Multidimensional Pharmacochemistry: Design of Safer Drugs

Multidimensional Pharmacochemistry: Design of Safer Drugs deals with techniques based on the theory of simultaneous statistical inference and the qualitative rules that can be applied in solving problems of high toxicity. This book points out that the multidimensional view of data analysis can be applied to solve problems in medicinal chemistry. Investigators use different approaches; a certain procedure can prove to be the most beneficial for a specific drug design. This text presents the theoretical assumptions that mathematicians make to derive the basis for their multivariate techniques. This book also describes, in nonmathematical terms, a set of methods that are valuable, as well as explain the different designs by using numerical examples. According to E.J. Ariens, drug action involves the pharmaceutical, pharmacokinetic-toxokinetic, and pharmacodynamics-toxodynamic phases. The multivariate structure-activity analysis (MASCA) Model of Pharmacochemistry is a highly unified multivariate approach to drug design. To develop a multidimensionally oriented pharmacology, the book notes that the investigator can use the "dynamic structure-activity analysis." This entails the experimentalist and chemist using quantitative approaches and intuitive elements from a small number of compounds toward larger groups, with successive changes being inputted in the desired biological activity. This book is strongly recommended for toxicologists, pharmacologists, applied mathematicians, medicinal and agricultural chemists.

Table of Contents:
PrefaceIntroductory Remarks1 Some Aspects of Medicinal Chemistry Today I. The Scientific Aspect II. The Multidimensional View III. Innovation Problems in Drug Design and Public Opinion2 Biochemical-Pharmacological Design I. Ariëns's Three Phases of Drug Action II. Spanning Biosystem-Parameter Space III. Influencing the Pharmacokinetic-Toxokinetic Phase through Molecular Manipulation IV. The Pharmacodynamic-Toxodynamic Phase V. Pharmacological-Toxicological Data Banks VI. Concluding Remarks3 Underlying Theory of Multivariate Statistics I. Definition of Statistical Terms II. The Structure of a Multivariate Design III. The MASCA Approach IV. If Many Biological Responses Must Be Analyzed, Why Can Many Univariate Approaches Not Be Employed? V. Multivariate Test Criteria and Significance Points VI. Requirements and Robustness of Multivariate Approaches VII. Operator Equations VIII. Recognition and Predictability IX. Concluding Remarks4 Multivariate Bioassay I. General Remarks II. One-Group Case: Repeated Measurements III. Test on Outliers IV. One-Group Case: Probabilistic Principal Component Analysis (PRINCO) V. Two-Group Case: Fixed Factors VI. Two-Group Case: Repeated Measurements VII. One-Way Classification of Multivariate Variance Analysis (MANOVA) VIII. Two-Way Classification of MANOVA IX. Multivariate Correlation and Regression Analysis (KANORA) X. Multivariate Covariance Analysis (MACOVA) XI. Multivariate Autocorrelation (MARA) XII. Analysis of Discrete Data XIII. Concluding Remarks5 Unity through Diversity I. Goal of the Analysis II. PRINCO III. KANORA IV. MANOVA and DISCRA V. CLASCA6 Physicochemical Parameters I. Synthesis Design II. Molecular Structure Coding III. "Multidimensionality" in Describing Substituent Effects IV. Physicochemical Data Banks V. Concluding Remarks7 The Multivariate Quantitative Structure-Activity Relationship I. Introduction to Univariate QSAR II. Principal Component Regression Analysis (PCRA) III. Overall-Response (OR) Parameter Analysis IV. KANORA Applied to QSAR V. Generalized Parabolic Model (Response Surface Optimization) VI. Grouped Observations VII. Summary8 Discussions and Future Perspectives in Multidimensional PharmacochemistryReferencesIndex