A Physiologically-Based Pharmacokinetic Simulation to Evaluate Approaches to Mitigate Efavirenz-Induced Decrease in Levonorgestrel Exposure with a Contraceptive Implant

Background: Levonorgestrel implant is a highly effective hormonal contraceptive, but its efficacy may be compromised when used with cytochrome enzyme inducers such as efavirenz. The primary aim of this study was to evaluate methods of mitigating the drug interaction. Methods: Using a physiologically-based pharmacokinetic (PBPK) model for levonorgestrel that we developed within the Simcyp® program, we evaluated a higher dose of levonorgestrel implant, a lower dose of efavirenz, and the combination of both, as possible methods to mitigate the interaction. In addition, we investigated the impact on levonorgestrel total and unbound concentrations of other events likely to be associated with efavirenz coadministration: changes in plasma protein binding of levonorgestrel (as with displacement) and high variability of efavirenz exposure (as with genetic polymorphism of its metabolism). The range of fraction unbound tested was 0.6% to 2.6%, and the range of efavirenz exposure ranged from the equivalent of 200 mg to 4800 mg doses. Results: Levonorgestrel plasma concentrations at any given time with a standard 150 mg implant dose are predicted to be approximately 68% of those of control when given with efavirenz 600 mg and 72% of control with efavirenz 400 mg. With double-dose levonorgestrel, the predictions are 136% and 145% of control, respectively. A decrease in levonorgestrel plasma protein binding is predicted to primarily decrease total levonorgestrel plasma concentrations, whereas higher efavirenz exposure is predicted to decrease total and unbound concentrations. Conclusions: Simulations suggest that doubling the dose of levonorgestrel, particularly in combination with 400 mg daily efavirenz, may mitigate the drug interaction. Changes in levonorgestrel plasma protein binding and efavirenz genetic polymorphism may help explain differences between model predictions and clinical data but need to be studied further.