REVENG: REVERSE ENGINEERING THE METABOLIC BIOLOGICAL PATHWAY

Most drugs undergo chemical transformations in the body, known as biotransformations, to generate metabolites that are more readily eliminated. These reactions are primarily mediated by metabolic enzymes, mainly in the liver, and are highly specific, with each enzyme favoring particular substrates. Identifying the enzymes responsible for metabolite formation is essential for elucidating metabolic pathways, predicting metabolic behavior, and assessing potential toxicity risks. Metabolite Identification (MetID) studies, typically conducted in vitro or in vivo, rely heavily on LC-MS/MS for detecting and structurally characterizing metabolites. However, most discovery studies provide limited insight into the enzymes involved, and experimental approaches for reaction phenotyping, such as recombinant enzyme incubations or chemical inhibition studies, are often time- and resource-intensive, limiting comprehensive pathway characterization.

To address this gap, we developed a workflow that integrates LC-MS/MS MetID data from in vitro incubations with computational predictions to identify enzymatic pathways responsible for metabolite formation, including both Phase I and Phase II reactions. The computational approach simulates interactions between xenobiotic compounds and human metabolic enzymes using their 3D structures, evaluating the exposure of reactive atoms to catalytic residues. Multiple docking poses are generated and scored based on energy contributions, and the best pose is normalized to provide a probability ranking.

MetID data were processed using MassMetaSite on the ONIRO server with LC-MS/MS datasets from Sciex and Thermo instruments, and predictions were performed using MetaSite 7 within Oniro. The model was applied to three compounds and validated against experimental CYP phenotyping data from the literature.

For dextromethorphan, Phase I metabolism studies using recombinant enzymes and human liver microsomes identified six metabolites formed through N- and O-dealkylation and hydroxylation after 30 minutes. Experimentally, CYP2D6 mediated O-dealkylation to dextrorphan, while CYP3A4 catalyzed N-dealkylation to 3-methoxymorphinan. The model accurately predicted both pathways with high probability, consistent with the experimental results.¹

Phase II metabolism of dextromethorphan was further evaluated in hepatocytes, and revealed a metabolite formed via O-dealkylation followed by glucuronidation after 140 minutes; the model correctly suggested CYP2D6 for the initial step and UGT enzymes for conjugation, in agreement with literature reports.²

Two additional compounds were also assessed for Phase I metabolism: thioridazine metabolism in human liver microsomes produced metabolites via S-oxidation and N-demethylation, with the model identifying CYP3A4 for 5-sulfoxide formation and suggested CYP1A2 as one enzyme involved in N-demethylation. The model also predicted CYP2D6 involvement in the formation of mesoridazine and its further conversion to sulphoridazine, consistent with published data.³ ⁴

For ethoxyresorufin, incubated with recombinant enzymes for 30 minutes, O-dealkylation to resorufin was observed in CYP1A2 incubations, and the model predicted involvement of both CYP1A2 and CYP1A1.⁵

Together, these results demonstrate the capability of the in silico functionality integrated within a MetID platform to predict Phase I and Phase II enzymatic pathways for LC-MS/MS identified metabolites.