Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings | Zendy

A. D. Revell | Zendy; D. Wang | Zendy; Robin Wood | Zendy; Carl Morrow | Zendy; H. Tempelman | Zendy; Raph L Hamers | Zendy; Gerardo AlvarezUria | Zendy; Adrian StreinuCercel | Zendy; Luminiţa Ene | Zendy; Annemarie M. J. Wensing | Zendy; F. DeWolf | Zendy; Mark Nelson | Zendy; Julio Montaner | Zendy; H. Clifford Lane | Zendy; B. A. Larder | Zendy

Open Access

Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings

Author(s) -

A. D. Revell,

D. Wang,

Robin Wood,

Carl Morrow,

H. Tempelman,

Raph L Hamers,

Gerardo AlvarezUria,

Adrian StreinuCercel,

Luminiţa Ene,

Annemarie M. J. Wensing,

F. DeWolf,

Mark Nelson,

Julio Montaner,

H. Clifford Lane,

B. A. Larder

Publication year - 2013

Publication title -

journal of antimicrobial chemotherapy

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.124

H-Index - 194

eISSN - 1460-2091

pISSN - 0305-7453

DOI - 10.1093/jac/dkt041

Subject(s) - human immunodeficiency virus (hiv) , genotype , medicine , antiretroviral therapy , intensive care medicine , resource (disambiguation) , computational biology , computer science , viral load , immunology , biology , genetics , gene , computer network

Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs.

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