Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models

Abstract

In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC(50) is at least one root mean square error (RMSE) from the high/low pIC(50) cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.

Activity-Limiting Role of Molecular Size: Size-Dependency of Maximum Activity for P450 Inhibition as Revealed by qHTS Data

“…P450s were already the subject of many QSAR studies (Ekins et al, 1999;Lewis et al, 2001;Riley et al, 2001;Wanchana et al, 2003;Hansch et al, 2004;Mao et al, 2006;Gleeson et al, 2007;Yamashita et al, 2008Yamashita et al, , 2011 as well as inhibitor/noninhibitor classification studies (Jensen et al, 2007;Choi et al, 2009;Cheng et al, 2011). For large ADME/Tox characterization panels, it has been repeatedly found that molecular size plays a major, defining role.…”

Computer-based substrate specifity prediction for cytochrome P450

“…В последние годы появились работы, в которых авторы для предсказания специфичности цитохромов используют нелинейные модели [35][36][37][38]. Так, используя метод опорных векторов (PM-CSVM и PP-CSVM), была разработана система для предсказания субстратов цитохромов 3A4, 2D6 и 2C9 [36].…”

“…QSAR модели часто позволяют предсказывать величины связывания, но они не всегда хорошо работают для разделения соединений на активные и неактивные. Поэтому в последнии годы в этом направлении наметилась тенденция использования для анализа и предсказания сложных статистических методов в надежде, что нелинейные модели могут лучше проводить данную селекцию [35][36][37][38]. Метод молекулярного докинга опирается только на знание структуры мишени, для его работы не требуются знания об известных лигандах для этого белка.…”

Time-Dependent Inhibition of CYP2C19 by Isoquinoline Alkaloids: In Vitro and In Silico Analysis

“…Although several mechanism-based inactivators have been identified for P450s, few attempts have been made to evaluate these compounds by exploiting molecular modeling (in silico) approaches. Several quantitative structure-activity relationship (QSAR) models have been published for CYP2C19 ligands (Suzuki et al, 2002(Suzuki et al, , 2004Lewis, 2004;Lewis et al, 2006;Gleeson et al, 2007;Foti et al, 2012), but thus far, none has specifically addressed mechanism-based inactivators.…”