Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen

TY - JOUR

T1 - Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen

AU - De Luca, Andrea

AU - Flandre, Philippe

AU - Dunn, David

AU - Zazzi, Maurizio

AU - Wensing, Annemarie M J

AU - Santoro, Maria Mercedes

AU - Günthard, Huldrych F

AU - Wittkop, Linda

AU - Kordossis, Theodoros

AU - García, Federico

AU - Castagna, Antonella

AU - Cozzi-Lepri, Alessandro

AU - Churchill, Duncan

AU - De Wit, Stephane

AU - Brockmeyer, Norbert H

AU - Imaz, Arkaitz

AU - Mussini, Cristina

AU - Obel, Niels

AU - Perno, Carlo Federico

AU - Roca, Bernardino

AU - Reiss, Peter

AU - Schülter, Eugen

AU - Torti, Carlo

AU - van Sighem, Ard

AU - Zangerle, Robert

AU - Descamps, Diane

AU - CHAIN and COHERE in EuroCoord

N1 - © The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: [email protected].

PY - 2016/5/1

Y1 - 2016/5/1

N2 - Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir.

AB - Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir.

KW - Adolescent

KW - Adult

KW - Aged

KW - Aged, 80 and over

KW - Anti-HIV Agents

KW - Darunavir

KW - Drug Resistance, Viral

KW - Europe

KW - Female

KW - Genotyping Techniques

KW - HIV Infections

KW - HIV Protease

KW - HIV-1

KW - Humans

KW - Male

KW - Microbial Sensitivity Tests

KW - Middle Aged

KW - Mutation

KW - Prognosis

KW - Treatment Outcome

KW - Young Adult

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1093/jac/dkv465

DO - 10.1093/jac/dkv465

M3 - Journal article

C2 - 26825119

SN - 0305-7453

VL - 71

SP - 1352

EP - 1360

JO - Journal of Antimicrobial Chemotherapy

JF - Journal of Antimicrobial Chemotherapy

IS - 5

ER -