Min Jee Sun, Kim Doyun, Park Jung Bae, Heo Hyunjin, Bae Soo Hyeon, Seo Jae Hong, Oh Euichaul, Bae Soo Kyung
Integrated Research Institute of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon.
Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea.
Drug Des Devel Ther. 2016 Sep 14;10:2959-2972. doi: 10.2147/DDDT.S109141. eCollection 2016.
Evaluating the potential risk of metabolic drug-drug interactions (DDIs) is clinically important.
To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, ()-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine.
The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol.
The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition.
This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.
评估代谢性药物相互作用（DDIs）的潜在风险在临床上具有重要意义。
建立盐酸沙格雷酯及其活性代谢物（）-1-{2-[2-（3-甲氧基苯基）乙基]-苯氧基}-3-（二甲基氨基）-2-丙醇（M-1）的基于生理的药代动力学（PBPK）模型，以预测沙格雷酯与临床相关细胞色素P450（CYP）2D6底物美托洛尔、地昔帕明、右美沙芬、丙咪嗪和托特罗定之间的药物相互作用。
基于体外和体内研究结果，结合盐酸沙格雷酯和M-1的理化性质和药代动力学特性建立PBPK模型。随后，通过将沙格雷酯和M-1的预测浓度-时间曲线和药代动力学参数与观察到的临床数据进行比较来验证该模型。最后，使用经过验证的模型模拟盐酸沙格雷酯与敏感CYP2D6底物之间的临床药物相互作用。通过比较盐酸沙格雷酯与美托洛尔联合给药后的预测结果与观察数据，评估模型的预测性能。
所建立的PBPK模型准确预测了单剂量或多剂量盐酸沙格雷酯后沙格雷酯和M-1的血浆浓度曲线。在存在盐酸沙格雷酯的情况下，美托洛尔的曲线下面积和最大血浆浓度与基线的模拟比值与观察到的比值高度一致。单剂量和多剂量口服盐酸沙格雷酯后，美托洛尔、地昔帕明、丙咪嗪、右美沙芬和托特罗定曲线下面积比值的预测增加倍数在≥1.25但<2倍的范围内，表明盐酸沙格雷酯在体内是CYP2D6的弱抑制剂。总体而言，预测的低药物相互作用表明盐酸沙格雷酯导致与CYP2D6抑制相关的显著药物相互作用的可能性有限。
本研究证明了应用PBPK方法预测盐酸沙格雷酯与CYP2D6代谢药物之间药物相互作用潜力的可行性。因此，这将有助于设计和优化以沙格雷酯作为体内CYP2D6抑制剂的临床药物相互作用研究。
