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Bioinformatics Tools for Pharmaceutical Drug Product Development

E-BookEPUB2 - DRM Adobe / EPUBE-Book
448 Seiten
Englisch
John Wiley & Sonserschienen am09.02.20231. Auflage
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.


Vivek Chavda, M. Pharm, is an assistant professor in the Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, India. He has more than 40 research articles in international journals.
Krishnan Anand, PhD, is a research scientist in the Department of Chemical Pathology, University of the Free State, Bloemfontein, South Africa. He has more than 40 research articles in international journals and his research interests are in organic chemistry, medicinal chemistry, chemical pathology, bioinformatics, and nanotechnology.
Vasso Apostolopoulos, PhD, is at the Institute for Health and Sport, Immunology and Translational Research Group, Victoria University, Melbourne, Australia. She received her PhD in immunology in 1995 from the University of Melbourne, and the Advanced Certificate in Protein Crystallography from Birkbeck College, University of London. Professor Vasso Apostolopoulos is a world-renowned researcher who has been recognized with over 100 awards for the outstanding results of her research and she was named one of the most successful Greeks abroad by the prestigious Times magazine. Vasso was the first in the world to develop the concept of immunotherapy for cancer in the early 1990s, which today is used by hundreds of labs around the world. Immunotherapy aims to boost specific immune cells and program them to kill cancer cells; it was used by Vasso to develop the world's first breast cancer vaccine with phase I, II, and III clinical trials completed. Of note, one of the studies now has long-term follow-up data showing that 20 years later those injected with the vaccine remain cancer free.mehr
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Produkt

KlappentextBIOINFORMATICS 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.


Vivek Chavda, M. Pharm, is an assistant professor in the Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, India. He has more than 40 research articles in international journals.
Krishnan Anand, PhD, is a research scientist in the Department of Chemical Pathology, University of the Free State, Bloemfontein, South Africa. He has more than 40 research articles in international journals and his research interests are in organic chemistry, medicinal chemistry, chemical pathology, bioinformatics, and nanotechnology.
Vasso Apostolopoulos, PhD, is at the Institute for Health and Sport, Immunology and Translational Research Group, Victoria University, Melbourne, Australia. She received her PhD in immunology in 1995 from the University of Melbourne, and the Advanced Certificate in Protein Crystallography from Birkbeck College, University of London. Professor Vasso Apostolopoulos is a world-renowned researcher who has been recognized with over 100 awards for the outstanding results of her research and she was named one of the most successful Greeks abroad by the prestigious Times magazine. Vasso was the first in the world to develop the concept of immunotherapy for cancer in the early 1990s, which today is used by hundreds of labs around the world. Immunotherapy aims to boost specific immune cells and program them to kill cancer cells; it was used by Vasso to develop the world's first breast cancer vaccine with phase I, II, and III clinical trials completed. Of note, one of the studies now has long-term follow-up data showing that 20 years later those injected with the vaccine remain cancer free.
Details
Weitere ISBN/GTIN9781119865704
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format Hinweis2 - DRM Adobe / EPUB
FormatFormat mit automatischem Seitenumbruch (reflowable)
Erscheinungsjahr2023
Erscheinungsdatum09.02.2023
Auflage1. Auflage
Seiten448 Seiten
SpracheEnglisch
Dateigrösse15922 Kbytes
Artikel-Nr.11059378
Rubriken
Genre9201

Inhalt/Kritik

Leseprobe

1
Introduction to Bioinformatics, AI, and ML for Pharmaceuticals

Vivek P. Chavda1*, Disha Vihol2, Aayushi Patel3, Elrashdy M. Redwan4,5 and Vladimir N. Uversky6â 

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...mehr

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