OBJECTIVE: To determine the impact of genotypic inhibitory quotient (GIQ) for lopinavir (LPV) in patients failing HAART with limited antiretroviral exposure. DESIGN: Retrospective analysis of a prospective trial. METHODS:Lopinavir GIQ was calculated as the ratio between the mean trough concentration (C(trough)) and the number of protease mutations using eight different HIV drug resistance mutation lists or algorithms. Early (by week 12) and confirmed (up to week 24) virological response (HIV-RNA< 400 copies/mL, ECVR) was used as dependent variable in logistic regression model. RESULTS: Seventy-one of 109 (65%) patients achieved ECVR. At multivariable logistic regression analysis, each mug/mL increase of GIQ was correlated with increasing probability of ECVR as far as the following mutations were computed: multi-protease inhibitor (PI) associated mutations listed by IAS (OR=1.17; 95% CI=0.99-1.39; P=0.058), mutations associated with LPV resistance by ANRS algorithm (OR=1.21; 95% CI=1.02-1.44; P=0.03), major mutations associated with LPV resistance by Stanford database (OR=1.16; 95% CI=1-1.35; P=0.05), and the whole set of mutations associated with LPV resistance in the same database (OR=1.22; 95% CI=1.02-1.46; P=0.03). Using ROC curve method, a specific threshold GIQ was assessed, above which this parameter could predict ECVR with the highest sensitivity (74.6% with GIQ obtained through Stanford LPV mutations) or specificity (89.5% with GIQ obtained through ANRS LPV mutations). CONCLUSIONS: Our results suggest that increasing GIQ can improve virological outcome even in patients with limited exposure to PIs. Further studies are necessary to understand what HIV protease mutations should be considered and whether such mutations should be weighted differently to improve LPV GIQ predictive value.
RCT Entities:
OBJECTIVE: To determine the impact of genotypic inhibitory quotient (GIQ) for lopinavir (LPV) in patients failing HAART with limited antiretroviral exposure. DESIGN: Retrospective analysis of a prospective trial. METHODS:Lopinavir GIQ was calculated as the ratio between the mean trough concentration (C(trough)) and the number of protease mutations using eight different HIV drug resistance mutation lists or algorithms. Early (by week 12) and confirmed (up to week 24) virological response (HIV-RNA< 400 copies/mL, ECVR) was used as dependent variable in logistic regression model. RESULTS: Seventy-one of 109 (65%) patients achieved ECVR. At multivariable logistic regression analysis, each mug/mL increase of GIQ was correlated with increasing probability of ECVR as far as the following mutations were computed: multi-protease inhibitor (PI) associated mutations listed by IAS (OR=1.17; 95% CI=0.99-1.39; P=0.058), mutations associated with LPV resistance by ANRS algorithm (OR=1.21; 95% CI=1.02-1.44; P=0.03), major mutations associated with LPV resistance by Stanford database (OR=1.16; 95% CI=1-1.35; P=0.05), and the whole set of mutations associated with LPV resistance in the same database (OR=1.22; 95% CI=1.02-1.46; P=0.03). Using ROC curve method, a specific threshold GIQ was assessed, above which this parameter could predict ECVR with the highest sensitivity (74.6% with GIQ obtained through Stanford LPV mutations) or specificity (89.5% with GIQ obtained through ANRS LPV mutations). CONCLUSIONS: Our results suggest that increasing GIQ can improve virological outcome even in patients with limited exposure to PIs. Further studies are necessary to understand what HIV protease mutations should be considered and whether such mutations should be weighted differently to improve LPV GIQ predictive value.
Authors: Jeffrey A Anderson; Hongyu Jiang; Xiao Ding; Leslie Petch; Terri Journigan; Susan A Fiscus; Richard Haubrich; David Katzenstein; Ronald Swanstrom; Roy M Gulick Journal: AIDS Res Hum Retroviruses Date: 2008-05 Impact factor: 2.205