Comparative metabolomics and lipidomics study to evaluate the metabolic differences between first‐ and second‐generation mammalian or mechanistic target of rapamycin inhibitors

Abstract

Mammalian or mechanistic target of rapamycin (mTOR) drives its fundamental cellular functions through two distinct catalytic subunits, mTORC1 and mTORC2, and is frequently dysregulated in most cancers. To treat cancers, developed mTOR inhibitors have been classified into first and second generations based on their ability to inhibit single (first‐generation) and dual (second‐generation) mTOR subunits. However, the underlying metabolic differences due to the effects of first‐ and second‐generation mTOR inhibitors have not been clearly evaluated. In this study, rapamycin (sirolimus) and AZD8055 and PP242 were selected as first‐ and second‐generation mTOR inhibitors, respectively, to evaluate the metabolic differences due to these two generations of mTOR inhibitors after a single oral dose using untargeted metabolomics and lipidomics approaches. The metabolic differences at each time point were compared using multivariate analysis. The multivariate and data analyses showed that metabolic disparity was more prominent within 8 h after drug administration and a broad class of metabolites were affected by the administration of both generations of mTOR inhibitors. Among the metabolite classes, changes in the pattern of fatty acids and glycerophospholipids were opposite, specifically at 4 and 8 h between the two generations of mTOR inhibitors. We speculate that the inhibition of the mTORC2 subunit by the second‐generation mTOR inhibitor may have resulted in a distinct metabolic pattern between the first‐ and second‐generation inhibitors. Finally, the findings of this study could assist in a more detailed understanding of the key metabolic differences caused by first‐ and second‐generation mTOR inhibitors.

Integrated comparative metabolomics and network pharmacology approach to uncover the key active ingredients of Polygonati rhizoma and their therapeutic potential for the treatment of Alzheimer’s disease

“…The numerous other components present in TCM make the screening and analysis of bioactive components extremely difficult, which is a great challenge to the research on TCM. In recent years, with the continuous progress in several high-throughput assembly technologies and the rapid development of computer technology, new strategies have emerged to study the material basis of TCM, such as comparative metabolomics, which is an emerging system biology technology that compares the metabolites of the control and experimental groups to identify the differences in their metabolic profiles ( Rashid et al, 2021 ). Researchers divide herbal medicines into control and experimental groups, and further screen active ingredients based on differential metabolites.…”

“…Accumulating evidence has demonstrated that it has great advantages in discovering multi-target drugs and providing a new insight into studying TCM ( Zhou Z. et al, 2020 ). Comparative metabolomics was identified as an emerging system biology technology that compares the metabolites of the control and experimental groups to identify the differences in their metabolic profiles ( Rashid et al, 2021 ). Furthermore, the systematic biological characteristics of comparative metabolomics are consistent with the theory of TCM, allowing us to thoroughly investigate TCM with complicated conditions and multiple factors ( Qu et al, 2021 ).…”