Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Office of New Drug Products, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
CPT Pharmacometrics Syst Pharmacol. 2020 Aug;9(8):456-465. doi: 10.1002/psp4.12541. Epub 2020 Jul 31.
Weak-base drugs are susceptible to drug-drug interactions (DDIs) when coadministered with gastric acid-reducing agents (ARAs). We developed PBPK models to evaluate the potential of such pH-dependent DDIs for four weak-base drugs, i.e., tapentadol, darunavir, erlotinib, and saxagliptin. The physiologically-based pharmacokinetic (PBPK) models of these drugs were first optimized using pharmacokinetic (PK) data following oral administration without ARAs, which were then verified with data from additional PK studies in the presence and absence of food. The models were subsequently used to predict the extent of DDIs with ARA coadministration. Sensitivity analysis was conducted to explore the impact of gastric pH on quantitative prediction of drug exposure in the presence of ARA. The results suggested that the PBPK models developed could adequately describe the lack of the effect of ARA on the PK of tapentadol, darunavir, and saxagliptin and could qualitatively predict the effect of ARA in reducing the absorption of erlotinib. Further studies involving more drugs with positive pH-dependent DDIs are needed to confirm the findings and broaden our knowledge base to further improve the utilization of PBPK modeling to evaluate pH-dependent DDI potential.
当与胃酸降低剂（ARAs）同时给药时，弱碱性药物易发生药物-药物相互作用（DDIs）。我们开发了基于生理的药代动力学（PBPK）模型，以评估四种弱碱性药物（即他喷他多、达芦那韦、厄洛替尼和沙格列汀）的这种 pH 依赖性 DDI 的潜力。这些药物的 PBPK 模型首先使用没有 ARAs 时口服给药的药代动力学（PK）数据进行优化，然后使用存在和不存在食物时的其他 PK 研究数据进行验证。随后，这些模型被用于预测与 ARA 共同给药的 DDI 程度。进行了敏感性分析，以探讨胃 pH 对存在 ARA 时药物暴露定量预测的影响。结果表明，所开发的 PBPK 模型能够充分描述 ARAs 对他喷他多、达芦那韦和沙格列汀 PK 无影响，并能够定性预测 ARA 降低厄洛替尼吸收的作用。需要进一步进行涉及更多具有阳性 pH 依赖性 DDI 的药物的研究，以证实这些发现并扩大我们的知识库，以进一步提高基于 PBPK 模型评估 pH 依赖性 DDI 潜力的应用。
