Ozbey Agustos C, Fowler Stephen, Leys Karen, Annaert Pieter, Umehara Kenichi, Parrott Neil
Roche Pharma Research and Early Development, F.Hoffmann-La Roche, Switzerland
Drug Metabolism, F. Hoffmann-La Roche Ltd, Switzerland.
Drug Metab Dispos. 2023 Oct 25;52(1):DMD-AR-2023-001487. doi: 10.1124/dmd.123.001487.
Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1 of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.
基于生理的药代动力学（PBPK）建模已成为预测人体药代动力学（PK）和药物 - 药物相互作用（DDI）的既定方法。通过非CYP酶代谢清除的药物数量一直在稳步增加，迄今为止，尚无关于非CYP酶清除药物的PBPK建模的综合概述。本综述旨在描述通过非CYP酶清除药物的PBPK建模的最新进展，识别成功策略，描述差距并提供克服这些差距的建议。为此，我们进行了详细的文献检索，发现了2023年1月1日前发表的58篇文章，其中包含62种非CYP酶底物的95个临床PBPK模型实例。综述文章涵盖了尿苷5'-二磷酸葡萄糖醛酸转移酶（UGT）、醛氧化酶（AO）、含黄素单加氧酶（FMO）、磺基转移酶（SULT）和羧酸酯酶（CES）介导的药物清除，其中UGT2B7、UGT1A9、CES1、FMO3和AO是最常涉及的酶。非CYP酶内在清除率的体外预测（IVIVE）和自下而上的PBPK建模仍然具有挑战性。我们观察到，80%的案例采用了中间向外建模方法，73%的模型对代谢参数进行了优化。我们的综述未能确定基于临床数据用于模型优化的标准化方法，最常采用的是手动优化。成功开发UGT2B7、UGT1A9、CES1和FMO3底物的模型为这些酶代谢的其他药物奠定了基础，并为创建这些家族中其他酶的PBPK模型指明了方向。我们的综述描绘了非CYP酶清除药物的PBPK建模的兴起。通过分析58篇文章和62种非CYP酶底物，我们发现UGT、AO、FMO、SULT和CES是主要涉及的酶家族，UGT2B7、UGT1A9、CES1、FMO3和AO是具有最强PBPK建模先例的个别酶。为这些酶建立的方法现在可以扩展到其他底物以及由特征相似的酶代谢的药物。
