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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics Pradeep N Sandeep Kautish Sheng-Lung Peng
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Sudhanshu Mishra, Palak Gupta, Smriti Ojha, Vijay Sharma, Vicky Anthony and Disha Sharma 4.1
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Selvasudha Nandakumar, Shah Alam Khan, Poovi Ganesan, Pushpa Sweety, Arul Prakash Francis, Mahendran Sekar, Rukkumani Rajagopalan and Dhanalekshmi Unnikrishnan Meenakshi
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Nishant Gaur, Rashmi Dharwadkar and Jinsu Thomas
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Telagam,
Kranti and Nikhil Chandra Devarasetti
Preface
Given the digital availability of knowledge today, deep learning (DL) has become a hot topic in the field of medicine in recent years. Deep learning is the general-purpose automatic learning procedure that is currently being widely implemented in a number of fields, including science, industry, and government. Since pharmaceutical formulation data consists of formulation combinations and methodological approaches that are neither image nor sequential data, this fully connected broad feed-forward network is a good option to predict pharmaceutical formulations. Moreover, targeted delivery of drugs to diseased tissues is another major challenge that can be solved by utilizing the deep learning framework. This book describes the importance of this framework for patient care, disease imaging/detection and health management. Since deep learning can play a major role in a patient’s healthcare management by controlling drug delivery to targeted tissues or organs, the main focus of the book is to leverage the various prospects of the DL framework for targeted therapy of various diseases. In terms of its industrial significance, this general-purpose automatic learning procedure is being widely implemented in pharmaceutical healthcare. This book provides the direction for future research in deep learning in terms of its role in targeted treatment, biological systems, site-specific drug delivery, risk assessment in therapy, etc. The profusely referenced and copiously illustrated 13 chapters are subdivided into various sections that were written by renown researchers from many parts of the world. It should be noted that since all chapters were deliberately reviewed and suitably revised once or twice, the information presented in this book is of the highest quality and meets the highest publication standards. Therefore, this book should be both immensely interesting and useful to researchers and those in industry working in the areas of clinical research, disease management, pharmaceuticals, R&D formulation, deep learning analytics, remote healthcare management, healthcare analytics, and deep learning in the healthcare industry.
Finally comes the best part, which is to thank everyone who helped to make this book possible. First and foremost, we express our heartfelt gratitude to the authors for their contributions, dedication, participation, and willingness to share their significant research experience in the form of written testimonials, without which this book would not have been possible. Lastly, we want to express our gratitude to Martin Scrivener of Scrivener Publishing for his unwavering support.
The Editors July 2022
Acknowledgement
Our sincere thanks to
Prof. P. K. Sharma
Pro-VC Galgotias University
Without his encouragement and support This task wouldn’t have been possible
Having an idea and turning it into a book is as hard as it sounds. The experience is both internally challenging and rewarding. At the very outset, we fail to find adequate words, with limited vocabulary to our command, to express our emotion to almighty, whose eternal blessing, divine presence, and masterly guidelines helps us to fulfill all our goals.
When emotions are profound, words sometimes are not sufficient to express our thanks and gratitude. We especially want to thank the individuals that helped make this happen. Without the experiences and support from my peers and team, this book would not exist.
No words can describe the immense contribution of our parents, friends, without whose support this work would have not been possible.
Last but not least, we would like to thank, our publisher for their support, innovative suggestions and guidance in bringing out this edition.
