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HIV Med. 2009 Nov;10(10):620-6. doi: 10.1111/j.1468-1293.2009.00734.x. Epub 2009 Jul 6.

Identification of new genotypic cut-off levels to predict the efficacy of lopinavir/ritonavir and darunavir/ritonavir in the TITAN trial.

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1
Pharmacology Research Laboratories, University of Liverpool, Liverpool, UK. microhaart@aol.com

Abstract

BACKGROUND:

Genotypic algorithms used to predict the clinical efficacy of lopinavir/ritonavir (LPV/r) have included a range of mutation lists and efficacy endpoints. Normally, HIV clinical trials are powered to detect a difference between treatment arms of 10-12% for the endpoint of viral load suppression <50 HIV-1 RNA copies/mL. The TITAN trial evaluated LPV/r vs. darunavir/ritonavir (DRV/r) in treatment-experienced patients with viral load >1000 copies/mL. This analysis aimed to re-evaluate resistance algorithms for LPV/r in the TITAN trial.

METHODS:

Baseline genotype data were classified using seven genotypic resistance algorithms: International AIDS Society USA (IAS-USA) LPV mutations (current cut-off=6), Abbott 2007 mutation list (cut-off=3), ANRS mutations (cut-off=4), FDA mutations (cut-off=3), Stanford, REGA and IAS-USA major protease inhibitor (PI) mutations. Efficacy in the TITAN trial (HIV-1 RNA <50 copies/mL at week 48) was correlated with the number of mutations from each list, to show the 'efficacy advantage cut-off level': the number of mutations from each list associated with a difference in efficacy between treatment arms of at least 12%.

RESULTS:

Multivariate logistic regression analysis identified lower genotypic cut-off levels than previously reported where there was at least 12% lower efficacy for LPV/r vs. DRV/r. These efficacy advantage cut-off levels were: IAS-USA LPV mutations, cut-off=3; Abbott 2007, cut-off=2; ANRS LPV, cut-off=3; FDA LPV mutations, cut-off=2; major IAS-USA PI mutations, cut-off=1; Stanford algorithm, cut-off=low-level LPV resistance; REGA algorithm, cut-off=intermediate-level LPV resistance. There were linear falls in HIV-1 RNA suppression rates with rising mutation counts in the TITAN, French LPV ATU, BMS-045 and RESIST trials.

CONCLUSIONS:

The analysis identified more sensitive cut-off levels for LPV genotypic algorithms, below those currently used.

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