Metabolism study and toxicological determination of mephtetramine in biological samples by liquid chromatography coupled with high-resolution mass spectrometry

The emerging market of new psychoactive substances (NPSs) is a global-scale phenomenon, and their identification in biological samples is challenging because of the lack of information about their metabolism and pharmacokinetic. In this study, we performed in silico metabolic pathway prediction and in vivo metabolism experiments, in order to identify the main metabolites of mephtetramine (MTTA), an NPS found in seizures since 2013. MetaSite (TM) software was used for in silico metabolism predictions and subsequently the presence of metabolites in the blood, urine, and hair of mice after MTTA administration was verified. The biological samples were analyzed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) using a benchtop Orbitrap instrument. This confirmed the concordance between software prediction and experimental results in biological samples. The metabolites were identified by their accurate masses and fragmentation patterns. LC-HRMS analysis identified the dehydrogenated and demethylated-dehydrogenated metabolites, together with unmodified MTTA in the blood samples. Besides unmodified MTTA, 10 main metabolites were detected in urine. In hair samples, only demethyl MTTA was detected along with MTTA. The combination of Metasite (TM) prediction and in vivo experiment was a powerful tool for studying MTTA metabolism. This approach enabled the development of the analytical method for the detection of MTTA and its main metabolites in biological samples. The development of analytical methods for the identification of new drugs and their main metabolites is extremely useful for the detection of NPS in biological specimens. Indeed, high throughput methods are precious to uncover the actual extent of use of NPS and their toxicity.

Automated summary

This study successfully identified the main metabolites of mephtetramine (MTTA) through a combination of in silico metabolic pathway prediction and in vivo metabolism experiments. This approach enabled the development of analytical methods for the detection of MTTA and its main metabolites in biological samples.