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Principal Component Analysis of CYP 2 C 9 and CYP 3 A 4 Probe Substrate / Inhibitor Panels

Cytochrome P450 (P450) inhibition often occurs in a strongly substrateand inhibitor-dependent manner, with a given inhibitor affecting the metabolism of different substrates to differing degrees and with a given substrate responding differently to different inhibitors. Traditionally, patterns of functional similarity and dissimilarity among substrates and inhibitors have been studied using clustering analysis of pair-wise correlation coefficients. Principal component analysis (PCA) is a widely used statistical technique that identifies the globally most significant independent trends in a set of data. Here, we show that PCA can be usefully applied to study the differential effects on a panel of P450 probe substrates by a panel of inhibitors, using published data on CYP3A4 (Kenworthy et al., 1999) and CYP2C9 (Kumar et al., 2006). PCA can detect functional similarities among substrates and inhibitors that are not readily apparent using pair-wise clustering analysis. PCA also allows identification of the functionally typical and atypical substrates that might be used in combination to fully explore the P450 functional landscape. The choice of optimal probe substrates for in vitro inhibition studies of cytochromes P450 (P450s), especially relevant to pharmacokinetic predictions and in vivo-in vitro correlation, is complicated by the diverse nature of substrate-inhibitor interactions for some of these enzymes (Kenworthy et al., 1999; Kumar et al., 2006; Foti and Wahlstrom, 2008). A single probe substrate can respond differently to various inhibitors; a single inhibitor can have different effects on a panel of probe substrates. The differential behavior of substrates and inhibitors with drug-metabolizing P450s is presumably due to their promiscuity and catalytic allosterism (Guengerich, 2001; Atkins, 2006; Nath and Atkins, 2008). Two recent studies provide valuable insight into the varied nature of substrate-inhibitor interactions and into the patterns of similarity among substrates and inhibitors. Houston and coworkers (Kenworthy et al., 1999) studied the effects of 34 different inhibitors on the metabolism of 10 probe substrates by CYP3A4. Subsequently, Tracy and coworkers (Kumar et al., 2006) studied how 21 different inhibitors affected the metabolism of five probe substrates by the CYP2C9 variants *1 and *3. Both groups used hierarchical clustering to analyze relative similarity among their respective panels of substrates and inhibitors; here, we show how principal component analysis (PCA), applied to the data sets of both groups, can serve as a powerful global alternative to clustering analysis. This application of PCA is similar to its previous use in functionally characterizing CYP102 variants generated by directed evolution (Bloom et al., 2007). PCA (Wall et al., 2003) is a mathematical technique to extract the most significant trends in a set of observations. PCA relies on the assumption that the most significant trends [called “principal components” (PCs)] are those that show the greatest covariance between different observables over multiple observations. For a data set with n observables, PCs can be thought of as vectors in n-dimensional space, and each observation can be transformed from the original observable space to PC space, where its coordinates (scores) reflect how much each component contributes to the observation. PCs are ranked in order of variance (i.e., significance); because there are typically more observables than significant PCs, PCA serves as a multidimensional scaling technique. The locations of different observations in component space provide a quick and global comparison of relative similarity and dissimilarity. Our intent is to demonstrate how PCA can be used to empirically analyze functional patterns among P450 substrates and inhibitors. We should emphasize that a compound’s score in a particular PC does not necessarily correlate with any one of its functional, physical, or chemical characteristics. More advanced statistical techniques, such as common factor analysis or partial least-squares regression, can be used to explain how specific characteristics of compounds contribute to their scores, but such analysis is beyond the scope of this paper. Materials and Methods CYP3A4 data from Kenworthy et al. (1999) consist of relative (percentage) inhibition of 12 probe reactions by single concentrations of 34 different inhibitors and are presented in Table 1. CYP2C9 data from Kumar et al. (2006) consist of Ki values measured for 21 inhibitors using five probe substrates, for allelic variants CYP2C9*1 and CYP2C9*3, presented in Table 2, respectively. To study the functional similarity of substrates, data were entered into a matrix, with each row representing a probe substrate and each column representing an inhibitor. PCA was performed using a script, available upon request, in the Python programming language with the SciPy module (http://www.scipy.org). In brief, columns were mean-centered, and compact singular value decompoThis work was supported by the National Institutes of Health Grant GM-32165. Article, publication date, and citation information can be found at http://dmd.aspetjournals.org. doi:10.1124/dmd.108.022061. ABBREVIATIONS: P450, cytochrome P450; PCA, principal component analysis; PC, principal component; TFA, terfenadine (C-hydroxylation); TFZ, terfenadine (N-demethylation); TS, testosterone; CY, cyclosporin; ER, erythromycin; DZ, diazepam; DX, dextromethorphan; NF, nifedipine; EROD, ethoxyresorufin; BROD, benzyloxyresorufin; MZ, midazolam; TZ, triazolam. 0090-9556/08/3611-2151–2155$20.00 DRUG METABOLISM AND DISPOSITION Vol. 36, No. 11 Copyright © 2008 by The American Society for Pharmacology and Experimental Therapeutics 22061/3377611 DMD 36:2151–2155, 2008 Printed in U.S.A. 2151 at A PE T Jornals on N ovem er 7, 2016 dm d.aspurnals.org D ow nladed from