefer, in the language we speak. Life sciences should be no exception, but there are several barriers to meeting this demand—regulations being one of them. Because regulations are a fact of life, developing standardised global website templates and frictionless translation workflow are essential to successful global content management. Modern technology platforms combine web content management with digital media management, targeting, testing, personalisation and localisation for high impact digital experiences. With this in mind, it is paramount that clinical products—and all related promotional materials, documents for educating sales professionals—are globalised, localised, personalised and comply with all local regulatory labeling requirements, which are often stringent and vary across countries. Technology and language services

With the potential negative consequences in mind that can result when localisation and translation goes wrong, it’s important www.pharmafocusasia.com

Strategy

Modern technology platforms combine web content management with digital media management, targeting, testing, personalisation and localisation for high impact digital experiences.

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quality translations on time and on budget. To ensure that things do not fall off during this process, make sure you are keeping the pace with the volume of content you are producing and that there is complete visibility and control over all documents and labels. Systems that are designed to work the way you do will enable you to scale as content and language demands increase, automate manual processes and reduce the cycle time and cost of language updates. Ultimately, to enable businesses to compete on a global scale, technology needs to keep pace with the volume and types of content needed. This enables the ability to produce, update and automate critical business processes that can create, manage, translate and publish content, providing complete visibility and control of all document and label versions. As a result, the business can scale appropriately as A u t h o r BIO

that pharmaceutical companies have a sound strategy in place for global content, incorporating both technology and services. When it comes to global health, life sciences organisations can’t afford to have any language barriers. Billions of dollars have been invested in entering new markets and patients’ lives are at stake. However, these resources will not be utilised effectively without getting the basics right. To successfully break into a new market, there needs to be a way to manage structured content and streamline the process of translation. Streamlining and optimising regulated global content delivery is the key during this process. With powerful translation memories, translation management systems and customisable workflows, organsations can ensure quality and consistency while lowering translation volumes and costs. This can be accomplished by employing technology solutions that are purpose built to supply flexibility and control at scale. A good first step is incorporating a technology platform that automates manual processes. This way, organisations can centralise all translation efforts, streamlining and controls translation projects to deliver

content and language demands increase. Technology on its own cannot solve this massive global content challenge. It can, however, power automation and work successfully when the right processes are in place to streamline operations. This combination of capabilities mixed with the right people that understand the industry and language in that specific market, make truly removing language barriers possible. It’s important to note that technology is a key piece to the puzzle, but the human element—especially in such a complex, nuanced and critical industry—is always needed. Best in class organisations today are adopting this approach, ensuring they can embrace globalisation as efficiently and effectively as possible—meeting regulatory guidelines and ultimately, bringing new products to market for patients in need worldwide.

Kaarin Gordon currently leads the global Life Sciences practice at SDL and helps leading pharma, medical device, and CROs more effectively develop, manage, translate, and publish global content. She has spent 20+ years focused on developing solutions for healthcare and life sciences and has extensive experience in the localisation industry working with both translation services and related technologies.

Strategy

Life Story The idea of product life cycle is more useful that you realise

lthough its ancestral ideas can be traced back to the late 19th and early 20th centuries, the concept of PLC as not at a strategic marketing tool is really the child of two later schools of thought. Rogers’ famous Diffusion of Innovation concept, with its ideas of innovators, early -adopter s and laggards, showed how and why the adoption of innovative new products changes over time. Then Levitt, amongst others, described how most competitors chose low-risk imitation of market leaders rather than high risk innovation. Gradually, these two streams of thinking flowed together to form the idea of the product life cycle, a term first used by Dean in 1950. Interestingly, he described it as “the cycle of competitive degeneration”, referring to products’ loss of differentiation over time. Others also developed the PLC idea into the 1950s 10

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and the classic curve we now all know, featuring stages of embryonic, growth, maturity and decline, was published by Forrester in 1959. It finally reached the text-books in 1964. In essence, what those early researchers noted was that it was typical for products to go through stages. Further, they saw that each stage was characterised by certain observable features, such as market growth rates, numbers and types of competitors and level of market penetration. Importantly, they realised a pattern in how those things vary that was reliably predictable. This history is of much more than academic interest: It tells us three things that many strategic marketers have forgotten. Firstly, PLC is not and was never about individual products. It is about product categories. Secondly, PLC shows that careful observation of current market

conditions can be used to predict the future. Finally, those PLC predictions can be used to manage brands and products for greater return on investment. These three things give us an appreciation of the real value of PLC that is much more useful than the simplistic view used by many current brand teams. Further, a careful reading of the research in this area reveals that there are five steps to releasing the value of PLC that its discoverers first intended. Step 1: Take a categorical view

The first step in using PLC is define the product category in which you are operating. A product category is simply the aggregate of all products that the customer perceives as interchangeable. Statins, Proton Pump Inhibitors and fMRI scanners are all product categories, individual products are not. Nor are

Strategy

The idea of Product Life Cycle (PLC) is familiar to any experienced executive in pharma or medtech. It’s often assumed to be synonymous with line extension tactics, such as reformulation or label extension, that can be used to defend against me-toos and generics. But the inventors of the concept meant PLC to be much more useful than just that. In this article, I’ll first visit the origins of this well-known strategic management tool and then show how it can be essential to optimising the value of a brand. Brian D Smith, Principal Advisor, PragMedic

substitutes that the customer sees as different, such as anti-depressants and talking therapies. This definition is important because the subsequent stages of using PLC depend on information that must be collected at product category level, not that of the brand or product. Step 2: Gather characteristic information

There are many observable factors that vary across the PLC but only a relatively small number of them are needed to give most of the diagnostic insight you need. In pharma and medtech markets, these characteristics include the primary basis of competition, level of product differentiation, rate of growth of market penetration, market share distribution and customer loyalty, as shown in figure 1. These factors are practically

The decades old history of PLC means that many of today’s marketers in pharma and medtech have forgotten, or have never been taught, the foundations, value and proper use of this strategic tool.

important because they can be measured relatively easily for any given product category. Indeed, this is exactly the sort of information that most companies collect routinely, so PLC analysis is rarely an expensive exercise. It does, however, require careful thought. Like any analytical tool, bad inputs lead to bad outputs, however well the tool is used. Step 3: Infer current PLC stage

Having gathered the information needed, the next step is to use it to infer the current stage of your product category’s life cycle. The complexity of pharma and medtech markets means that this is more of a craft than a simple calculation. It involves judgment, wisdom and perspective applied in two steps. Firstly, it involves making a judgement about what PLC stage is inferred by www.pharmafocusasia.com

Strategy

Diagnostic Characteristic of Product Category Life Cycle Stages Life cycle stage/ Characteristics of category

Embryonic (e.g. CART therapy)

Growth (e.g. Biologics in oncology)

Mature (e.g. â&#x20AC;&#x153;Novelâ&#x20AC;? anticoagulants)

Decline (e.g. Statins)

Primary basis of competition

Efficacy of product

Acceptance into patient pathways

Accessibility and ease of use

Price

Level of product differentiation

Products vary significantly

Convergence onto similar technologies and products

Marginal differences

No difference

Rate of growth of market penetration

Erratic

Fast

Slow

Negative

Market share distribution

Very uneven and unstable

Rapid entry and exit of competitors

Consolidation into few large players with 80:20 distribution

Reduction to 2-3 dominant competitors

Customer loyalty

Trial use with frequent experimentation

Trial activity leading to confirmed preference

High degree of loyalty based on familiarity and trust

Low loyalty and commoditisation

Table 1

each of the individual characteristics. Secondly, It requires the aggregation of all those separate judgements into an overall assessment of PLC stage. In some cases, all of the indicators point to the same stage and it is a relatively simple take to decide the PLC stage. For other product categories, the indicators may appear to contradict each other. This is usually due to them evolving at different speeds and your final judgement should consider not only the current data but also its direction and speed of change. In table 1, the typical characteristics of each PLC stage are illustrated.

and medtech markets means it is rarely possible to be precise about the timing of the changes but it is often possible to do this to an approximate level. For example, if the characteristics are divided between embryonic and growth, it is likely that

the market is some time away from maturity, whereas if the characteristics are mostly growth stage with one mature indicator, it implies that market maturity is imminent. This also reveals another value of PLC; the direction of market

Extension

Step 4: Anticipate market changes

Inference of the current PLC stage allows the anticipation of where the market is headed. Each of the characteristics is likely to develop into those typical of the next stage, so the PLC allows you to anticipate the broad and powerful trends in your market. The variability of pharma 12

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Embryonic Stage

Growth Stage

Mature Stage

Decline Stage

Strategy

Step 5: Develop anticipative tactics

By using PLC to anticipate where the market is heading, you have given yourself the powerful strategic advantage of prescience. There are many ways to use this gift at the various stages in the pharma and medtech value chain. At product discovery and development stage, PLC-derived information can be used to inform investment and business development decisions. For example, investment in or acquisition of a product at embryonic or growth stage may be justified by the anticipation of volume growth. By contrast, if the PLC analysis indicates a mature category then that would imply a future pricing decline that may make investment or acquisition a bad choice. Approaching and around launch, insight derived from PLC analysis might inform pricing and market access strategy.

For example, some firms use evidence of market maturity to argue that market prices will decline in future. This not only helps their own pricing decisions, it also helps them make coherent and evidenced arguments to Health Technology Assessment bodies. Equally, PLC analysis helps to inform how product launch might accelerate the category’s PLC. In competitive strategy, the PLC analysis provides a view as to how competitors will behave and what market trends might be. This is important for the choice of competitive activity. For example, for a product category at growth phase it might make sense to invest in clinical trials designed to provide evidence that demonstrates efficacy. At more mature stages, however, the investment might be more appropriately allocated to health economic outcomes research, based on real world data, to demonstrate health economic superiority. Equally, appreciation that the market will mature might direct resources towards marketing projects, such as brand building, or line-extensions, such as extended

A u t h o r BIO

or competitor research. For example, if the PLC indicates the decline stage, you should focus on identifying the next product category that is likely to replace the existing one.

indications or services that provide value ‘beyond the product’. Mostly neglected, because PLC information often remains siloed in marketing departments, is insight that can inform operations and sales. For example, imminent growth would push the company to invest in manufacturing, supply chain and sales teams in order to exploit the market. By contrast, imminent decline might direct the company to pull resources away from sales and towards investments that would reduce costs. Overall, thoughtful use of PLC in the way its inventors intended shows it to be widely useful across the whole of the life cycle and the whole of the value chain. Hidden value

The decades old history of PLC means that many of today’s marketers in pharma and medtech have forgotten, or have never been taught, the foundations, value and proper use of this strategic tool. Brand team leaders, who in many cases were born a generation after PLC was developed, often mistakenly confuse PLC with its limited, narrow use in line extension activity. A better understanding of PLC’s parentage and application reveals that it has hidden value for brand teams that can be realised across the value chain. PLC is tool to predict the future, helping us to make better decisions about what products to develop, how to launch them, how to compete with them and how to extract value as they age. In an increasingly competitive market, with increasingly demanding shareholders, this is knowledge that we can’t afford to leave hidden.

Brian D Smith is the world’s leading authority on the evolution of the life sciences industry. Working at Bocconi University, Italy and University of Hertfordshire, UK, his most recent book is “Darwin’s Medicine: How Life Science Business Models are Evolving”. He welcomes questions and comments at brian.smith@pragmedic.com

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CLINICAL TRIALs

Use of Real World Evidence Increasing throughout Asia

Real World Evidence (RWE) can be used to inform decisions throughout the life cycle of a drug, from development through postapproval. Across Asia, many countries have begun incorporating real world evidence, and while some are fast adopters in terms of RWE collection and use, other countries have shown less inclination to implement this approach. Zachary Peter Smith Tufts Center for the Study of Drug Development Tufts University Medical School Christopher-Paul Milne Tufts Center for the Study of Drug Development Tufts University Medical School

rue experiments such as randomised controlled trials (RCT)—the gold standard when it comes to the approval of pharmaceuticals— are often criticised as being unrealistic. The results of these trials do not always generalise well and may not be applicable in the real world. The use of real world data and real world evidence to supplement RCTs may be one solution to this problem. According to the US FDA, real world data is “data collected from sources outside of traditional clinical trials,” and real world evidence is the evidence derived from this data. These 14

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CLINICAL TRIALs

real world sources include observational studies, registries, health surveys, and even administrative or insurance claims databases, and they provide a great deal of insight into how a drug may perform when patient adherence is not rigorously supervised. For these reasons many companies are beginning to look at real world evidence to help inform pricing and reimbursement decisions, as well as other critical decisions in drug development. A recent study by the Tufts University Center for the Study of Drug Development in Boston, Massachusetts asked a working group of eight major international biopharma companies to rate their perceived appropriateness of different data sources for reimbursement decisions (Figure 1). While many companies attempt to discern how real world evidence should be used in drug development, many countries around the world are debating its use in regulatory decision making. In Europe, real world evidence has been used to support drug pricing and reimbursement for years. In 2015 the EMA conducted a pilot program for their Adaptive Pathways approval path which would incorporate RWE into the drug

The Indian Health Ministry is developing legislation that would protect the privacy of its citizens while improving the country’s ability to collect and store personal health data.

