Real World Drug Discovery

Аннотация

Drug discovery increasingly requires a common understanding by researchers of the many and diverse factors that go into the making of new medicines. The scientist entering the field will immediately face important issues for which his education may not have prepared him: project teams, patent law, consultants, target product profiles, industry trends, Gantt charts, target validation, pharmacokinetics, proteomics, phenotype assays, biomarkers, and many other unfamiliar topics for which a basic understanding must somehow be obtained. Even the more experienced scientist can find it frustratingly difficult to get an overview of the many factors involved in modern drug discovery and often only after years of exploring does a whole and integrated picture emerge in the mind of the researcher. Real World Drug Discovery: A Chemist's Guide to Biotech and Pharmaceutical Research presents this kind of map of the landscape of drug discovery. In a single, readable volume it outlines processes and explains essential concepts and terms for the recent science graduate wondering what to expect in pharma or biotech, the medicinal chemist seeking a broader and more timely understanding of the industry, or the contractor or collaborator whose understanding of the commercial drug discovery process could increase the value of his contribution to it.Key Features: - Interviews with well-known experts in many of the fields involved, giving insightful comments from authorities on many of the sub-disciplines important to cutting edge drug discovery. - Helpful suggestions gleaned from years of experience in biotech and pharma, which represents a repository drug discovery &quote;lore&quote; not previously available in any book. - &quote;Periodic Table of Drugs&quote; listing current top-selling drugs arranged by target and laid out so that structural similarities and differences are plain and clear, with regular updates available at the book's website. - Extensive use of diagrams to illustrate concepts like biotech startup models, preteomic profiling for target identification, Gantt charts for project planning, etc.