Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC–HRMS and untargeted data processing

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Abstract

Background:

Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction, high-resolution accurate mass LC–MS and MS vendor independent data processing. Retrospective evaluation of predictions for 14 14C-ADME studies published in the period 2007–January 2012 indicates that on average 90% of the major metabolites in human plasma can be identified by searching for accurate masses of predicted metabolites. Furthermore, the workflow can identify unexpected metabolites in the same processing run, by differential analysis of samples of drug-dosed subjects and (placebo-dosed, pre-dose or otherwise blank) control samples. To demonstrate the utility of the workflow we applied it to identify tamoxifen metabolites in serum of a breast cancer patient treated with tamoxifen.

Results & Conclusion:

Previously published metabolites were confirmed in this study and additional metabolites were identified, two of which are discussed to illustrate the advantages of the workflow.

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