P46 Comparative analyses of in vitro biotransformation in human liver microsomes and MetaSite based in silico predictions for 70 new chemical entities

Mithat Gunduz , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Jennifer L. Bushee , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Upendra A. Argikar , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Giuliano Berellini , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Kevin Colizza , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Hong Jiang , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Michelle Dennehy , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Amanda Cirello , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Nigel Waters , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Amin Kamel , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Franco Lombardo , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
Shawn Harriman , Metabolism and Pharmacokinetics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA
MetaSite, a software program developed by Molecular Discovery Ltd., that predicts CYP-mediated sites of metabolism based on the CYP enzyme’s active site, and orientation of a substrate via GRID molecular interaction field descriptors. It further accounts for accessibility and reactivity of a given CYP and a given substrate respectively, to determine each atom’s probability of metabolism1. MetaSite is routinely employed in drug discovery to understand metabolic liabilities of new chemical entities2. The objective of the present investigation was (i) to compare in vitro human liver microsomal metabolite identification results with in silico soft-spot predictions by Metasite and (ii) to understand the molecular and ADME properties leading to high predictive accuracy as well as those drivers implicated in low predictive accuracy. To this effect, human liver microsomal biotransformation studies were carried out for 70 new chemical entities. Top three metabolic soft spots were computed by Metasite, for liver as a metabolic model, with reactivity correction enabled. Comparison between in silico and in vitro results revealed approximately 70% accuracy of prediction by Metasite for new chemical entities. A weighted average of MetaSite’s top three sites of metabolism was calculated from the Metasite score and was compared with human microsomal Clint. Preliminary observations indicate a tendency that the prediction of MetaSite is better for new chemical entities with high microsomal extraction ratios i.e. higher Clint (>300 uL/min/mg of microsomal protein). Similarly a general tendency toward increase in confidence of prediction by Metasite for the top three metabolic soft spots was seen with increase in the number of observed metabolites in human liver microsomal incubations. 1. Cruciani, G.; Carosati, E.; De Boeck, B. et al. (2005) J. Med. Chem. 48, 6970-6979. 2. Trunzer, M.; Faller, B.; and Zimmerlin, A. (2009) J. Med. Chem. 52:329-335.