Frontiers in pharmacology · 2026
Coproporphyrin I PBPK model in the open systems pharmacology suite enables precise prediction of OATP 1B- mediated drug interactions through calibrated In vivo inhibition constants.
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Kanacher Tobias, Pellowe Moriah, Kovar Christina, Eriksson Johanna, Wu Jing, Busse David, Gómez-Mantilla Jose David, Stopfer Peter, Ince Ibrahim, Huang Fenglei
Аннотация
BACKGROUND: Organic Anion Transporting Polypeptide (OATP) 1B1/3 inhibition can lead to clinically significant drug-drug interactions (DDIs). Coproporphyrin-I (CP-I) is an established endogenous biomarker for assessing the OATP1B inhibitory activity of new chemical entities. However, current physiologically based pharmacokinetic (PBPK) models for CP-I are restricted to proprietary software, limiting widespread adoption and collaboration.
METHODS: We developed and qualified an open-source PBPK model for CP-I using the Open Systems Pharmacology (OSP) Suite. The model describes the endogenous synthesis and OATP1B1/MRP2-mediated disposition of CP-I. It was integrated into a DDI network comprising the precipitants rifampicin, probenecid, and cyclosporine A, and the object drugs atorvastatin, pitavastatin, and rosuvastatin. A key methodological feature was the estimation of in vivo inhibition constants (Ki,OATP1B) for precipitants based on clinical CP-I data rather than relying solely on in vitro measurements.
RESULTS: The CP-I model successfully captured baseline plasma concentration-time profiles with a Geometric Mean Fold Error (GMFE) of 1.15. In the DDI network analysis, using CP-I-derived in vivo Ki,OATP1B values for the strong OATP1B inhibitors rifampicin and cyclosporine yielded superior predictive accuracy compared to in vitro values, with most predicted DDI ratios for statins falling within the 2-fold acceptance range. Specifically for cyclosporine, the model required an adjustment of the endogenous CP-I synthesis rate to account for baseline differences in the specific study population; incorporating this allowed for the estimation of an in vivo Ki,OATP1B (4.7 nM) that accurately predicted interactions with pitavastatin and rosuvastatin. Notably, probenecid, a weak OATP1B inhibitor, showed an in vivo Ki,OATP1B estimate within a similar range of its in vitro measured value. Consequently, the scaling factor between in vitro and in vivo Ki,OATP1B appears to be a compound-specific parameter requiring independent identification for each precipitant.
CONCLUSION: This study presents a qualified, freely available PBPK framework for CP-I. It demonstrates that calibrating participant models using early clinical biomarker data enables reliable prospective prediction of OATP1B-mediated DDIs. This workflow supports model-informed drug development (MIDD) strategies, potentially reducing the need for dedicated clinical DDI studies.
METHODS: We developed and qualified an open-source PBPK model for CP-I using the Open Systems Pharmacology (OSP) Suite. The model describes the endogenous synthesis and OATP1B1/MRP2-mediated disposition of CP-I. It was integrated into a DDI network comprising the precipitants rifampicin, probenecid, and cyclosporine A, and the object drugs atorvastatin, pitavastatin, and rosuvastatin. A key methodological feature was the estimation of in vivo inhibition constants (Ki,OATP1B) for precipitants based on clinical CP-I data rather than relying solely on in vitro measurements.
RESULTS: The CP-I model successfully captured baseline plasma concentration-time profiles with a Geometric Mean Fold Error (GMFE) of 1.15. In the DDI network analysis, using CP-I-derived in vivo Ki,OATP1B values for the strong OATP1B inhibitors rifampicin and cyclosporine yielded superior predictive accuracy compared to in vitro values, with most predicted DDI ratios for statins falling within the 2-fold acceptance range. Specifically for cyclosporine, the model required an adjustment of the endogenous CP-I synthesis rate to account for baseline differences in the specific study population; incorporating this allowed for the estimation of an in vivo Ki,OATP1B (4.7 nM) that accurately predicted interactions with pitavastatin and rosuvastatin. Notably, probenecid, a weak OATP1B inhibitor, showed an in vivo Ki,OATP1B estimate within a similar range of its in vitro measured value. Consequently, the scaling factor between in vitro and in vivo Ki,OATP1B appears to be a compound-specific parameter requiring independent identification for each precipitant.
CONCLUSION: This study presents a qualified, freely available PBPK framework for CP-I. It demonstrates that calibrating participant models using early clinical biomarker data enables reliable prospective prediction of OATP1B-mediated DDIs. This workflow supports model-informed drug development (MIDD) strategies, potentially reducing the need for dedicated clinical DDI studies.