Regulatory agencies worldwide expect that clinical pharmacokinetic drug–drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug’s safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus^® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4′-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ’s nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent–metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus^®. The model was utilized for Drug–Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ’s nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUC_last ratio was 0.49 (slightly outside of the two-fold range). CBZ’s nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Conventional exposure–response (E–R) analyses, such as logistic regression and time‐to‐event analysis using summary exposure metrics, are often conducted in oncology using data from the pivotal trial(s) with a single dose level to support dosing decisions. However, these E–R analyses, affected by multiple confounding factors, may mischaracterize true E–R relationships, potentially limiting their utility in dosing decisions. This study investigates potential mischaracterization in such analyses influenced by the following time‐dependent confounding factors: exposure accumulation, dose modification patterns, and event onset time. We used a simulation‐based approach to evaluate two E–R scenarios: ER1, where event time generated with a Weibull distribution is not affected by drug exposure, and ER2, where the response is driven by drug exposure via a joint PK‐tumor size model. Our analyses indicate that when using time‐dependent exposure metrics (e.g., average concentration till event/censoring), exposure accumulation tends to induce an inverse E–R trend, while dose modifications (interruptions/reductions) likely induce a positive E–R trend. Simulations suggest that employing static exposure metrics (e.g., first‐cycle or steady‐state) minimizes these biases. Additionally, adopting an E max model aligned with the underground truth in ER2 in the E–R analyses could reduce bias. When significant dose modifications are present, including relevant data from a dose‐range study and employing modified methods for time‐dependent exposure derivation may help mitigate bias. Overall, using multiple exposure metrics (including static ones) to assess E–R consistency and interpreting the potential causal effects with totality of evidence (including dose–response results) should be implemented to better inform dosing decisions.