Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis

In drug discovery, metabolite identification data are used to identify metabolic soft spots in research molecules to facilitate reduced metabolism in subsequently designed compounds. In addition, knowledge about exact metabolite structures enables the assessment of risks associated with active, reactive, or toxic metabolites. In the present work, we exemplify how metabolite identification data are generated and utilized at AstraZeneca. We share metabolite transformation scheme data derived from incubations in human hepatocytes for a set of 120 compounds. Comparison with other in-house generated metabolite identification data, both in terms of chemical space analysis and <i>in vitro</i> properties, is performed, including the characterization of observed metabolic pathways. For selected compounds, the correlation between <i>in vitro</i> and <i>in vivo</i> metabolite data in animal species is provided. Finally, the usage of shared metabolite identification data for drug metabolism prediction using machine learning and artificial intelligence approaches is discussed.