Bioinformatics Tools for Pharmaceutical Drug Product Development E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries. The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall. Readers will find in this book: * Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; * The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; * The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
Part I: BIOINFORMATICS TOOLS
1 Introduction to Bioinformatics, AI, and ML for Pharmaceuticals
1.1 Introduction
1.2 Bioinformatics
1.3 Machine Learning (ML)
1.4 Conclusion and Future Prospects
References
2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling
2.1 Introduction
2.2 Artificial Intelligence in Drug Discovery
2.3 AI in Virtual Screening
2.4 AI for
De Novo
Design
2.5 AI for Synthesis Planning
2.6 AI in Quality Control and Quality Assurance
2.7 AI-Based Advanced Applications
2.8 Discussion and Future Perspectives
2.9 Conclusion
References
3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability
3.1 Introduction
3.2 Points to be Considered for Peptide-Based Delivery
3.3 Overview of Peptide-Based Drug Delivery System
3.4 Tools for Screening of Peptide Drug Candidate
3.5 Various Strategies to Increase Serum Stability of Peptide
3.6 Method/Tools for Serum Stability Evaluation
3.7 Conclusion
3.8 Future Prospects
References
4 Data Analytics and Data Visualization for the Pharmaceutical Industry
4.1 Introduction
4.2 Data Analytics
4.3 Data Visualization
4.4 Data Analytics and Data Visualization for Formulation Development
4.5 Data Analytics and Data Visualization for Drug Product Development
4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management
4.7 Conclusion and Future Prospects
References
5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics
5.1 Introduction
5.2 Mass Spectrometry - Protein Interaction
5.3 MS Analysis
5.4 Validating Specific Interactions
5.5 Mass Spectrometry – Qualitative and Quantitative Analysis
5.6 Challenges Associated with Mass Analysis
5.7 Relative vs. Absolute Quantification
5.8 Mass Spectrometry – Lipidomics and Metabolomics
5.9 Mass Spectrometry – Drug Discovery
5.10 Conclusion and Future Scope
5.11 Resources and Software
Acknowledgement
References
6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology
6.1 Introduction
6.2 Bioinformatics Tools
6.3 The Genetic Basis of Diseases
6.4 Proteomics
6.5 Transcriptomic
6.6 Cancer
6.7 Diagnosis
6.8 Drug Discovery and Testing
6.9 Molecular Medicines
6.10 Personalized (Precision) Medicines
6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic
6.12 Prognosis of Ailments
6.13 Concluding Remarks and Future Prospects
Acknowledgement
References
7 Clinical Applications of “Omics” Technology as a Bioinformatic Tool
Abbreviations
7.1 Introduction
7.2 Execution Method
7.3 Overview of Omics Technology
7.4 Genomics
7.5 Nutrigenomics
7.6 Transcriptomics
7.7 Proteomics
7.8 Metabolomics
7.9 Lipomics or Lipidomics
7.10 Ayurgenomics
7.11 Pharmacogenomics
7.12 Toxicogenomic
7.13 Conclusion and Future Prospects
Acknowledgement
References
Part II: BIOINFORMATICS TOOLS FOR PHARMACEUTICAL SECTOR
8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery
Abbreviations
8.1 Introduction
8.2 Informatics and Drug Discovery
8.3 Computational Methods in Drug Discovery
8.4 Conclusion
References
9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products
9.1 Introduction
9.2 Current Scenario in Pharma Industry and Quality by Design (QbD)
9.3 AI- and ML-Based Formulation Development
9.4 AI- and ML-Based Process Development and Process Characterization
9.5 Concluding Remarks and Future Prospects
References
10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing
Abbreviations
10.1 Introduction to Artificial Intelligence and Machine Learning
10.2 Different Applications of AI and ML in the Pharma Field
10.3 AI and ML-Based Manufacturing
10.4 AI and ML-Based Drug Product Marketing
10.5 Future Prospects and Way Forward
10.6 Conclusion
References
11 Artificial Intelligence and Machine Learning Applications in Vaccine Development
11.1 Introduction
11.2 Prioritizing Proteins as Vaccine Candidates
11.3 Predicting Binding Scores of Candidate Proteins
11.4 Predicting Potential Epitopes
11.5 Design of Multi-Epitope Vaccine
11.6 Tracking the RNA Mutations of a Virus
Conclusion
References
12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products
Abbreviations
12.1 Introduction
12.2 AI and ML for Pandemic
12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development
12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products
12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products
12.6 Way Forward
12.7 Conclusion
References
Part III: BIOINFORMATICS TOOLS FOR HEALTHCARE SECTOR
13 Artificial Intelligence and Machine Learning in Healthcare Sector
Abbreviations
13.1 Introduction
13.2 The Exponential Rise of AI/ML Solutions in Healthcare
13.3 AI/ML Healthcare Solutions for Doctors
13.4 AI/ML Solution for Patients