1
Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science
Dhanalekshmi Unnikrishnan Meenakshi1*, Selvasudha Nandakumar2, Arul Prakash Francis3, Pushpa Sweety4, Shivkanya Fuloria5, Neeraj Kumar Fuloria5, Vetriselvan Subramaniyan6 and Shah Alam Khan1†
1College of Pharmacy, National University of Science and Technology, Muscat, Oman
2Department of Biotechnology, Pondicherry University, Puducherry, India
3Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry, India
4Anna University, BIT Campus, Tiruchirappalli, India
5Faculty of Pharmacy, AIMST University, Bedong, Malaysia
6Faculty of Medicine, Bioscience and Nursing, MAHSA University, Selangor, Malaysia
Abstract
Site-specific drug delivery [SSDD] is a smart localized and targeted delivery system that is used to improve drug efficiency, decrease drug-related toxicity, and prolong the duration of action by having protected interaction between a drug and the diseased tissue. SSDD system in association with the computational approaches is employed in discovery, design, and development of drugs to improve treatment outcomes. Artificial intelligence [AI] networks and tools are playing a prominent role in developing pharmaceutical products by employing fundamental paradigms. Among many computational techniques, deep learning [DL] technology utilizes artificial neural networks [ANN], belongs to machine learning [ML] approach that holds the key to measuring and forecasting a drug’s
affinity for specific targets. It can reduce both cost and time by speeding up the drug development process rationally with careful decisions. DL is considered as the primary strategy to predict bioactivity as it shows improved performance compared with other technologies in the field. DL can assist in evaluating the success of a target-based drug design and development before the actual laboratory synthesis or production of the drug molecule. This chapter highlights the potential applications of DL in assigning a specific drug target site by predicting the structure of the target protein and drug affinity for a successful treatment. It also spotlights the impactful applications of many types of DL in SSDD and its advantages over conventional SSDD systems. Furthermore, some formulations that are intended to lead to the target or site-specific delivery and DL role in docking and pharmacokinetics profiling are also addressed. Ongoing challenges, skepticism about the likelihood of success, and the paths to overcome by future technological advancements are also dealt with briefly. Due emphasis is given to the use of DL in reducing the economic burden of pharmaceutical industries to overcome costly failures and in developing target specific new drug candidate[s] for a successful therapeutic regimen beneficial to human life.
Keywords: Site-specific, target, drug delivery, deep learning, machine learning, artificial intelligence, computational approach, precision medicine
1.1 Introduction
Site-specific drug delivery (SSDD) is an almost a century-old strategy but successful delivery of drugs to the target site without producing offsite unwanted adverse effects has not been realized yet. Random testing assays in the traditional development of SSDD identify only 3% of compounds that warrant further laboratory tests, and hence, it is vital to explore the drug-target interactions for every single pharmaceutical molecule. Modern drug discovery, which includes identifying and preparing drug-molecular targets with precision, is emerging to fill traditional SSDD gaps. Target-specific drug delivery promotes the delivery of medications to target sites without creating unwanted side effects elsewhere. Despite numerous publications and attention paid to the site-specific delivery that promises to “deliver” the medicine at the diseased site, the generation of target-specific therapeutic products has still been a challenge for researchers [1]. The obstructions met during the drug formulation process are mainly associated with the inability to foresee the impact of the combination of active pharmaceutical ingredients [APIs] and materials on the formulation parameters. A new drug formulation development process and the associated procedures need to satisfy the site-specific delivery and
release profile. Moreover, it is a laborious task and the protocols to perform in vitro characterizations or modifications to obtain the desired profile are difficult for the formulators [2]. To bridge the knowledge gap and reduce the time required for selecting the best molecule for drug development, researchers have devised computational modeling approaches like molecular dynamics simulations, docking studies [3], and cheminformatics [4]. These helps in the evaluation of novel insights about the complex drug delivery systems, especially in atomic/cellular scale which experimental techniques cannot provide [5–7]. A revolution in data science has been observed in the last decades due to the usage of the graphics processing unit [GPU]. A large volume of drug-related data and techniques were generated and analyzed using artificial intelligence [AI] to predict drug interaction with the diseased targets in drug discovery. AI networks and tools are playing a prominent role in the development of pharmaceutical products by employing fundamental paradigms. In medicinal chemistry, several computational methods contributed to designing new drug candidates by relating the drug candidate’s physicochemical properties, biological activity, and binding affinity [8]. Machine learning [ML], the branch of AI, has gained importance in drug discovery protocols and has become the most attractive and prominent research areas. ML supports the advancement of effective formulation through data-driven predictions using experimental data. A well-designed ML technique can significantly speed up the optimization of formulations with reduced cost [9]. Knowledge acquisition about the molecular characteristics of lead molecules has been made with the help of ML techniques like partial least squares [PLS], k-nearest neighbors [kNN], and artificial neural networks [ANN] [10]. ANN is the most prevalent ML technique in formulation prediction [9, 10].
