RS-8359

Aldehyde oxidase (AOX) is an important drug-metabolizing enzyme. However, the current in vitro models for evaluating AOX metabolism are sometimes misleading, and preclinical animal models generally fail to predict human AOX-mediated metabolism. In this study, we report a combined computational and experimental investigation of drug-like molecules that are potential aldehyde oxidase substrates, of which multiple sites of metabolism (SOMs) mediated by AOX and their preferences for the reaction can be unambiguously identified. In addition, the proposed strategy was used to evaluate the metabolism of newly designed c-Met inhibitors, and a success switch-off of AOX metabolism was observed. Overall, this study provide useful information to guide lead optimization and drug discovery based on AOX-mediated metabolism.

Metabolism by aldehyde oxidase (AO) has been responsible for a number of drug failures in clinical trials. The main reason is the clearance values for drugs metabolized by AO are underestimated by allometric scaling from preclinical species. Furthermore, in vitro human data also underestimates clearance. We have developed the first in silico models to predict both in vitro and in vivo human intrinsic clearance for 8 drugs with just two chemical descriptors. These models explain a large amount of the variance in the data using two computational estimates of the electronic and steric features of the reaction. The in vivo computational models for human metabolism are better than in vitro preclinical animal testing at predicting human intrinsic clearance. Thus, it appears that AO is amenable to computational prediction of rates, which may be used to guide drug discovery, and predict pharmacokinetics for clinical trials.