Computational-aided understanding of metabolic mechanism: A case study

Abstract

This study presents an innovative computational method, combined with experimental validation, to predict and elucidate the metabolic pathways of SILA-123, a novel FLT3 inhibitor. Using UFLC/Q-TOF MS, we identified 21 metabolites generated through key reactions (oxidation, reduction, hydrolysis, cleavage, deamination, and glucuronidation). Advanced computational techniques were applied to identify key metabolic enzymes, with results confirmed experimentally. Our findings represent a significant advancement in the field of metabolic prediction. By integrating computational methods with experimental data, we have established a robust framework that can be applied to other therapeutic compounds. This approach not only enhances our understanding of SILA-123's metabolic pathways but also provides a novel strategy for guiding metabolic prediction.