A comprehensive understanding of thioTEPA metabolism in the mouse using UPLC-ESI-QTOFMS-based metabolomics

ThioTEPA, an alkylating agent with anti-tumor activity, has been used as an effective anticancer drug since the 1950s. However, a complete understanding of how its alkylating activity relates to clinical efficacy has not been achieved, the total urinary excretion of thioTEPA and its metabolites is not resolved, and the mechanism of formation of the potentially toxic metabolites S-carboxymethylcysteine (SCMC) and thiodiglycolic acid (TDGA) remains unclear. In this study, the metabolism of thioTEPA in a mouse model was comprehensively investigated using ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) based-metabolomics. The nine metabolites identified in mouse urine suggest that thioTEPA underwent ring-opening, N-dechloroethylation, and conjugation reactions in vivo. SCMC and TDGA, two downstream thioTEPA metabolites, were produced from thioTEPA from two novel metabolites 1,2,3-trichloroTEPA (VII) and dechloroethyltrichloroTEPA (VIII). SCMC and TDGA excretion were increased about 4-fold and 2-fold, respectively, in urine following the thioTEPA treatment. The main mouse metabolites of thioTEPA in vivo were TEPA (II), monochloroTEPA (III) and thioTEPA-mercapturate (IV). In addition, five thioTEPA metabolites were detected in serum and all shared similar disposition. Although thioTEPA has a unique chemical structure which is not maintained in the majority of its metabolites, metabolomic analysis of its biotransformation greatly contributed to the investigation of thioTEPA metabolism in vivo, and provides useful information to understand comprehensively the pharmacological activity and potential toxicity of thioTEPA in the clinic.

Modelling zero-inflated count data when exposure varies: with an application to tumor counts)

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with thiotepa on the number of new bladder tumors.