Why do life-saving prescription drugs cost so much? Drug companies insist that prices reflect the millions they invest in research and development. In this gripping expose, Merrill Goozner contends that American taxpayers are in fact footing the bill twice: once by supporting government-funded research and again by paying astronomically high prices for prescription drugs. Goozner demonstrates that almost all the important new drugs of the past quarter-century actually originated from research at taxpayer-funded universities and at the National Institutes of Health. He reports that once the innovative work is over, the pharmaceutical industry often steps in to reap the profit. Goozner shows how drug innovation is driven by dedicated scientists intent on finding cures for diseases, not by pharmaceutical firms whose bottom line often takes precedence over the advance of medicine. A university biochemist who spent twenty years searching for a single blood protein that later became the best-selling biotech drug in the world, a government employee who discovered the causes for dozens of crippling genetic disorders, and the Department of Energy-funded research that made the Human Genome Project possible--these engrossing accounts illustrate how medical breakthroughs actually take place. The $800 Million Pill suggests ways that the government's role in testing new medicines could be expanded to eliminate the private sector waste driving up the cost of existing drugs. Pharmaceutical firms should be compelled to refocus their human and financial resources on true medical innovation, Goozner insists. This book is essential reading for everyone concerned about the politically charged topics of drug pricing, Medicare coverage, national health care, and the role of pharmaceutical companies in developing countries. (source: Nielsen Book Data)
Part 1 3D-QSAR - the integration of QSAR with molecular modelling: chemometrics and molecular modelling--3D QSAR methods--GOLPE - philosophy and applications in 3D QSAR.
Part 2 Rational use of chemical and sequence databases: molecular similarity analysis - applications in drug discovery-- clustering of chemical structure databases for compound selection-- receptor mapping and phylogenetic clustering.
Part 3 Advanced statistical techniques: continuum regression - a new algorithm for the prediciton of biological activity-- molecullar taxonomy by correspondence factorial analysis (CFA)-- analysis of embedded data - k-nearest neighbour and single class distinction-- quantitative analysis of structure-activity-class relationships by (Fuzzy) adaptive least squares-- alternating conditional expectations in QSAR.
Part 4 Neural networks and expert systems in molecular design: neural networks - a tool for drug design-- rule induction applied to the derivation of quantitative structure-activity relationships.
(source: Nielsen Book Data)
Drug discovery is extremely expensive and time consuming. This handbook outlines computational methods of drug discovery and design, showing how they are applied and the pitfalls to avoid. Practical examples are used to illustrate the scope and limitations of each method with an emphasis on making the industrial process more efficient in terms of cost and time. (source: Nielsen Book Data)