Clinical data sets of human immunodeficiency virus type 1 reverse transcriptase-resistant mutants explained by a...

Area of Science:

• Virology
• Mathematical Biology
• Immunology

Background:

• Treatment of human immunodeficiency virus type 1 (HIV-1) infection with reverse transcriptase (RT) inhibitors reduces viral load but often leads to drug resistance.
• HIV-1 evolves mutations in the RT gene, enabling it to circumvent antiviral drugs.
• Understanding the dynamics of drug resistance evolution is crucial for optimizing HIV-1 treatment strategies.

Purpose of the Study:

• To develop and validate a mathematical model simulating the evolution of drug-resistant HIV-1 mutants.
• To analyze clinical data on lamivudine and zidovudine resistance to assess model accuracy.
• To investigate factors influencing the rate of drug resistance evolution and viral load rebound.

Main Methods:

• Development of a mathematical model distinguishing quiescent and activated CD4+ T cells, modeling productive HIV-1 infection.
• Incorporation of empirical estimates for drug resistance and mutation frequencies of HIV-1 drug-resistant mutants.
• Analysis of clinical data on the evolution of drug-resistant mutants for lamivudine and zidovudine.

Main Results:

• The model accurately accounts for the evolutionary sequence of drug-resistant mutants for both lamivudine and zidovudine.
• Lamivudine demonstrates higher effectiveness than zidovudine in preventing the evolution of drug resistance.
• A critical treatment threshold was identified, below which wild-type HIV-1 can rebound before resistant mutants emerge.

Conclusions:

• The model's predictions align with clinical data, validating current estimates of mutation frequencies and drug resistances.
• Viral load rebound under zidovudine treatment is attributed to both wild-type virus and the first resistant mutant.
• Drug resistance evolution is slower with zidovudine due to its lower efficacy and competition between wild-type and resistant strains.