Cleary Yumi, Milani Nicolo, Ogungbenro Kayode, Aarons Leon, Galetin Aleksandra, Gertz Michael
Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK.
Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
AAPS J. 2025 Apr 29;27(4):83. doi: 10.1208/s12248-025-01064-3.
During drug development, the design, interpretation and risk assessment of drug-drug interaction (DDI) are generally performed with physiologically-based pharmacokinetic (PBPK) modelling. Critical parameters are the hepatic metabolic fraction (fm) and intestinal availability (F) which are commonly informed by clinical data. In this study, two methods for the simultaneous estimation of these parameters are proposed which utilize the distinctive changes in substrate's plasma concentration profiles in response to inhibition of intestinal and hepatic enzymes. The two-dimensional DDI (2D-DDI) method estimates the fm and F values directly from the ratios of area-under-curve (AUCR) and maximum concentration (CR), while the population PBPK method utilizes the full concentration-time data of a substrate without or with an inhibitor. The utility of both methods was demonstrated for a broad range of > 50,000 virtual and six actual CYP3A substrates. The 2D-DDI method is fast, reliable, and does not require a priori PBPK model development. The population PBPK method can estimate the population parameters and inter-individual variabilities of fm and F and is applicable to more complex DDIs (e.g., multiple pathways/dynamic inhibitor concentration-time profiles) without the need for IV data. Like other approaches, both methods show an increasing uncertainty for substrates with high hepatic extraction and sensitivity to the assumed degree of enzyme inhibition. While both methods were evaluated for CYP3A substrates, the methodology equally applies to other enzymes. Additionally, this study provides guidance for clinical DDI study design to facilitate robust DDI extrapolation necessary to inform drug labels on concomitant medications in lieu of clinical trials.
在药物研发过程中，药物相互作用（DDI）的设计、解读和风险评估通常采用基于生理学的药代动力学（PBPK）模型来进行。关键参数是肝脏代谢分数（fm）和肠道生物利用度（F），它们通常由临床数据提供信息。在本研究中，提出了两种同时估算这些参数的方法，这两种方法利用底物血浆浓度曲线在肠道和肝脏酶受到抑制时的独特变化。二维DDI（2D-DDI）方法直接从曲线下面积比（AUCR）和最大浓度比（CR）估算fm和F值，而群体PBPK方法则利用有无抑制剂时底物的完整浓度-时间数据。这两种方法在超过50,000种虚拟和六种实际CYP3A底物中的效用都得到了验证。2D-DDI方法快速、可靠，且无需事先开发PBPK模型。群体PBPK方法可以估算fm和F的群体参数及个体间变异性，适用于更复杂的DDI（例如，多途径/动态抑制剂浓度-时间曲线），无需静脉注射数据。与其他方法一样，对于肝脏提取率高的底物，这两种方法的不确定性都在增加，并且对假定的酶抑制程度敏感。虽然这两种方法都是针对CYP3A底物进行评估的，但该方法同样适用于其他酶。此外，本研究为临床DDI研究设计提供了指导，以促进在无需进行临床试验的情况下，为药物标签提供关于合并用药的可靠DDI推断。
