Investigation of Piperine Metabolites via Computational and Experimental Biophysical Chemistry Approaches

In this work, it is presented a pioneering approach called Computational Predictive and Electrochemical Detection of Metabolites (CP-EDM) aimed at accelerating the identification of drug metabolites. This approach focuses on piperine, a bioactive natural compound with a well-documented metabolite profile. However, its computational metabolism is unpredictable when using Biotransformer v3.0. To address this, it was devised an electrochemical reaction to oxidize piperine, resulting in a diverse array of metabolites. These metabolites were subsequently identified through Liquid Chromatography-Mass Spectrometry (LC-MS) analysis. Through the analysis of ion fragmentation patterns, it was predicted several chemically feasible metabolites. These predicted metabolites were then docked into the active site of the enzyme CYP3A4 using Autodock4.2 software. The low-energy clustering profile of piperine within the active site indicated that the most probable site of metabolism is the benzo[d][1,3]dioxole motif. This inference was based on the intermolecular distances to the Fe-oxo active site. The metabolic profile was further validated through comparison with existing literature. The electrochemical reaction successfully produced plausible metabolites, demonstrating the power of this combined technique. By integrating electrochemical detection with computational binding pose evaluation, It was showcased an effective method for predicting and confirming metabolic pathways. Overall, our novel approach merges diverse data sources to predict and validate the metabolic outcomes for bioactive molecules, offering a robust tool for drug metabolism studies.