Quantitative Molecular Pharmacology and Informatics in Drug DiscoveryQuantitative 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. |
Contents
Drug Discovery | 1 |
Measurement of Drug Affinity | 33 |
Efficacy | 63 |
Pharmacological Assays used in Screening for Therapeutic | 97 |
Finding the optimal assay format for the chemical target | 135 |
Mathematical and Statistical Framework for Problems in | 171 |
47 | 189 |
Statistical Methods for Target Identification and Validation | 255 |
Experimental Design | 299 |
Analysis and Interpretation of Data | 357 |
| 401 | |
| 407 | |
Common terms and phrases
Abscissa affinity alleles allosteric allosteric antagonist allosteric ligand analysis of variance ANOVA antagonism binding assay biochemical biological calcitonin receptor calculated cells Chem chemical combinatorial library competitive antagonist compounds concentration conformations constitutive receptor constitutively active correlation cyclic AMP dataset descriptors detect disease distribution dose-response curve drug discovery effect efficacy endogenous equation equilibrium error estimate example experimental design factors functional assay G-protein genetic algorithm genotype Glaxo Wellcome high-throughput screening histogram human genome identify increase interaction inverse agonists isoproterenol ligand linear linkage analysis marker maximal response mean measure methods molecular molecules monomers neural networks non-linear observed optimization outliers parameters Pharmacol phenotype plot population potency predictors prenalterol produce protein radioligand random ratio receptor activity receptor occupancy receptor systems recombination regression relationship replicates residuals sample selection sequence shows signal significant specific standard deviation statistical studies Table target ternary complex tissue transfected values variability