approval process. Still some countries shy away from the use of RWE. Brazil has several public administrative databases that are filled with data, however Brazil has strict laws in place to protect the privacy of its citizens, and these laws restrict the use of these databases. Additionally, there are significant issues with the connectivity of these databases, as well as the quality and completeness of the data they contain. Similar to the pattern seen around the world, the use of RWE in Asia varies from country to country, and spans the entire spectrum. Some countries work

to fully embrace real world evidence, like Japan, which recently began its “Rational Medicine” initiative in an attempt to make the Japanese health care system more patient-centric and evidence-based. Other countries, like India, seem far less interested in the use of real world evidence. Many other countries, like China, fall somewhere in the middle. As mentioned, one country leading the charge in terms of real world evidence is Japan. Dr. Tatsuya Kondo, Chief Executive of Japan’s Pharmaceuticals and Medical Devices Agency, recently described the new ‘Rational Medicine’ initiative, which has the goals of providing ‘better insight into the risk/ benefit balance of drugs, medical devices, and regenerative medical products’ and creating ‘a medical environment where the care provided is strictly evidence based.’ In order to move toward these goals, the PMDA intends to use real world data to increase the sophistication of their safety measures. In addition to the ‘Rational Medicine’ Initiative, Japan has several registries and databases, including the Medical Information Database Network (MID-NET), which has been in development since 2011. MID-NET collects laboratory and other types of data and can be analysed to assess drug safety and answer a variety of other questions. Analysis of the data is expected to be in full operation in 2018, however some analysis has already begun. MID-NET is expected to be used to contribute to regulatory decision-making. Another country that appears to be incorporating real world evidence is Taiwan. In 2012, researchers looked at the amount of healthcare data being collected in several Asian countries, as well as the utilisation of the data. Among these countries, Taiwan appeared be one of the most active in terms of collecting and utilising real world data from a variety of sources. Sources of real world data in Taiwan include: the administrative data collected by the National Health Insurance (NHI), www.pharmafocusasia.com

CLINICAL TRIALs

Appropriateness of Different Data Sources for Reimbursement Decisions 8

MOST

less

leAsT

6 5 4 3 2 1 0

RCTs

Pragmatic CTs

Modelling

Observational Trials

Registries

"RWE" Databases

Figure 1

surveillance systems established by the Center for Disease Control to monitor epidemics and outbreaks, the Cancer Registry, the Bureau of Labor Insurance’s ‘labor insurance claim data set,’ and multiple annual health surveys. Many of the data sets within Taiwan have been linked together by the Office of Statistics within the Department of Health, further improving the usability and comprehensiveness of the data sets. Additionally, many of the data sets are readily accessible to the public for research purposes, and the protocol for accessing other data is well established. RWE in Taiwan is frequently used for decision making in the health sector. It is also used for research, and for health care technology assessments. Singapore also seems eager to incorporate real world evidence. In 2010 the Health Sciences Authority (HSA), Singapore’s drug regulatory agency, partnered with MIT to explore an Adaptive Licensing pathway similar to the one recently piloted by the EMA. The Adaptive Licensing pathway allows a drug to be granted conditional approval for a limited patient population at first, with the limitations being increased or decreased as safety data is obtained from the real world. 16

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Other countries have found a use for RWE, as well. In the Philippines, companies are required to conduct postmarketing studies on all marketed drugs. Post-marketing studies are observational studies meant to examine the safety, tolerability, and effectiveness of a drug in more diverse populations than the populations seen in RCTs. In Thailand, as the government searches for ways to lower healthcare costs, payers are using RWE to determine which therapies offer the best value, both in cost and outcome. In particular, payers in Thailand are looking at patient, disease, and product registries. While some countries are rapidly incorporating real world evidence into industry processes, other countries have opted for a slower paced adoption. Among these countries is China, where we can see signs of the market turning toward the use of RWE, but where there remain some significant challenges. Like Japan and Taiwan, China has many sources of real world evidence. There are registries for infectious diseases, rich hospital-level data, and even the use of wearable devices, another potential source of real world data, is on the rise in China. There are also two government databases with medical insurance

information. However, these data bases belong to separate departments within the government and are not linked together. Additionally, hospitals all use their own information systems and do not share data. In some cases medical records and files are not fully digitised, making the data harder to access. In spite of the incompatibility of the various sources of data, the use of RWE by decision-makers and in health service research is expected to rise. As the Chinese government works towards reducing healthcare expenditure, big companies have begun to move towards ‘evidencebased, scientific-driven sales models.’ While the demand for RWE by the market and regulators is expected to rise in China, researchers have already conducted several studies there. For example, the Shanghai Clinical Center for Endocrine and Metabolic Diseases has completed several studies on non communicable diseases and the risk factors of noncommunicable diseases, the China Cardiometabolic Registries have been used to research treatment outcomes for cardiovascular and metabolic diseases, and researchers have conducted a retrospective study investigating the risk of HIV transmission in serodiscordant couples.

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Salah Alkowaiter, Director of Quality and Compliance, SPIMACO, KSA

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CLINICAL TRIALs

South Korea, like China, is moving toward the use RWE at a more moderate pace. Like China, South Korea has many databases which could be analysed. One of the largest is the National Health Insurance Corporation database, which represents the entire South Korean population. However, this database only collects information on procedures covered by the National Health Insurance system, which means it is incomplete in regards to total procedures conducted. The validity of this database has been questioned, as well, due to inaccurate coding. Despite this, by 2012, the NHIC database had been used to conduct two studies of disease burden in South Korea. Additionally, in August 2016, the Korean Ministry of Health and Welfare announced that 16 Big Data analysis centres would be established around the country. These centres would provide claims data from the NHIC database to private sectors in order to assist with research. Although many countries are moving toward the collection and use of real world evidence, in some countries there is little movement in this direction. India is among the countries showing little, though not zero, interest in real world evidence. One hurdle the use of RWE in India must face is a lack of sources of data. Few people in India have health insurance, so most medical treatments are paid for out of pocket. As a result, there is no insurance claims database, and little incentive to develop other sources of RWE such as registries or other databases. A few medical registries have been developed, such as the National Cancer Registry and the Indian Transplant Registry, but there remain far fewer than are typically seen in many other countries. (Table 1) Even without these sources of RWE, India has made some strides towards its use. The Indian Health Ministry is developing legislation that would protect the privacy of its citizens while improving the country’s ability to collect and store personal health data. The legislation 18

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would also help India move toward the development and use of Electronic Health Records. These Electronic Health Records are part of the Digital India initiative, an application that will receive health data from hospitals around India in real time. India may currently lack much of the infrastructure necessary to capitalise on RWE, however some large international companies are bringing the needed technology to India. IBM has announced the ‘Watson for Oncology’ platform for Manipal Hospital’s communal training facilities. ‘Watson for Oncology’ will be able to help oncologists offer more personalised healthcare through the analysis of data and recommendation of evidence-based treatment options. In 2015, Fitbit distributed their fitness trackers to 300 towns around India. As more companies bring their technology to India, we may begin to see an acceleration in India’s adoption of RWE.

However how countries view RWE is only one side of the coin. As more and more companies begin using RWE to support pricing and reimbursement, formulate marketing strategies, and even begin incorporating it into product development, it is important to mention how these companies view Asia—as an increasingly important market. Bayer recently announced the completion of the first Pan-Asian real world evidence study examining the use of Rivaroxaban for stroke prevention in patients with atrial fibrillation. The study looked at data from 10 countries and results were consistent with previous smaller studies. With Asia Pacific making up 25 per cent of Bayer’s global pharmaceutical sales in 2015, senior vice president and head of commercial operations Claus Zieler says it is reassuring to have real world evidence showing that their products have the same effects in the real world as they do in controlled clinical trials.

Sources of Real World Data in Asia Source of RWD

Available In

Used In

Consumer Data

Japan

Social Media/Wearable Devices

China, India

China

Claims Databases

Thailand, South Korea, Taiwan, Japan, China

South Korea, Taiwan, China

Test Results, Lab Values, Pathology Results

Malaysia, Japan

Japan

Hospital Visits, Service Details

Thailand, China, South Korea, Taiwan, Japan, Malaysia

China, Taiwan

Mortality and Other Registries

China, Taiwan, Malaysia, Japan, India, Thailand

China, Taiwan, Thailand

Pharmacy Data

South Korea

Electronic Medical and Health Records

China, Taiwan, Malaysia, Japan, India, South Korea

China, Taiwan, Japan, South Korea

Health Surveys

Thailand, China, Taiwan, Japan, Malaysia

China, Taiwan

Supplemental Data from RCT

China

Table 1

CLINICAL TRIALs

being conducted, one analysis of clinicaltrials.gov indicates that Asia is quickly catching up. According to this 2017 analysis, 321 real world studies have been or are being conducted in the US, 268 in Europe, and 234 in Asia. The use of real world evidence is on the rise around the world, and this is true throughout Asia as well. Although individual countries are following this trend at their own pace, it is clear

A u t h o r BIO

Other companies are turning to Asia precisely because of geographic differences in diseases and drug performance. “We are doing some work … where the pattern of diabetes in Asia is entirely different than in the US and European Union” said global chief of Quintiles’ Real-World and Late Phase Research, Nancy Dreyer. Large companies are not the only ones conducting more real world research around Asia; increasingly, universities and hospitals are using RWE to answer research questions. In South Korea, RWE studies have been conducted investigating dementia and antipsychotic drug use in the elderly, and misuse of medications among the elderly. In Malaysia, researchers have looked at drug related ER visits, and the effects of paediatric acute otitis media on parental quality of life using real world data. Hong Kong is another location where significant amounts of real world research is conducted. As a result of this increased real world attention from large companies and universities, Asia has become something of a hub for real world research. While the US remains the world leader in terms of number of real world studies

that they are all moving toward the incorporation of RWE in some capacity. And while each country sets its own pace in terms of regulations, there are indications that many large corporations and universities are looking to quickly turn Asia into a hub of real world research. References are available at www.pharmafocusasia.com

Before coming to the Tufts Center for the Study of Drug Development, Zachary Peter Smith worked as a research assistant in several labs including the Etter Lab at the University of Massachusetts, and the Evolutionary Psychology Lab at Harvard University. He completed his B.S. at Florida Southern College, and completed his M.A. at Brandeis University.

Christopher-Paul Milne joined the Center for the Study of Drug Development, Tufts University School of Medicine (TUSM) in 1998, and is currently a TUSM Associate Professor and Director of Research at the Center. He has published over 75 book chapters and papers on biopharmaceutical regulatory and policy issues worldwide, while serving as an Innogen Center Associate (University of Edinburgh), and recently as Visiting Professor at Kyushu University in Japan.

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Knowing vs Hunting for Targets Selecting a target for setting-up the drug discovery path is a tough decision and often this choice is based on borrowed and incomplete knowledge from the literature. In many cases, this has led to serious challenges in the late stages of clinical trials, dealing with patients. In most cases, these drug discovery programmes are missing the consideration of 'human disease biology complexity'. At least, this is haunting the community in one such disease area: neuro drug discovery. The lack of relevant biological models that are close to real disease and our insufficient knowledge about the targets are the two serious mountains to climb, and require, building highly integrated, working models. These challenges are driving the change in our thinking i.e. moving away from single target-based, biased studies to clinically relevant, unbiased phenotypic programmes and ask for different thinking strategies to making a progress on this tough journey. Prabhat Arya, Distinguished Research Professor in Chemistry and Chemical Biology Dr. Reddy's Institute of Life Sciences (DRILS), University of Hyderabad Campus

t is common knowledge that drug discovery is a tough business to be in, and requires constant questioning as well as refining of ongoing strategies. In this game, the early days were rewarding for those employing the classical approaches that rely upon identifying the target, and building a programme around these well-established target(s), to produce novel drug candidates for a variety of biological disorders (see Figure 1).In recent years, the failure of some of the expensive late stage clinical trials are beginning to question our classical approach in this arena. For example, in 2016, the drug candidate LMTX for Alzheimerâ&#x20AC;&#x2122;s, could not stand successful in a large phase III clinical trial, although it may still show some promise for patients

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research & development

Classical Approaches to Drug Discovery

Pre-genomic era

Working with isolated targets (for example, enzymes)

Structural information on the target (i.e. finding the pocket)

• Well defined • Compact • Deep pocket

Structural guided chemistry / medicinal chemistry (Lipinski’s rule of 5)

Figure 1

suffering from Alzheimer’s disease. This expensive clinical trial dealt with nearly 900 patients who had moderate symptoms of Alzheimer’s disease. Is the target really true and dependable, and is it worth building a time and money-consuming programme around? Typically, in our early days, the acceptance of the target(s) was taken in the absence of complex human machinery as well as diversity. In many challenging disease areas, such as cancer and neurological disorders, our classical thinking of chasing single isolated target(s) is beginning to haunt us; at the same time, it is also forcing us seriously to question our presently practiced drug discovery approaches. In general, the acceptance of a given biological target for setting-up the drug discovery path is based on partial information or biased thinking that is commonly associated with the target. In many cases, the clinical validity of the biological targets in the context

Newer approaches are needed to accessing compounds that are capable of modulating large surface areabased interactions, and this, alone is creating a huge challenge for the next generation, medicinal chemistry community.

of "patient information" is generally missing. Our quest to develop the next generation of drugs for neurological disorders is also badly missing this. It is now well-accepted that the lack of relevant models closely representing the brain disorders are some of the major limiting factors in this area. Overcoming these challenges will allow development of new research models that utilise, for example, patient-derived, induced pluripotent stem cells and the generation of neurons from these cells for further studies. The drug discovery community has been trained to think about the isolated target(s) and then seeking the structural information which further sets the stage for building the medicinal chemistry programme. In general, this approach utilises well-accepted medicinal chemistry rules for obtaining small molecule candidates (for example, Lipinski rule of 5). Then came the post-genomic era: with a message that proteins do not www.pharmafocusasia.com

research & development

PARADIGM SHIFT IN DRUG DISCOVERY ARENA! Post-genomic era - a new thinking!

• Proteins do not function in isolation; part of complex networks in inducing functions

• Involve multiple, protein-protein interactions

• Commonly known as signaling pathways

• Regulation (normal) and de-regulation (disease)

• Dynamic and temporal processes!

