Drug metabolism studies are a critical component of the drug development process. Drug metabolite identification is a bottleneck of such studies due to the time-consuming chromatographic separation and purification and the need for large quantities of materials for structural confirmation using NMR. LC-MS provides higher throughput, but identification of unknowns can be limited relying on just MS and MS/MS fragmentation alone. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) timescale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion’s gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation of the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this talk, I will first discuss our effort in building a large-scale database of drug and drug metabolite CCS values (a total of 4419 values). We generated metabolites of 2000 drug or drug-like molecules using high-throughput in vitro reactions with human liver fractions and then measured their CCS values using a rapid IM-MS analysis with automated data processing. Metabolite annotation was carried out using known drug metabolites in DrugBank, in silico-predicted metabolites using Biotransformer, and MS/MS spectrum matching score against in silico fragments of potential metabolites generated using MetFrag. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D molecular descriptors. The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models are available to the public at https://CCSbase.net/dmccs_predictions.

Enhancing the confidence of drug metabolite identification using ion mobility-mass spectrometry and machine learnin

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.