Jed Zaretzki, Charles Bergeron, Tao-wei Huang, Kristin Bennett, and Curt M. Breneman*

1. Mechanism Legend Csp3 Hydroxylation O-Dealkylation Csp2 Hydroxylation N-Dealkylation Aromatic Ring N-Oxide Formation Non-Aromatic Ring SII Oxidation Aldehyde Oxidation SIV Oxidation Alcohol Oxidation S with 2 Oxygens Phosphorous Experimentally determined site of metabolism RS-Predictor Metabolic Site Predictions Molecular Structures Analyze results from a mechanistic standpoint Prediction Legend Calculate Descriptors Predicted First Predicted Second Predicted Third Incorrectly Predicted Analyze Models Calculate MIRank Machine Learning Models 533 descriptors/atom: Descriptors breakdown Includes: 66 Charge related 140 Valence related 33 Mono-centric energy related 140 Bi-centric energy related 19 Topological 127 Physical-environment 8 Other MIRank is a combination of SVM ranking and multiple instance algorithms which allow the characteristics of each metabolic site to be considered separately, but where the trends represented in each molecule can be combined in an intelligent manner to produce a single global ranking model of metabolic site selectivity Signatures of specific oxidative mechanisms are identified during model development Metabolic outcomes can be predicted with good accuracy using RS-Predictor Comparison of the models derived from different CYP isozymes reveal the relative electronic and shape components of their specificity Modeling the regioselectivity of cytochrome P450-mediated metabolism:  Development of an effective in silico solution – “RS-Predictor”  Jed Zaretzki, Charles Bergeron, Tao-wei Huang, Kristin Bennett, and Curt M. Breneman* Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180 e-mail: brenec@rpi.edu Mechanistic Analysis Introduction Results A small number of cytochrome P450 isozymes are responsible for metabolizing 90% of all marketed drugs through a variety of oxidative mechanisms. CYP isozymes are capable of catalyzing many different transformations, including aromatic and aliphatic oxidation, N-,O-,and S-dealkylation, N-oxidation, sufloxide/sulfone formation, oxidative deamination, desulfuration, and dehalogenation, among others. Useful and accurate computational methods that can predict both the mechanism and likely sites of metabolism are becoming essential for efficient early drug discovery efforts. To address this need, we present RS-Predictor, a method that specifically addresses the unique machine learning and reactivity descriptor challenges that must be met in order to provide reliable site-specific prediction of the products of oxidative metabolism.    • Results are expressed as a percentage accuracy of correctly predicted molecules. • A molecule is correctly predicted if the experimental sight of metabolism is ranked first or second 3A4 Potential Sites Mechanism Breakdown - 4147 Sites Descriptors Goal: To rank potential sites and mechanisms of metabolism of drug-like molecules by specific CYP450 isozymes. 3A4 Mechanism Breakdown - 376 Sites A molecule of Lidocaine: 7 potential sites of metabolism (metabolophores), 3 mechanism types (Csp3 Hydroxylation, N – Hydroxylation, Aromatic Hydroxylation) RS-Predictor Flowchart Summary • RS-Predictor incorporates customized descriptors and exploits a novel machine learning framework to make predictions on a difficult problem with limited experimental data • Current results meet or exceed previously published results. Other methods are proprietary or commercial while RS-Predictor is publically available. • RS-Predictor is completely automated and will soon be available online for isozyme-specific predictions on entire databases. Runtime is currently 2 minutes per molecule per Linux processor. References • R.P. Sheridan, K.R. Korzekwa, R.A. Torres, and M.J. Walker, “Empirical Regioselectivity Models for Human Cytochromes P450 3A4, 2D6, and 2C9,” Journal of Medicinal Chemistry, vol. 50, Jul. 2007, pp. 3173-3184. • C. Bergeron, J. Zaretzki, C. Breneman, and K.P. Bennett, “Multiple instance ranking,” Proceedings of the 25th international conference on Machine learning, Helsinki, Finland: ACM, 2008, pp. 48-55. • G. Cruciani, E. Carosati, B. De Boeck, K. Ethirajulu, C. Mackie, T. Howe, and R. Vianello, “MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist,” Journal of Medicinal Chemistry, vol. 48, Nov. 2005, pp. 6970-6979.   • D. Korolev, K.V. Balakin, Y. Nikolsky, E. Kirillov, Y.A. Ivanenkov, N.P. Savchuk, A.A. Ivashchenko, and T. Nikolskaya, “Modeling of Human Cytochrome P450-Mediated Drug Metabolism Using Unsupervised Machine Learning Approach,” Journal of Medicinal Chemistry, vol. 46, 2003, pp. 3631-3643. • S. Rendic and F.J. Di Carlo, “Human cytochrome P450 enzymes: a status report summarizing their reactions, substrates, inducers, and inhibitors,” Drug Metabolism Reviews, vol. 29, Feb. 1997, pp. 413-580.