Quantitative Molecular Pharmacology And Informatics In Drug Discovery

Quantitative Molecular Pharmacology and Informatics in Drug Discovery Michael Lutz, Section Head, Cheminformatics Group and Terry Kenakin, Principal Research Scientist, Glaxo Wellcome Research and Development, Research Triangle Park, NC, USA Quantitative Molecular Pharmacology and Informatics in Drug Discovery combines pharmacology, genetics and statistics to provide a complete guide to the modern drug discovery process. The book discusses the pharmacology of drug testing and provides a detailed description of the statistical methods used to analyze the resulting data. Application of genetic and genomic tools for identification of biological targets is reviewed in the context of drug discovery projects. Covering both the theoretical principles upon which the techniques are based and the practicalities of drug discovery, this informative guide. * outlines in step-by-step detail the advantages and disadvantages of each technology and approach and links these to the type of chemical target being sought after in the drug discovery process; and, * provides excellent demonstrations of how to use powerful pharmacological and statistical tools to optimize high-throughput screening assays. Written by two internationally known and well-regarded experts, this book is an essential reference for research and development scientists working in the pharmaceutical and biotechnology industries. It will also be useful for postgraduates studying pharmacology and applied statistics.

This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process. With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools -- from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition. An invaluable resource for drug developers and medicinal chemists in academia and industry.

Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. Provides practical information on how to choose and use appropriate computational tools Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics

In the literature, several terms are used synonymously to name the topic of this book: chem-, chemi-, or chemo-informatics. A widely recognized de- nition of this discipline is the one by Frank Brown from 1998 (1) who defined chemoinformatics as the combination of “all the information resources that a scientist needs to optimize the properties of a ligand to become a drug. ” In Brown’s definition, two aspects play a fundamentally important role: de- sion support by computational means and drug discovery, which distinguishes it from the term “chemical informatics” that was introduced at least ten years earlier and described as the application of information technology to ch- istry (not with a specific focus on drug discovery). In addition, there is of course “chemometrics,” which is generally understood as the application of statistical methods to chemical data and the derivation of relevant statistical models and descriptors (2). The pharmaceutical focus of many developments and efforts in this area—and the current popularity of gene-to-drug or si- lar paradigms—is further reflected by the recent introduction of such terms as “discovery informatics” (3), which takes into account that gaining kno- edge from chemical data alone is not sufficient to be ultimately successful in drug discovery. Such insights are well in accord with other views that the boundaries between bio- and chemoinformatics are fluid and that these d- ciplines should be closely combined or merged to significantly impact b- technology or pharmaceutical research (4).

This textbook provides a fresh, comprehensive and accessible introduction to the rapidly expanding field of molecular pharmacology. Adopting a drug target-based, rather than the traditional organ/system based, approach this innovative guide reflects the current advances and research trend towards molecular based drug design, derived from a detailed understanding of chemical responses in the body. Drugs are then tailored to fit a treatment profile, rather than the traditional method of ‘trial and error’ drug discovery which focuses on testing chemicals on animals or cell cultures and matching their effects to treatments. Providing an invaluable resource for advanced under-graduate and MSc/PhD students, new researchers to the field and practitioners for continuing professional development, Molecular Pharmacology explores; recent advances and developments in the four major human drug target families (G-protein coupled receptors, ion channels, nuclear receptors and transporters), cloning of drug targets, transgenic animal technology, gene therapy, pharmacogenomics and looks at the role of calcium in the cell. Current - focuses on cutting edge techniques and approaches, including new methods to quantify biological activities in different systems and ways to interpret and understand pharmacological data. Cutting Edge - highlights advances in pharmacogenomics and explores how an individual’s genetic makeup influences their response to therapeutic drugs and the potential for harmful side effects. Applied - includes numerous, real-world examples and a detailed case-study based chapter which looks at current and possible future treatment strategies for cystic fibrosis. This case study considers the relative merits of both drug therapy for specific classes of mutation and gene therapy to correct the underlying defect. Accessible - contains a comprehensive glossary, suggestions for further reading at the end of each chapter and an associated website that provides a complete set of figures from within the book.

This book brings together drug design practitioners, all leaders in their field, who are actively advancing the field of quantitative methods to guide drug discovery, from structure-based design to empirical statistical models - from rule-based approaches to toxicology to the fields of bioinformatics and systems biology. The aim of the book is to show how various facets of the drug discovery process can be addressed in a quantitative fashion (ie: numerical analysis to enable robust predictions to be made). Each chapter includes a brief review of the topic showing the historical development of.

Quantitative structure-activity relationships (QSARs) represent predictive models derived from the application of statistical tools correlating biological activity or other properties of chemicals with descriptors representative of molecular structure and/or property. Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment discusses recent advancements in the field of QSARs with special reference to their application in drug development, predictive toxicology, and chemical risk analysis. Focusing on emerging research in the field, this book is an ideal reference source for industry professionals, students, and academicians in the fields of medicinal chemistry and toxicology.

With more restrictions upon animal experimentations, pharmaceutical industries are currently focusing on a new generation of experiments and technologies that are considerably more efficient and less controversial. The integration of computational and experimental strategies has led to the identification and development of promising compounds. Computer Applications in Drug Discovery and Development is a pivotal reference source that provides innovative research on the application of computers for discovering and designing new drugs in modern molecular biology and medicinal chemistry. While highlighting topics such as chemical structure databases and dataset utilization, this publication delves into the current panorama of drug discovery, where high drug failure rates are a major concern and properly designed virtual screening strategies can be a time-saving, cost-effective, and productive alternative. This book is ideally designed for chemical engineers, pharmacists, molecular biologists, students, researchers, and academicians seeking current research on the unexplored avenues and future perspectives of drug design.