Cluster Gauss‐Newton method analyses of PBPK model parameter combinations of coproporphyrin‐I based on OATP1B‐mediated rifampicin interaction studies. (9th August 2022)

• Record Type: Journal Article
• Authors: Yoshikado, Takashi; Aoki, Yasunori; Mochizuki, Tatsuki; Rodrigues, A. David; Chiba, Koji; Kusuhara, Hiroyuki; Sugiyama, Yuichi
• Is Part Of: CPT: pharmacometrics & systems pharmacology. Volume 11:Number 10(2022)
• Page Start: 1341
• Page End: 1357
• Publication Date: 2022-08-09
• Subjects: Pharmacokinetics -- Periodicals; Pharmacology -- Periodicals; Pharmacokinetics; Periodicals; 615.05
• DOI: 10.1002/psp4.12849
• Languages: English
• ISSNs: 2163-8306

Abstract

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 (CLint, all ), the rate of biosynthesis ( v syn ), and the OATP1B inhibition constant of RIF( K i, u, OATP ) 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, CLint, all, K i, u, OATP, and v syn were sensitive parameters. The obtained K i, u, OATP for CP‐I was 5.0‐ and 2.8‐fold lower than that obtained for statins, confirming our previous findings describing substrate‐dependent K i, u, OATP 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.