Trends in HIV-1 Drug Resistance Mutations from a U.S. Reference Laboratory from 2006 to 2017

Area of Science:

• Virology
• Infectious Diseases
• Pharmacogenomics

Background:

• Antiretroviral drug resistance mutations (DRMs) in Human Immunodeficiency Virus type 1 (HIV-1) impact treatment efficacy.
• Monitoring DRM trends is essential for clinical management and the development of new antiretroviral therapies.

Purpose of the Study:

• To evaluate trends in drug resistance mutations for nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and integrase strand transfer inhibitors (INSTIs) in a large US reference laboratory database.
• To assess the clinical implications of observed DRM trends on HIV-1 treatment strategies.

Main Methods:

• Analysis of deidentified HIV-1 subtype B drug resistance data from a US reference laboratory database between 2006-2017.
• Evaluation of DRMs with a Stanford HIV Drug Resistance Database mutation score ≥10 for NRTI/NNRTI/PI and INSTI drug classes.
• Assessment of DRM prevalence in single and multi-class resistance scenarios, and in serially tested patients.

Main Results:

• Overall sequences with NRTI, NNRTI, or PI single- or multi-class DRMs declined from 48.9% to 39.3%.
• High-level dual- and triple-class resistance significantly decreased, while single-class resistance increased.
• Prevalence of some DRMs associated with newer regimens (e.g., rilpivirine, etravirine, elvitegravir, dolutegravir) showed an increase, while DRMs linked to older regimens declined.
• In serially tested patients, a substantial proportion developed new DRMs after initial testing.

Conclusions:

• Trends indicate a shift in HIV-1 drug resistance patterns, with a decrease in resistance to older drug classes and an increase in resistance to newer agents.
• The findings support the importance of baseline genotypic resistance testing to inform optimal antiretroviral therapy selection.
• Genotypic testing following virologic failure is valuable for detecting treatment-emergent DRMs and guiding subsequent treatment decisions.