Figure 2

function in isolation, and rather, are a part of complex networks, commonly known as signalling pathways (see Figure 2). In general, these pathways are composed of highly complex and dynamic, multiple, protein-to-protein interactions to induce biological functions. It is also now well-accepted that these pathways are highly organised in normal functions and their subsequent de-regulation leads to various disease states. Not only is it necessary to know enough about these pathways, our biological question(s) should also be well-aligned, keeping in mind the relevance of complex human machinery. More and more our quest to address clinically-relevant biological questions is becoming the backbone of the modern drug arena. This is a major paradigm shift for all of us; it is also forcing us to create novel approaches that are out of the box and develop highly integrated research models. A typical small molecule chemical arsenal that was the hall mark 22

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of classical enzyme pocket-based drug discovery may not be challenging enough to undertake complex biological targets dealing with multiple protein-protein interactions. Newer approaches are needed to access compounds that are capable of modulating large surface area-based interactions, and this alone is creating a huge challenge for the next generation, medicinal chemistry community. Moving on from the single isolated target(s) that are usually undertaken in drug discovery programmes, we are now beginning to appreciate the power of clinically-relevant, un-biased, functional biological screens leading the discovery of novel chemical probes which can lead to establishing the drug discovery path (see Figure 3).One of the key advantages in this approach is the use of the patient (for example, in cancer and neurological disorders) as the source of developing functional biological screens to address specific questions. In doing so, one tries

to keep the biases aside (to reaching the information about the target at early stages) while developing screening assays. The goal here is to discover novel small molecules exhibiting specific phenotypic biological effects; serious efforts are then placed for collecting the information of the biological target(s) being affected / modulated by a specific small molecule. Given the complexity of diseases such as cancer and neurological disorders, it is naive to consider a single biological target a safe starting point for the drug discovery. Cancer can be considered as a combination of several diseases; in general, several components of the cells/signalling pathways get de-regulated. This can further vary from patient to patient. The thought of having a magic bullet taking care of several de-regulated processes in this course of action is asking too much from a single small molecule. It is not known when we will be seeing the finish line of the personalised cancer

research & development

Growing Faith in Emerging Chemical Biology Model

Clinically-relevant questions

Modern Organic Synthesis novel chemical toolbox to explore the scope of biologically relevant chemical space // natural product-inspired compounds

Phenotypic

Cell Signaling Biology (biological questions/ novel assays) cellular/zebrafish studies in vivo studies

Unbiased Screens

Discovery of functional small molecules

understanding the mode of action â&#x20AC;&#x201C; biophysics, genomic, CRISPR-Cas9 tools

placing them on to the drug discovery path!

Figure 3

clinically-relevant functional screens, narrowing down our understanding for providing information on targets modulated by small molecules can be highly intense and a time consuming exercise. Making progress on this tough journey requires working with several different skill-sets, such as genomic and CRISPR-Cas9 tools, target pull-out

A u t h o r BIO

medicine. It will be a long and tough road to cover before enjoying the fruit of these highly specialised â&#x20AC;&#x2DC;next generationâ&#x20AC;&#x2122; medicines. On the positive side, however, the accessibility of patient-derived cancer samples in developing some of these modern drug discovery approaches is highly encouraging. The day is not too far when we start seeing the benefits of all this progress impacting the cancer patient population. That being said, the neurological disorders present much higher degree of challenges compared to other diseases. As the scientific community comes up with clinicallyrelevant models in brain disorders drug discovery and further exploration of their applications in phenotypic screening of novel functional small molecules, we might be able to produce better outcomes in curing patients in the long run. With the discovery of novel chemical probes through these unbiased

studies, small molecule-target binding information by protein NMR, SPR, and X-ray studies. This is almost the reverse of what we have been trained to do in the drug discovery culture, over the years. The newer approaches require working with patients as the starting point and seeking information on biological targets (in addition to our ability to observing their modulation by small molecule approaches) are key to developing clinically-relevant drug discovery programmes. A recent publication from Roche titled "Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery" is a testimony to this emerging, no-target centric-based research model that deeply involves unbiased, phenotypic screen(s). Building new research models that are inclusive of different skill-sets ranging from genomic/CRISPR-Cas9 science, clinically-relevant signalling pathway biology, phenotypic screens, and modern synthetic approaches, is expected to lead the way on this tough journey. As we progress on this road, our ability to embrace different skillsets for addressing challenging disease biology-related questions would go far in making this tough journey enjoyable and beneficial to human kind. This water remains to be tested in coming years. References are available at www.pharmafocusasia.com

The research in Arya group aims at building a novel, natural productinspired chemical toolbox for undertaking clinically relevant, signaling pathway-based challenging targets, considered difficult to achieve by conventional medicinal chemistry approaches. He is a Distinguished Research Professor in Chemistry and Chemical Biology at the Dr. Reddy's Institute of Life Sciences (DRILS), located on the University of Hyderabad campus. Prior to moving back to India, he worked at the National Research Council of Canada (and a brief stint at the Ontario Institute of Cancer Research) for nearly 20 years.

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research & development

Single-Use Method Using Filter Aid Easy Removal of Midstream Cells Discussing midstream as the gap-filling technology between up- and downstream and alluvial filtration as the solution for efficient cell removal in just one step, this article shows how efficiency in cell removal is increased in a singleuse format which also allows for easy scale-up. Ralph Daumke, Market Manager Biologics Corinne Luechinger, Head of FILTROX Academy

he removal of cells and cell debris takes place between fermentation (upstream) and product purification (downstream) and is referred to as midstream. The midstream process very often involves a combination of several operation units 1. A highly efficient method for this is alluvial filtration (cake filtration) and can be done with FILTRODISCâ&#x201E;˘ BIO SD. Continuous process optimisation is a key factor in the bioprocessing industry. With higher and higher particle loads (>108 cells/ml), standard technologies, e.g. centrifugation, separation, membrane- and depth filtration, reach their limits. Especially mammalian cells are sensitive to breakage during clarification with high shear stress (centrifugation, separation). This results in the release of host cell proteins which can have an influence 24

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on product stability and purity and leads to additional purification steps2. The following method describes the clarification of fermentation broths with alluvial filtration. This technology leads to a maximum product yield and highest economic efficiency. Midstream, filling the gap between up- and downstream

The industry is divided in their opinion about where to put the line between upand downstream. The clarification of fermentation broths is very often treated as the stepchild of bioprocessing and assigned to either up- or downstream. Due to the importance of the clarification step within the whole process, the link between up- and downstream is called midstream (figure1). Midstream, the clarification of fermentation broths, is the most important step in bioprocesses

(figure 1). Meanwhile, cell cultures are the most important systems to produce therapeutics and diagnostics. For this purpose, the use of mammalian cells is predominant, but also bacteria, yeast and insect cells are used. Involved in the process design for the right cell removal system are questions about: process efficiency, process robustness, economic feasibility, as well as legal aspects. Challenges in process efficiency are higher and higher cell titers, amount of cell debris, scalability, robustness and flexibility in terms of process changes and future process adaptations and process optimisations. The industry asks for more efficient and more economic methods. Efficiency increase with alluvial filtration

Alluvial filtration (cake filtration) is a well-established and economical type

research & development

Figure 1 Exemplary scheme of a bioprocess showing midstream as the link between up- and downstream.

of depth filtration. The pharmaceutical industry has been relying on this method for decades (e.g. plasma fractionation). Instead of using just a static depth filter medium, filter aid (e.g. diatomaceous earth, DE) is added to constantly build up a filter cake during filtration. The filter cake with its resistance acts then as the actual filter medium. Alluvial filtration as a dynamic type of depth filtration, therefore, leads to a higher filter capacity 3â&#x20AC;&#x201D;especially with compressible particles, e.g. microbial or mammalian cells â&#x20AC;&#x201C; and will extend the life cycle of subsequent sterilizing filter membranes with accomplishment of batch filtration in a much quicker and efficient manner 4. Diatomaceous earth is like the Swiss army knifeâ&#x20AC;&#x201D;the all-purpose tool for downstream processing. (David Delvaille, MerckSerono France 5)

Figure 2 Principle of standard depth filtration (a) vs. alluvial filtration (a) where new filter surface is continuously generated during filtration.

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research & development

Without filter aid

With filter aid

Figure 3 Increase of filtration time and therefore filtered volume per square meter due to the addition of filter aid

The clarification of fermentation broths is very often treated as the stepchild of bioprocessing and assigned to either up- or downstream.

Throughout the filtration, the filter aid particles (for e.g. diatomites) are deposited alongside the compressible solids ( for e.g. cells; figure2, b). Due to the physical properties of the filter aid particles, the permeability of the cake is sustained throughout filtration despite the compressible debris and cells. Thereby, a capacity for extraordinary particle loads will be 26

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generated 5. The filtration time - and therefore the filtered volume per square meter of filter area - can be increased up to 4-fold compared to standard depth filtration (figure 3). FILTRODSICâ&#x201E;˘ BIO SD is the first microfiltration system, which combines the advantages of standard depth filters with alluvial filtration in a singleuse system, resulting in new possibilities for midstream and downstream. Depth filters are also known for removing host cell proteins (HCP), DNA, viruses,

and endotoxins3. Instead of a two-step cell removal system with centrifuges or acoustic separators as a first step and depth filters as a second step, just one step is sufficient to remove cells, microorganisms and cell debris out of a fermentation broth. The centrifugation or acoustic separation step can be completely eliminated. Scale-up and Optimisation

FILTRODISCâ&#x201E;˘ BIO SD provides solutions from process development

Figure 4 Linear scalability of FILTRODISCâ&#x201E;˘ BIO SD from process development to production scale

research & development

Conclusion

The use of alluvial filtration in midstream processing is one of the most effective, efficient, robust and easy to use methods for cell removal. Diatomite filter aids are suitable for the use in cGMP pharmaceutical processing environment6. FILTRODISC™ BIO SD provides a state of the art single-use technology for midstream processing in just one step. Literature 1] Process Scale Bioseparations for Biopharmaceutical Industry, Chapter One: Harvest of Therapeutic Protein Product; Elisabeth Russell, Alice Wang, and Anurag S. Rathore; Taylor & Friends Group, 2007 2] Mammalian Cell Culture Clarification: A Case Study Using

Chimeric Anti-Cea Monoclonal Antibodies: Mohamed Ali Abol Hassan, Abdul Wahab Mohammad, and Badarulhisam Abdul Rahman, ILUM Engineering Journal, Vol. 12, No. 4, 2011 3] Dynamic Depth-Filtration: Proof of Principle; W.E. Hurst; Technical Note AMC06; Advanced Minerals 4] Technical Bulletin, Disposable Body Feed System DBF, ManCel Associates,

May 2008 5] Filtration Improvements Yield Many Benefits Down the Line, Susan Aldrige, Genetic Engineering & Biotechnology News, Vol. 30, No. 21, 2010 6] Advances in Disposable Diatomite Filter Aid Systems for cGMP Bioseparations, T. Sulpizio and J. Taniguchi, AFSS Annual Meeting, May 2008

Ralph Daumke received his diploma degree in biotechnology from the University of Applied Sciences in Berlin. Being in various positions in Sales and Marketing over the last 20 years, he is since 2014 responsible for the FILTROX Group worldwide business development in the field of biologics.

A u t h o r BIO

to production scale with a simple linear scale-up (figure 4.). Therefore, a feasible filtration optimisation and a scale-up are very simple. The cake volume per litre of filtered liquid, which was determined and optimised during lab trials, is directly proportional to the cake volumes with the larger sizes of filter modules. This is also shown in the following formula: C_P = (V_P ×C_L) / V_L C =h ×A C: cake volume [m 3] V: filtered volume [L] L: labscale P: production scale h: cake height [m] A: filter area [m 2] Scale-up calculation for alluvial filtration does not directly include the filter area, as it is dependent on the available space for the filter cake which is the important parameter in this case. Besides cells and cell debris, the FILTRODISC™ BIO SD system can remove impurities (for e.g. DNA or HCP), resulting in cost reduction for the following chromatography steps in downstream purification. A change in pH and the addition of flocculants are not necessary with this technology.

Corinne Luechinger received her master’s degree from the Federal Institute of Technology in Zurich, Switzerland (ETHZ). Since 2007 in the filtration industry, she has accumulated substantial knowledge on diverse applications which is shared in publications and seminars all over the world.

