An Automated High-Throughput Metabolic Stability Assay Using an Integrated High-Resolution Accurate Mass Method and Automated Data Analysis Software

• DOI: 10.1124/dmd.116.072017
• Publication Date: 2016-07-15T01:53:54Z
• Authors: Pranav Shah; Edward Kerns; Dac-Trung Nguyen; R. Scott Obach; Amy Q Wang; Alexey Zakharov; John McKew; Anton Simeonov; Cornelis E. C. A Hop; Xin Xu
• Keywords: 0301 basic medicine; Automation; 03 medical and health sciences; Cytochrome P-450 Enzyme System; Humans; Mass Spectrometry; Software; Chromatography, Liquid; Half-Life

Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (P450) isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets. The substrate depletion method [in vitro half-life (t1/2) method] was chosen to determine CLint The assay (384-well format) consisted of three parts: 1) a robotic system for incubation and sample cleanup; 2) two different integrated, ultraperformance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and 3) an automated data analysis system. The CYP3A4 assay was evaluated using two long t1/2 compounds, carbamazepine and antipyrine (t1/2 > 30 minutes); one moderate t1/2 compound, ketoconazole (10 < t1/2 < 30 minutes); and two short t1/2 compounds, loperamide and buspirone (t½ < 10 minutes). Interday and intraday precision and accuracy of the assay were within acceptable range (∼12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human P450 isozymes.