Among the various methodologies of AI, deep learning [DL] had gained significant attention in several areas because of its ability to extract features from data [11]. Leading pharmaceutical industries in collaboration with different AI organizations are trying to develop effective and ideal drug candidates in the field of oncology and CNS complications. In recent years, several trials involving the combination of nanotechnology and DL are underway to study their potential role in drug formulation with SSDD. The role of DL in drug development and manufacturing is depicted in Figure 1.1. DL methods are representation-learning techniques that can discover multiple-level representations of increasing complexity from the raw data using nonlinear models [12]. Several recent trials have connected nanotechnology and DL to study their potential role in drug formulation with site-specific drug delivery [SSDD]. DL can predict the probable drug carrier candidate through target-based drug designing and development.
* Drug design
* Drug Screening
* Poly Pharmacology
* Chemical Synthesis
* Drug Repurposing
* Pharmaceutical Product Management
* Market Positioning, Prediction and Analysis
* Pharma Product Development
* Deciding Excipients
* Monitoring Development Process
* Pharmaceutical manufacturing and Approval
* Automated Manufacturing
* Personalised Medicine
* QA and QC
* Clinical Trial Design
DL methods play a significant role in drug delivery by predicting (i) drug loading in the carrier, (ii) the enhancement in permeability through the body barriers, and choosing the stable drug delivery systems from different carriers and matrices [13].
DL has proved to be an effective tool for virtual screening and predicting quantitative structure-activity relationships from large chemical libraries [14]. Golkov et al. reported that the DL is very useful in predicting the biological functions of several chemical compounds from the raw data based on their electronic arrangements [15]. A previous study on DL revealed that it has collected evidence from the vast amount of data sets related to the genome and utilized for drug repurposing and precise treatments [16]. Various DL models have been used to forecast interactions between protein-ligand, scoring docking poses, and virtual screenings. Thus, DL has been utilized to discover several endpoints in medicinal chemistry [17].
A study on predicting protein-ligand interactions using molecular fingerprints and protein sequences as vector input showed that the essential amino acid residues responsible for drug-target interactions were predicted using vectors obtained from the model [18]. A previous study by Lee et al. detailed a predictive model to represent the DeepConv-drug-target interactions [DTIs] in ligand-target complex. The predictive models were built using over 32,000 drug-target structures from the DrugBank, IUPHAR, and KEGG data sets. DNN outperforms similarity-based models and traditional
Figure 1.1 Role of DL in drug development and manufacturing.
protein representations, according to the findings [19]. For the prediction of novel DTIs between marketed medications and targets, Wen et al. used a successful DL method called deep belief network [DBN] and developed a methodology called DeepDTIs. This method was tested using an appropriate method and associated to suitable algorithms, such as random forest [RF], Bernoulli Naive Bayesian [BNB], and decision tree [DT]. Results showed that the algorithm used in this method achieves comparatively high prediction performance and could be used for drug repositioning in the future [20]. Another study proved that DNN surpasses support vector machines [SVM] used internal testing to predict the drugs and therapeutic categories after ten-fold cross-validation using gene expression data [16]. Recently, the combination of DL-based predictors with the conformal prediction framework to create highly extrapolative models and further evaluated their performance on toxicology in the 21st century [Tox21] data [21]. The results suggested that the utility of conformal predictors is an appropriate way to provide toxicity predictions with assurance. Another study introduced QuantitativeTox (a DL-based framework) to predict toxicity endpoints like LD50, IGC50, LC50, and LC50-DM [22].
