Abstract
The screening and identification of drug metabolites in biological matrices is challenging, and ultra-high performance liquid chromatography-Q-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) has become a powerful technological tool for drug metabolites analysis due to its high sensitivity. However, the spectral information contained in existing chemical standards and databases is very limited, and the UHPLC-Q-Exactive Orbitrap MS technique alone cannot satisfy the identification of complex and diverse metabolites. Therefore, there is an urgent need for a new strategy to achieve comprehensive drug metabolic profile. Based on this, we have innovatively constructed a “recursive tree” analysis strategy and bridged it with network pharmacology for elucidating the pharmacological mechanisms of drugs. In this paper, we investigated the overall metabolic profile of formononetin as an example and utilized the primary branching metabolites of formononetin as effective ingredients for the study of the anti-NAFLD mechanism. The results showed that a total of 131 metabolites (prototype drug included) were detected and identified. Among them, 106 metabolites were found in rats and 31 metabolites were found in liver microsomes. Glucose conjugation, demethylation, sulfation, glucuronidation, and their complex reactions were the major processes of formononetin biotransformation. Network pharmacology results screened 104 potential targets and 20 major signaling pathways. Their mechanisms may be additive and/or synergistic effects. In addition, the therapeutic effects of formononetin against NAFLD were investigated based on palmitic acid / oleic acid-induced HepG2 cells. In summary, the recursive tree analysis strategy provides a convenient method for the identification of metabolites, and its seamless integration with network pharmacology lays the foundation for studying the pharmacological activities of natural products.
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