Integration of computational models to predict botanical phytochemical constituent clearance routes by the Extended Clearance Classification System (ECCS)

Abstract

The Extended Clearance Classification System (ECCS) is a framework that predicts a chemical's predominant rate-determining clearance route: metabolism, hepatic uptake, or renal clearance. The ECCS prediction is based upon molecular weight, ionization state, and membrane permeability, which could be predicted by quantitative structure-activity relationship (QSAR) models. The ECCS also indicates potential chemical interactions via drug-metabolizing enzymes and transporters. This study used the ECCS to evaluate phytochemical constituents and predicted drug-metabolizing enzyme and transporter pathways to understand botanical clearance in humans. First, 82 phytochemical constituents were classified into six ECCS classes based on QSAR-predicted properties. Next, constituents in classes 1A and 2 were further explored as potential substrates for 18 drug-metabolizing enzymes followed by predictions for hepatic clearance, while constituents in classes 3 and 4 leveraged predictions for glomerular filtration and renal transporters. Finally, potential interactions between phytochemical constituents and drugs were discussed. Results showed that more than half of the phytochemical constituents were in ECCS class 2, whose Phase I metabolism were predicted to be predominantly mediated by CYP3A4, CYP2D6, and CYP1A2. Additionally, over 20 % of the phytochemical constituents fell into ECCS class 4, which were predicted to be predominantly cleared in unchanged forms by glomerular filtration and active renal secretion by OAT1/3 or OCT2. Classes 1A and 2 compounds exhibit high interaction potential via CYPs, while classes 3 and 4 compounds have relatively low potential for renal uptake transporter mediated interactions. This study represents a data-driven framework for exploring and contextualizing botanical constituent information to inform safety evaluations.