Decrease of atazanavir and lopinavir plasma concentrations in a boosted double human immunodeficiency virus protease inhibitor salvage regimen

Prediction of area under the concentration-time curve for lopinavir from peak or trough lopinavir concentrations in patients receiving lopinavir-ritonavir therapy

PURPOSE

A "single time point" strategy for predicting the area under the concentration-time curve (AUC) for lopinavir in patients receiving ritonavir-boosted lopinavir therapy was investigated.

METHODS

Linear regression equations describing the relationships of lopinavir peak and trough concentrations to lopinavir AUC values were established using pharmacokinetic data from published studies of patients or healthy subjects receiving lopinavir and ritonavir at standard dosages. The resulting "trough-AUC model" and "peak-AUC model" were used to predict lopinavir AUC values in the evaluated study populations (total n = 479); those values were then compared with reported AUC values.

RESULTS

Lopinavir peak or trough concentrations were strongly correlated with lopinavir AUC values (r = 0.9947 and r = 0.9541, respectively). For about 94% of calculations using the peak-AUC model and 87% of calculations using the trough-AUC model, differences between predicted and observed AUC values were in the range of 0.76-1.5 fold; the associated r values were 0.9514 (p < 0.001) and 0.9345 (p < 0.001), respectively. The mean absolute predictive error was less than 6% with the use of either the peak-AUC model or the trough-AUC model, with corresponding values for root-mean-square error of 17.6% and 23.5%, respectively.

CONCLUSION

Equations incorporating lopinavir peak and trough concentrations were found to satisfactorily predict lopinavir AUC values in data sets describing patients receiving lopinavir with ritonavir boosting. Variability in predictions was higher with use of the trough-AUC model.

Population pharmacokinetics of lopinavir/ritonavir (Kaletra) in HIV-infected patients

BACKGROUND

A relationship between plasma concentrations and viral suppression in patients receiving lopinavir (LPV)/ritonavir (RTV) has been observed. Therefore, it is important to increase our knowledge about factors that determine interpatient variability in LPV pharmacokinetics (PK).

METHODS

The study, designed to develop and validate population PK models for LPV and RTV, involved 263 ambulatory patients treated with 400/100 mg of LPV/RTV twice daily. A database of 1110 concentrations of LPV and RTV (647 from a single time-point and 463 from 73 full PK profiles) was available. Concentrations were determined at steady state using high-performance liquid chromatography with ultraviolet detection. PK analysis was performed with NONMEM software. Age, gender, height, total body weight, body mass index, RTV trough concentration (RTC), hepatitis C virus coinfection, total bilirubin, hospital of origin, formulation and concomitant administration of efavirenz (EFV), saquinavir (SQV), atazanavir (ATV), and tenofovir were analyzed as possible covariates influencing LPV/RTV kinetic behavior.

RESULTS

Population models were developed with 954 drug plasma concentrations from 201 patients, and the validation was conducted in the remaining 62 patients (156 concentrations). A 1-compartment model with first-order absorption (including lag-time) and elimination best described the PK. Proportional error models for interindividual and residual variability were used. The final models for the drugs oral clearance (CL/F) were as follows: CL/F(LPV)(L/h)=0.216·BMI·0.81(RTC)·1.25(EFV)·0.84(ATV); CL/F(RTV)(L/h) = 8.00·1.34(SQV)·1.77(EFV)·1.35(ATV). The predictive performance of the final population PK models was tested using standardized mean prediction errors, showing values of 0.03 ± 0.74 and 0.05 ± 0.91 for LPV and RTV, and normalized prediction distribution error, confirming the suitability of both models.

CONCLUSIONS

These validated models could be implemented in clinical PK software and applied to dose individualization using a Bayesian approach for both drugs.

Atazanavir: its role in HIV treatment

Azatanavir is a protease inhibitor (PI) approved for the treatment of HIV-1 infection. Atazanavir is a substrate and inhibitor of cytochrome P450 isozyme 3A and an inhibitor and inducer of P-glycoprotein. It has similar virologic efficacy as efavirenz and ritonavir-boosted lopinavir in antiretroviral-naive individuals. Its impact on lipids is less than other PIs and it is suitable for those in whom hyperlipidemia is undesirable. Ritonavir boosting of atazanavir enhances the bioavailability of atazanavir but may result in some elevation of lipids and is recommended for treatment-experienced patients and those receiving efavirenz or tenofovir. Ritonavir-boosted atazanavir has similar antiviral activity as ritonavir-boosted lopinavir in both antiretroviral therapy-naive and -experienced patients. Atazanavir causes unconjugated bilirubinemia in over 40% of patients but results in less than 2% discontinuations. Atazanavir is licensed for once-daily use and atazanavir/ritonavir competes with lopinavir/ritonavir as the most commonly prescribed PI.