Influence of pharmacogenetic on pharmacokinetic interindividual variability of indinavir and lopinavir in HIV patients (COPHAR2 – ANRS 111 trial)
J. Bertrand (1), X. Panhard (1), A. Tran (2), E. Rey (2), S. Auleley (1), X. Duval (1, 3, 5), D. Salmon (4), J.M. Tréluyer (2), F. Mentré (1) and the COPHAR2- ANRS 111 study group
(1) INSERM, U738, Paris, France ; Université Paris 7, Paris, France ; AP-HP, Hôpital Bichat, Paris, France ; (2) AP-HP, Hôpital Cochin, Département de pharmacologie clinique – EA3620, Paris, France ; (3) AP-HP, Hôpital Bichat, Service des Maladies Infectieuses B, Paris, France ; (4) Université Paris 5, AP-HP, Hôpital Cochin, Service de Médecine interne Paris, France ; (5) CIC 007, Hôpital Bichat, Paris, France
Objectives: To evaluate the effect of genetic polymorphisms on indinavir and on lopinavir pharmacokinetic (PK) variability in HIV-infected patients initiating a protease inhibitor (PI) containing highly active antiretroviral therapy (HAART) and to study the link between concentrations and short term response or metabolic toxicity.
Methods: Among the patients enrolled in the COPHAR 2 - ANRS 111 trial, 34 and 38 PI naive-patients initiating lopinavir/ritonavir (LPV) or indinavir/ritonavir (IDV), respectively, were studied. At week 2, 4 blood samples were taken before and up to 12 hours following the drug intake to measure PI plasma levels. Genotyping for CYP3A4, 3A5, and MDR1 (exon 21 and 26) was performed. For each PI (LPV and IDV), a population PK model was developed in order to estimate the model parameters and to analyse the genotypes influence. The SAEM algorithm implemented in the Monolix software version 2.1 was used [1, 2]. For each patient, mean (Cmean), minimum (Cmin) and maximum (Cmax) plasma concentrations were derived from the estimated individual PK parameters. For each PI, the link between plasma level and HIV RNA level variation between day 0 and Week 2 or variations of metabolic levels between day 0 and Week 4 were studied using Spearman nonparametric correlation tests.
Results: A one-compartment model with first-order absorption and elimination best described LPV and IDV concentrations. No significant genetic effect was found on LPV pharmacokinetics. For IDV, patients *1B/*1B for CYP3A4 gene had an IDV absorption divided by 3.70 compared to *1A/*1B or *1A/*1A genotypes (p= 0.02). With respect to link between PK and short-term efficacy, Cmean and Cmin were positively correlated to HIV RNA variation (p=0.02, p=0.03, respectively) in the IDV group. No significant relationship was found between LPV or IDV concentrations and metabolic toxicity. However, in the IDV group, patients with the *1A/*1B or *1A/*1A genotypes had significantly higher increase in triglycerides during the 4 weeks of treatment (p=0.02).
Conclusions: This study points out the role of genetic polymorphisms on IDV pharmacokinetic variability and confirms the link between IDV exposure and short term efficacy. No such effect could be found, in this sample, for LPV.
References:
[1] Kuhn, E. & Lavielle, M. Maximum likelihood estimation in nonlinear mixed effects models. Comput. Stat. Data Anal. 49, 1020-1038 (2005).
[2] http://software.monolix.org/.
