Background: Lopinavir is a protease inhibitor indicated for the treatment of HIV infection. It is coformulated with low doses of ritonavir in order to enhance its pharmacokinetic profile. After oral administration, plasma concentrations of lopinavir can vary widely between different HIV-infected patients.
Objective: To develop and validate a population pharmacokinetic model for lopinavir and ritonavir administered simultaneously in a population of HIV-infected adults. The model sought was to incorporate patient characteristics influencing variability in the drug concentration and the interaction between the two compounds.
Methods: HIV-infected adults on stable therapy with oral lopinavir/ritonavir in routine clinical practice for at least 4 weeks were included. A concentration-time profile was obtained for each patient, and blood samples were collected immediately before and 1, 2, 4, 6, 8, 10 and 12 hours after a morning lopinavir/ritonavir dose. Lopinavir and ritonavir concentrations in plasma were determined by high-performance liquid chromatography. First, a population pharmacokinetic model was developed for lopinavir and for ritonavir separately. The pharmacokinetic parameters, interindividual variability and residual error were estimated, and the influence of different patient characteristics on the pharmacokinetics of lopinavir and ritonavir was explored. Then, a simultaneous model estimating the pharmacokinetics of both drugs together and incorporating the influence of ritonavir exposure on oral clearance (CL/F) of lopinavir was developed. Population analysis was performed using nonlinear mixed-effects modelling (NONMEM version V software). The bias and precision of the final model were assessed through Monte Carlo simulations and data-splitting techniques.
Results: A total of 53 and 25 Caucasian patients were included in two datasets for model building and model validation, respectively. Lopinavir and ritonavir pharmacokinetics were described by one-compartment models with first-order absorption and elimination. The presence of advanced liver fibrosis decreased CL/F of ritonavir by nearly half. The volume of distribution after oral administration (Vd/F) and CL/F of lopinavir were reduced as alpha1-acid glycoprotein (AAG) concentrations increased. CL/F of lopinavir was inhibited by ritonavir concentrations following a maximum-effect model (maximum inhibition [Imax] = 1, concentration producing 50% of the I(max) [IC50] = 0.36 mg/L). The final model appropriately predicted plasma concentrations in the model-validation dataset with no systematic bias and adequate precision.
Conclusion: A population model to simultaneously describe the pharmacokinetics of lopinavir and ritonavir was developed and validated in HIV-infected patients. Bayesian estimates of the individual parameters of ritonavir and lopinavir could be useful to predict lopinavir exposure based on the presence of advanced liver fibrosis and the AAG concentration in an individual manner, with the aim of maximizing the chances of treatment success.