This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

01 Jul 2007·Journal of Medicinal ChemistryQ1 · MEDICINE

Empirical Regioselectivity Models for Human Cytochromes P450 3A4, 2D6, and 2C9

Q1 · MEDICINE

Article

Author: Korzekwa, Kenneth R. ; Torres, Rhonda A. ; Sheridan, Robert P. ; Walker, Matthew J.

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR method show that other factors than the immediate chemical environment and the accessibility of the hydrogen affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330-1336) and predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970-6979).

01 Oct 2006·Journal of Medicinal ChemistryQ1 · MEDICINE

Recursive Partitioning for the Prediction of Cytochromes P450 2D6 and 1A2 Inhibition: Importance of the Quality of the Dataset

Q1 · MEDICINE

Article

Author: Vercauteren, Daniel P. ; Barberan, Olivier ; Ijjaali, Ismail ; Michel, André ; Petitet, François ; Burton, Julien

The purpose of this study was to explore the use of detailed biological data in combination with a statistical learning method for predicting the CYP1A2 and CYP2D6 inhibition. Data were extracted from the Aureus-Pharma highly structured databases which contain precise measures and detailed experimental protocol concerning the inhibition of the two cytochromes. The methodology used was Recursive Partitioning, an easy and quick method to implement. The building of models was preceded by the evaluation of the chemical space covered by the datasets. The descriptors used are available in the MOE software suite. The models reached at least 80% of Accuracy and often exceeded this percentage for the Sensitivity (Recall), Specificity, and Precision parameters. CYP2D6 datasets provided 11 models with Accuracy over 80%, while CYP1A2 datasets counted 5 high-accuracy models. Our models can be useful to predict the ADME properties during the drug discovery process and are indicated for high-throughput screening.

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