Metabolite identification of bentysrepinine (Y101), a novel anti-HBV agent in rats using a five-step strategy based on a combined workflow with two different platforms of liquid chromatography-tandem mass spectrometry

Bentysrepinine (Y101), a derivative of repensine (a compound isolated from Dichondrarepens Forst), is a novel phenyalanine dipeptide inhibiting DNA-HBV and cccDNA activities and is currently under development for the treatment of hepatitis B virus (HBV)-infected hepatitis. Our previous study implied that there might be an existence of extensive metabolism of Y101 in rats. Therefore, it is necessary to perform metabolic profiling study to further evaluate its safety and drug-like properties. In this study, the metabolism of Y101 in rats was investigated by a convincible five-step strategy to characterize metabolites in plasma and that excreted into urine, bile and feces. The five-step strategy was realized by using an combined workflow on two different MS, platforms, including various scan modes of liquid chromatography with hybrid quadruple-linear ion trap mass spectrometry (LC-QTRAP-MS/MS) and various post-acquiring data mining tools of liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS). QTOF MS/MS was employed as a powerful complementary tool to enable high confidence of metabolites identification using its functions of accurate MS and MS/MS fragmentation. As a result, a total of 30 metabolites were detected, including 25 phase I and 5 phase II metabolites. Among them, four primary metabolites (M6-M9) were further identified by comparing with the authentic standards chemically synthesized. The possible metabolic pathways of Y101 in rats were proposed to be amide hydrolysis, monohydroxylation, dihydroxylation, N-oxidation, demethylation, methylation, glucosidation and glucuronidation. This is the first study of the metabolism of Y101 in rats. The five-step strategy was successfully used to systematically characterize metabolites of Y101 in rats, and it would be generally applied for metabolite identification of new drug candidate. (C) 2016 Elsevier B.V. All rights reserved.