Physiologically‐based pharmacokinetic model‐based translation of <scp>OATP1B</scp>‐mediated drug–drug interactions from coproporphyrin I to probe drugs

Mochizuki, Toshiaki; Aoki, Yasunori; Yoshikado, Takashi; Yoshida, Kenta; Lai, Yurong; Hirabayashi, Hideki; Yamaura, Yoshiyuki; Rockich, Kevin; Taskar, Kunal S.; Takashima, Tadayuki; Chu, Xiaoyan; Zamek‐Gliszczynski, Maciej J.; Mao, Jialin; Maeda, Kazuya; Furihata, Kenichi; Sugiyama, Yuichi; Kusuhara, Hiroyuki

doi:10.1111/cts.13272

Cited by 17 publications

(26 citation statements)

References 37 publications

(86 reference statements)

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“…We found that CL int ,all of CP‐I could be determined based primarily on the baseline‐to‐peak phase of its blood concentration through analysis of the clinical data where cyclosporin A, another strong inhibitor for OATP1B, was used as the OATP1B perpetrator. 29 CL int ,all in the present study could also be determined from the observed baseline‐to‐peak phase under the condition that the rate‐limiting step (uptake) of hepatic elimination was strongly inhibited by RIF. This subsequently enabled the determination of v syn from the steady‐state data without RIF.…”

Section: Discussionmentioning

confidence: 75%

“…K i ,u,OATP could be primarily determined from the peak‐to‐baseline phase of the CP‐I blood concentration with the OATP1B inhibitor 29 . The quartiles for the estimated RIF K i ,u,OATP for CP‐I (using the reported model for CP‐I, 22 and the modified model for RIF) were similar between the analyses of the 2‐Dose‐RIF and the 3‐Dose‐RIF studies (0.044–0.055 μM and 0.046–0.060 μM, respectively; Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, CL int ,all and v syn obtained by the CGNM were close to the values obtained by the conventional method, both of which well‐reproduce PK of CP‐I and DDI effect by RIF as summarized in a table in the Supplementary Text. We found that CL int ,all of CP‐I could be determined based primarily on the baseline‐to‐peak phase of its blood concentration through analysis of the clinical data where cyclosporin A, another strong inhibitor for OATP1B, was used as the OATP1B perpetrator 29 . CL int ,all in the present study could also be determined from the observed baseline‐to‐peak phase under the condition that the rate‐limiting step (uptake) of hepatic elimination was strongly inhibited by RIF.…”

Section: Discussionmentioning

confidence: 99%

“…The flowchart for the PBPK‐CGNM analyses is shown in Figure S2a . After running the CGNM, parameter combinations that could well explain the blood concentration data were selected by the cutoff method composed of two steps 29 : [i] Chi‐square method, which rejects parameter combinations with significantly larger SSRs than the minimum SSR by assuming a Chi‐square distribution of SSR (alpha value = 0.05), and [ii] the Elbow method, which chooses the cutoff so that the sum of the areas of two trapezoids in the order SSR plot among the SSRs that are not rejected by [i] becomes a minimum (Figure S2b ). Although it is assumed that multiple parameter combinations exist with exactly the same minimum SSR, such multiple parameter combinations were not found due to an artifact of numerical computation.…”

Section: Methodsmentioning

confidence: 99%

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

Yoshikado

Aoki

Mochizuki

et al. 2022

CPT Pharmacom & Syst Pharma

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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 int,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, CL int,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.

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Yoshikado

Aoki

Mochizuki

et al. 2022

CPT Pharmacom & Syst Pharma

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“…However, this preliminary analysis was made based on a very limited data set. A comprehensive analysis using a more diverse compound set is needed to determine whether Method 2 consistently yields a good performance for OATP1B-mediated DDI predictions for (Mochizuki et al, 2022a;Yoshikado et al, 2022).…”

Section: Clinical Relevance Of Trans-inhibition Mechanism To Oatp1b1-...mentioning

confidence: 99%

Preincubation Time-Dependent, Long-Lasting Inhibition of Drug Transporters and Impact on the Prediction of Drug−Drug Interactions

Nozaki

Izumi

2023

Drug Metab Dispos

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E 2 G, estradiol-17β-glucuronide; EMA, European Medicines Agency; FDA, U.S. Food and Drug Administration; HBV, hepatitis B virus; HDV, hepatitis D virus; HEK293, human embryonic kidney 293; IC 50 , half maximal inhibitory concentration; K i , inhibition constant; K p,uu , intracellular-to-extracellular or tissue-to-plasma unbound drug concentration ratio; LAT, L-type amino acid transporter; MATE, multidrug and toxin extrusion; MDCK, Madin-Darby canine kidney; MHLW, Ministry of Health, Labour and Welfare; NTCP, Na + /taurocholate cotransporting polypeptide; OAT, organic anion transporter; OATP/Oatp, organic anion transporting polypeptide; OCT, organic cation transporter; SLC, solute carrier; statin, 3hydroxy-3-methylglutaryl-CoA reductase inhibitor; TCA, taurocholic acid; TDI, time-dependent inhibition; TLC, taurolithocholic acid.

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Utilization of OATP1B Biomarker Coproporphyrin‐I to Guide Drug–Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry

Kikuchi,

Chothe,

Chu

et al. 2023

Clin Pharma and Therapeutics

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Drug‐drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin‐I (CP‐I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP‐I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP‐I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP‐I exposure changes. Static models to predict changes in exposure of CP‐I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP‐I area‐under‐the‐curve ratio (AUCR) or maximum concentration ratio (CmaxR) and AUCR of substrate drugs. In general, the CP‐I CmaxR was equal to or greater than the CP‐I AUCR. CP‐I CmaxR <1.25 was associated with absence of OATP1B‐mediated DDIs (AUCR <1.25) with no false negative predictions. CP‐I CmaxR <2 was associated with weak OATP1B‐mediated DDIs (AUCR <2). A correlation was identified between CP‐I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP‐I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP‐I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP‐I exposure that may be used to prioritize, delay or replace clinical DDI studies.

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Physiologically‐based pharmacokinetic model‐based translation of OATP1B‐mediated drug–drug interactions from coproporphyrin I to probe drugs

Cited by 17 publications

References 37 publications

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

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

Preincubation Time-Dependent, Long-Lasting Inhibition of Drug Transporters and Impact on the Prediction of Drug−Drug Interactions

Utilization of OATP1B Biomarker Coproporphyrin‐I to Guide Drug–Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry

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