Presenting Author: Wei Yin, PhD – Takeda Pharmaceutical Company Limited
Eric T Ballard, PhD – Takeda Pharmaceutical Company Limited; Liming Zhang, PhD – Takeda Pharmaceutical Company Limited; Lawrence Cohen, PhD – Takeda Pharmaceutical Company Limited; Mackenzie C Bergagnini-Kolev, PhD – Certara UK Ltd; Ian E Templeton, PhD – Certara UK Ltd; Hannah M Jones, PhD – Certara UK Ltd; Hongxia Jia, PhD – Takeda Pharmaceutical Company Limited
Soticlestat (TAK-935) is in phase 3 development for adjunctive treatment of seizures in Dravet syndrome and Lennox–Gastaut syndrome, and is metabolized predominantly via UDP glucuronosyltransferase 2B4 and less so via cytochrome P450 (CYP) 3A4. In vitro, soticlestat is an inhibitor of CYP2C8, CYP2C9, CYP2C19, CYP3A4 and P-glycoprotein (P-gp). We developed a physiologically based pharmacokinetic (PBPK) model to predict potential drug–drug interactions (DDIs) of soticlestat.Methods
The PBPK model was developed and refined based on in vitro and clinical PK data, and verified using data from a single- and a multiple-rising-dose study, and two clinical DDI studies. The verified model was applied to evaluate soticlestat as a victim of CYP inhibition and induction, and as a perpetrator of CYP and P-gp inhibition. Virtual populations simulated for model development, refinement, and verification were matched to the clinical studies for sample size, sex, and age. For model application, a virtual population of 100 North European healthy volunteers aged 20–50 years (50% female) was used. All analyses were completed using the SimcypTM v20 Population-Based Simulator.Results
Simulated area under the plasma concentration–time curve from time zero to infinity (AUC0-inf) and maximal drug concentration (Cmax) based on the final PBPK model for all doses evaluated were within 2-fold of the observed values from single- and multiple-rising-dose studies. For soticlestat 300 mg (maximum twice-daily [BID] dose in phase 3 studies), the model-simulated AUC0-inf and Cmax geometric mean ratios (GMRs) were 0.88 fold and 0.78‑fold of the observed values, respectively. For soticlestat with and without coadministration of itraconazole (strong CYP3A4 inhibitor), the model-simulated versus observed AUC0-inf and Cmax GMRs were 1.05 and 1.10-fold, respectively. For soticlestat with and without coadministration of rifampin (strong CYP3A4 inducer), the model under-predicted the DDI, with simulated AUC0-inf and Cmax GMRs of >2.9-fold of the observed values.
The model predicted a weak interaction for soticlestat with strong CYP3A4 inhibition and no interactions with moderate and weak CYP3A4 inhibitors (Table 1). Weak-to-moderate interactions were predicted with strong CYP3A4 inducers and a weak interaction was predicted with moderate CYP3A4 inducers. No clinically significant DDIs were predicted following coadministration of multiple doses of 300 mg BID soticlestat with sensitive CYP2C8, CYP2C9, CYP2C19, CYP3A4, and P-gp substrates.Conclusions
PBPK modeling is an important tool that allows the prediction of DDIs when clinical data are limited and can inform dose adjustment guidance in clinical practice. Our verified soticlestat PBPK model reasonably predicted DDIs, except for with rifampin, and will support the clinical development of soticlestat and regulatory submissions. Funding
Study funded by Takeda Pharmaceutical Company Ltd