13.5 AI Solutions for Administrators
13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector
13.7 AI/ML Based Healthcare Start-Ups
13.8 Opportunities and Risks for Future
13.9 Conclusion and Perspectives
References
14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy
Abbreviations
14.1 Introduction
14.2 Machine Learning Algorithm Models
14.3 Artificial Learning in Radiology
14.4 Application of Artificial Intelligence and Machine Learning in Radiotherapy
14.5 Implementation of Machine Learning Algorithms in Radiotherapy
14.6 Deep Learning Models
14.7 Clinical Implementation of AI in Radiotherapy
14.8 Current Challenges and Future Directions
References
15 Role of AI and ML in Epidemics and Pandemics
15.1 Introduction
15.2 History of Artificial Intelligence (AI) in Medicine
15.3 AI and MI Usage in Pandemic and Epidemic (COVID-19)
15.4 Cost Optimization for Research and Development Using Al and ML
15.5 AI and ML in COVID 19 Vaccine Development
15.6 Efficacy of AI and ML in Vaccine Development
15.7 Artificial Intelligence and Machine Learning in Vaccine Development: Clinical Trials During an Epidemic and Pandemic
15.8 Clinical Trials During an Epidemic
15.9 Conclusion
References
16 AI and ML for Development of Cell and Gene Therapy for Personalized Treatment
16.1 Fundamentals of Cell Therapy
16.2 Fundamentals of Gene Therapy
16.3 Personalized Cell Therapy
16.4 Manufacturing of Cell and Gene-Based Therapies
16.5 Development of an Omics Profile
16.7 Machine Learning in Gene Expression Imaging
16.8 AI in Gene Therapy Target and Potency Prediction
16.9 Conclusion and Future Prospective
References
17 Future Prospects and Challenges in the Implementation of AI and ML in Pharma Sector
17.1 Current Scenario
17.2 Way Forward
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Various bioinformatics and AI-driven tools applied in the pharmacy d...
Chapter 2
Table 2.1 Input and output data patterns for the development of AI algorithms ...
Chapter 5
Table 5.1 Implementation of mass spectrometry in various scientific field [1].
Table 5.2 Identified hurdles in various steps of mass spectrometry [28].
Chapter 7
Table 7.1 Overview of the omics arms and their applicability.
Table 7.2 Pros and cons of different omics approaches.
Chapter 8
Table 8.1 List of selected programs and software for protein modeling.
Table 8.2 List of selected programs and software for docking.
Table 8.3 List of selected programs and software for QSAR modeling.
Table 8.4 List of selected programs and software for pharmacophore modeling.
Table 8.5 List of selected programs and software for ADMET prediction.
Chapter 10
Table 10.1 Applications of AI–ML in pharma.
Table 10.2 AI ML tools for manufacturing.
Table 10.3 AI ML tools for marketing.
Chapter 11
Table 11.1 A summary of studies that have been done on protein—ligand scoring ...
Table 11.2 A summary of databases available for binding scores.
Table 11.3 Summary of online tools with application in multiepitope vaccine de...
Chapter 12
Table 12.1 Bioinformatics tools used in pre-clinical drug development [59].
Table 12.2 Use of ML algorithms in preclinical drug development [60].
Table 12.3 Key terminologies and concepts related to machine learning in clini...
Chapter 13
Table 13.1 AI/ML based healthcare start-ups in India.
Chapter 14
Table 14.1 List of application of AI based technology applied, type of cancer ...
Chapter 15
Table 15.1 The most frequently used AI algorithm.
List of Illustrations
Chapter 1
Figure 1.1 Applications of bioinformatics, AI, and ML in the pharmacy sector.
Chapter 2
Figure 2.1 The process of constructing an AI planner in general.
Figure 2.2 General flowchart to describe model optimization techniques.
Chapter 3
Figure 3.1 Analytical tool for analysis of its strengths, weaknesses, opportun...
Figure 3.2 Various cyclization of peptide.
Figure 3.3 Example of terminal modification.
Chapter 4
Figure 4.1 PharmaSD program for the
in silico
formulation of solid dispersion....
Figure 4.2 Graphical illustration of HyperDC. (Reprinted with permission from ...
Figure 4.3 SeDem diagram. (Reprinted with permission from Suñé-Negre, 2014 [37...
Chapter 5
Figure 5.1 Implementation of mass spectrometry in drug discovery, development ...
Chapter 6
Figure 6.1 Functional Genomics is the study of how the genome, transcripts (ge...
Figure 6.2 Application of proteomics.
Figure 6.3 Application of transcriptomics.
Figure 6.4
In silico
drug design platform approach (Accelerating Therapeutics ...
Figure 6.5 Stages of protein drug discovery and development.
Figure 6.6 Structure of molecular medicine.
Figure 6.7 Application of personalized (precision) medicines.
Chapter 7
Figure 7.1 Omics science and their interaction.
Figure 7.2 Evolution of omics technology.
Figure 7.3 Workflow for the integrated omics.
Figure 7.4 Application of genomics in medicines.
Figure 7.5 Applications of nutrigenomics.
Figure 7.6 Applications of comparative transcriptomics.
