Sugiyama Laboratory, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Kanagawa, Japan (S.K., K.T., Y.S.); College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Gwanak-gu, Seoul, Korea (W.L.); and National Institute of Informatics, Chiyoda-ku, Tokyo, Japan (Y.A.).
Sugiyama Laboratory, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Kanagawa, Japan (S.K., K.T., Y.S.); College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Gwanak-gu, Seoul, Korea (W.L.); and National Institute of Informatics, Chiyoda-ku, Tokyo, Japan (Y.A.)
Drug Metab Dispos. 2021 Apr;49(4):298-304. doi: 10.1124/dmd.120.000023. Epub 2021 Feb 8.
Bosentan is a high-affinity antagonist of endothelin receptors and one of the earliest examples for target-mediated drug disposition [a type of nonlinear pharmacokinetics (PKs) caused by saturable target binding]. The previous physiologically based PK (PBPK) modeling indicated that the nonlinear PKs of bosentan was explainable by considering saturable hepatic uptake. However, it remained unexamined to what extent the saturable target binding contributes to the nonlinear PKs of bosentan. Here, we developed a PBPK model incorporating saturable target binding and hepatic uptake and analyzed the clinical bosentan PK data using the cluster Gauss-Newton method (CGNM). The PBPK model without target binding fell short in capturing the bosentan concentrations below 100 nM, based on the PK profiles and the goodness-of-fit plot. Both global and local identifiability analyses (using the CGNM and Fisher information matrix, respectively) informed that the target binding parameters were identifiable only if the observations from the lowest dose (10 mg) were included. By analyzing blood PK profiles alone, the PBPK model with target binding yielded practically identifiable target binding parameters and predicted the maximum target occupancies of 0.6-0.8 at clinical bosentan doses. Our results indicate that target binding, albeit not a major contributor to the nonlinear bosentan PKs, may offer a prediction of target occupancy from blood PK profiles alone and potential guidance on achieving optimal efficacy outcomes, under the condition when the high-affinity drug target is responsible for the efficacy of interest and when the dose ranges cover varying degrees of target binding. SIGNIFICANCE STATEMENT: By incorporating saturable target binding, our physiologically based pharmacokinetic (PBPK) model predicted in vivo target occupancy of bosentan based only on the blood concentration-time profiles obtained from a wide range of doses. Our analysis highlights the potential utility of PBPK models that incorporate target binding in predicting target occupancy in vivo.
波生坦是内皮素受体的高亲和力拮抗剂，也是最早的靶向介导药物处置（一种由饱和靶结合引起的非线性药代动力学（PKs））的例子之一。之前的基于生理的 PK（PBPK）模型表明，考虑到饱和肝摄取，波生坦的非线性 PKs 是可以解释的。然而，到什么程度饱和靶结合对波生坦的非线性 PKs 有贡献仍未得到检验。在这里，我们开发了一个包含饱和靶结合和肝摄取的 PBPK 模型，并使用聚类高斯-牛顿法（CGNM）分析了临床波生坦 PK 数据。基于 PK 曲线和拟合优度图，没有靶结合的 PBPK 模型无法捕捉低于 100 nM 的波生坦浓度。全局和局部可识别性分析（分别使用 CGNM 和 Fisher 信息矩阵）表明，只有包括最低剂量（10 mg）的观察值，靶结合参数才是可识别的。仅通过分析血液 PK 曲线，具有靶结合的 PBPK 模型可产生实际可识别的靶结合参数，并预测临床波生坦剂量下最大靶占有率为 0.6-0.8。我们的结果表明，尽管靶结合不是导致波生坦非线性 PKs 的主要因素，但在高亲和力药物靶标负责感兴趣的疗效且剂量范围涵盖不同程度的靶结合的情况下，它可以提供仅从血液 PK 曲线预测靶占有率的预测，并为实现最佳疗效结果提供潜在指导。意义陈述：通过纳入饱和靶结合，我们的基于生理的药代动力学（PBPK）模型仅基于从广泛剂量获得的血药浓度-时间曲线，预测了波生坦的体内靶占有率。我们的分析强调了在预测体内靶占有率方面，纳入靶结合的 PBPK 模型的潜在效用。
