Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
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HIV-1 drug resistance and resistance testing.

The global scale-up of antiretroviral (ARV) therapy (ART) has led to dramatic reductions in HIV-1 mortality and incidence. However, HIV drug resistance (HIVDR) poses a potential threat to the long-term success of ART and is emerging as a threat to the elimination of AIDS as a public health problem by 2030. In this review we describe the genetic mechanisms, epidemiology, and management of HIVDR at both individual and population levels across diverse economic and geographic settings. To describe the genetic mechanisms of HIVDR, we review the genetic barriers to resistance for the most commonly used ARVs and describe the extent of cross-resistance between them. To describe the epidemiology of HIVDR, we summarize the prevalence and patterns of transmitted drug resistance (TDR) and acquired drug resistance (ADR) in both high-income and low- and middle-income countries (LMICs). We also review to two categories of HIVDR with important public health relevance: (i) pre-treatment drug resistance (PDR), a World Health Organization-recommended HIVDR surveillance metric and (ii) and pre-exposure prophylaxis (PrEP)-related drug resistance, a type of ADR that can impact clinical outcomes if present at the time of treatment initiation. To summarize the implications of HIVDR for patient management, we review the role of genotypic resistance testing and treatment practices in both high-income and LMIC settings. In high-income countries where drug resistance testing is part of routine care, such an understanding can help clinicians prevent virological failure and accumulation of further HIVDR on an individual level by selecting the most efficacious regimens for their patients. Although there is reduced access to diagnostic testing and to many ARVs in LMIC, understanding the scientific basis and clinical implications of HIVDR is useful in all regions in order to shape appropriate surveillance, inform treatment algorithms, and manage difficult cases.

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