CPT: Pharmacometrics & Systems Pharmacology (Oct 2022)

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

  • Takashi Yoshikado,
  • Yasunori Aoki,
  • Tatsuki Mochizuki,
  • A. David Rodrigues,
  • Koji Chiba,
  • Hiroyuki Kusuhara,
  • Yuichi Sugiyama

DOI
https://doi.org/10.1002/psp4.12849
Journal volume & issue
Vol. 11, no. 10
pp. 1341 – 1357

Abstract

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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 (vsyn), and the OATP1B inhibition constant of RIF(Ki,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, Ki,u,OATP, and vsyn were sensitive parameters. The obtained Ki,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 Ki,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.