Liu Yitong, Lawless Michael, Roe Amy L, Ferguson Stephen S
Division of Toxicology, Office of Chemistry and Toxicology, Office of Laboratory Operations and Applied Science, Human Foods Program, U.S. Food and Drug Administration, Laurel, MD, USA.
Simulations Plus, Inc. Lancaster, California, USA.
Toxicol Appl Pharmacol. 2025 Jul;500:117385. doi: 10.1016/j.taap.2025.117385. Epub 2025 May 11.
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.
扩展清除分类系统（ECCS）是一个预测化学物质主要限速清除途径的框架：代谢、肝脏摄取或肾脏清除。ECCS预测基于分子量、电离状态和膜通透性，这些可通过定量构效关系（QSAR）模型进行预测。ECCS还指出了通过药物代谢酶和转运体产生的潜在化学相互作用。本研究使用ECCS评估植物化学成分，并预测药物代谢酶和转运体途径，以了解人体中的植物清除情况。首先，根据QSAR预测的特性，将82种植物化学成分分为六个ECCS类别。接下来，进一步探究1A类和2类中的成分作为18种药物代谢酶的潜在底物，随后预测肝脏清除情况，而3类和4类中的成分则利用肾小球滤过和肾脏转运体的预测结果。最后，讨论了植物化学成分与药物之间的潜在相互作用。结果表明，超过一半的植物化学成分属于ECCS 2类，预计其I相代谢主要由CYP3A4、CYP2D6和CYP1A2介导。此外，超过20%的植物化学成分属于ECCS 4类，预计主要通过肾小球滤过和OAT1/3或OCT2的主动肾脏分泌以未改变的形式清除。1A类和2类化合物通过细胞色素P450表现出较高的相互作用潜力，而3类和4类化合物通过肾脏摄取转运体介导相互作用的潜力相对较低。本研究代表了一个数据驱动的框架，用于探索植物成分信息并将其置于背景中，以指导安全性评估。
