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

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 Acqui...

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Autores principales: Tunc, Huseyin, Sari, Murat, Kotil, Seyfullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069423/
https://www.ncbi.nlm.nih.gov/pubmed/37020854
http://dx.doi.org/10.7717/peerj.15033
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author Tunc, Huseyin
Sari, Murat
Kotil, Seyfullah
author_facet Tunc, Huseyin
Sari, Murat
Kotil, Seyfullah
author_sort Tunc, Huseyin
collection PubMed
description 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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spelling pubmed-100694232023-04-04 Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy Tunc, Huseyin Sari, Murat Kotil, Seyfullah PeerJ Computational Biology 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. PeerJ Inc. 2023-03-31 /pmc/articles/PMC10069423/ /pubmed/37020854 http://dx.doi.org/10.7717/peerj.15033 Text en ©2023 Tunc et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Computational Biology
Tunc, Huseyin
Sari, Murat
Kotil, Seyfullah
Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title_full Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title_fullStr Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title_full_unstemmed Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title_short Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy
title_sort machine learning aided multiscale modelling of the hiv-1 infection in the presence of nrti therapy
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069423/
https://www.ncbi.nlm.nih.gov/pubmed/37020854
http://dx.doi.org/10.7717/peerj.15033
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