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research & development

Delivering patient relevant healthcare and patient centric drug discovery and innovation based approach globally is the new and emerging requirement. It means, the very starting material, knowledge and data is to be connected with the patient and only patient. Conventional methods relying on extrapolation of data from animal systems and models is slowly going to be a thing of the past as the emerging field of human stem cells and their role in drug discovery and development is quickly gaining momentum. Subhadra Dravida, A Soorneedi Transcell Biologics

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research & development

Emerging Directions in R&D of Drug Discovery and Development The patient specificity paradigm

sing stem cells as platforms for drug discovery not only allows us to recreate the microenvironment usually found inside the body but it also allows us to decipher the molecular intricacies that play an important role in the success of precision medicine and ways to further improve it. Traditional drug discovery practice includes the early phases of research (Pre-Discovery stage), Discovery phase 1 (Identification of hit molecules), Discovery phase 2 (Lead and Optimisation), Discovery phase 3 (Pre-clinical safety and efficacy) leading to Development stage involving clinical trials. The early stages of the process is known to take approximately upto six years and then, researchers hope to identify, develop a suitable drug candidate to further optimise in the lab, animal models, and then in clinics for another ten to twelve years. The story of drug discovery from academic or clinical research or from the commercial sector, till date has been revolving

around the elusive â&#x20AC;&#x2DC;Targetâ&#x20AC;&#x2122;, which is anything within a living organism to which a druggable candidate is directed, resulting in the function that is expected of in a disease state. Ninety nine per cent of the hypothesis-driven drug research is centered around known and tested targets (biased) from the literature or in the mind of the lead researcher; thousands of compounds accessed from shared libraries or made libraries from the known drug scaffold/chemistry are screened, followed by hunting for molecules that would dock in, which is a wild goose chase. Recent advances in molecular medicine and tools to enhance computational capacity have claimed to have enabled researchers to only understand the inner workings of human disease to extrapolate in research on validated Targets. The traditional high throughput (computational) screening process which is very popular during the early phases of research can leverage automation between validated www.pharmafocusasia.com

research & development

Large Scale (HTS) Primary drug screening Disease target known

Moderate Scale (HCS) Target identification Disease target known

Small Scale Personlised drugs Disease target known

target points and large number of drug-like compounds. Very recent emerging thought process -driven direction in early phases of drug research is to develop patient specific drugs taking into consideration the patients history. The introspection and interruption points could be the starting materials (both chemistry and biology) along with the process employed. The promise of personalised medicine (or precision medicine) is to get the right treatment to the right patient at the right dose the first time, through the use of targeted/selected therapies with desired outcomes. Equipped with phenotype screening platforms to discover the desired functions, the pre-discovery phase has a great opportunity to integrate patient sourced samples, especially in oncology and neurodegenerative diseases drug research. These emerging advancements not only offer selectivity/targeted relevant options to patients but also nicely complement complexity to the R&D process directed towards functional multi-target discoveries on the patients for the patients. 30

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The R&D in valid in taking hits to leads and optimising leads traditionally again is very target-centric with repetitive screening performed on either easily available animal or transformed cell based or engineered artificial reporter assay platforms. These are far away from the natural biological systems and the data obtained need extrapolation to the patient and the human disease indications. Although phenotypic assays validate drug candidates in intact and relevant biological platforms, integration of patient sourced and prepared biological platforms in optimising leads is relevant minimising any false positive data at this stage of crucial discovery process, setting stage for successful drug development in clinics. Pre-clinical animal-based safety and efficacy phases of drug research is the only known and applied procedure for investigational new drug candidate that has passed the preliminary stages of research and validation. This approach comes under heavy criticism from many anti-animal usage groups advocating actively against the use of animals for drug testing. It was reported that

approximately 20 million animals are used annually in medical experiments or for testing drug candidates. Depending on animal research and testing to discover drugs for humans is expensive, timeconsuming, unreliable and not patient relevant. Extrapolation of information/ data from animal research to developing patient specific drugs has always had erratic baseline in anatomy, organ structure/function, metabolism, drug absorption. Despite the fact that several hundreds of animals are used for testing a single drug candidate, generating volumes of safety data, approximately 95 per cent of the drug candidates do not pass clinical trials while the disease burden continues to rise. It is to the common man's and the patient's knowledge that in spite of several hundreds of animals used for testing one drug candidate and the volumes of safety and efficacy data generated on all the investigational new drugs till date, approximately 95 per cent of the drug candidates that enter human clinical testing fail while the disease burden has continued to rise. The recent discovery of human donor

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important and expensive reasons that led the R&D process to a dead end and setbacks from finding the holy grail. Target biased and based approach, in silico methods of high throughput assays along with the discovery on non-patient systems, coupled with extrapolating to the human disease have surfaced to be the stumbling blocks. Integrating nature-inspired new and complex stereochemistry addressing the undruggable /druggable targets, functional phenotype based high throughput screening along with privileged biology having high physiological relevance, such as those that use human primary cell types, organoids derived from such primary cell types, stem-cell derived cells, and/or

A u t h o r BIO

sourcing and inducing pluripotency has revolutionised the very approach and the hypothesis of integrating human/ donor/patient stem cell based systems to evaluate the safety and efficacy of drug candidates pushing the data generated closer to reality and not extrapolation, that has always resulted in desperate situations for clinics. The behaviour of cells in phenotypic assays is monitored microscopically, providing sub-cellular ultra deep resolution of biological responses that the human cells display to drugs. Human tissues from donors can provide yet another approach, as well as animal models. Additionally, as the liver plays a critical role in how the body metabolises drugs and produces key proteins, the existing animal models are being used to study physiology and pharmacology in an intact system, patient, or donor derived stem cell coaxed liver models with both the complex micro-architecture and diverse cell makeup developed in the lab as platforms for drug evaluations. During the century-old drug discovery and development research, investigators have uncovered certain

patient cells, are the emerging directions that are proven to increase predictive validity, improving R&D productivity. Drug discovery and development using patient sourced biological platforms and predictive models embracing genomics inspired target identification has to become the new norm and direction with highest probabilities of success predicted. This practice while helping improve the precision of novel drugs would also benefit the R&D sector economically. Target tricks don't work in drug discovery and development anymoreâ&#x20AC;Ś. Do or Do not! There is no try.. is the new order for drug hunters!

A Scientist by profession, Subhadra Dravida led global stem cell research and commercialisation initiatives in regenerative medicine and drug discovery domains for over 12 years. She holds over two dozen patents in the field of regenerative medicine and has significant expertise in converting promising research into business opportunities.

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Mass Spectrometry in Research and Development of Protein Biologics

he protein drugs constitute almost everything of the biopharma today. This include signalling proteins (e.g. insulin, erythropoietin), monoclonal antibodies (e.g. Bevacizumab, Rituximab) that are used as drugs, peptide drugs (e.g. Liraglutide, Icatibant), antibodydrug conjugates (e.g. Trastuzumab emtansine), modern recombinant protein vaccines (e.g. Tetanus toxoid) etc. The production of these protein drugs is majorly done through manipulations of biological processes and entities. Since the systems that are used for this purpose are extremely complex, delicate and sensitive living forms, the drug development process is highly complicated in nature. Characterisation of these protein drugs, their variants and impurities is far more arduous as compared to small molecule

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The soft ionisation revolution in mass spectrometry took place in last decade of 20th century. After that, mass spectrometry quickly rose to prominence in the life science laboratories. The paradigm of the pharmaceutical R&D has also changed over the same time and biological drugs—generally called ‘big molecules’— are brought to market, a lot many are expected in coming decades. Thus, it is hardly a coincidence that the role of mass spectrometry in biological drug discovery is increasing. It is already an indispensable tool in biopharma R&D and it is on its way to occupy the centre stage of this industry. Mallikarjun Dixit, VP, Accutest Biologics Private Limited Heramb M Kulkarni, Research Scientist Accutest Biologics Private Limited

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drugs. Further, pre-clinical and clinical studies of these protein drugs are laden with problems that are unique to this class of therapeutics. Mass spectrometry based proteomics is a technology that helps in all these phases and it is an only available platform for addressing many of the roadblocks. The objective of this article is to summarise the different mass spectrometry-based workflows that are currently being used during the R&D of the therapeutic proteins. Although soft ionization mass spectrometry has been commodiously used to analyse the carbohydrates, lipids, nucleic acids and small metabolic molecules, we shall focus on the protein-related applications. Characterisation of a therapeutic protein

A protein molecule is nothing but one or more polypeptide chains folded in a specific way. The polypeptide chains are unique sequences of amino acid residues produced by living cells via process of translation. Some post-translational modifications are incorporated and the proteins are ensured to be folded to their respective functional state. Sometimes the finished protein product is artificially processed after recovery to include some more chemical changes in the molecule. Thus the characterisation of the therapeutic protein product comprises following aspects; Molecular mass

This is usually called ‘intact mass’ to differentiate from the ‘reduced mass’ (explained later). It is the exact molecular mass of the protein. This analysis quickly provides information on the heterogeneity of the product. It can also be used to determine whether and how much of the intended protein is present in the given sample. Intact mass analysis is performed using time-of-flight (TOF) analysers. The ionisation method could be MALDI or ESI. The data obtained from ESI-TOF generally needs further software processing to find the intact

Quantisation of drug in biological matrices for the checking of bioequivalence (BE) and bioavailability (BA) using triplequadruple mass analyser is a standard practice in clinical studies.

mass. The sample intended for intact mass analysis should be reasonably pure. Higher is the number of proteins in the sample, lower is the probability that the protein of interest gets properly ionised and identified. Reduced mass

When the protein is composed of more than one polypeptides hold together, the masses of all the polypeptides can be separately checked. The polypeptide chains are covalently held together by disulphide links. These links are broken using a reducing agent (hence the name ‘reduced mass’) and alkylated so that their spontaneous re-formation can be avoided. Once the polypeptide chains are separated, the intact mass of the chains can be assessed using the TOF analysers as explained earlier. Peptide mapping

The complete amino-acid sequences of all the polypeptides present in the protein under enquiry are determined using the peptide mapping technique. The protein is reduced, alkylated, digested with a site-specific protease, and the peptides generated from the protease activity are extracted. These peptides are separated through reversephase chromatography (RP-HPLC) and fed online to ESI mass spectrometer.

Then the peptides are subjected to fragmentation in collision cell and these fragments’ masses are reported in the form of a ‘fragment ion spectrum’. This technique belongs to the ‘bottom-up proteomics’ philosophy. Since this is a tandem MS method the systems capable of tandem MS are to be used for peptide matching. There are variety of mass analysers that can be used for this analysis which include various ion traps and TOF. The method described here is the mainstream technique. Many variations have been and can be successfully attempted using ‘top-down’ approach, using offline coupling of RP-HPLC to MALDI based machines and using different kinds of peptide separation methods other than RP-HPLC. The data obtained from peptide mapping is extremely complex and huge. Specialised analysis software has to be used for processing the raw data and obtain the amino-acid sequence of the protein. The sequence of the protein is to be added to the sequence database of the software. The software processes that sequence in siilico to get theoretical digest of the protein and the theoretical fragment ion spectra for all the peptides. The experimentally obtained data is matched with the in silico data and the identification is done. With the advent of technology, new software have been being developed which are capable of analysing bigger data, more number of proteins, and better models of fragment ion spectrum matching. Disulphide mapping

Identification of locations of interchain and intra-chain disulphide links is a highly recommended since they define the folding of the protein to a large extent. The correct folding of the protein is required for its biological activity. The protein under enquiry is digested using a site-specific protease without reducing the disulphide links. This digestion yields di-peptides held together by disulphide links along with other peptides. The peptides and www.pharmafocusasia.com

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di-peptides thus formed are subjected to tandem mass spectrometry as described in peptide mapping section. The fragment ion spectra of the disulphide containing di-peptides are analysed using software tools to get an idea of the disulphide link locations. The software used for peptide mapping is equipped with the disulphide identification as well. If the expected locations of disulphide links are already known (as in case of bio-similar development), there are specialised software available to suit to their speedy analysis. Although technically disulphide links are one of the post-translational modifications (PTMs), their mapping is separately covered since the methodology used in that assay is different than that used for other PTMs. Identification of post-translational modifications

Several other PTMs are, for example deamidation, phosphorylation, addition of carbohydrate or lipid moieties etc can be efficiently analysed by mass spectrometry. The general workflow resembles to that of peptide mapping, but the in silico processing is quite challenging. For some modifications like N-linked glycans, the glycans are

enzymatically separated from the protein and are analysed separately to identify the set of all the glycans present. This information is subsequently utilised while analysing the peptide mapping data, and the locations of each type of glycans on the protein can be identified. The approaches to be taken for PTM characterization are as varied as the PTMs. Quantisation of therapeutic protein

Quantisation of drug in biological matrices for the checking of bioequivalence (BE) and bioavailability (BA) using triple-quadruple mass analyser is a standard practice in clinical studies. Performing similar study on therapeutic proteins is ridden with various problems. Being a protein, the drug may interact non-specifically with other components interfering the quantisation. Mass spectrometric identification of an intact protein present in a complex mixture of other proteins is not feasible. Thus this quantisation is performed by a ‘signature peptide’ approach. A signature peptide is a peptide of the protein under enquiry which is formed during its digestion using site-specific protease, and is uniquely different from

Figure 1 Determination of intact mass of recombinant insulin using RP-UHPLC (Shimadzu Nexera) coupled Ab Sciex 5600. Bayecian reconstruction and deconvolution is performed using Analyst software from Ab Sciex. The theoretical mass of this molecule is 5808.00 Da, while experimental result is 5807.00 Da.

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the other peptides present in the digests of proteins in that biological matrix. The choice of signature peptide also needs consideration of its chemical nature, ease of ionisation and the reproducibility of the quantisation. The approach requires both, software support and user’s conjecture. The quantisation experiment consists of digesting the entire biological matrix with site-specific protease and running it onto RP-HPLC coupled with ESI based mass spectrometers. The biological matrix may be pre-processed to enrich the protein of interest and remove some unwanted high abundant proteins before the digestion. The calibration curve is obtained by spiking the synthesised sequence of signature peptide in blank matrix in various concentrations. The assay broadly follows the quantisation principles of ELISA experiments. The analysis too is performed in a similar way using the peak areas of the signature peptide in total ion chromatogram. This assay can be performed using almost all routinely used mass analysers, including the triple quadrupole analysers used ubiquitously in the traditional small molecule drugs BA/BE sector. Structural studies on protein interactions

Study of drug-target interactions can be performed using special applications like hydrogen-deuterium exchange (HDX) and native mass spectrometry. In fact HDX can be used to certain extent to study folding of a given protein too. Identification of interacting partners of the protein under enquiry can be performed by co-immunoprecipitation / pull-down assay followed by mass spectrometry based identification of the proteins. This approach is ordinarily called 'interactomics'. The field has a lot of scope of development and currently this potential of mass spectrometry is largely unutilised. More automation and software support will spice up structural studies on therapeutic proteins using mass spectrometry.

