A Computational Integration Strategy Driven by Chemical Similarity Uncovers Comprehensive Metabolic Profiles of Small...

The current limited understanding of small bioactive peptide metabolism hinders effective doping control. A primary challenge lies in distinguishing suspicious features from extensive mass spectral datasets contaminated by biological matrix interference and background noise. In this study, we introduce a strategy integrating in silico prediction and nontargeted data mining to achieve more comprehensive metabolic profiling through ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). The strategy operates by identifying and applying chemical similarity (CSIM) rules of peptides (such as LC/MS behaviors and specific biotransformation) to mine unknown metabolites. With this strategy, a semiautomated workflow utilizing computational software and custom script was constructed and applied to the human liver microsomal metabolism of two significant kisspeptin analogues with doping potential (TAK-448 and TAK-683). The characteristic behaviors induced by CSIM enabled effectively in-depth data mining from redundant background signals, leading to the identification of 13 metabolites (three were validated via synthetic standards) and two uncommon biotransformation pathways (N-terminal vinylation and N-terminal carboxylation) for both investigated compounds. Notably, the two biotransformation pathways offered an innovative perspective on small peptide metabolism, which was further confirmed in rats, and produced two promising long-term metabolites for monitoring doping abuse. Further drug activity evaluation indicated higher or retained performance-enhancing effects of these metabolites, alerting doping control subjects to pay attention to more information. The study provided the first comprehensive characterization of the metabolic profiles of TAK-448 and TAK-683, while also offering an effective tool for metabolism research on small peptide doping agents.