Analysis of HIV latent infection model with multiple infection stages and different drug classes

Alshorman, Areej; Al-Hosainat, Nidal; Jackson, Trachette L.

doi:10.1080/17513758.2022.2113828

Cited by 2 publications

(1 citation statement)

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“… Chen, Teng & Zhang (2021) included saturated incidence and distributed infection delays into the standard two-strain T — T ∗ — V model and investigated the effects of those novel incidence terms on the long-time behaviour of the dynamics. Additionally, the effect of drug adherence on the virological failure of ARTs ( Rosenbloom et al, 2012 ), the effect of time-dependent drug efficiencies on ART response ( Rong, Feng & Perelson, 2007 ; Vaidya & Rong, 2017 ), competition between susceptible and resistant strains in the viral dynamics ( Ball, Gilchrist & Coombs, 2007 ; Lythgoe, Pellis & Fraser, 2013 ), the role of latently infected CD4+ T cell reservoirs on the evolution of strains ( Doekes, Fraser & Lythgoe, 2017 ), comparison of entry inhibitors with the RTIs and PIs according to viral resistance ( Alshorman, Al-hosainat & Jackson, 2022 ), investigation of optimal timing for ART Rouzine (2022) have been proposed through within-host models. The proposed mathematical models assume the co-existence of susceptible and resistant strains and generally investigate the response to antiretroviral therapy (ART).…”

Section: Introductionmentioning

confidence: 99%

Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy

Tunc

Sarı

Kotil

2023

PeerJ

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Human Immunodeficiency Virus (HIV) is one of the most common chronic infectious diseases in humans. Extending the expected lifetime of patients depends on the use of optimal antiretroviral therapies. Emergence of the drug-resistant strains can reduce the effectiveness of treatments and lead to Acquired Immunodeficiency Syndrome (AIDS), even with antiretroviral therapy. Investigating the genotype-phenotype relationship is a crucial process for optimizing the therapy protocols of the patients. Here, a mathematical modelling framework is proposed to address the impact of existing mutations, timing of initiation, and adherence levels of nucleotide reverse transcriptase inhibitors (NRTIs) on the evolutionary dynamics of the virus strains. For the first time, the existing Stanford HIV drug resistance data have been combined with a multi-strain within-host ordinary differential equation (ODE) model to track the dynamics of the most common NRTI-resistant strains. Overall, the D4T-3TC, D4T-AZT and TDF-D4T drug combinations have been shown to provide higher success rates in preventing treatment failure and further drug resistance. The results are in line with the genotype-phenotype data and pharmacokinetic parameters of the NRTI inhibitors. Moreover, we show that the undetectable mutant strains at the diagnosis have a significant effect on the success/failure rates of the NRTI treatments. Predictions on undetectable strains through our multi-strain within-host model yielded the possible role of viral evolution on the treatment outcomes. It has been recognized that the improvement of multi-scale models can contribute to the understanding of the evolutionary dynamics, and treatment options, and potentially increase the reliability of genotype-phenotype models.

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