Abstract
Background and objective
Understanding interindividual pharmacokinetic variability and potential for drug interactions is of clinical importance for pomalidomide given its long-term use in patients with relapsed and/or refractory multiple myeloma (RRMM). This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of pomalidomide that integrates multiple sources of pharmacokinetic (PK) variability, including RRMM-related physiology, ethnicity, hepatic impairment, food effects, drug interactions, and cigarette smoking.Methods
A PBPK model was developed incorporating in silico-predicted and experimentally measured physicochemical and biopharmaceutical properties, together with physiological parameters representing disease state, liver function, prandial status, and ethnic differences. Changes in PK and systemic exposure associated with multiple sources of variability, evaluated as shifts in central tendency, were predicted by the model.Results
The model provided reliable estimates of observed PK profiles across all evaluated scenarios, with more than 90% of predicted PK metrics falling within 0.80- to 1.25-fold of clinically reported values. Changes in pharmacokinetics associated with RRMM in people of European and Japanese ancestry, as well as severity-graded hepatic impairment, were reasonably predicted, despite slight overestimation of systemic exposure in severe hepatic impairment (prediction-to-reported ratio ≈ 1.55). Physiologically based pharmacokinetic model predictions of the effect of co-administration with high-fat, high-calorie meals (15-30% reduction in Cmax) were also consistent with clinical observations in both European and Chinese cohorts. In addition, the PBPK model demonstrated good predictive performance in simulating moderate interactions perpetrated by fluvoxamine (cytochrome P450 1A2 [CYP1A2] inhibition) and tobacco smoking (CYP1A2 induction) and the negligible effect of ketoconazole co-administration (CYP3A4 inhibition), with predicted-to-reported Cmax and AUC ratios all within the 0.90-1.05 range.Conclusions
Overall, this work highlights the utility of a PBPK modelling framework in providing mechanistic insights into multiple sources of pomalidomide PK variability, with potential broader applicability to other drugs.