A Generalized Model and High Throughput Data Analysis System for Functional Modulation of Receptor-Agonist Systems Suitable for use in Drug Discovery

Positive allosteric modulators (PAMs) of receptors represent a class of pharmacologic agents having the desirable property of acting only in the presence of cognate ligands. Discovery and optimization of the structure activity relationships of PAMs is complicated by the requirement of a second ligand to manifest their action, and by the need to quantify both affinity and intrinsic efficacy. Multivariate regression analysis is a statistical method capable of simultaneously obtaining affinity and intrinsic efficacy parameters from curve fits of multiple agonist dose-response functions generated in the presence of varying concentrations of PAMs. Capitalizing on the advantages of multivariate regression analysis for PAM optimization requires a theoretical framework and a system that facilitates efficient flow of information from data generation through data analysis, storage, and retrieval. We describe here the experimental design, mathematical model and informatics workflow enabling a multivariate regression approach for rapidly obtaining affinity and intrinsic efficacy values for PAMs in a drug discovery setting.