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Matrix-Based Activity Pattern Classification as a Novel Method for the Characterization of Enzyme Inhibitors Derived from High-Throughput Screening

Auld, Douglas and Busby, Scott and Chen, Kiki and Jimenez, Marta and Yue, Qing and Bowes, Scott and Wendel, Gregory and Smith, Thomas and Zhang, Ji (2016) Matrix-Based Activity Pattern Classification as a Novel Method for the Characterization of Enzyme Inhibitors Derived from High-Throughput Screening. Journal of Biomolecular Screening, 21 (10). pp. 1075-1089. ISSN 1552-454X

Abstract

One of the central questions in the characterization of enzyme inhibitors is determining the mode of inhibition (MOI). Classically, this is done with a number of low-throughput methods in which inhibition models are fitted to the data. The ability to rapidly characterize the MOI for inhibitors arising from high-throughput screening in which hundreds to thousands of primary inhibitors may need to be characterized would greatly help in lead selection efforts. Here we describe a novel method for determining the MOI of a compound without the need for curve fitting of the enzyme inhibition data. We provide experimental data to demonstrate the utility of this new high-throughput MOI classification method based on nonparametric analysis of the activity derived from a small matrix of substrate and inhibitor concentrations (e.g., from a 4S × 4I matrix). Lists of inhibitors from four different enzyme assays are studied, and the results are compared with the previously described IC50-shift method for MOI classification. The MOI results from this method are in good agreement with the known MOI and compare favorably with those from the IC50-shift method. In addition, we discuss some advantages and limitations of the method and provide recommendations for utilization of this MOI classification method.

Item Type: Article
Keywords: enzyme assays enzyme inhibitors inhibitor classification mode of inhibition
Date Deposited: 25 Oct 2017 00:45
Last Modified: 25 Jan 2019 00:45
URI: https://oak.novartis.com/id/eprint/28798

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