Many researchers reported the applications of DL in drug design using different models. The development of new applications and methodologies makes this system a reliable tool in the collections available to discover new drug candidates. In terms of drug discovery and development, DL techniques still have a long way to go and the applications of DL methods in target-specific drug delivery are focused on in this chapter. It also discusses how DL can be used to assign a specific drug target site by estimating the target protein’s structure and drug affinity for successful therapy. It also highlights the important applications of DL in SSDD, as well as the benefits of DL over traditional SSDD systems. Furthermore, some formulations intended to lead to the target or site-specific delivery and DL role in docking and pharmacokinetic profiling are also addressed. Ongoing challenges, skepticism about the likelihood of success, and the paths to overcome by future technological advancements are also dealt with. The application of DL in minimizing the economic burden of pharmaceutical enterprises to overcome costly failures and produce target-specific new drug candidate[s] for a successful therapeutic regimen beneficial to human life is discussed briefly in this chapter.
1.2 Drug Discovery, Screening and Repurposing
Drug discovery is a complex, tedious, lengthy, costly, and challenging multistep process with a very high failure rate. That is why a new drug
approximately requires 10 to 15 years to enter from the bench to the bedside. Despite rapid development in the field of chemical and biological sciences, only an average of 25 new molecular entities [NME] per year were approved over the last two decades, indicating obvious challenges and obstacles associated with the currently used methods for the discovery of drugs [23]. In the late 20th century, target-based drug discovery programs [TBDD] focused on the identification of promising target proteins trailed by high-throughput screening [HTS] to recognize potential drug candidates based on their interaction with the target protein. However, HTS screening program is a costly, time-consuming process with low success rates. Therefore, pharmaceutical industries during 2001 to 2020 mainly relied on virtual screening [VS], i.e., in silico computational methods to design and discover new drugs that resulted in the approval of 498 NME by the US-FDA. VS predicts drug-target interaction and is carried out just before the HTS to increase success rate with less cost. One study reported that the hit rate to identify a suitable protein tyrosine phosphatase-1B enzyme inhibitor by VS approach was much higher [34.8%] than the HTS method [0.021%] [24]. The chemical [small bioactive molecules] and biological [protein target structures] databases are expanding at a rapid pace [high volume, velocity, and variety] due to advancements in technology. To speed up drug discovery, DL seems to be a popular approach for mining suitable drug targets from big data. DL is helpful in drug discovery process, prediction of physicochemical characteristics, quantitative structure-activity relationship [QSAR] studies, bioactivities, ligand-based and structure-based virtual screening, toxicity, mechanism of action, drugtarget interaction, protein-protein interactions, design of dosage form, etc. Zhavoronkov and co-workers in 2019 used DL method namely generative tensorial reinforcement learning [GENTRL] and discovered potent inhibitors of discoidin domain receptor 1 [DDR1] merely in 3 weeks. One of the inhibitors showed promising activity against fibrosis and a favorable pharmacokinetic profile in experimental animals [25]. Stokes et al. in 2020 discovered a broad-spectrum antibiotic halicin employing a DNN model of DL. The chemical structure of bactericidal halicin is very different from the core of existing antibiotic molecules and was identified from the drugrepurposing hub through the prediction of antibacterial activities [26].
DL uses chemical and protein data to accelerate the drug design and development protocols. The big data in chemical space are stored in databases, such as ChemSpider, ChEMBL, ChemMine, ChemBank, DrugCentral, GDB-17, ZINC, and PubChem, while 3D images of proteins are available in protein data bank PDB, BindingDB, and KEGG ligand. DisGeNET database provides useful information on the relationship
between human disease-associated genes and variants [27]. Another important database for drug discovery is STITCH, which provides information on interactions between small chemical molecules and target proteins along with binding affinity [28]. For drug discovery, DL employs several subsets of ANNs, including deep neural networks [DNNs], recurrent neural networks [RNNs], and convolutional neural networks [CNNs]. DNN can be used either to generate the structure of bioactive compounds from the pool of chemical libraries and training sets [generative DNN] or to predict physicochemical properties of novel bioactive chemicals [predictive DNN] [29, 30]. RNNs are primarily used to process sequential data. It works on a self-learning method and helps to create a descriptive simplified molecular-input line-entry system [SMILES] for characterization and synthesis of molecules [4, 31]. CNNs are the most effective tool of DL that can convert 2D to 3D data. CNN is used to differentiate data for the identification of gene mutations, disease target, lead candidate, and their interaction based on microscopy images and fingerprints. It is a very good DL algorithm for handling 2D data but requires a long training time [32–34]. In the recent past, graph neural networks [GNNs] are preferred over RNNs and CNNs that present model data in a graph instead of representation in Euclidean space [35]. GNN molecular graphs for small bioactive molecules are a useful drug development process to predict molecular attributes and generate molecular tasks [36].
