Objectives: To use a viral dynamics model to compare the effectiveness of in vivo viral inhibition of several doses of maraviroc (MVC;UK-427,857) and to use a modeling approach to support design decisions for a monotherapy study using various dosing regimens of maraviroc given with and without food.
Design: The pharmacokinetic-pharmacodynamic model was developed using clinical data from a first monotherapy study (study A4001007). This was a randomized, double-blind, placebo-controlled, multicenter study of maraviroc in 44 asymptomatic HIV-1-infected patients. Patients received maraviroc under food restrictions at 25 mg once daily or 50, 100, or 300 mg twice daily, or placebo for 10 days.
Methods: Antiviral responses were assessed by measuring plasma HIV-1 RNA levels during screening, during randomization, at baseline, and daily during the 10 days of treatment and at days 11 to 15, 19, 22, 25, and 40. An integrated pharmacokinetic-pharmacodynamic model was developed using the mixed effects modeling approach with patients' pharmacokinetic profiles on the last day of treatment, HIV-1 RNA levels over time, and the individual viral susceptibility. The parameters derived from the viral dynamic model were used to calculate average viral inhibition fraction, decay rate of actively infected cells, and basic reproductive ratio for each treatment group. Monte Carlo simulation was then used to determine the distribution of viral load change across simulated patients over time for each regimen to be studied in another monotherapy study, A4001015.
Results: The decline rate in the 300 mg twice daily group was comparable to that induced by potent protease inhibitor monotherapy, but was significantly slower than that in patients receiving combination therapy including both protease inhibitor and reverse transcriptase inhibitors. The efficacy of inhibition in vivo was estimated to range from 0.15 to 0.38 for the 25 mg once daily dose group and from 0.88 to 0.96 for the 300 mg twice daily dose group.
Conclusions: The model has aided the analysis and interpretation of the clinical data. The use of a model-based approach for selecting doses can accelerate drug development by replacing some arms or trials with simulations.