Figure 7.7 Proteomics technologies at a glance.
Figure 7.8 Standard proteomics study template. (Created with Biorender.com usi...
Figure 7.9 Typical metabolomics/lipidomics workflow: (1) after establishing a ...
Figure 7.10 Applications of pharmacogenomics.
Figure 7.11 Relationship of toxicogenetic and toxicogenomic.
Chapter 8
Figure 8.1 Overview of drug discovery process.
Figure 8.2 Schematic representation of CADD strategies.
Chapter 9
Figure 9.1 QbD roadmap for pharmaceutical product development. (Reprinted with...
Figure 9.2 Illustration of the study conducted by Gentiluomo
et al.
[22]. The ...
Figure 9.3 Challenges to implementing AI into pharma manufacturing processes.
Figure 9.4 AI/ML based modern pharmaceutical manufacturing process. (Adopted u...
Chapter 10
Figure 10.1 AI ML and Industry 4.0.
Figure 10.2 AI in pharmaceutical manufacturing.
Figure 10.3 AI in drug product marketing.
Chapter 11
Figure 11.1 Different definitions of Artificial Intelligence.
Figure 11.2 Total steps of sequence analysis using DL methods can be observed ...
Chapter 12
Figure 12.1 Successive use of conventional analytical tools in preclinical and...
Figure 12.2 Implementation of AI in clinical trials (EMR: Electronic Medical R...
Figure 12.3 AI/ML application in product lifecycle management.
Chapter 14
Figure 14.1 Application of artificial intelligence in radiotherapy.
Figure 14.2 Mask R-CNN network architecture diagram. RoI generates a region of...
Chapter 15
Figure 15.1 Pandemic preparedness and response using digital technology. (Adop...
Figure 15.2 A schematic diagram of using ML (Machine Learning) and DL (Deep Le...
Figure 15.3 Process of disease diagnosis using AI.
Figure 15.4 The combination of artificial intelligence and systems biology for...
Chapter 16
Figure 16.1 The clinical applications of translational bioinformatics. (Figure...
Figure 16.2 Overview of AI/ML application for cell and gene therapy developmen...
Figure 16.3 Goals of AI in precision medicine.
Figure 16.4 Possible involvement of AI and ML in cell and gene therapy product...
Figure 16.5 A brief overview of stem cell prediction using morphology recognit...
Chapter 17
Figure 17.1 Summary of the challenges for the implementation of AI/ML in pharm...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Bioinformatics Tools for Pharmaceutical Drug Product Development
Edited by
Vivek Chavda
Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, India
Krishnan Anand
Department of Chemical Pathology, School of Pathology, University of the Free State, Bloemfontein, South Africa
and
Vasso Apostolopoulos
Institute for Health and Sport, Immunology and Translational Research Group, Victoria University, Melbourne, Australia
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86511-7
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
For a new drug to be developed and brought to market, approximately US$1.8 billion and a minimum of 15 years in development are required. In most instances, only a few drugs make it to market because the process of creating a new drug can fail at different steps along the way, with most of them failing in the final stages of development. Some reasons for this can be attributed to the lack of extensive clinical data, unexpected toxicities and long-term side effects; as well as the highly competitive market, which puts a strain on the development of new drugs. A way to reduce some of the costs and increase the likelihood of success is to maximize the information gained via basic science and the design of better translational approaches and clinical trials. As such, bioinformatics approaches are becoming more essential in drug discovery and vaccine design, not only in academia, but also in the pharmaceutical industry. Bioinformatics involves the use of software tools and computer programming to understand biological data, particularly when the data is large and complex. The development of large data warehouses and algorithms to analyze large data, the identification of biomarkers and novel drug targets, computational biochemistry, genomics, drug discovery and design have all been at the forefront of translational drug discovery in recent years. Bioinformatics has revolutionized disease-based and drug-based approaches as well as improved knowledge of biological targets. It has ushered in a new era of research with the aim to accelerate the design and development of drug and vaccine targets, improve validation approaches as well as facilitate in identifying side effects and predict drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market and, most importantly, make it a more cost-effective process overall.
Since there has been much emphasis placed on developing bioinformatics tools for pharmaceutical drug development, this book is a timely and important addition to the evolving field. The 17 chapters are categorized into three sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design and omics technologies. The following two sections include bioinformatics tools for the pharmaceutical and healthcare sectors.
In this book, experts from around the world provide comprehensive overviews of the many important steps involved in—and the critical insights needed for—the successful development of therapeutic drug products with the help of bioinformatics, artificial intelligence and machine learning. The amount of work put into these 17 chapters required significant collaboration and input, and there are many people who are worthy of our thanks. We thank the support of over 20 thought leaders in AI/ ML from across the globe who contributed to the chapters. Without their contributions, the book would not have been possible. We offer our sincere gratitude to the Department of Pharmaceutics and Pharmaceutical Technology, L.M. College of Pharmacy, Ahmedabad-Gujarat, India; the Department of Chemical Pathology, School of Pathology, University of the Free State, Bloemfontein campus, Free State, South Africa; and Victoria University, Institute for Health and Sport for their ongoing support in publishing this volume. We also thank Scrivener Publishing and their staff for their editorial support throughout the publication process.