QSAR modeling is a computational technique used in drug discovery. It develops a quantitative relationship between the physiochemical features of tiny chemical compounds and their biological activities using mathematical models. Some of the web-based models developed for QSAR studies include; Cloud 3D-QSAR, FL-QSAR, QSAR-Co-X, Meta-QSAR, and Vega platform, etc., [37]. AlphaFold is an AI-based tool developed by Google’s DeepMind to identify protein interaction [38]. This CNN-based tool can help in structure-based VS for drug discovery. Al Quraishi in 2019 also developed a similar DL-based tool, known as Recurrent Geometric Network, for the prediction of 3D structures of proteins [39].
The Monte Carlo tree search (MCTS) technique is a computationalbased NN system and is very effective in generating various chemical synthetic pathways and in providing a solution to the total synthesis problems [40]. AiZynthFinder is recently developed by Genheden et al., using MCTS approach for retrosynthesis planning [41]. DeepScreening is a DL algorithm-based, user-friendly online server developed by Liu et al., in 2019 for drug discovery. It assists in VS of chemical compounds either from the public database or as defined by the user for a particular target protein [42]. The deep Reinforcement Learning for Structural Evolution
[ReLeaSE] program, which is based on the stack-augmented RNN, could be used to develop chemical libraries. ReLeaSE performs the de novo drug design through generative and predictive DNNs [43, 44]. Bai et al. in 2020 designed a soft tool called MolAIcal to design 3D drugs in 3D protein pockets. It utilizes DL and genetic algorithms for de novo drug design using US- FDA-approved drugs followed by DL-based molecular docking [45]. Drug discovery applications of DL are briefly presented in Table 1.1.
As discussed, conventional drug design and development might take a long period, expensive, off-target delivery, and high risk, with enormous difficulties and challenges; as a result, efforts are made to repurpose existing medications [60, 61]. Drug repurposing [or drug repositioning] is an approach that helps to speed up the applications of an already approved existing drug for a new indication, thus reducing the difficulties of discovering new drug molecules [62]. The advancement in the large-scale, heterogeneous biological networks provided unique opportunities in in silico drug repurposing methods as discussed elsewhere in this chapter [60]. These appealing properties have piqued biopharmaceutical companies’ interest in scanning existing medications for potential repurposing applications. According to an estimate, approximately 30% of FDA-approved new drug products were made available through medication repurposing [63]. Using various biological networks, significant data collection from molecular, genomic, and phenotypic data facilitates the advanced development in drug repurposing [62]. Mechanism-based repurposing approaches are likely to find new indications for individual patients, given the current demand for PM and personalized therapy. These approaches consider the patient’s complexity and heterogeneity, lowering the risk of drug toxicity and interpatient variability therapeutic efficacy [62].
Computer-assisted drug repositioning plays a leading role to improve the safety and efficacy of repurposing approaches utilizing the advantage of computational modeling through the data obtained from preclinical and clinical studies. With the advancement of computational drug design, various anticancer drugs, like Gefitinib, Erlotinib, Sorafenib, Crizotinib, and so on, were profitably discovered, which has been considered a milestone in this area. Collaboration of computational and AI methods are creating new promising outcomes in drug development research, and the role of DL is valued by pharma industries [37, 64]. DL creates a unique perspective on how drug molecules bind to target molecules, the changes in their physicochemical characteristics that result, and how these changes impact phenotypic alterations. Furthermore, this technique aids in the identification of novel therapeutic targets from large-scale data sets gathered by numerous programs [65].
