Adherence and Population Pharmacokinetics of Atazanavir in Naïve HIV-Infected Patients using Medication Events Monitoring System (MEMS) for drug intake timing
Radojka Savic (1,2), Aurélie Barrail-Tran (3) , Xavier Duval (1,3), Georges Nembot (1,3), Xavière Panhard (1), Diane Descamps (3), Bernard Vrijens (4), France Mentré (1,3), Cécile Goujard (3), Anne-Marie Taburet (3) and the ANRS 134 study group
(1) INSERM UMR 738 (2) Stanford University, Division of Clinical Pharmacology, Stanford, USA (3) AP-HP Hôpital Bichat, Paris, France (4) Pharmionic Research Center, Visé, Belgium.
Objectives: Individual drug pharmacokinetics (PK) and treatment adherence are key determinants of HIV sustained virological response. Assessment of adherence performed with MEMS, which records exact times of bottle opening for drug intake, in combination with a reliable population PK model, allows quantification of individual drug exposure. The aim of this analysis is to describe population PK of atazanavir using accurate patient dosing-histories, and to demonstrate how different dosing-history assumptions may impact the population PK analysis outcomes.
Methods: A prospective study was conducted in 35 HIV-infected naïve pts. Atazanavir (300 mg), ritonavir (100 mg), and tenofovir (300 mg) + emtricitabine (400 mg) were given once daily during 6 months. All drugs were supplied in bottles with a MEMS cap. Blood samples were drawn at week 4, then bimonthly. Population PK analysis was performed using non-linear mixed effects under three dosing-history assumptions: (i) all patients are at steady state (SS) and the last reported time of dose intake by the patient before a PK visit is accurate, (ii) full dosing-histories as recorded by MEMS are exact, and (iii) “reliable” dosing-history data consists only of MEMS records concordant (within 3 hours) with last reported time of dose intake before a PK visit (gold standard). Dosing-history assumption impact on population PK analysis outcomes were compared to the gold standard reference.
Results: A one compartment model best described plasma atazanavir concentrations. Apparent clearance (CL) and volume of distribution (Vd) were 6.93 L/hr and 81.1 L, with associated inter-individual variabilities of 40% and 31%. The transit compartment model described the absorption well with absorption rate constant of 3.1 hr-1, mean transit time of 1.35 hr and 11.5 transit compartments. Assuming SS in all patients gave rise to significant quantifiable inter-occasion variability in CL (26.5% CV), while using unmodified MEMS dosing-history led to biased Vd parameter estimates and numerical difficulties during estimation procedure thereby potentially adversely affecting individual patient drug exposures.
Conclusions: The proposed model described the atazanavir PK well. It is important to critically assess MEMS data in order to collect reliable dosing records. Erroneous dosing-history assumptions without taking into account adherence information may lead to biased parameter estimates and significant inter-occasion variability. In combination with exact dosing history as recorded by MEMS, the proposed model provides a useful tool for correct quantification of an individual patient’s drug exposure which is essential information for understanding individual virological response and potential success/failure of the therapy.
