An algorithm for thorough background subtraction from high-resolution LC/MS data: application to the detection of troglitazone metabolites in rat plasma, bile, and urine

✍ Scribed by Haiying Zhang; Li Ma; Kan He; Mingshe Zhu

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 359 KB
• Volume: 43
• Category: Article
• ISSN: 1076-5174
• DOI: 10.1002/jms.1432

✦ Synopsis

Abstract

Interferences from biological matrices remain a major challenge to the in vivo detection of drug metabolites. For the last few decades, predicted metabolite masses and fragmentation patterns have been employed to aid in the detection of drug metabolites in liquid chromatography/mass spectrometry (LC/MS) data. Here we report the application of an accurate mass‐based background‐subtraction approach for comprehensive detection of metabolites formed in vivo using troglitazone as an example. A novel algorithm was applied to check all ions in the spectra of control scans within a specified time window around an analyte scan for potential background subtraction from that analyte spectrum. In this way, chromatographic fluctuations between control and analyte samples were dealt with, and background and matrix‐related signals could be effectively subtracted from the data of the analyte sample. Using this algorithm with a ± 1.0 min control scan time window, a ± 10 ppm mass error tolerance, and respective predose samples as controls, troglitazone metabolites were reliably identified in rat plasma and bile samples. Identified metabolites included those reported in the literature as well as some that had not previously been reported, including a novel sulfate conjugate in bile. In combination with mass defect filtering, this algorithm also allowed for identification of troglitazone metabolites in rat urine samples. With a generic data acquisition method and a simple algorithm that requires no presumptions of metabolite masses or fragmentation patterns, this high‐resolution LC/MS‐based background‐subtraction approach provides an efficient alternative for comprehensive metabolite identification in complex biological matrices. Copyright © 2008 John Wiley & Sons, Ltd.