RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4
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RS-predictor : a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4. / Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M.
I: Journal of Chemical Information and Modeling, Bind 51, Nr. 7, 25.07.2011, s. 1667-1689.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - RS-predictor
T2 - a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4
AU - Zaretzki, Jed
AU - Bergeron, Charles
AU - Rydberg, Patrik
AU - Huang, Tao-wei
AU - Bennett, Kristin P
AU - Breneman, Curt M
PY - 2011/7/25
Y1 - 2011/7/25
N2 - 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.
AB - 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.
KW - Former Faculty of Pharmaceutical Sciences
U2 - 10.1021/ci2000488
DO - 10.1021/ci2000488
M3 - Journal article
C2 - 21528931
VL - 51
SP - 1667
EP - 1689
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
SN - 1549-9596
IS - 7
ER -
ID: 35458079