Volume 19 - Number 2

April - June 2017



Review Article

The Evolving Landscape of HIV Drug Resistance Diagnostics for Expanding Testing in Resource-Limited Settings


Seth C. Inzaule, Raph L. Hamers, Roger Paredes, Chunfu Yang, Rob Schuurman and Tobias F. Rinke de Wit

Dept. of Global Health, Academic Medical Center of the University of Amsterdam, and Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands


Abstract

Global scale-up of antiretroviral treatment (ART) has dramatically changed the prospects of HIV/AIDS disease rendering life-long chronic care and treatment a reality for millions of HIV-infected patients. Affordable technologies to monitor ART are needed to ensure long-term durability of limited available drug regimens. HIV drug resistance tests can complement existing strategies in optimizing clinical decision-making for patients with treatment failure, in addition to facilitating population-based surveillance of HIV drug resistance. This review assesses the current landscape of HIV drug resistance technologies and discuss the strengths and limitations of existing assays available for expanding testing in resource limited settings (RLS). These include sequencing-based assays (Sanger sequencing assays and next-generation sequencing), point mutation assays and genotype-free data-based prediction systems. The Sanger assays are currently considered gold standard genotyping technology, though available at a limited number of RLS reference and regional laboratories, but high capital and test cost have limited their wide expansion. The point mutation assays present opportunities for simplified laboratory assays, but HIV genetic variability, extensive codon redundancy at or near the mutation target sites with limited multiplexing capability have restricted their utility. Next-generation sequencing (despite high cost) may have potential to reduce the testing cost significantly through multiplexing in high-throughput facilities, although the level of bioinformatics expertise required for data analysis is currently still complex and expensive and lacks standardization. Web-based genotype-free prediction systems may provide enhanced ART decision-making without the need for laboratory testing, but require further clinical field evaluation and implementation science research in resource-limited settings.


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