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

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.

Metabolism of a G Protein-Coupled Receptor Modulator, Including Two Major 1,2,4-Oxadiazole Ring-Opened Metabolites and a Rearranged Cysteine-Piperazine Adduct

“…Computer algorithms for background subtraction from LC/high-resolution MS data have been demonstrated to be effective approaches to reveal drug metabolites in complex biological matrices (Zhang et al, 2008b;Zhang and Yang, 2008;Zhu et al, 2009). Unlike radioactivity monitoring of metabolites for which the physical properties of the radioactive nucleus (e.g., 14 C) are not affected by biotransformation at the molecular level, the LC-MS signal-response efficiency of metabolites varies, sometimes dramatically, such as with the acidic metabolites M1 and M2 of the basic parent compound P in the present study.…”

Drug Metabolite Profiling and Identification by High-resolution Mass Spectrometry

“…However, the challenges of background subtraction with nominal mass resolution data have been that isobaric ions are typically not resolved, and sample components shift between chromatograms, both of which affect the quality of background-subtracted spectra. LC/HR-MS data sets significantly enhance the potential to selectively subtract background ions; however, the algorithms must be able to accommodate intensity and retention time fluctuations of individual sample matrix components so that common components between the test and control samples can be thoroughly subtracted even with not so perfectly matched control sample data sets (30,33,36,57,58). These improved background subtraction approaches have been demonstrated to be quite sensitive and selective in detecting in vitro (supplemental Fig.…”

“…These improved background subtraction approaches have been demonstrated to be quite sensitive and selective in detecting in vitro (supplemental Fig. 1B) and in vivo (30,33,36,57) metabolites in complex matrices. Of course, the background subtraction approach requires a good control sample.…”

“…For example, multiple MDFs were applied to remove ions from profiles generated from EIC for in vitro screening of oxidative metabolites (31). Another example of this strategy was the use of MDF to improve the selectivity of background subtraction for in vivo profiling of troglitazone metabolites (33). Although the data mining techniques described here are optimized for detection of metabolites, very similar applications of MDF, background The asterisks indicate background ions and/or drug-related components that are not GSH adducts.…”

“…In the past 6 -8 years, new and improved HR-MS instruments (24 -27) and data processing techniques (18, 28 -38) have been developed for metabolite identification. As a result, HR-MS instruments are now capable of accomplishing all requisite metabolite identification tasks with significantly improved productivity and quality (33,(37)(38)(39)(40). In this minireview, we discuss a new approach for drug metabolite identification with HR-MS, including data acquisition and data mining technologies, employed in support of metabolite identification in drug discovery and development.…”