September 13-16, 2016
Eastbourne Winter Gardens, Eastbourne, UK
Platform Agnostic Data Processing Routine for Targeted and Untargeted Metabolite Identification in Drug Discovery
Richard Lee, Vitaly Lashin, Alexandre Sakarov
Abstract: Information gained from metabolite analysis plays a critical role in drug discovery and development. The principle method of recognizing metabolic "hotspots" are through interpretation of mass spectrometry data, resulting in elucidation of biotransformation pathways. A new software algorithm was investigated for batch processing high resolution LC/MSn data files representing a study across multiple incubation time points acquired from major instrument vendors. Part of the automated process involved metabolite prediction based on a regio-selectivity model, which was used as a potential metabolite target list. To complement the prediction driven approach, a data driven untargeted analysis was also performed. The software was able to assign fragment ions of the parent and metabolites to their respective MS2 spectra. Structures of metabolites were verified and scores provided by comparing the assigned fragments and common neutral losses between it and the parent. In situations where the software was not able to provide a discrete structure, Markush notations were employed until users were able to apply manual changes to the substructure. The software routine then combined both predicted and unexpected metabolites into a single biotransformation map, where all mass spectra were associated to each of the structures and uploaded to a knowledge management system for review.
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