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Clinical Trial
. 2012 Nov;92(5):575-83.
doi: 10.1038/clpt.2012.137. Epub 2012 Oct 3.

Effect of adherence as measured by MEMS, ritonavir boosting, and CYP3A5 genotype on atazanavir pharmacokinetics in treatment-naive HIV-infected patients

Collaborators, Affiliations
Clinical Trial

Effect of adherence as measured by MEMS, ritonavir boosting, and CYP3A5 genotype on atazanavir pharmacokinetics in treatment-naive HIV-infected patients

R M Savic et al. Clin Pharmacol Ther. 2012 Nov.

Abstract

We investigated population pharmacokinetics and pharmacogenetics of ritonavir-boosted atazanavir (ATV), using drug intake times exactly recorded by the Medication Event Monitoring System. The ANRS 134-COPHAR 3 trial was conducted in 35 HIV-infected treatment-naive patients. ATV (300 mg), ritonavir (100 mg), and tenofovir (300 mg) + emtricitabine (200 mg), in bottles with MEMS caps, were taken once daily for 6 months. Six blood samples were collected at week 4 to measure drug concentrations, and trough levels were measured bimonthly. A model integrating ATV and ritonavir pharmacokinetics and pharmacogenetics used nonlinear mixed effects. Use of exact dosing data halved unexplained variability in ATV clearance. The ritonavir-ATV interaction model suggested that optimal boosting effect is achievable at lower ritonavir exposures. Patients with at least one copy of the CYP3A5*1 allele exhibited 28% higher oral clearance. We provide evidence that variability in ATV pharmacokinetics is defined by adherence, CYP3A5 genotype, and ritonavir exposure.

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Conflict of interest statement

Conflict of interest: None

Figures

Figure 1
Figure 1
Observed atazanavir concentration, full profile at week 4 (top) and troughs at various occasions (bottom)
Figure 2
Figure 2
Adherence patterns in the study population for atazanavir (top) and ritonavir (bottom). Green fields indicate correct dose intake, red bars represent days with missed doses, black bars indicate overdosing and blue fields are missing data because of absence of MEMS (patients CASL had atazanavir MEMS data for 4 weeks only). Each patient is shown as a horizontal bar.
Figure 3
Figure 3
Comparison of individual model fits with the transit compartment model and first order absorption model with a lag time for three patients. The best, average and worst fits were chosen based on the median value of absolute individual weighted residuals.
Figure 4
Figure 4
Visual predictive check for the final atazanavir model stratified for different CYP3A5 genotypes: homozygote *3*3 shown in the left panel and heterozygote *1*3 and homozygote *1*1 shown in the right panel. Red solid line indicates observed data median, red dotted lines are 95% observed percentiles, grey shaded area is the simulated median with uncertainty and dark grey shaded areas are simulated 95% percentiles with uncertainty.
Figure 5
Figure 5
Schematic view of: left, effect of CYP3A5 genotype on ritonavir - atazanavir first pass effect; right, final linked ritonavir – atazanavir model. CYP3A5*3*3 are low expressors and CYP3A5*1*3, *1*1 are high expressors.
Figure 6
Figure 6
PK profile over 6 months derived based on individual parameter estimates and full dosing histories (grey line) for one patient with VL > 40 cp/mL at week 24. Cumulative time below MEC is shown with a red stair-case line. The target MEC of 150 ng/mL is shown with a dark red broken line.

References

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