Abstract
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
Combinatorial Chemistry & High Throughput Screening
Title:A Generalized Model and High Throughput Data Analysis System for Functional Modulation of Receptor-Agonist Systems Suitable for use in Drug Discovery
Volume: 16 Issue: 3
Author(s): Aaron C. Pawlyk*, R. Kyle Palmer, Dennis Sprous and Chip Allee
Affiliation:
- ,0
Keywords: Allosteric modulators, dose response, lead optimization, membrane potential, multivariate regression, partial agonist, surrogate agonist, TRPM5 ion channels
Abstract: 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.
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Cite this article as:
C. Pawlyk Aaron*, Kyle Palmer R., Sprous Dennis and Allee Chip, A Generalized Model and High Throughput Data Analysis System for Functional Modulation of Receptor-Agonist Systems Suitable for use in Drug Discovery, Combinatorial Chemistry & High Throughput Screening 2013; 16 (3) . https://dx.doi.org/10.2174/1386207311316030005
DOI https://dx.doi.org/10.2174/1386207311316030005 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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