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基于 OATP1B 介导的利福平相互作用研究的粪卟啉原-I 的 PBPK 模型参数组合的聚类高斯牛顿法分析。

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies.

机构信息

Laboratory of Clinical Pharmacology, Yokohama University of Pharmacy, Yokohama, Kanagawa, Japan.

Laboratory of Quantitative System Pharmacokinetics/Pharmacodynamics, School of Pharmacy, Josai International University, Tokyo, Japan.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2022 Oct;11(10):1341-1357. doi: 10.1002/psp4.12849. Epub 2022 Aug 9.

Abstract

Coproporphyrin I (CP-I) is an endogenous biomarker supporting the prediction of drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically-based pharmacokinetic (PBPK) model for CP-I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP-I were estimated using the cluster Gauss-Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least-squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CL ), the rate of biosynthesis (v ), and the OATP1B inhibition constant of RIF(K ) were estimated by fitting to the observed CP-I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CL , K , and v were sensitive parameters. The obtained K for CP-I was 5.0- and 2.8-fold lower than that obtained for statins, confirming our previous findings describing substrate-dependent K values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP-I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP-I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP-I.

摘要

粪卟啉原 I(CP-I)是一种内源性生物标志物,可用于预测涉及肝脏有机阴离子转运多肽 1B(OATP1B)的药物相互作用(DDI)。我们之前使用 OATP1B 抑制剂利福平(RIF)的临床 DDI 数据构建了 CP-I 的基于生理学的药代动力学(PBPK)模型。在这项研究中,使用聚类高斯-牛顿法(CGNM)估计了 CP-I 的 PBPK 模型参数,CGNM 是一种用于寻找非线性最小二乘问题的多个近似解的算法。通过拟合涉及改变 RIF 剂量的两项不同临床研究中观察到的 CP-I 血药浓度,估计了 8 个未知参数,包括肝总体内在清除率(CL)、生物合成速率(v)和 RIF 的 OATP1B 抑制常数(K)。CGNM 获得了多个参数组合,可以很好地捕捉临床数据。其中,CL、K 和 v 是敏感参数。获得的 CP-I 的 K 值比他汀类药物低 5 倍和 2.8 倍,证实了我们之前描述的底物依赖性 K 值的发现。总之,通过 CGNM 对 PBPK 模型参数组合的分析,可以估计 CP-I 的三个基本参数,以捕捉 DDI 谱,即使其他参数仍然未知。CGNM 还阐明了在 RIF 影响下,为了捕获 CP-I 浓度时间过程,适当组合其他未识别参数的重要性。所描述的 CGNM 方法还可以支持构建除 CP-I 之外的其他转运蛋白生物标志物的稳健 PBPK 模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c0f/9574750/0411d7af84eb/PSP4-11-1341-g004.jpg

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