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Figure 2B Peptide mapping of chain B of recombinant insulin using RP-UHPLC (Shimadzu Nexera) coupled Ab Sciex 5600. Tandem MS data is analysed using Protein Pilot. The chain B is characterised with 100% sequence coverage.

High-throughput proteomics studies

High-throughput proteomics is useful in the initial exploratory studies for identification of potential protein therapeutics. The purpose of such exploratory studies can also be identification of biomarkers for specific disorder, or studying of effects of certain factor on the proteome of the model. Such studies are more common for fundamental research in academic scenario. In this approach, a large number of proteins from given biological matrix or complex sample are identified and quantified at a very rapid rate. The collection of data normally takes few hours followed by in silico analysis. Given the increasing capacity of new generation mass spectrometers and analysis softwares, few thousands of proteins can be identified and

quantified in a single experiment. Such an enormous amount of data is usually viewed on the background of the overall molecular functioning of the model system, hence it requires a lot of bioinformatic tools and expertise to make sense out of it. The quantification of the proteins in high-throughput mode can be performed using either label-free mode or using labelling technologies. The most widely used labelling methods are isobaric chemical labels and isotope based labels. As of now, orbitrap mass analysers are unmatched for such exploratory studies, but linear ion traps also have been used for this purpose with reasonable success before the orbitraps hit the proteomics labs. Currently high-throughput proteomics and big-data intensive discovery is a gold mine of therapeutic and diagnostic molecules.

Identification of host protein contamination/carryover

The proteins from the host system in which the recombinant therapeutic protein is synthesised, need to be completely got rid of in the final preparation. The purification process development adopts variety of ways depending upon the nature of the contaminants. It is helpful to know the identity of the contaminating host proteins at every step to decide the further course of purification. Using mass spectrometry based high throughput identification the host protein contamination can be determined accurately and sensitively. De novo sequencing

All the approaches described so far are based on an assumption that the sequence of the protein under enquiry is known. In case of high-throughput proteomics, the database of the entire proteome is www.pharmafocusasia.com

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Figure 2A Peptide mapping of chain A of recombinant insulin using RP-UHPLC (Shimadzu Nexera) coupled Ab Sciex 5600. Tandem MS data is analysed using Protein Pilot. The chain A is characterized with 100% sequence coverage.

used. But there are instances when the sequence of the protein or peptide is unknown or partially known. The screening and discovery of antimicrobial peptides, neuropeptides, non-ribosomal peptides, insect/reptile venoms, impurities in chemically synthesised peptides need de novo sequencing for their sequence identification. De novo sequencing means the identification of the sequence of the peptides directly from the fragment ion spectra, without sequence database. Many software tools have been developed dependent on different models of scoring of fragment ion matches. For a very few number of peptides a trained and experienced person can manually perform de novo sequencing. The power of this technique makes possible invention of new therapeutics from biological sources on which no information is available. For de novo sequencing, tandem MS and good quality of fragment ion spectra are must. Different mass analysers may be used as per the chemical nature of the peptide under enquiry. In spite of being a powerful technology, mass spectrometry has not yet occupied as much space it deserves. The 36

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main hurdle is a high cost of purchasing and maintaining mass spec instruments and analysis software. Besides, the training of manpower in biological mass spectrometry takes far longer time as compared to the traditional techniques. Many of the quantitative analyses could be performed using mass spectrometry with more defined way and more refined data output as compared to the traditional techniques. But the bio-similar development industry tends to prefer traditional techniques over the modern ones as they want to match their data with innovator's data. Still, mass spectrometry based assays is a growing trend in R&D of new therapeutic proteins. With the development of more and more therapeutic proteins, mass spectrometry is set to become a most important tool in bio-pharma research.

A u t h o r BIO Dixit (graduate from University of Illinois) is a scholastic personality with several patents and publications to his credit and with more than 25 years of rich experience in pharmaceuticals, biopharmaceutical and CRO industries in the area of drug discovery and bio-analytical services. He has extensively worked on method development and validation of various assay platforms such as LC-MS, ELISA, MSD, SPR, RIA/RIPA and Cell based assays for Immunogenicity and pharmacokinetics evaluation of non-clinical and clinical study samples under GLP and GCLP compliant practices for regulatory submission studies. As a test facility management and head of bioanalytical laboratory he has successfully faced multiple sponsorâ&#x20AC;&#x2122;s and regulatory audits. Dr. Dixit with his expertise is currently guiding the team biologics in the delivery of quality compliant bioanalytical and Characterisation services for biologics and biosimilars for submission studies. Heramb M Kulkarni is a researcher in biopharmaseuticals with a special interest and expertise in proteomics, biological mass spectrometry and bioinformatics. He is also well acquainted with cell biology, microbiology and modern biophysical techniques. He has been working with Accutest Biologics Pvt Ltd as a Research Scientist from last few years. He is a Ph.D. in Life Sciences from a premier research institute in India, CSIR-Centre for Cellular and Molecular Biology, Hyderabad, and has also worked there as a post-doctoral fellow. He has authored seven publications in globalhigh-impact peer reviewed research journals, which include some of the most renowned journals in the field of Proteomics and Microbiology. His passion for biological mass spectrometry dates back to his masters in Biotechnology from Shivaji Univeristy, Kolhapur, India. Heramb is interested in pursuing a research career in the biopharmaseuticals and contribute to development of new drug molecules includingbut not limited to- proteins, peptides, hybrid molecules, aptamers etc.

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COVER STORY

Changing the Game in Drug Development Modelling & Simulation To improve the Return on Investment (ROI) for drug development, the biopharmaceutical industry has invested in methods, technologies and operational programs to reduce cost and improve efficiency. Modelling and simulation lives at the intersection of biology and technology. It is used to reliably and predictably optimise crucial drug development decisions, decreasing cost and time while improving safety and minimising risk. Mikku Nagata, Business Development Director, Certara GK

s Sir Winston Churchill one famously stated: “Those who fail to learn from history are doomed to repeat it.” That statement certainly holds true for the drug development industry. Researchers who cannot learn from a new drug candidate’s complete history—both good and bad—will not make the best, most informed decisions about its future. Knowledge-sharing, both within the sponsor biopharmaceutical company and across the industry, is of paramount importance. Data siloes actively contribute to poor outcomes by ensuring that all the historic data and resulting wisdom cannot be factored into critical dose selection and go/no-go decisions for new drug candidates. Those decisions should be based on all of the sponsor’s pre-clinical and clinical data 38

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combined with published data about similar compounds and indications being studied by other companies. M&S Applications

One of the many strengths of computational Modelling and Simulation (M&S) is that it enables knowledge and wisdom gained from pre-clinical and clinical successes and failures to be transferred between phases in the drug development process. As new data are collected, they are used to further refine the models being developed. During drug discovery, M&S is used to identify the best molecule designs, those that can deliver the highest efficacy with the least toxicity. In the pre-clinical phase, it is employed to determine the optimal drug dose that will achieve the best therapeutic window. During clinical development, M&S is used to identify potential drug-drug and drug-food interactions, and to model different clinical trial designs in order to optimise trial outcomes. Most recently, it has been used to increase the precision with which individual patients are treated. In these precision dosing cases, the patients’ genomic data, physiological profile, and certain biomarkers are incorporated into the mathematical model to ensure that they get the best possible treatment. While the clinical focus for precision dosing is currently on complex patient cases—for example, those who have had bariatric surgery, or a cell transplant, or have cancer or an HIV infection—its application is expected to become more widespread over the next few years. Ultimately, it will become a routine part of patient care. Why do drugs fail?

New drug candidates generally fail for one of five reasons. Researchers picked the wrong target, wrong patients, wrong drug dose, wrong clinical trial design (e.g. they might not have measured the response at the right time) or the wrong endpoint (e.g. they may be blocking an enzyme that has nothing to do with the pathophysiology of the indication). 40

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Avoiding failure

There are also some privacy and ethics concerns about the use of personal genetic data that would need to be overcome before patients can experience the full benefit of precision dosing.

These issues are more prevalent than one might imagine. Approximately 90 per cent of drugs/biologics that are tested in humans are never submitted to the US Food and Drug Administration (FDA) for approval.1 Phase 2 trials have the lowest success rate, with only 30.7 per cent of candidates advancing to phase 3.2 Furthermore, progress to phase 3 does not always herald ultimate success. The FDA published a report in January 2017 in which it reviewed 22 drug, vaccine or medical device case studies in which promising phase 2 clinical trial results were not confirmed in phase 3 clinical testing.1 In those examples, the experimental product failed due to lack of effectiveness in 14 cases, safety in 1 case, and both safety and effectiveness in seven cases. Even worse, in two instances the product actually exacerbated the problem it was intended to solve. Also, safety issues do not always end after a drug is approved. Out of 222 novel therapeutics approved by the FDA between 2001 and 2010, 32 per cent were affected by post market safety events, resulting in three withdrawals, 61 boxed warnings, and 59 safety communications.3 Part of the issue is that post approval, the drug is taken by patient subpopulations that were never studied clinically. M&S can help there as well.

Quantitative Systems Pharmacology (QSP), which combines computational modelling of a biological system and experimental methods, can help to reduce those drug failures, especially the phase 2 attrition. QSP examines the mechanistic relationships between a drug, the biological system, and the disease process. It explores how the drug dose and the pharmacological response it generates relate to the disease and its progression. It combines quantitative drug data with knowledge of the drug’s mechanism of action. In the past, medical researchers thought that a single target—a receptor, channel, protein or enzyme—was responsible for causing a particular disease. However, that was found to be true in only a few cases. The remaining diseases are the result of a complex interaction of environmental and genetic factors. Fortunately, QSP focuses on the behaviour of complete biological systems and not just the behaviour of individual components. “QSP helps to draw a map of biological networks and identify hot spots that need to be hit simultaneously to generate the desired effect. It provides vital intelligence for the drug discovery process by showing researchers what combined effects a successful drug candidate needs to have,” said Certara Vice President of QSP Professor Piet van der Graaf, PharmD, PhD. “QSP can also assist in identifying the best dose and combination of drugs for an individual based on their own phenotypic traits,” added Professor van der Graaf. Therefore, QSP is an asset when evaluating complex, heterogeneous diseases that require multiple therapies, such as cancer. FDA has also adopted QSP. It published on its website about using a QSP model which connected bone turnover markers with bone mineral density. Using this QSP model led the FDA to propose a different dosing

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regimen for a biologic from what the sponsor had proposed.4 Conducting virtual trials

M&S allows clinical trials to be conducted virtually that could not be performed for practical, legal or ethical reasons in the real world. For example, clinical trials are rarely conducted in vulnerable patient populations such as pregnant women, paediatric patients or those with impaired liver/kidney function. It can also be difficult to study patients with comorbidities in clinical trials due to polypharmacy. Other issues include small and dispersed patient populations, which often occur with orphan or ultra-orphan diseases. Whole body simulation can be used to predict the pharmacokinetics (PK) and pharmacodynamics (PD) of small molecule and biological medicines using laboratory-derived data. PK examines how the human body absorbs, distributes, metabolises, and excretes a drug. In contrast, PD describes what the drug does to the body. By using unique genetic, physiological and epidemiological databases, virtual populations can be

simulated with different demographics and ethnicities. M&S permits ‘what if ’ clinical questions to be answered using virtual patient populations. This approach can be used to determine first-in-human dose selection, evaluate new drug formulations, and predict drug-drug interactions (DDIs) and PK outcomes in clinical populations. In addition, M&S has replaced the need for certain clinical trials—such as using in vitro in vivo equivalence models to attain a waiver for bioequivalence studies—saving both time and money. It has also been used to optimise the design of clinical trials, making them much less complicated and more predictable and reliable. M&S has also been employed to inform drug label claims, resulting in the inclusion of new information about potential DDIs, dosing regimens, or specific patient populations. Using virtual data to improve realworld results

To maximise the use of M&S, sponsors are starting to develop parallel virtual and real-world drug development paths.

