Thesis (Ph.D.)--University of Washington, 2021Confident identification of xenobiotic and endogenous metabolites is central to applications including metabolomics, lipidomics, and drug metabolism studies. The integration of ion mobility spectroscopy with mass spectrometry enhances the information gained from such studies without significantly impacting their analytical throughput and increases confidence in identification of unknown metabolites through the measurement of collision cross section (CCS). The diversity of small molecule chemical space necessitates the ability to predict CCS with high accuracy, rather than relying solely upon experimental CCS databases for annotation of unknowns. This dissertation aims to demonstrate the various applications of IM-MS in the large- scale determination of endogenous and xenobiotic metabolites, in addition to theory-based CCS calculation and machine learning-based CCS-prediction. First, I discuss the curation of a comprehensive and diverse database of experimental CCS values sourced from the literature and the development of a comprehensive CCS prediction model using this large experimental database, while providing insight into the structural characteristics of endogenous and xenobiotic metabolites that determine their CCS. Next, I examine the IM-MS characteristics of a panel of drugs and in vitro-generated metabolites using human liver microsomes and S9 fraction, with in-depth computational modeling and theoretical CCS calculation to rationalize experimental observations. I then present the results from scaling the in vitro drug metabolite generation and IM-MS analysis to a high-throughput format and its application to a diverse collection of over 2000 drug and drug-like compounds in order to build a drug- and drug metabolite-specific CCS database for use in building a ML-based CCS prediction model for drugs and drug metabolites. Next, I discuss the development of a bioinformatic tool for the analysis of lipidomics data, which includes specialized models for the prediction of CCS and HILIC retention time, demonstrating how specialized predictive models can be built for specific chemical classes that leverage class- specific structural trends to produce high-accuracy CCS predictions. Finally, I summarize the principal conclusions of this collective work and provide perspective on how research in this area may continue to expand