The Editors
Vivek Chavda
K. Anand
Vasso Apostolopoulos
December 2022
Part IBIOINFORMATICS TOOLS
1Introduction to Bioinformatics, AI, and ML for Pharmaceuticals
Vivek P. Chavda1*, Disha Vihol2, Aayushi Patel3, Elrashdy M. Redwan4,5 and Vladimir N. Uversky6†
1Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, Gujarat, India
2Department of Phytopharmacy and Phytomedicine, School of Pharmacy, Gujarat Technological University, Ahmedabad, Gujarat, India
3Pharmacy Section, L. M. College of Pharmacy, Ahmedabad, Gujarat, India
4Department of Biological Sciences, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
5Therapeutic and Protective Proteins Laboratory, Protein Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications, New Borg EL-Arab, Alexandria, Egypt
6Department of Molecular Medicine and Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
Abstract
Bioinformatics is a growing field that has emerged in recent years. The use of computational applications for protein sequence analysis in the early 1960s created the groundwork for bioinformatics. Alongside, developments in molecular biology techs evolved DNA analysis, leading to simpler manipulation of DNA, its sequencing, and computer science, suggesting the development of compatible and more powerful computers with innovative software for performing bioinformatics tasks. Biological Big Data collection when analyzed with bioinformatics tools leads to powerful predictive results with repeatability. Due to advancements in the merging of computer science and biology, even subdisciplines like synthetic biology, systems biology, and whole-cell modeling are emerging rapidly.
Keywords: Bioinformatics, artificial intelligence, machine learning, AI/ML, pharmaceuticals, drug product development
1.1 Introduction
In the context of Artificial Intelligence (AI), Machine Learning (ML), and Big Data, the healthcare industry explores the medication research process, evaluating how emerging technologies can enhance efficacy [1]. Artificial intelligence and machine learning are seen as the way of the future in a variety of fields and sectors, including pharmaceuticals. In a world, where a single authorized medicine costs millions of dollars and requires years of rigorous testing before being licensed, saving money and time is a priority.
Producing new pharmacological compounds to combat any disease is an expensive and time-consuming procedure, yet it goes unchecked. The most important aspect of drug design is to take advantage of the collected data and seek fresh and unique leads. Once the medication target has been determined, numerous multidisciplinary domains collaborate to develop enhanced pharmaceuticals using AI and ML technologies [2]. These technologies are utilized at every phase of the computer-aided drug discovery process, and combining them results in a proven track record of success in finding hit molecules. Furthermore, the fusion of AI and ML with high-dimension data enhanced the capabilities of computer-aided drug discovery and design [3]. Clinical trial output prediction using AI/ML integrated models might decrease the costs of the clinical trial, while simultaneously increasing their success rates. In this study, we will be covering the potential of AI and ML technologies in enabling computer-aided drug creation, along with challenges and opportunities for the pharmaceutical sector.
1.2 Bioinformatics
When biological data along with genetic information is analyzed using computer technology for calculating and obtaining mathematically and statistically approved results, is called Bioinformatics. The computational means are utilized for addressing data-intensive, large-scale biological challenges [4]. It includes the development and application of databases, algorithms, computational and statistical tools, and theory to tackle formal and practical issues emerging from biological data administration and analysis [5, 6].
Bioinformatics allows rapid molecular modeling of biological processes from the collected big data for obtaining meaningful conclusions through various stages such as compilation of the statistical information from biological data, creation of a computational model, the resolution of computational modeling issues, and the assessment of the resulting computational algorithm [7]. Genomics and proteomics are the branches of bioinformatics that aim at understanding the organizing principles encoded inside the nucleic acid and protein sequences, respectively. Image and signal processing enables usable conclusions to be extracted from vast volumes of raw data [5]. It helps in decoding genetics by facilitating text mining of biological literature, comparing, analyzing, and interpreting genetic, genomic, and proteomic data, assessing gene and protein expression, detecting mutations, sequencing and annotating genomes, and interpreting evolutionary aspects with disease pathways [8, 9]. Moreover, it helps to simulate and model DNA, RNA, proteins, and biomolecular interactions in structural biology. All of these can be achieved by correlating the biological data for understanding the effect of the diseased condition on the body’s normal cellular functions [10]. Hence, bioinformatics has progressed now to the point, where analysis and interpretation of diverse forms of data is the most important challenge.
Omics technologies offer a chance to investigate changes in DNA, RNA, proteins, and other biological components across intraspecies and interspecies. The analysis of these components is interesting from a toxicity assessment view as they can alter in response to chemical or drug exposure in cells or tissues. Genomic research, which generates enormous amounts of data, is one area, where bioinformatics is very valuable. Bioinformatics aids in the interpretation of data, which may then be used to provide a diagnosis for a patient suffering from a rare ailment, track and monitor infectious organisms as they spread across a community, or determine the best therapy for a cancer patient [11]. Genomics sequences assemble and analyze the structure and function of genomes using recombinant DNA, DNA sequencing technologies, and bioinformatics. Various software used in bioinformatics includes accessibility of protein surface and secondary structure predictions using NetSurfP, prediction of beta-turn sites in protein sequences using NetTurnP, and AutoDock for Automated Docking Tool Suite [4].
