Quantitative estimation of circulating metabolites without synthetic standards by ultra‐high‐performance liquid chromatography/high resolution accurate mass spectrometry in combination with UV correction

Abstract

An early assessment of metabolite exposure in preclinical species can provide quantitative estimation on possible active or toxic metabolites. Frequently, synthetic metabolite standards are not available at the preclinical stage, precluding the quantitation of metabolites by means of calibration curves and quality control (QC) samples. We present here an approach to determine the extent of circulating metabolites using 'metabolite standards' generated by in vitro incubations in combination with the correction for mass spectrometry response based on UV response. The study was done by coupling ultra-high-performance liquid chromatography (UHPLC) to LTQ-Orbitrap high-resolution mass spectrometry, and the quantitation was based on full scan high-resolution accurate mass analysis in combination with retention time. First, we investigated the separation capacity of a 10.5 min UHPLC method and the quantitative capability of an LTQ-Orbitrap for full scan accurate mass quantitation by spiking chemical standards of buspirone and its six metabolites in blank plasma. Then we demonstrated the use of a UV correction approach to quantitatively estimate buspirone and its metabolites in plasma samples from a rat pharmacokinetics study. We compared the concentration versus time profiles of buspirone and its six metabolites in rat plasma samples obtained using three different approaches, including using UV correction, using individual standard curves for each metabolite prepared from the synthetic standard, and using a calibration curve of the parent compound buspirone. We demonstrated the estimated metabolite exposure of buspirone using this UV correction approach resulted in rank ordering of metabolite exposure within three-fold of the value obtained with metabolite standards, in contrast to eight-fold without UV correction. The approach presented in this paper provides a practical solution to an unmet bioanalytical need for quantitative information on metabolites without standards in preclinical in vivo studies.