Combined Computational Metabolite Prediction and Automated Structure-Based Analysis of Mass Spectrometric Data

ABSTRACT As high-throughput technologies have developed in the pharmaceutical industry, the demand for identification of possible metabolites using predominantly liquid chromatographic/mass spectrometry-mass spectrometry/mass spectrometry (LC/MS-MS/MS) for a large number of molecules in drug discovery has also increased. In parallel, computational technologies have also been developed to generate predictions for metabolites alongside methods to predict MS spectra and score the quality of the match with experimental spectra. The goal of the current study was to generate metabolite predictions from molecular structure with a software product, MetaDrug. In vitro microsomal incubations were used to ultimately produce MS data that could be used to verify the predictions with Apex, which is a new software tool that can predict the molecular ion spectrum and a fragmentation spectrum, automating the detailed examination of both MS and MS/MS spectra. For the test molecule imipramine used to illustrate the combined in vitro/in silico process proposed, MetaDrug predicts 16 metabolites. Following rat microsomal incubations with imipramine and analysis of the MSn data using the Apex software, strong evidence was found for imipramine and five metabolites and weaker evidence for five additional metabolites. This study suggests a new approach to streamline MS data analysis using a combination of predictive computational approaches with software capable of comparing the predicted metabolite output with empirical data when looking at drug metabolites.

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