Non-targeted metabolite characterization in microsomal assay using liquid chromatography coupled to high-resolution mass spectrometry: Application to carisoprodol

Understanding the metabolic fate of pharmaceutical compounds is critical for assessing drug safety and efficacy. A combination of advanced analytical techniques and in vitro models allows for detailed investigation of biotransformation processes. This study presents an integrated workflow using carisoprodol as a case study to demonstrate the application of modern analytical strategies for metabolic profiling. An analytical platform based on liquid chromatography–high-resolution mass spectrometry (LC-HRMS) was employed, operating in both MS¹ and MS² modes to investigate fragmentation behaviour and identify metabolites. Chromatographic separation was performed using a core-shell C18 column under gradient elution. In vitro metabolic stability studies were conducted using rat liver microsomes, and a deuterated analogue was also tested to assist in structural elucidation of hydroxylated metabolites. Additionally, in silico metabolite prediction tools were applied and compared with experimental results. The compound showed slow metabolic degradation (t₁/₂ = 233.72 ± 3.09 min) and low intrinsic clearance (CLint, in vitro = 5.930 ± 0.078 µL/min/mg). LC-HRMS enabled identification of meprobamate and a hydroxylated derivative as major metabolites. MS/MS analysis of the deuterated metabolite excluded hydroxylation on the n-pentyl chain as reported in the literature, indicating alternative modification sites. In silico predictions correctly identified meprobamate but misassigned hydroxylation positions for the other metabolite. This study highlights the effectiveness of a multi-technique analytical approach for elucidating drug metabolism. The integration of LC-HRMS, isotopic labelling, and computational tools provides a comprehensive platform for metabolic characterization, while emphasizing the necessity of experimental validation in refining in silico predictions.