At each step, the virtual experiment is conducted first and the knowledge gained is used to inform the live study. Once that live study is complete, it provides additional information that is fed into the next virtual one, and so on. Drug development has evolved from a linear path into a spiral, a series of conjoined circles. This iterative process provides a highly-informative positive feedback loop and ensures that the most complete data set is used at each step. After all, dosing a virtual patient will always be less risky than dosing a real one. Growing adoption of M&S

M&S is now encouraged by global regulators, who have been quoted referring to M&S as ‘a regulatory necessity.’ The FDA employs M&S extensively; it has been used to inform more than 100 drug labels in the past few years for indications as diverse as cancer, schizophrenia, deep vein thrombosis, and Gaucher disease. M&S is also being used by the European Medicines Authority and the China Food and Drug Administration for evaluating new drug submissions. PMDA adoption of M&S

The Japanese Pharmaceuticals and Medical Devices Agency (PMDA) is actively encouraging its biopharmaceutical industry to use pharmacometric approaches to achieve more efficient drug development. The agency has held two important meetings on the subject recently—the PMDA-Keio Joint Symposium on Pharmacometrics in 2015 and the Pharmacometrics Symposium earlier in 2017. In addition, the PMDA intends to begin using pharmacometric approaches for in-house analysis regarding regulatory decisions. This agency support is important because there are currently fewer government grants for pharmacometrics research in Japan than for preclinical research into drug transport, absorption www.pharmafocusasia.com

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M&S implementation in Japan

The most successful implementation of pharmacometrics and PKPD concepts in Japan to date has been in infectious diseases, helping to support the development of new antibiotics to combat the increasing incidence of antibiotic-resistant bacterial infections. It is also being applied in oncology to develop new targeting agents and support optimal treatment of patients. Japan’s antibiotic M&S strategy proved successful because it combined in vitro knowledge of the minimum inhibitory concentration of the antibiotic required to kill the bacteria with clinical data. The researchers also had a good in-vitro system for measuring the threshold concentration that would kill the bacteria. A similar approach could also be applied with antifungal or antiviral agents. PMDA has now implemented a prescription drug monitoring program to track the use of antibiotics in clinical practice. Traditionally, oncology researchers in Japan focused on determining the maximum tolerated dose for chemotherapeutic agents. But now, the new drugs target the cellular signaling pathways that have become dysregulated in cancer cells. So, their goal is to 42

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The Japanese Pharmaceuticals and Medical Devices Agency intends to begin using pharmacometric approaches for in-house analysis regarding regulatory decisions.

determine the effective dose rather than a toxic one. They find the drug target and then determine whether they’ve been able to modulate it sufficiently by measuring biomarkers to identify the drug concentration required for efficacy. Then using the PK information, they can calculate the optimal dose required for a patient to reach the effective concentration. There is also interest in precision dosing based on therapeutic drug monitoring (TDM) but it is currently confined to specific patient cohorts such as those taking antibiotics or immuno-suppressant drugs after an organ transplant. On the other hand, pharmacogenomics-based precision dosing is not being used for individual patients yet due to the limited information available about the influence of genetic variability on drug efficacy and toxicity. For example, only one pharmacogenetic test is currently being used for patient care in Japan – the UGT1A1 test for irinotecan toxicity. It will take several years before pharmacogenetic tests are implemented

A u t h o r BIO

or metabolism. There are also very few teaching positions in that field. As a result, there is a paucity of pharmacometricians in Japan and few experts with the requisite experience to teach Japanese students about M&S, a field that is growing rapidly in importance worldwide. To meet this need, Professor Yusuke Tanigawara PhD, professor of Clinical Pharmacokinetics and Pharmacodynamics at Keio University School of Medicine, has developed an open course to teach students, industrial researchers and regulators about pharmacometrics. Professor Tanigawara is considered to be Japan’s leader in PK/PD and Keio University is the country’s oldest institute of higher education.

routinely in general patient care in Japan. This delay is due in part to the absence of in-depth pharmacogenomics training; it is not included in the standard curricula at Japanese medical schools or pharmacy schools. There are also some privacy and ethics concerns about the use of personal genetic data that would need to be overcome before patients can experience the full benefit of precision dosing. Conclusion

M&S can improve a broad range of activities throughout the drug discovery and development continuum. It has the potential to save researchers time and money by enabling them to make better decisions regarding new drug candidates, improving clinical trial design, and removing the need for certain studies. It also has the power to improve patient care by tailoring drug doses to meet the needs of individual patients. References 1. 22 Case Studies Where Phase 2 and Phase 3 Trials Had Divergent Results, US FDA report, January, 2017 2. Clinical Development Success Rates, 2006-2015, BIO report 3. Downing NS. Postmarket Safety Events Among Novel Therapeutics Approved by the US Food and Drug Administration Between 2001 and 2010. JAMA. 2017;317(18):18541863. 4. FDA Center for Drug Evaluation and Research Clinical Pharmacology Review for Natpara® (rhPTH[1-84]) for injection. 2013. https://www. accessdata.fda.gov/drugsatfda_docs/nd a/2015/125511Orig1s000ClinPharm R.pdf.

Mikku Nagata is Certara GK’s business development director. Certara is the leading provider of decision support technology and consulting services for optimising drug development and improving health outcomes. Mr. Nagata joined Certara’s Japan team in 2014 bringing 10+ years of business development experience in the pharmaceutical, electronics and semiconductor industries.

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Cultural Excellence as the Foundation For effectiveness of the quality system

In pharmaceutical companies, it is universally understood that a robust Pharmaceutical Quality System (PQS) provides key elements of assurance and oversight for pharmaceutical manufacturing and quality control laboratory processes. However, despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence. The research shows that a high degree of PQS Effectiveness is accompanied with a high level of Cultural Excellence. Thomas Friedli, Professor for Production Management, University of St.Gallen Stephan Kรถhler, Research Associate, University of St.Gallen Paul Buess, Research Associate, University of St.Gallen

n pharmaceutical companies, it is universally understood that a robust Pharmaceutical Quality System (PQS) provides key elements of assurance and oversight for pharmaceutical manufacturing and quality control laboratory processes: It ensures that patients are provided with medications that are safe, effective, and reliably produced at a high level of quality. However, despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence, and can result in recalls, withdrawals, or harm to patients (Woodcock & Wosinska, 2013; Yu & Kopcha, 2017). Additionally, quality issues have been linked to the rise in critical drug shortages (ISPE & PEW, 2017).

Pharmaceutical Production System Model

The St.Gallen Pharmaceutical Production System Model (PPSM) is a holistic model that illustrates the system - understanding 44

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Pharmaceutical Production System Model

Customer Complaint Rate

Result System

PQS Effectiveness [S]

Level 4

Level 3

Enabling System

PQS Excellence [S]

Lot Acceptance Rate / 1-Rejected Batches

Supplier Reliability [S]

Inva lidat ed O OS

PQS Efficiency [S]

Operational Stability [S]

Level 2

CAPA Effectiveness

Level 1

Cultural Excellence [S]

Lab Quality & Robustness [S]

Structural Factors

Level 5

Figure 1

of a Pharmaceutical Production System. The model was built as part of the quality metrics research funded by FDA to enable the University of St.Gallen to conduct a structured analysis how to achieve PQS Excellence. From a scientific perspective, the model is inspired by two business excellence models. It follows the argumentation of the Sand Cone Model, which suggests that there is a hierarchy in a sequential order of the four competitive capabilities Quality, Dependability, Speed and Cost Efficiency (Ferdows & De Meyer, 1990). In addition, the PPSM follows the same approach as the European Foundation for Quality Management (EFQM) Model, which promotes the consideration of two key aspects when undertaking improvement programs, the Enablers (how) and the Results (what)(Figure1). The PPSM allows to put the three metrics suggested in the revised FDA

Quality Metrics Draft Guidance1 into the broader context of the PQS. This allows to analyse the relationships of the FDA proposed Quality metrics from an overall PQS perspective. The data behind the PPSM is based on the St.Gallen Operational Excellence (OPEX) database which currently encompasses 339 pharmaceutical manufacturing sites from over 124 different companies around the world. It combines Key Performance Indicators, Enablers, and Structural Factors of the given organisation. According to Yu and Kopcha (2017) a critical enabler for product quality is the culture of quality within an organisation. This concurs with the understanding of the research team which is demonstrated by placing the category Cultural Excellence as the foundation of the PPSM. The system for implementing Corrective Actions 1 Submission of Quality Metrics Data - Guidance for Industry Draft (Food and Drug Administration, 2016)

and Preventive Actions (CAPA) is a fundamental part of any PQS. The level of effort and documentation should be proportionate to the level of risk. The CAPA system may be considered effective if it achieves the key objective to support the improvement of product and processes as well as enhance the understanding of product and processes. The CAPA Effectiveness is therefore an inevitable element of the PPSM. According to the ICH Q 10 Guideline, the PQS extends to the control and review of the supplier. In order to assess the reliability of external suppliers the PPSM comprises the category Supplier Reliability covering the supplierâ&#x20AC;&#x2122;s ability to deliver products of high quality on-time. Operational Stability within the PPSM equates to capable and reliable processes and equipment. Recalling the Sand Cone Model, Operational Stability embodies the core capabilities of Quality and www.pharmafocusasia.com

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Dependability. The FDA proposed Quality Metric Lot Acceptance Rate can be located in this category. To have a comprehensive view of the production system and to cover the whole value chain from supply to release within a pharmaceutical company, Supplier Reliability and Operational Stability are complemented with the category Lab Quality & Robustness. This category is also seen as one pillar of the risk-based approach of FDAâ&#x20AC;&#x2122;s Quality Metrics Initiative (Yu, 2017). Lab Quality & Robustness comprises the FDA metric Invalidated OOS and additional indicators of the quality level and robustness of the lab operations. The roof of the PPSM is built on the two-fold objective of effectiveness and efficiency of Operational Excellence (Friedli, Bellm, Werani, & Basu, 2013). The category PQS Effectiveness addresses the question of how well the PQS is working and if it achieves its objective. The FDA proposed Quality Metric Customer Complaint Rate is allocated to PQS Effectiveness. The second building block of the PPSM roof is PQS Efficiency. This category is considering how much resources have been deployed to reach the targeted level of effectiveness. Superior performance in both categories leads to PQS Excellence. The Linkage between Quality Maturity and Quality Behaviour

In both domains, research and industry, Cultural Excellence is deemed to be the basis for PQS Effectiveness and it is recognised to play an important role beside Quality Metrics (Patel et al., 2015). In the 2014 PDA Quality Culture study the associationâ&#x20AC;&#x2122;s objective was to determine whether there is a relationship between Quality Maturity and Quality Behaviour (Patel et al., 2015). In addition the authors aimed to identify certain Quality Maturity attributes that may be used as surrogates to assess Quality 46

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The St.Gallen Pharmaceutical Production System Model allows to put the three metrics suggested in the revised FDA Quality Metrics Draft Guidance into the broader context of the Pharmaceutical Quality System.

Culture. Quality Behaviour summarises all quality related behaviours of an individual that can be observed in an organisation covering aspects such as commitment, engagement, transparency and active assistance from supervisors. Quality Maturity comprises implementable elements of the quality system such as methods and tools. Following the hypothesis of Patel et al. (2015) that Cultural Excellence is driven by Quality Behaviour and Quality Behaviour is driven by Quality Maturity the St.Gallen research team had the objective to analyse these relationships translating the PDA understanding of Quality Behaviour and Maturity on data from the St.Gallen OPEX Benchmarking database.

For the analysis the Operational Excellence (OPEX) Enablers from the St.Gallen OPEX Benchmarking had to be assigned to either Quality Behaviour or Quality Maturity first. In total the Benchmarking comprises 114 Enablers covering different areas of OPEX, i.e. Total Productive Maintenance (TPM), Total Quality Management (TQM), Just-In-Time (JIT), Effective Management System (EMS) and Basic Elements. An Enabler can either be a tool, a methods or more general an effort that is spend to improve the respective area (e.g. TPM). Enablers therefore describe how to achieve Performance in the respective area. 26 of the OPEX Enablers were assigned to Quality Behaviour and 36 to Quality Maturity. 52 Enablers were not assigned as those do not have a direct link to any of the two categories. In order to assess the linkage between Quality Maturity and Behaviour the research team used a scatter plot to identify whether a high Quality Maturity (x-axis) is accompanied with a high Quality Behaviour (y-axis). For both categories a score was calculated that represents the average of the implementation level of all attributes of the respective category. The result of the St.Gallen research team confirms the previous findings of PDA. The statistical measure adjusted R2 of 0.66 means that based on the St.Gallen

Figure 2 Linkage between Quality Maturity and Quality Behaviour - PDA results (left) (Patel et results (right)

al., 2015) and St.Gallen

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data 66 per cent of the variation of the Quality Behaviour Score can be explained with the Quality Maturity Score. This supports and enhances the finding of a PDA study in 2014 which showed a degree of determination of 34 per cent (Patel et al. 2015). Both analysis, the PDA study and the more recent work of the St.Gallen research team show that a high Quality Maturity is accompanied with a high degree of Quality Behaviour. The confirmation of the previous findings of PDA was the starting point for the St.Gallen research team to enhance the analysis in the field of Cultural Excellence. Cultural excellence as the foundation for PQS effectiveness

The importance of Cultural Excellence and Quality Culture is widely discussed in the industry and generally understood on a qualitative basis (Barney, 1986; Digalwar & Sangwan, 2011; Jochimsen & Napier, 2013; Yu & Kopcha, 2017). The St.Gallen research team aimed to analyse if this qualitative understanding of the role of Cultural Excellence can be confirmed quantitatively. The researcher’s objective was to identify whether there is a significant impact of Cultural Excellence on PQS Effectiveness of pharmaceutical manufacturing sites. To identify if there is a significant impact the overall sample of sites from the St.Gallen OPEX Benchmarking database was split into two peer-groups to perform a statistical t-Test. To identify sites for the respective peer-group the PQS Effectiveness surrogate Service Level Delivery (OTIF) was used. The first peer-group includes all PQS Effectiveness High Performer sites (10 per cent best performing sites for OTIF). The second peergroup included all PQS Effectiveness Low Performer (10 per cent worst performing sites for OTIF). Cultural Excellence in this analysis represents an aggregated score of the Quality Behaviour Score, Quality Maturity Score and Engagement Metrics Score. The Quality Behaviour and Maturity

Scores each represent an average of the implementation level of all attributes of the respective category. The Engagement Metrics Score represents the relative position of each site to the overall sample for this category based on the percentile rank calculation, enabling the research team to include Engagement Metrics with different scales (e.g. days and percentage). Based on the t-Test the researchers were able to show that the PQS Effectiveness High Performer (peer-group 1) have a significantly higher implementation level of Cultural Excellence compared to the PQS Effectiveness Low Performer (peer-group 2) Further more, the research team identified that the relationship between Cultural Excellence and the two peer-groups does apply to each sub-category of Cultural Excellence as well. For Quality Maturity, Quality

Behaviour and Engagement Metrics the PQS Effectiveness High Performer have a statistical significantly higher implementation level compared to the PQS Effectiveness Low Performer. The research shows that a high degree of PQS Effectiveness is accompanied with a high level of Cultural Excellence. Taking into account that there are other influencing factors to achieve a high PQS Effectiveness (e.g. Operational Stability and Supplier Reliability) the research finding still shows a significant relationship between the two categories of the PPSM. As a consequence, the widely discussed and understood importance of Cultural Excellence and Quality Culture can be confirmed with the data of the St.Gallen Operational Excellence Benchmarking database. References are available at www.pharmafocusasia.com

A u t h o r BIO Thomas Friedli is a Professor for Production Management at University of St.Gallen in Switzerland. His main research interests are in the fields of managing operational excellence, global production management and management of industrial services. He is a lecturer in the (E) MBA programs in St.Gallen, Fribourg and Salzburg. Hespent several weeks as Adjunct Associate Professor at the Purdue University in West Lafayette, USA. He is responsible for the St.Gallen OPEX Benchmarking in the Pharmaceutical Industry, the largest independent Benchmarking in this field.