Rapid advances in genomics and other molecular research tools, along with advances in information technology, have resulted in a massive volume of molecular biology knowledge during the last few decades [12]. Bioinformatics will continue to advance as a result of the integration of many technologies and techniques that bring together professionals from other domains to build cutting-edge computational and informational tools tailored to the biomedical research business [4, 9]. Table 1.1. represents various bioinformatics and AI-driven tools, which can be applied in the pharmacy department and industry.
Table 1.1 Various bioinformatics and AI-driven tools applied in the pharmacy department and industry.
Computational tools
Application
Reference
BLAST
The Basic Local Alignment Search Tool (BLAST) is used for searching local similarity regions between sequences and compares to the available database for calculating the statistical significance of matches. The matching infers functional and evolutionary relationships between sequences and identifies genetically related families.
[
15
]
ChEMBL
ChEMBL is designed manually to maintain a database of bioactive molecules. It correlates genomic data with chemical structure and bioactivity for the development of new drugs.
[
16
]
geWorkbench
The genomics Workbench (geWorkbench) comprises the tools for performing management, analysis, visualization, and annotation of biomedical data. It supports data for microarray gene expression, DNA and Protein Sequences, pathways, Molecular structure – prediction, Sequence Patterns, Gene Ontology, and Regulatory Networks
[
11
]
GROMACS
It is software for high-performance molecular dynamics and output analysis, especially for proteins and lipids.
[
7
]
IGV
Integrative Genomics Viewer (IGV) is a high-performance, user-friendly, interactive tool for the visualization and exploration of genomic data.
[
17
]
MODELLER
A protein three-dimensional structural homology or comparative modeling tool.
[
18
]
SwissDrugDesign
SwissDrugDesign provides a collection of tools required for Computer-Aided Drug Design (CADD).
[
19
]
UCSF Chimera
UCSF Chimera is an interactive tool for the visualization and analysis of molecular structures.
[
20
]
AlphaFold
It is an AI system developed to computationally predict protein structures with unprecedented accuracy and speed.
[
21
]
Cyclica
The ML tool address challenges faced across the drug discovery life cycle by correlating biophysics, medicinal chemistry, and systems biology.
[
21
]
DeepChem
It is a deep learning framework for drug discovery.
[
18
]
DeltaVina
Gives docking scores for protein-ligand binding affinity
[
17
]
Exscientia
The AI engineers precision medicines more rapidly and efficiently by accelerating pre-clinical drug discovery phases through monitoring and analysis of drug design and experiments.
[
13
]
Hit Dexter
It estimates how likely a small molecule is to trigger a positive response in biochemical and biological assays.
[
4
]
ORGANIC
Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) is a tool for creating molecules with desired properties.
[
8
]
Somatix
It is a real-time gesture detection technology that enables passive data collection of indoor and outdoor patients for enhancing medication adherence rates, data reliability, and cost-effectiveness.
[
22
]
1.2.1 Limitations of Bioinformatics
In the case of drug discovery by applying in silico approaches, prediction of ligand affinity and inhibitory activity, where adequate training data are available is possible at certain levels but search for the mechanism-based inhibitors remains a highly challenging task. In structure-based approaches to various binding affinities, free energy calculations without extensive data collection at times leads to adequate results but high chances of false-positive rates remain a limiting factor. While chemical structure mining, the bioinformatics tools are efficiently able to produce a large number of metabolites but it is by no means possible to find ways of ranking metabolites accurately. Prediction of metabolic rates is generally not possible with bioinformatics tools. The in vivo predictions by applying bioinformatics for assessing biological activity and toxicological effects detect most toxico-phores but the prediction of time-dependent inhibitors remains a difficult task [13].
Furthermore, it requires a sophisticated laboratory for the in-depth study and collection of biomolecules, and the establishment of such laboratories requires significant funds. The system operation is a complex mechanism so specially trained individuals are required for handling computer-based biological data. Uninterrupted electricity supply for biological investigations using computational applications is the basic requirement, abrupt interruption can lose huge data from the system memory. The system must also be virus-protected, which, if not controlled, can lead to the deletion of data and corruption of the programs [14].
1.2.2 Artificial Intelligence (AI)
Artificial intelligence (AI) is a branch of computer science that unlike natural intelligence is demonstrated by machines or computers and is defined as a system that analyzes its environment and performs a series of actions to achieve goals similar to a human-being process. The objectives of AI include gathering information, establishing rules on usage of the information, reasoning, data representation, organizing, planning, learning, problem-solving, processing, and the ability to manipulate data or self-correction [9, 21].
