Quantitative prediction of OATP1B-mediated drug–drug interactions using endogenous biomarker coproporphyrin I. (3rd April 2022)

• Record Type: Journal Article
• Authors: Takubo, Hiroaki; Bessho, Koji; Watari, Ryosuke; Shigemi, Ryota
• Is Part Of: Xenobiotica. Volume 52:Number 4(2022)
• Page Start: 397
• Page End: 404
• Publication Date: 2022-04-03
• Subjects: Coproporphyrin I -- OATP1B -- drug–drug interaction -- biomarker -- prediction -- endogenous substrate; Metabolism -- Periodicals; Drugs -- Physiological effect -- Periodicals; Food additives -- Periodicals; Chemicals -- Physiological effect -- Periodicals; Biochemistry -- Periodicals; Pharmaceutical Preparations -- metabolism -- Periodicals; Metabolism -- Periodicals; 574.133
• DOI: 10.1080/00498254.2022.2085210
• Languages: English
• ISSNs: 0049-8254

Abstract

Abstract: Evaluation of the organic anion transporting polypeptide (OATP) 1B-mediated drug–drug interaction (DDI) potential is important for drug development. The focus of this study was coproporphyrin I (CP-I), an endogenous OATP1B biomarker. We investigated a new approach to OATP1B-mediated DDI prediction based on the mechanistic static pharmacokinetics (MSPK) model. The ratio of the area under the plasma concentration–time curve (AUCR) with and without co-administration of rifampicin (a typical OATP1B inhibitor) was found for CP-I and OATP1B substrate, respectively, and was then used to derive the correlation curve equation. The AUCR with and without co-administration of another OATP1B inhibitor than rifampicin was then predicted for the OATP1B substrates by substituting the AUCR of CP-I in the correlation curve equation to verify the predictability of the AUCR of the OATP1B substrates. The derived correlation curve equation between CP-I and the OATP1B substrates of the AUCRs with and without co-administration of rifampicin matched the observed AUCRs well. Regarding pitavastatin, rosuvastatin, and pravastatin, 92.9% of the predicted AUCR values were within a two-fold range of the observed values, indicating that this approach may be a good way to quantitatively predict DDI potential.