Metabolism of the Isoflavone Derivative Structural Isomers ACF-02 and ACF-03 in Human Liver Microsomes

Background/Objectives: Flavonoids are widely used as lead structures in drug discovery, and their pharmacological and metabolic properties are strongly influenced by structural features such as positional isomerism. This study aimed to compare the metabolic profiles and underlying mechanisms of two isoflavone-based positional isomers, ACF-02 (2-(4-hydroxy-3-methoxyphenyl)-6,7-dimethoxy-3-(4-methoxyphenyl)-4H-chromen-4-one) and ACF-03 (2-(3-hydroxy-4-methoxyphenyl)-6,7-dimethoxy-3-(4-methoxyphenyl)-4H-chromen-4-one). Methods: The metabolic pathways of synthetically prepared ACF-02 and ACF-03 were investigated using an in vitro incubation system with human liver microsomes (HLMs) supplemented with an NADPH-regenerating system, followed by liquid chromatography–high-resolution tandem mass spectrometry (LC–HRMS/MS) analysis. Metabolites were identified based on LC–HRMS/MS data and molecular networking-based node connectivity with the parent compounds. Major metabolites were further characterized by CYP phenotyping using recombinant CYP450 isoforms, and the potential for drug–drug interactions of ACF-03 was evaluated using a CYP probe substrate cocktail approach. Results: HLM incubation of ACF-02 and ACF-03 produced both hydroxylated and O-demethylated metabolites, with O-demethylation as the predominant pathway; notably, the most abundant O-demethylated metabolite differed in an isomer-dependent manner, occurring at the B2 ring for ACF-02 and at the A ring for ACF-03, with distinct CYP isoform involvement. Molecular networking supported the relationships between the parent compounds and their metabolites, and both compounds exhibited relatively high metabolic stability with limited CYP inhibition. Conclusions: Despite differing only in the position of a single methyl substituent, ACF-02 and ACF-03 exhibited distinct isomer-dependent metabolic profiles. These findings demonstrate that even subtle positional isomerism can significantly influence metabolic behavior and should be carefully considered during lead optimization and drug design.