PI-095 - BUILDING AN OATP1B-MATRIX: PERFORMANCE VERIFICATION OF IVIVE-PBPK MODELS IN THE PREDICTION OF OATP1B1/1B3 MEDIATED DRUG-DRUG INTERACTIONS

J. Dinh¹, O. Hatley¹, L. Curry¹, C. Cross¹, U. Ezuruike¹, M. Harwood², K. Crewe¹, S. Neuhoff²; ¹Certara UK, Sheffield, England, United Kingdom, ²Certara UK, Sheffield, UK.

Background: Organic Anion Transporter Polypeptides (OATP) 1B1/1B3 transporters can modulate hepatic drug distribution. As such, drug-drug interactions (DDIs) involving OATP transporters can result in changes to drug pharmacokinetics. In vitro-to-in vivo extrapolation linked to physiologically based pharmacokinetic (IVIVE-PBPK) models have been used to assess transporter-mediated DDI risk in silico. However, these models must be verified before routine use in regulatory applications. Thus, the overall aim of this project was to evaluate the performance of a large set of IVIVE-PBPK models in predicting the magnitude of OATP1B mediated DDIs, and to provide an OATP1B-matrix that gives sufficient confidence to be used for regulatory submissions.

Methods: For the matrix, clinical studies where an OATP1B DDI was reported were identified using the Certara Drug Interaction Solution Database (Certara, USA). IVIVE-PBPK models for OATP1B substrates and perpetrators were newly developed or verified models were obtained from The Simcyp PBPK Simulator (V23 R2). Multiple trial simulations were performed matching study design and population characteristics to the DDI scenarios. Observed and simulated DDI maximum plasma concentration and area under the concentration-time profile ratios (CmaxR and AUCR, respectively) were used to estimate matrix performance.

Results: The database search yielded 221 publications, of which 103 were excluded. Exclusion criteria were case reports, studies in patients, or populations not available in The Simulator. The final matrix included 118 publications with >130 study arms. Twelve substrates (e.g., several statins, methotrexate, pemafibrate, and endogenous biomarkers like coproporphyrin-I) and 7 perpetrators (e.g., probenecid and rifampicin) were identified. The initial percentage of CmaxR and AUCR that were predicted within 2-fold of observed data was 89% and 92%, respectively. This could be improved by considering the impact of inhibiting metabolites of the perpetrators within the model (e.g., cyclosporine and gemfibrozil). Of the 39 unique substrate-perpetrator pairs, 36 were simulated within the established Guest-criteria.

Conclusion: The majority of CmaxR and AUCR in the matrix were predicted within 2-fold of observed data, supporting the use of these IVIVE-PBPK models to predict the extent of OATP1B DDIs.