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

Author(s): Aaron C. Pawlyk*, R. Kyle Palmer, Dennis Sprous, Chip Allee

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 16 , Issue 3 , 2013

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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.

Keywords: Allosteric modulators, dose response, lead optimization, membrane potential, multivariate regression, partial agonist, surrogate agonist, TRPM5 ion channels

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Article Details

Year: 2013
Published on: 06 February, 2013
Page: [199 - 209]
Pages: 11
DOI: 10.2174/1386207311316030005

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