Stephan Köhler is a Research Associate at the University of St.Gallen, Switzerland. At the Institute of Technology Management in the division of Production Management he works in the Operational Excellence team with a special focus on the pharmaceutical industry. He graduated from RWTH Aachen University with a master’s degree in Industrial Engineering and Management. Currently he is doing his PhD at the University of St.Gallen with a focus on Operational Excellence in Quality Control Laboratories in the pharmaceutical industry.

Paul Buess is a Research Associate at the Institute of Technology Management of the University of St.Gallen, Switzerland. He is part of the Operational Excellence team with a focus on the pharmaceutical industry and LEAN management. He holds a Master’s degree in Industrial Engineering and Management from the Karlsruhe Institute of Technology (KIT), Germany and AALTO University, Espoo, Finland. Currently he is doing his PhD at the University of St.Gallen with a focus on production and quality systems in the pharmaceutical industry.

www.pharmafocusasia.com

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manufacturing

A Hard Pill to Swallow

Measuring texture in novel oral dosage forms

As the pharmaceutical industry embraces new delivery mechanisms to improve efficacy and patient compliance, the author discusses how to develop novel oral dosage drugs that are robust during processing and throughout transport and storage, yet easy to use, safe and effective for the patient. Jo Smewing Applications Manager Stable Micro Systems

ral drug delivery is one of the most convenient, and most common, methods of delivering medication. Its simplicity and cost-effectiveness have made it particularly popular with manufacturers in the pharmaceutical industry, but it is not always the most popular with consumers. 50

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The trouble with tablets

Traditionally, orally-administered drugs are dispensed in a solid form, such as capsules or tablets, and liquid forms, such as solution or emulsion. Tablets are widely accepted due to their ease of manufacture and convenience in terms of self-administration and compactness, yet a large number of people have difficulty swallowing solid tablets. Children, the elderly and people with certain illnesses or disabilities may not take their medications as prescribed due to swallowing problems. To overcome these difficulties, patients may crush, break or chew their tablets, which can lead to changes in drug absorption or variation in dosage sizes. As the pharmaceutical industry continues to grow and diversify, NPD and quality control departments have never been busier – or more challenged. In today’s tough environment, multiple complex regulatory requirements have to be juggled with innovative ingredients, new packaging formats, changing consumer demand and financial pressures. Never before has it been so difficult to perfect a product and achieve long-term commercial success. Finding an alternative

Consumers have come to expect a personalised experience in many aspects of their lives, and the way they take their medication is no exception. The wealth of information available has allowed them to become more educated than ever before, and this knowledge is being used to request specific products and treatments in their personal therapy. Patients that have difficulty swallowing, as well as those caring for people who are struggling, expect to be able to receive their medication in a way that is convenient to them. With pharmaceutical products needing to appeal to a wider range of preferences, manufacturers need to develop new, patient-friendly dosage forms. Effervescent tablets, lozenges, films and chewable tablets are examples of dosages forms that are not only more

appealing, but also tackle the challenges faced by the consumers who struggle to swallow solid tablets. Testing novel dosage forms

Sensation in the mouth, taste and ease of intake are the main factors that influence a patient’s experience when taking medication. When developing new dosage forms, the large amount of research necessary may be intimidating to manufacturers. New products need to maintain the same high quality and safety standards while also delivering a format that is appealing to a wider range of patients. This means that stringent testing must take place to perfect a product and achieve long-term commercial success. The analysis of textural (or rheological) properties is an assessment or measurement of a particular characteristic, such as adhesiveness, hardness, break strength or elasticity. Contrary to verbal description (sticky, tacky, gooey, gummy), texture analysis made these rheological characteristics quantifiable – and therefore comparable. Fast disintegrating tablets

One alternative method to deliver medication is Orally Disintegrating

Texture analysis instruments can imitate the downward movement of the finger, measuring the force needed to actuate the spray and administer the dose.

Tablets (ODTs), also known as fast melting or fast dissolving tablets. ODTs are designed to rapidly dissolve in the mouth, in less than three minutes, before swallowing. No other source of liquid is required for ODTs, making them a popular option for patients with dysphagia or children too young to swallow. The key properties of ODTs are fast absorption of water into the core of the tablets and disintegration of associated particles into individual components for fast dissolution. In addition, they must be strong enough to endure manufacturing and shipping yet friable enough to deliver an optimum dissolution rate. To ensure that the tablets meet all requirements, thorough testing is required. Equipment is available that facilitates the assessment of these properties. Rigs can replicate the in vivo conditions of a human mouth, with the dry tablet sample secured to a probe with a ‘channelled’ design that allows fluid to flow freely around the tablet. The test shows the water absorption and disintegration of the tablet, allowing manufacturers to record its disintegration time and behaviour and giving a valuable insight into performance and efficacy. Films

Thin-film drug delivery is another delivery method that has emerged as an alternative to the traditional tablet. In addition to being convenient to administer and easier to swallow, films also allow the medication to bypass the first pass delivery, which gives the medication more bioavailability than conventional tablets. Ideally, films should exhibit flexibility, elasticity and softness, whilst also being resistant to breakage and taste compliant with minimum disintegration time. There is a number of testing methods available to evaluate a film’s performance. Tensile characterisation involves subjecting the film to tensile stress, allowing the properties of the film in its solid state to be defined. The use of Tensile Grips or Pneumatic Grips can provide details on the force require to www.pharmafocusasia.com

manufacturing

results. One well-known instrument provides a circular testing area contains the sample before a compression with a cylinder probe. The maximum force and energy are then recorded and used as an indication of the compressibility of the granules. Sprayable drugs

Lozenges

Lozenges are a popular option for administering medication to children. Using sugar and solidified gum, they are consumed by light chewing that allows them to dissolve in the mouth. Lozenges are most commonly used to medicate the throat and mouth or for the slow administration of vitamins, with flavour options and textures that appeal to the age group and making it easier to integrate them into a daily routine. Lozenges are made by pouring a thick liquid mixture into a powdered, sugared or waxed mould. The liquid mixture is commonly based on starch and gum arabic, emulsifying with added oils and extracts and binding them. The combination of starch and gum arabic also reduces the dissolution rate and moderates the delivery of active substances, while the gum arabic also hardens the pastilles for easier storage and transportation. Firmness and stickiness are the two most important properties of lozenges, with hardness providing an insight into the effects of different formulations and stickiness showing useful measurements, such as the potential of tooth pulling. In the majority of cases, a simple penetration 52

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test is suitable to evaluate the firmness of the gel. Stickiness can also be easily measured by lowering a small, cylinder probe down to the productâ&#x20AC;&#x2122;s surface and recording the force needed to withdraw it upwards. Orally disintegrating granules

Orally Disintegrating Granules (ODG) are currently used for pain relief and allergies in adults, but singular paediatric 4mg granules are suitable for children aged six months to five years. Granules can either be administered directly or mixed with a spoonful of soft food, such as apple sauce or ice-cream, increasing their popularity as a method of easy administration. Granule compressibility testing provides crucial measurement of physical strength and breakdown characteristics. The testing of single granules is discouraged as repeatability is compromised. Testing a fixed area of sample instead creates an averaging effect over a larger number of granules, improving the quality of

A u t h o r BIO

stretch the film to a given distance and/ or breaking point.

Oral sprays deliver drug-containing aqueous droplets to the mouth. It is currently a suitable drug delivery method for sufferers of angina, insomnia and multiple sclerosis, and an alternative to injectable insulin formulations for those with diabetes. Self-administration creates an even greater need for the safe and accurate delivery of any drug. Metering valve systems can be affected by alterations to design and product performance is dependent on the interaction between the elastomeric components and the drug formulation. Texture analysis instruments can imitate the downward movement of the finger, measuring the force needed to actuate the spray and administer the dose. A rig which mimics the movement of the finger onto the inhaler, can measure the force needed to administer the dose. Conclusion

In a highly competitive market, pharmaceutical manufacturers need to be able to deliver high-quality, customised products to meet their customersâ&#x20AC;&#x2122; needs. Providing oral dosages of medication in forms other than in traditional targets makes products more accessible to a large number of people. By working with the right partners, providing novel oral dosages forms does not need to come at the cost of quality.

Jo Smewing has been the applications manager at Stable Micro Systems for 23 years. Stable Micro Systems is a leading designer and manufacture of texture analysis equipment. These instruments are used in laboratories worldwide for testing in pharmaceuticals, medical devices, food, packaging, personal care, paints and coatings and other manufacturing industries.

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manufacturing

Real-Time inLine Monitoring

For high shear wet granulation This article discusses why HSWG is important and the difficulties it poses for scale-up and monitoring in the pharmaceutical industry. It also introduces the fundamentals of a drag force flow technique and how that can potentially address the limitations associated with other HSWG process monitoring methods by providing high frequency and high resolution in-line granule property data. Jamie Clayton, Operations Director, Freeman Technology Ltd.

igh Shear Wet Granulation (HSWG), a critical step in many applications in the pharmaceutical industry, transforms fine powder blends into more free-flowing granules optimised for solid dosage form production. Manufacturers rely extensively

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on granulation as it can enhance blend uniformity, minimise segregation, improve compression properties and reduce dusting. In drug development and manufacture, granules are typically not the end product, but rather an

intermediate, though important, step in the production process. This can make it difficult to identify the Critical Process Parameters (CPPs) that influence Critical Quality Attributes (CQAs) of the finished product, and challenging to implement a Quality by Design

manufacturing

(QbD) approach that may rely on gathering data at all relevant points within the process. At-line techniques, such as dynamic powder testing, can be successfully employed to optimise a HSWG process, but this article explores the application of a continuous, in-line analytical tool to monitor the granulated mass in realtime. It considers the importance of HSWG, introduces the concept of Drag Force Flow (DFF) measurement, and examines its potential for monitoring a HSWG process, presenting a case study which evaluates in-line data alongside dynamic powder tests that have previously proved to be valuable in optimising HSWG operations. 1 Understanding the importance of HSWG

HSWG energetically combines liquid, usually water, with a blend of active ingredients and excipients to form homogeneous granules, ideally with properties suited to further downstream processing. For example, in tableting, where the objective would be uniform granules with suitable compression profiles that enable high throughput as well as the production of tablets with desired CQAs. Parameters that impact granule properties include: • Amount of water added • Rate at which water is added • Impeller and/or chopper speed • Granulation time. Altering one or more of these variables changes the properties of the granulate produced, and understanding the impact of each of the variables often involves time-consuming empirical studies. Characterising dynamic flow properties of the wet mass/granules extracted from the process has been shown to produce valuable data that correlate with the CQAs of a finished tablet1. However, in-line, real-time solutions deliver significant benefit as monitoring is continuous and the process does not need to be interrupted to extract samples.

DFF - real-time monitoring of HSWG

A Drag Force Flow (DFF) sensor is a thin hollow cylindrical needle approximately 1-4 mm diameter, which can be mounted inside processing equipment, such as a mixer, granulator or feeder, to provide real-time local measurement of the forces associated with the flow of material within the process. As powders or granules flow against the needle it is deflected, and the magnitude of this deflection is captured via two optical strain gauges fixed to the inside walls of the needle. The gauges are composed of Fiber Bragg Gratings (FBGs) whose relative spectra shift in proportion to the amount they are compressed or stretched, with the signal transmitted by optical fibres to an interrogator to assesses the response. DFF measurements are directly influenced by fundamental parameters of the material, such as density and shear viscosity, and can therefore be used to track the progression of granulation where these attributes are known to change. DFF signals are reported as a Forced Pulse Magnitude (FPM), a differential measurement which is

therefore not subject to baseline drift, and is complemented by a temperature measurement. Sensor sensitivity is defined by the length, diameter and material of the needle, with tip deflection as low as one micrometer being detectable. The sensors employ no moving parts, have no material traps and are relatively insensitive to material build-up on the sensor surface. The small diameter of the sensor offers minimal intrusion to the flow of material and high frequency measurement rates of up to 500 samples per second are achievable. Figure 1 shows an example of a probe installed in a batch HSWG system. The following case study illustrates how this technology can be applied to monitor a HSWG process. Case study: Monitoring HSWG with in-line and at-line technology

Trials were carried out to assess the value of in-line DFF measurements for tracking a granulation process in realtime. The study investigated correlations between DFF data gathered using an

Figure 1 In-line sensor installation within a granulator www.pharmafocusasia.com

manufacturing

In drug development and manufacture, granules are typically not the end product, but rather an intermediate, though important, step in the production process.

LFS system (measurement range +/- 3N, Lenterra Inc., USA) and measurements of basic flow ability energy (BFE) using an FT4 Powder RheometerÂŽ (Freeman Technology, UK). Methods

Testing was carried out on two kilogram batches of three placebo pharmaceutical formulations produced with different levels of hydroxypropyl cellulose (1 per cent , 3 per cent and 5 per cent w/w HPC). Each batch was granulated with 800g of water in a 10 L high shear wet granulator (Pharma-ConnectÂŽ, GEA). In-line data was gathered using a DFF sensor mounted in the granulator lid, positioned 8.2 cm off the blade rotation axis and 2.5 cm above the granulator blade (Figure 1). Each granulation run consisted of 3 minutes of dry mixing, followed byup to 3 minutes of water addition and up to 5 min of wet massing. The impeller tip speed and chopper speed were maintained at 4.8 m/s and 1,000 rpm respectively for all the batches, and from dry mixing through to the end of the wet massing phase for each individual batch. These conditions were set with reference to previous optimisation studies2. 56

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Figure 2 Real-time DFF data clearly show the change in consistency of the granulating mass changes with the addition of water (a) 1% w/w HPC b) 3% w/w HPC c) 5% w/w HPC).

