Research Project
6990722
DESCRIPTION (provided by applicant): Failure of molecules in the late stages of drug development are to a large extent attributable to poor ADME/Tox properties. These properties are generally predictable in the earlier, cheaper stages of drug discovery. The goal of this work is to predict metabolism and toxicity using a computational suite called MetaDrug. This integrates human endogenous and xenobiotic metabolic as well as signalling pathways and can also incorporate gene expression, and experimental data. Under phase I, novel algorithms for predicting major CYP-mediated pathways were generated and successfully validated along with rules for predicting metabolites and reactive metabolites formed which are likely to be toxic. This algorithm development enabled the prediction of substrates and metabolites, the affinity and the rate of metabolism as well as interactions with other endogenous, metabolic and signalling pathways. With phase II funding we will develop large comprehensive datasets (>1000 molecules) for in vitro drug-drug interactions with the major CYPs, and use these for generating machine learning algorithms for these human drug metabolizing enzymes. We will also annotate rat and mouse data for drug metabolism and the transcriptional regulation of these enzymes, capturing the kinetic data which can also be used for predictive model building. We will also generate a novel algorithm for the accurate prediction of metabolites using the metabolite rules from phase I to produce a molecular fingerprint for known drugs. The database of molecules with known human metabolites will then be used as an input for a machine learning algorithm. We will combine the predictions from our various QSAR models for enzyme affinity and rate of metabolism, the relative contributions of these enzymes and their tissue distribution, to ultimately predict the clearance of a drug. The proposed work will enable GeneGo to develop a unique tool that will improve the prediction of metabolism and toxicity. These new features and database content will then be marketed to pharmaceutical companies and academia.
