Torborinone

A majority of xenobiotics are metabolized by cytochrome P450 (CYP) enzymes. The discovery of drug candidates with low propensity to form reactive metabolites and low clearance can be facilitated by understanding CYP-mediated xenobiotic metabolism. Being able to predict the sites where reactive metabolites form is beneficial in drug design to produce drug candidates free of reactive metabolite issues. Herein, we report a pragmatic protocol using first-principle density functional theory (DFT) calculations for predicting sites of epoxidation and hydroxylation of aromatic substrates mediated by CYP. The method is based on the relative stabilities of the CYP-substrate intermediates or the substrate epoxides. Consequently, it concerns mainly the electronic reactivity of the substrates. Comparing to the experimental findings, the presented protocol gave excellent first-ranked epoxidation site predictions of 83%, and when the test was extended to CYP-mediated sites of aromatic hydroxylation, satisfactory results were also obtained (73%). This indicates that our assumptions are valid and also implies that the intrinsic reactivities of the substrates are in general more important than their binding poses in proteins, although the protocol may benefit from the addition of docking information.

In recent years, considerable effort has been invested in the development of classification models for prospective hERG inhibitors, due to the implications of hERG blockade for cardiotoxicity and the low throughput of functional hERG assays. We present novel approaches for binary classification which seek to separate strong inhibitors (IC50<1 µM) from ‘non‐blockers′ exhibiting moderate (1–10 µM) or weak (IC50≥10 µM) inhibition, as required by the pharmaceutical industry. Our approaches are based on (discretized) 2D descriptors, selected using Winnow, with additional models generated using Random Forest (RF) and Support Vector Machines (SVMs). We compare our models to those previously developed by Thai and Ecker and by Dubus et al. The purpose of this paper is twofold: 1. To propose that our approaches (with Matthews Correlation Coefficients from 0.40 to 0.87 on truly external test sets, when extrapolation beyond the applicability domain was not evident and sufficient quantities of data were available for training) are competitive with those currently proposed in the literature. 2. To highlight key issues associated with building and assessing truly predictive models, in particular the considerable variation in model performance when training and testing on different datasets.