In-line DFF measurements were gathered during each of the runs and at the end of each run, three representative samples of the wet mass were extracted for immediate evaluation with the FT4 Powder Rheometer. Figure 2 shows the FPM profile for the dry powder blend (profile A) and for granulation runs of 1, 2, 3, 4, 6 and 8 minutes following the start of water addition (profiles B-G). Results

In-line DFF data showed excellent repeatability, with the derived FPM readings reflecting the change in consistency of the granulating mass during the process (figure 2).

The FPM profiles shown in figure 2 are generated using a rolling average over 300 blade sweeps to smooth the raw data; each 300 blade sweeps corresponded to a 20 s interval. The FPM profiles for each batch demonstrate high reproducibility and the three formulations produced similar profiles. The initial work (shear) to mix the dry powders did not result in significant changes in FPM, however, as water is added, FPM rises rapidly as larger, denser, less compressible and more adhesive granules are generated. A peak FPM is observed shortly after the end of water addition, after which FPM declines as the continued mixing generates smaller granules. It was

manufacturing

Figure 3 Comparison of in-line Force Pulse Magnitude (FPM) measurements from the Drag Force Flow (DFF) sensor with at-line Basic Flowability Energy (BFE) measurements.

whereas the BFE values for the highest concentration of HPC, and all three FPM profiles, peak shortly after water addition is complete. However, the data demonstrate how both techniques are able to track granule development in a HSWG process. Combining in-line and at-line measurements

The value of at-line dynamic powder characterisation and in-line DFF measurements for monitoring and controlling the HSWG process is clearly demonstrated by the case study presented.

A u t h o r BIO

also observed that FPM increases with respect to HPC percentage, suggesting that higher binder concentration results in stronger, denser and larger granules. The BFE profiles clearly support the FPM data. Again, a rising profile is seen during water addition with a subsequent decay as the addition of water ends. Also, as the quantity of the HPC binder increases, BFE values increase. Data from the highest concentration of HPC binder (5 per cent) in particular showed close similarities. The sensitivity of DFF measurement is illustrated by the magnitude of the increase in signal associated with water addition compared to the corresponding increase in BFE, particularly for the lower HPC concentrations. For the formulations with the two lower concentrations of HPC, BFE values appear to peak during water addition,

The data build on previous studies that have shown how at-line powder rheology techniques can provide valuable information for optimising HSWG processes. Granulate quality is a function of several parameters, including size, shape, surface texture, density and porosity rather than a single physical property of the granules. Techniques that focus on properties of the granulate as a bulk, and in particular how they alter with respect to water content, formulation changes or other variables within a process, have considerable potential. In-line DFF measurements correlate with at-line dynamic properties and demonstrate that variations in granule quality can be monitored in real-time in order to ensure that granules suitable for downstream processing are produced. For HSWG, which is a critical operation across many manufacturing sectors, including the pharmaceutical industry, but recognised as notoriously difficult to control and scale-up, the robust, real-time, continuous measurement capability offered by drag force flow sensors for routine monitoring delivers significant benefits. References: [1] Freeman, T. (2014) In Pursuit of Wet Granulation Optimization. Pharmaceutical Manufacturing. [2] Narang, AS. (2016) Process Analytical Technology for High Shear Wet Granulation: Wet Mass Consistency Reported by In-Line Drag Flow Force Sensor Is Consistent with Powder Rheology Measured by At-Line FT4 Powder Rheometer®. Journal of Pharmaceutical Sciences. 105:185-187

Jamie Clayton is Operations Director at powder characterisation company Freeman Technology, and is based at the company’s headquarters in Tewkesbury, UK. He graduated from University of Sheffield with a degree in Control Engineering and is responsible for all daily activities of the company, including overall management of the administration, production, R&D, sales and customer support teams. Jamie also works with the company’s clients to provide application based support.

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manufacturing

A Novel Continuous Pharmaceutical Manufacturing Pilot-Plant Advanced model predictive control

Currently, pharmaceutical industries are going under paradigm shift from traditional batch to novel continuous manufacturing. Such a continuous plant has been built at C-SOPS which is being adapted by several pharmaceutical companies. Real time process control is highly desired for efficient Quality by Design (QbD)-based continuous pharmaceutical manufacturing. A control system ensures the predefined end product quality, satisfies the high regulatory constraints, facilitates real time release of the product, and optimises the resources. In this work, an advanced Model Predictive Control (MPC) system has been developed and implemented into direct compaction continuous pharmaceutical tablet manufacturing pilotplant. The closed-loop process performance has been practically demonstrated through experiments. It has been found that, MPC performs better than PID. Ravendra Singh, C-SOPS, Department of Chemical and Biochemical Engineering Rutgers, The State University of New Jersey

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ontinuous Manufacturing (CM) is evolving into a preferred platform for pharmaceutical products involving solid dosage forms. The US Food and Drug Administration (FDA) has recently approved some pharmaceutical products to be manufactured in continuous line, with several others on the way1. Therefore, the pharmaceutical industry is going through a paradigm shift from conventional batch manufacturing to advanced CM2. One advantage of CM is that the product quality can be controlled in real time thereby opening up the possibility of achieving Quality by Control (QbC)

manufacturing

The proposed systematic control framework supports the paradigm shift of pharmaceutical tablet manufacturing from conventional QbT-based batch-wise, open-loop production to QbD-based continuous, closedloop production.

and Real Time Release (RTR) paradigm. Indeed, CM has a strong impact on drug quality1. The objective of this work is to demonstrate the real time advanced model predictive control of a novel continuous direct compaction pharmaceutical tablet manufacturing pilot-plant. 1. Process and Pilot-plant description

The snapshot of the pilot-plant developed at Rutgers along with the control system overview is shown in Figure 1 (whole plant is not shown).

The pilot plant is built in three levels at different heights to take advantage of gravitational material flow. The top level is used for feeder placement and powder storage, the middle level is used for delumping and blending, and the bottom level is used for compaction. Each level consists of 10x10 square feet working area. There are three gravimetric feeders (K-Tron)-with the capability of adding more- that feed the various formulation components (API, excipient, lubricant etc.). A co-mill (Glatt) is also integrated after the feeder hopper primarily for de-lumping the powders and creating contact between www.pharmafocusasia.com

manufacturing

Local Level Control

Supervisory Feedback Control

Supervisory Feedforward Control

components. The lubricant feeder is added after the co-mill to prevent over lubrication of the formulation in the co-mill. These feed streams are then connected to a continuous blender (Glatt) within which a homogeneous powder mixture of all the ingredients is generated. The chute is placed in between blender and tablet press. The chute has interface to integrate the sensors. Finally, the outlet from the blender is fed to the tablet press via feed frame. The pilot-plant consist of some inbuilt local level control system in feeder (to control mass flow rate) and tablet press (to control main compression force) unit operation. In this article, the implementation of supervisory control system consisting of feedback and feed forward loops is described.

Figure 1 Continuous direct compaction continuous tablet manufacturing pilot-plant

Figure 2 Advanced model predictive control architecture for continuous direct compaction continuous tablet manufacturing process 60

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2.2. Implementation of control architecture

Two types of sensors are used for real time monitoring and control of continuous pharmaceutical tablet manufacturing process: spectroscopic and non-spectroscopic. In terms of control system implementation, the basic difference between these type

Relay

OPC

CHARM

Controller

Actuators

Ethernet Connection Non-Spectroscopic Sensor

Real Time Prediction

OPC/Fieldbus/Serial port connection

OPC

Control Variable

A control architecture for direct compaction continuous tablet manufacturing process has been developed as shown in Figure 2. A combination of MPC and PID has been used to utilize the advantages of both control algorithms. As shown in the figure, the drug concentration has been measured using NIR sensor. The measured drug concentration is the input for the master controller, which generates the feeder ratio set point. Based on this ratio set point and the total powder flow rate, the individual flow rate set points for API, excipients and lubricant feeders are calculated and then controlled by manipulating the respective feeder RPMs using built-in feeder controllers. The powder level in chute has been controlled by manipulating the turret speed. In the tablet press, the tablet weight is controlled through a cascade control arrangement using one master loop and one slave loop. Master loop is used to control the tablet weight that provides the set point of slave controller which has been designed to control the Main Compression Force (MCF). MCF has been controlled by manipulating the fill depth. A feedforward control loop has been also added. The real time measured powder bulk density is the input to feedforward controller (FFC) thatâ&#x20AC;&#x2122;s manipulating fill depth. FFC has been added to take proactive actions to mitigation the effects of variations in powder bulk density. The tablet hardness has been controlled by manipulating the punch displacement.

Control Platform OPC Connection

Control Variable

2. Advanced hybrid model predictive control architecture 2.1. Development of control architecture

Spectroscopic Sensor

Figure 3 Systematic procedure for implantation of control system into continuous pharmaceutical manufacturing pilot-plant

of the sensor is that the spectroscopic sensors need addition tool for real time prediction while the signal from a non-spectroscopic sensor can be directly send to the control platform. The implementation procedure of a control system is shown in Figure 3. The implementation of the control loop in case of spectroscopic sensor has been previously reported and therefore has not been repeated here2. A non-spectroscopic sensor can be directly integrated with the control platform as shown in Figure 3. A sensor that generates 4-20 mA signal has been considered to demonstrate the concept of control system implementation. As shown in the Figure, the sensor is integrated with the control panel at relay through serial ports. From relay, the signal transmitted to charm and from charm to controller. From controller block (placed in control panel), the

signal transmitted to control platform where the control loop is implemented. In the control platform, the mA signal is converted into a relevant variable to be monitored and control. From control platform, the signal goes to the plant using the standard communication system (OPC, serial ports, profibus). 3. Results and Discussions

The advanced model predictive control system has been successfully used to control the continuous direct compaction pharmaceutical tablet manufacturing pilot-plant. The closed-loop response in case of non-spectroscopic sensor has been evaluated here. The closed-loop performance of MPC for spectroscopic sensor has been previously reported2. The powder level control has been considered here as a demonstrative example. The powder level is measured in real time using Triflex (Fluidwell Ins.) sensor. The www.pharmafocusasia.com

manufacturing

level control has been developed. It includes, an advanced model predictive controller and an electric filed based sensor. The powder level exhibits an integrating response meaning that it is a non-self-regulating process and must be controlled. The proposed systematic control framework supports the paradigm shift of pharmaceutical tablet manufacturing from conventional QbT-based batch-wise, open-loop production to QbD-based continuous, closed-loop production. Acknowledgements

Figure 4 Real time feedback control (powder level) of continuous pharmaceutical manufacturing pilot-plant

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per cent) very effectively through MPC. The controller can track the set point and can handle the fluctuations in flow rate. The performance of the MPC has been compared with PID and found to be better. 4. Conclusions

An efficient control system is required to operate the continuous tablet manufacturing pilot-plant safely and to achieve the predefined end product quality consistently. A systematic procedure to implement the control system in case of both spectroscopic and non-spectroscopic sensors have been developed and applied to the continuous pharmaceutical manufacturing process. A novel method for powder

A u t h o r BIO

closed-loop response is shown in Figure 4. As shown in the figure, the powder level was controlled at 50 per cent. Then a step change in powder level set point has been made from 50 per cent to 60 per cent. The result shows that, the MPC brings the powder level signal back to the new set point. At steady state, the difference between set point and actual value is very less meaning that the perfect control has been achieved. No overshoot has been overserved and settling time is very less. There is some process delay and therefore powder level took some time to response. But, MPC has taken this dead time into account very efficiently. When the signal become stable at 60 per cent powder level then a bigger step change from 60 per cent to 40per cent has been made. The MPC again bring the signal back to new set point efficiently meaning that the developed model predictive controller is valid for a bigger range of powder level. Finally, the powder level set point has been changed again to original set point (50 per cent). As expected, the MPC was able to bring the signal back to 50 per cent. The developed MPC is robust as it can be seen in actuator response. The results demonstrate that; the powder level can be controlled at desired set point (50

This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, Rutgers Research Council and US Food and Drug Administration (FDA). References: 1. Yu, L. (2016). Continuous Manufacturing Has a Strong Impact on Drug Quality. U.S. Food and Drug Administration (FDA). https://blogs. fda.gov/fdavoice/index.php/2016/04/ continuous-manufacturing-has-astrong-impact-on-drug-quality/ 2. Singh, R., Sahay, A., Karry, K. M., Muzzio, F., Ierapetritou, M., Ramachandran, R. Implementation of a hybrid MPC-PID control strategy using PAT tools into a direct compaction continuous pharmaceutical tablet manufacturing pilot-plant. International Journal of Pharmaceutics. 2014b; 473, 38â&#x20AC;&#x201C;54.

Ravendra Singh is Research Assistant Professor at C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA, working in Pharmaceutical System Engineering research field. He is also serving as a manager and key researcher of â&#x20AC;&#x153;multi million dollars projects funded by NSF, FDA and pharmaceutical companies. He is the recipient of prestigious EFCE Excellence Award given in Recognition of an Outstanding PhD Thesis, from European Federation of Chemical Engineering. He has published more than 44 research papers, written 7 book chapters, presented at over 89 international conferences and currently editing one pharmaceutical book from Elsevier. He is actively serving as a conference session chair, Journal reviewer and guest editor.

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