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A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug–Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates

基于生理学的药代动力学模型 利托那韦 CYP3A4型 药理学 药代动力学 酮康唑 化学 CYP2D6型 药品 生物制药 药物相互作用 细胞色素P450 医学 体外 生物活性 生物化学 人类免疫缺陷病毒(HIV) 免疫学 抗真菌 生药学 皮肤病科 抗逆转录病毒疗法 病毒载量
作者
Sumit Arora,Amita Pansari,Peter Kilford,Masoud Jamei,David B. Turner,Iain Gardner
出处
期刊:European Journal of Drug Metabolism and Pharmacokinetics [Adis, Springer Healthcare]
卷期号:47 (4): 483-495 被引量:7
标识
DOI:10.1007/s13318-022-00765-w
摘要

Background and ObjectivesDue to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug–drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir.MethodsA PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively.ResultsMaximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (Cmax) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of Cmax and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all Cmax and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for Cmax and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively.ConclusionsThe current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.
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