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Clinical Trial
. 2007 Sep;51(9):3067-74.
doi: 10.1128/AAC.00388-07. Epub 2007 Jun 18.

Predictive genotypic algorithm for virologic response to lopinavir-ritonavir in protease inhibitor-experienced patients

Martin S King  1 , Richard Rode, Isabelle Cohen-Codar, Vincent Calvez, Anne-Geneviève Marcelin, George J Hanna, Dale J Kempf
Affiliations
Clinical Trial

Predictive genotypic algorithm for virologic response to lopinavir-ritonavir in protease inhibitor-experienced patients

Martin S King et al. Antimicrob Agents Chemother. 2007 Sep.

Erratum in

  • Antimicrob Agents Chemother. 2008 Feb;52(2):811

Abstract

Several genotypic resistance algorithms have been proposed for quantitation of the degree of phenotypic resistance to the human immunodeficiency virus (HIV) protease inhibitor (PI) lopinavir (LPV), including the original LPV mutation score. In this study, we retrospectively evaluated 21 codons in HIV protease known to be associated with PI resistance in a large antiretroviral agent-experienced observational patient cohort, "Autorisation Temporaire d'Utilization" (ATU), to assess whether a more optimal algorithm could be derived by using virologic response data from patients treated with LPV in combination with ritonavir (LPV/r). Five of the 11 mutations constituting the LPV mutation score were not associated with a virologic response, while 4 additional mutations not included in this score demonstrated an association. Therefore, the LPV ATU score, which includes mutations at codons 10, 20, 24, 33, 36, 47, 48, 54, 82, and 84, was constructed and shown in two different types of multivariable analyses of the ATU cohort to be a better predictor of the virologic response than the LPV mutation score. The LPV ATU score was also more strongly associated with a virologic response when it was applied to independent clinical trial populations of PI-experienced patients receiving LPV/r. This study provides the basis for a new genotypic resistance algorithm that is useful for predicting the antiviral activities of LPV/r-based regimens in PI-experienced patients. The refined algorithm may be useful in making clinical treatment decisions and in refining genetic and pharmacologic methods for assessing the activity of LPV/r.

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Figures

FIG. 1.
FIG. 1.
Observed virologic response rates (circles and triangles) and logistic regression-estimated virologic response rates (lines) in ATU cohort by baseline LPV mutation score. The sizes of the circles and triangles are proportional to the sample size for each category.
FIG. 2.
FIG. 2.
Logistic regression analysis of representative codons included or excluded from the LPV ATU score. (A) Codon 10, included in the LPV mutation score and the LPV ATU score; (B) codon 90, included in the LPV mutation score but excluded from the LPV ATU score; (C) codon 36, not included in the LPV mutation score but included in the LPV ATU score; (D) codon 30, not included in either score; the presence of a mutation in this codon was marginally associated with an improved virologic response. Gray and black bars, observed response rates with and without a mutation, respectively, at the codon of interest; gray and black lines, predicted response rate with and without a mutation, respectively, at the codon of interest. The calculation of the LPV mutation score in each panel excludes the codon of interest.
FIG. 3.
FIG. 3.
Observed virologic response rates (circles and triangles) and logistic regression-estimated virologic response rates (lines) in ATU cohort by baseline LPV ATU score. The sizes of the circles and triangles are proportional to the sample size for each category.
FIG. 4.
FIG. 4.
Classification tree analysis for assessment of the probability of a 1.0-log10 decrease in the plasma HIV-1 RNA from the baseline.
See this image and copyright information in PMC

References

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