Two advanced metabolomics-based data processing approaches for comprehensive drug metabolite identification using high-resolution mass spectrometry

Abstract

In our previous study, we developed a dose-response technique coupled with stable isotope tracing (SIT) for drug metabolite identification. The efficacy and comprehensiveness of this method requires further validation. We employed two incubation methods and two replicate datasets to identify drug metabolites using our developed approach. A total of 24 potential rosiglitazone (ROS) metabolite ions with proposed structures were identified in this study. Our developed approach demonstrated impressive consistency between two replicates in the coincubation datasets when identifying potential ROS metabolites. In coincubation datasets-where ROS and its isotope-labeled compound were incubated in the same tube-12 out of 13 ions were consistently identified across the two replicates, where by contrast, only 13 out of 20 ions were consistently identified across the two replicates in separate incubation datasets. Interestingly, the separate incubation datasets yielded a higher number of potential metabolites for screening (n = 20) compared with the coincubation dataset (n = 13). We compared our approach with another approach-mass defect filtering (MDF) coupled with SIT-in the identification of ROS metabolite ions from these datasets. We observed similar trends when MDF coupled with SIT was used. These approaches could complement each other's limitations, offering a more comprehensive analytical strategy.

Keywords: Dose-response relationship; Mass defect filtering; Rosiglitazone; Stable isotope tracing.