In the pharmaceutical industry, AI plays role in providing technologies for drug discovery, such as drug target identification, optimization, designing, formulation development, manufacturing, break-down R&D costs, analyzing biomedicine information for recruiting suitable patients for clinical trials, drug repurposing, diagnostic tools and assistance, and optimizing medical treatment processes [23]. Moreover, AI optimizes innovation, enhances the efficiency of research by data management, and creates computational tools for researchers, physicians, and regulators with minimizing human intervention and errors [24]. Generally, two main classes of AI technology developments are incorporated in the case of drug discovery. The first component comprises computing methodologies including systems simulating human experiences along with outputs. The second one consists of models mimicking Artificial Neural Networks (ANNs) for real-time data correlation with the management of AI technology evolution [25].
Various in silico models predict pharmacokinetics and simulate molecular docking of the drugs to ease down drug discovery phases along with predictions of in vitro and in vivo responses [26, 27]. AI in drug development includes predictions of probable synthetic pathways for drug-like molecules, pharmacokinetic and dynamic properties, protein identification and characterization, bioactivity, drug-target, and drug-drug interactions [18, 28, 29]. Moreover, AI incorporates various omics for identifying new disease pathways with targets using novel biomarkers and therapeutic targets [30]. In the case of clinical trials, AI improves candidate selection criteria by identifying the best patients with human-relevant biomarkers of disease and gene targets for the study and ensuring the most suitable trial results. It also helps in removing the trail hindering elements and reduces the time for conducting large database analysis [24]. An example of such an AI platform is AiCure, a mobile application subjected to phase 2 clinical trial patients with schizophrenia for assessing improvement in patient medication adherence [31]. The AI-driven PAT (Process analytical technology) proves to be a necessary tool in terms of quality control and assurance while manufacturing. Improvements can be observed in product yield, utilization, and cost-saving with less waste generation [32]. To assess real-time manufacturing aspects, the Manufacturing Execution System (MES) is utilized. It complies with regulatory guidelines and ensures high-quality product development through risk management, shortened production cycles, optimized resource use, and controlled batch release [33]. Then the Automatic Process Control System (APCS) is used for ensuring a safe and profitable process by monitoring and optimizing process variables like concentration, flow, pressure, temperature, and vacuum [34].
Furthermore, AI can be utilized in drug repurposing, where a drug gets qualified to enter Phase II trials for different use without going through Phase I clinical trials and toxicology testing which reduces costing and time of the trials [35]. Not only AI can be used for drug discovery, but it can also be used in polypharmacology, the ‘one-disease–multiple targets theory. Databases such as BindingDB, ChEMBL, DrugBank, Ligand Expo, PubChem, PDB, and ZINC are available for information on binding affinities, biological activities, chemical properties, crystal structures, drug targets, and pathways [36].
The digitalization of health and medicine has created an opportunity for AI in hospital pharmacies for performing tasks, such as maintaining electronic medical records and patients’ medication history, designing treatment approaches and dosage forms, medication safety, drug interactions, ADME consultations, and providing healthcare aid to patients. This way AI agrees with share-risk agreements and decision-making in Pharmacy and Therapeutics Committees [37, 38]. Electronic health records (EHR) are collected routinely and can be classified into structured and unstructured data. Structured data refers to the collection of data in an organized manner with standard units and ranges (e.g., vital signs), unlike unstructured data with unclear management (e.g., imaging results) [39]. This data is collected by AI in real-time for analyzing clinical data management and practice which can give insights into novel drug discovery, pharmacovigilance, drug-associated adverse events, patient medication adherence, and prescription errors [40]. With the information of EHR, various patient-omics data (i.e., genomics, microbiomics, proteomics) can be integrated for the creation of the Electronic Medical Records and Genomics (eMERGE) network which helps in identifying unknown diseases with associations to the gene bank obtained [41]. AI can also predict an epidemic outbreak. Even predictions of shipment times of therapeutics can be carried out efficiently by incorporating AI tools in the case of e-pharmacy. Moreover, AI can be used as a diagnostic tool for disease analysis and status by grading system with reproducibility. It improves the accuracy of the treatment decisions and predicts prognosis. Even data can be collected from uncooperative patients [42].
Advantages of AI include providing real-time data management, error minimization and producing efficient output, multitasking, patient data management, adverse effects or side effects data collection, medication designing with disease correlation, streamlining tasks, inventory management, and assisting research in the development of drug delivery formulations. Nevertheless, disadvantages are also a part of the AI and they are the need for human surveillance, expensive building and launching of AI tools, chances of false report generation, lack of data collection and method standardization, may overlook social variables, raises unemployment, no creativity, the risk of data leakage and mass-scale destruction, and acceptance within the healthcare sector [37, 43].
1.3 Machine Learning (ML)
Machine learning (ML) is a subclass of AI, where algorithms process big data to detect patterns, learn from them, and solve problems statistically and autonomously. ML is categorized into supervised, unsupervised, and reinforcement learning. Supervised learning includes the application of regression and classification methods which forms a predictive model upon data insertion from input and output sources. The predictive models can be disease diagnosis or drug efficacy and ADMET predictions [44]. In unsupervised mode, solely input data are utilized and interpreted using clustering and feature-finding methods. The output comprises discovering a disease with its probable targets [45, 46]. Lastly, reinforcement learning depends majorly on decision-making in the applied or specific environment with maximum performance ability. By applying modeling and quantum chemistry, outputs such as de novo drug design can be executed with this learning mode [47].
