Development of a metabolomics-based data analysis approach for identifying drug metabolites based on high-resolution mass spectrometry.

A metabolomics-based approach to data analysis is required for drug metabolites to be identified quickly. This study developed such an approach based on high-resolution mass spectrometry. Our approach is a two-stage one that combines a time-course experiment with stable isotope tracing. Pioglitazone (PIO) was used to improve glycemic management for type 2 diabetes mellitus. Consequently, PIO was taken as a model drug for identifying metabolites. During Stage I of data analysis, 704 out of 26626 ions exhibited a positive relationship between ion abundance ratio and incubation time in a time-course experiment. During Stage II, 25 isotope pairs were identified among the 704 ions. Among these 25 ions, 18 exhibited a dose-response relationship. Finally, 14 of the 18 ions were verified to be PIO structure-related metabolite ions. Otherwise, orthogonal partial least squares-discriminant analysis (OPLS-DA) was adopted to mine PIO metabolite ions, and 10 PIO structure-related metabolite ions were identified. However, only four ions were identified by both our developed approach and OPLS-DA, indicating that differences in the designs of metabolomics-based approaches to data analysis can result in differences in which metabolites are identified. A total of 20 PIO structure-related metabolites were identified by our developed approach and OPLS-DA, and six metabolites were novel. The results demonstrated that our developed two-stage data analysis approach can be used to effectively mine data on PIO metabolite ions from a relatively complex matrix.

Mass defect filter technique combined with stable isotope tracing for drug metabolite identification using high-resolution mass spectrometry.

Metabolism studies are one of the important steps in pharmaceutical research. LC-MS combined with metabolomics data-processing approaches have been developed for rapid screening of drug metabolites. Mass defect filter (MDF) is one of the LC/MS-based metabolomics data processing approaches and has been applied to screen drug metabolites. Although MDF can remove most interference ions from an incubation sample, the true positive rate of the retaining ions is relatively low (approximately 10%). To improve the efficacy of MDF, we developed a two-stage data-processing approach by combining MDF and stable isotope tracing (SIT) for metabolite identification. Pioglitazone (PIO), which is an antidiabetic drug used to treat type 2 diabetes mellitus, was taken as an example drug. Our results demonstrated that this new approach could substantially increase the validated rate from about 10% to 74%. Most of these validated metabolite signals (13/14) could be verified as PIO structure-related metabolites. In addition, we applied this approach to identify uncommon metabolite signals (a mass change beyond the window of 50 Da around its parent drug, MDF1). SIT could remove most interference ions (approximately 98%) identified by MDF1, and four out of five validated metabolite signals could be verified as PIO structure-related metabolites. Interestingly, a lot of the verified metabolites (10/17) were novel PIO metabolites. Among these novel metabolites, nine were thiazolidinedione ring-opening signals that might be related to the toxicity of PIO. Our developed approach could significantly improve the efficacy in drug metabolite identification compared with that of MDF.