RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  • Jed Zaretzki
  • Charles Bergeron
  • Patrik Rydberg
  • Tao-wei Huang
  • Kristin P Bennett
  • Curt M Breneman
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.
TidsskriftJournal of Chemical Information and Modeling
Udgave nummer7
Sider (fra-til)1667-1689
StatusUdgivet - 25 jul. 2011

ID: 35458079