ML includes a subdivision consisting of Deep Learning (DL), which engages Artificial Neural Networks (ANNs) for adapting and learning the experimental data. These networks are similar to human biological neurons responsible for electrical impulses transmission in the brain, which allows real-time data collection and interpretation [48]. This big data in association with algorithm application can help in discovering new drugs with more potency and can improve the personalized medicine sector based on genetic markers [47].
Machine learning in healthcare performs multiple tasks, such as classification, recommendations, clustering and correlation of cases, prediction, anomaly detection, automation, and ranking of information [49]. The disease progression and development mechanism within a body is a complex system that cannot be understood by simple data collection. Usually, real-time data collection and compilation are carried out by high throughput approaches such as the usage of the pre-defined set of machine learning applications. This software not only provides diagnostic approaches but also helps in identifying hypothetical therapeutics for drug development. The benefits of incorporating machine learning are infinite availability of data storage and high flexibility in its management. Various data sets include assay information, biometrics, images, omics data, textual information, and data collected from wearables [50, 51]. Various ML applications such as Python, Spark, MLLib, and Jupyter notebooks have been utilized by pharma industries for data mining and predictive intelligence for solving daily tasks along with moderately tedious challenges [52].
1.3.1 Applications of ML
a) Research and development of new drug:
ML utilizes a feedback-driven drug development process by interpreting existing results from sources, such as computational modeling data, literature surveys, and high-throughput screening. This process helps in identifying lead compounds with efficiency and reduced randomness, errors, and time-lapse. In the approach such as de novo design, inputs require a compound library gained through in silico methods and virtual screening applications which mimic bioactivity and toxicity models [53]. The drug discovery can be carried out by following a series of steps starting from the identification of novel bioactive compounds through docking studies and molecular dynamics. A hit compound can be found while screening chemical libraries, computer simulation, or screening naturally isolated materials, such as plants, bacteria, and fungi. Then the recognized hits are screened in cell-based assays consisting of animal models of disease to assess efficacy and safety. Once the activity of the lead molecule is confirmed, chemical modifications can be carried out in search of a novel compound consisting of maximal therapeutic benefits with minimal harm [54, 55]. Hence, incorporating algorithm datasets in conjugation with chemical structures and targets is utilized for the optimization of new leads and is preferable to the laborious target-specific lead identification for the R&D sector of a pharma company.
b) Claims Databases for finding patients:
The Claims Database includes the use of applications such as APLD (Anonymized Patient-Level Data) and Truven Marketscan for identifying patients exhibiting characteristic symptoms correlating to the diagnosis code available in the software, with this, an undiagnosed case can also be identified. This approach can be utilized for finding orphan diseases, which are often left undiagnosed. By applying ML, early disease progression can be identified, and pharma companies can utilize this information for creating orphan drugs which are usually very expensive per patient revenue compared to the cost allotted for drug discovery. Some examples of ML-based approaches for identifying rare diseases are CART (Classification and Regression Tree) models such as C5, standard decision trees, and random forests.
c) Patient medical history and treatment pathways:
A patient medical history reveals the journey through various treatment and therapy approaches which can be further utilized to identify disease pathways and dosage regimens [52]. In this sector, machine learning is utilized as means of avoiding data clustering from scoring models by creating a time series data correlated with treatment pathways for faster processing and improved efficiency, by incorporating database tools like kdb+, and Tensorflow.
Figure 1.1 Applications of bioinformatics, AI, and ML in the pharmacy sector.
d) Enhancing Commercial market survey:
Physical methods as means of surveying physicians’ drug prescribing patterns is often a tedious task for marketing individuals in the pharma sector. These surveys are usually conducted for identifying dosage trends that can be further utilized for new drug developments [52]. ML models, such as Associative Rules Mining or “apriori”, make the laborious task easier by providing quantitative variables related to Rx records, which can then be applied for large data assessment. Figure 1.1 summarizes some of the applications of bioinformatics, AI, and ML in the pharmacy sector.
1.3.2 Limitations of ML
The major issue while using ML is obtaining accurate diagnostic values. In ML, a predefined set of algorithms are set for a particular disease but it is not generally quantitative as the human diseased state is due to innumerable complex pathways going inside. Rather than quantitative assessment in identifying a disease and designing the formulation for them, experience and expertise are needed for diagnosis and dose requirement calculation. Nevertheless, ML can be utilized for creating a huge dataset that can give fairly quantified data for further usage [52].
