LB-018 - BUILDING AN OAT-MATRIX: PERFORMANCE VERIFICATION OF IVIVE-PBPK MODELS IN THE PREDICTION OF OAT1/OAT3 MEDIATED DRUG-DRUG INTERACTIONS

O. Hatley¹, J. Dinh¹, U. Ezuruike¹, C. Brochot², S. Neuhoff³; ¹Certara UK, Sheffield, England, United Kingdom, ²Certara UK, Sheffield, England, ³Certara UK, Sheffield, UK.

Background: Organic Anion Transporters (OAT) 1 and OAT3 are located in proximal tubular basolateral membranes and play an important role in the renal excretion of various drugs. As such, drug-drug interactions (DDIs) involving OAT 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 OAT1 and OAT3 mediated DDIs, and to provide an OAT-matrix that gives sufficient confidence to be used for regulatory submissions.

Methods: For the matrix, clinical studies where an OAT DDI was reported were identified using the Certara Drug Interaction Solution Database (Certara, USA). IVIVE-PBPK models for OAT 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 area under the concentration-time profile and renal clearance ratios (AUCR and CLrR, respectively) were used to estimate matrix performance.

Results: The database search yielded 51 publications, of which 6 were excluded. Exclusion criteria were cocktail studies, studies in patients, repeated publications, or no matching control arm. The final matrix included twenty DDIs. Eight substrates (adefovir, baricitinib, furosemide, methotrexate, oseltamivir carboxylate, pravastatin, penicillin G, and tenofovir) and two perpetrators (probenecid and gemfibrozil) were identified. Eight unique substrate-perpetrator pairs were simulated. The initial percentage of AUCR that were predicted within 2- and 1.25-fold of observed data was 100% and 70%, respectively. The AFE (bias) and AAFE (precision) were 0.89 and 1.22 for AUCR and 0.97 and 1.28 for CLrR, respectively.

Conclusion: All but one AUCR and CLrR in the matrix were predicted within 2-fold of observed data, supporting the use of these IVIVE-PBPK models to predict the extent of renal OAT1/OAT3 DDIs.