Metabolism Site Prediction Based on Xenobiotic Structural Formulas and PASS Prediction Algorithm

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6

Computational Approaches for Screening Drugs for Bioactivation, Reactive Metabolite Formation, and Toxicity

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely conjugate to protein and DNA, in a process known as bioactivation, and prompt adverse reaction, drug candidate attrition, or market withdrawal. Experimental assays are low-throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. Reactive metabolites also elude in vivo detection, as they are transitory and generally do not circulate. In contrast, computational methods are high-throughput and cheap to screen millions of potentially toxic molecules during early stages of the drug development pipeline. This work computationally models sequences of metabolic transformations, i.e., pathways, between an input molecule and a corresponding, optional reactive metabolite(s). Additionally, an accurate graph neural network model was developed to assess importance of intermediate metabolites and extract connected subnetworks of relevance to bioactivation. Connecting multiple site of metabolism and structure inference models, we developed an integrated model of metabolism and reactivity to evaluate bioactivation risk driven by epoxidation, quinone formation, thiophene sulfur-oxidation, and nitroaromatic reduction. We applied this framework to an understudied substructure, the isoxazole ring, that is gaining traction in a class of drugs known as bromodomain inhibitors that may potentially drive quinone formation. Finally, we attend to toxicity associated with drug-drug interactions, particularly with NSAID usage reported in electronic health records