1.4 Conclusion and Future Prospects
It is determined that a summary of this interdisciplinary subject is formed by producing a unique precise understanding that is provided by the reaction of biology and computer science, as well as certain evaluation aspects such as statistics and mathematics, to end in the recently hatched field after this powerful reaction. Bioinformatics has a promising outlook in many biological and living fields, but one of the most critical difficulties that must be addressed is the integration of a large and diverse set of data sources and databases for the improvement of life and a massive biological change.
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Notes
*
Corresponding author
:
†
Corresponding author
:
: ORCID:
https://orcid.org/0000-0002-4037-5857
2Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling
Isha Rani1, Kavita Munjal2, Rajeev K. Singla3,4 and Rupesh K. Gautam5*
1Spurthy College of Pharmacy, Marasur Gate, Bengaluru, Karnataka, India
2MM College of Pharmacy, MM (Deemed to be) University-Mullana, Ambala, Haryana, India
3Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
4iGlobal Research and Publishing Foundation, New Delhi, India
5Department of Pharmacology, Indore Institute of Pharmacy, IIST Campus, Rau, Indore (M.P.), India
Abstract
Drug development is a time-consuming, expensive and extremely risky procedure. Up to 90% of drug concepts are discarded due to challenges such as safety, efficacy and toxicity resulting in significant losses for the investor. The use of artificial intelligence (AI), namely machine learning and deep learning algorithms, to improve the drug discovery process is one technique that has arisen in recent years. AI has been effectively used in drug discovery and design. This chapter includes these machine learning approaches in depth, as well as their applications in medicinal chemistry. The current state-of-the-art of AI supported pharmaceutical discovery is discussed, including applications in structure and ligand-based virtual screening, de novo drug design, drug repurposing, and factors related, after introducing the basic principles, along with some application notes, of the various machine learning algorithms. Finally, obstacles and limits are outlined, with an eye towards possible future avenues for AI-supported drug discovery and design.
Keywords: Artificial intelligence, drug development, drug discovery, lead optimization, molecular modelling, virtual screening, de novo drug design, drug repurposing
Abbreviations
AI
Artificial intelligence
RNA
Ribonucleic acid
R & D
Research and develoment
ML
Machine Learning
SBVS
Structure-based virtual screening
VS
Virtual screening
CADD
Computer aided drug design
PDB
Protein data bank
SAS
Synthetic accessibility score
2.1 Introduction
The development of pharmaceutical drugs is a time-consuming and costly process. Pharmaceutical and biotechnology companies often spend over $1 billion to develop a drug to the market, and can take anywhere from 10 to 20 years. This process is extremely risky with up to 90% of new drug concepts are discarded due to difficulties such as safety and efficacy, resulting in significant loss for the investor [1, 2]. Traditional drug discovery methods are target-driven, in which a known target is used to screen for small molecules that either interact with it or affect its function in cells. These approaches work well for easily druggable targets with well-defined structures and well-understood cellular interactions. However, due to the complex nature of cellular interactions and limited knowledge of intricacies, these methods are severely limited [1, 3].
The term “artificial intelligence” (AI) refers to intelligence displayed by computers. When a computer exhibits cognitive behavior similar to that of humans, such as learning or problem solving, this term is employed [4]. AI makes use of systems and software that can read and learn from data in order to make independent judgments in order to achieve certain goals. Machine learning, for example, is a well-established technology for learning and predicting novel features [5]. By finding novel relationships and inferring the functional importance of distinct components of a biological pathway, AI can overcome these obstacles. Complex algorithms and machine learning are employed by AI to extract useful information from enormous datasets. As such, a dataset of RNA sequencing can be used to discover genes whose expression correlates with a specific biological situation. AI can also be used to discover compounds that could bind to ‘undruggable targets,’ or proteins with unknown structures. A predicted collection of compounds may be easily identified in a relatively short length of time by iterative simulations of different compounds’ interactions with tiny portions of a protein [6]. Companies that are commercializing AI drug discovery platforms and AI-discovered pharmaceuticals have demonstrated that using algorithms can reduce the multi-year process down to a few months [7]. This large reduction in development time, as well as the quantity of compounds that must be manufactured for laboratory testing provides for significant cost savings, addressing two key challenges in pharmaceutical R&D. The drug development process covers mainly virtual screening, de novo drug design, lead optimization (predicting and optimizing compound properties), planning chemical synthesis, pre-clinical testing, translation to human clinical trials, as well as manufacturing processes, scale up etc. All of these procedures add to the difficulty of identifying effective drugs to treat an illness. As a result, the most important concern facing pharmaceutical businesses is how to manage the process’s cost and pace [8–10].
All of these issues need to be answered in a simple and scientific manner, reducing the time and expense of the procedure. Furthermore, the rise in data digitization in the pharmaceutical and healthcare industries stimulates the use of AI to solve the issues of analyzing complicated data [11, 12]. This chapter will comprise the role of AI in drug discovery with respect to virtual screening (VS), de novo design, synthesis planning, quality control and quality assurance etc. Various AI applications in drug design and discovery, including primary and secondary screening, drug toxicity, drug pharmacokinetics, medication dose efficacy, drug repositioning, poly-pharmacology, and drug-target interactions etc. all of which are presented herein.
