Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 34 min ago
PreprintCC BY 4.0arXiv

Mathematical Modelling of Within-Host HIV Dynamics with Cytotoxic Immune Response and Antiretroviral Therapy

Abstract

We develop and analyse a mechanistic ODE model of within-host HIV dynamics that includes uninfected CD4+ T cells, latently and productively infected cells, free virions, and cytotoxic immune effectors. Antiretroviral therapy is described by two time-dependent efficacy functions that separately reduce new infections and virion production. We show that solutions remain non-negative and bounded, identify the infection-free and endemic equilibria, and derive the basic reproduction number together with the local stability condition for the infection-free state. We also obtain treatment-dependent suppression thresholds and an analytical estimate of when this threshold is crossed as treatment efficacy declines. Numerical simulations consider untreated infection, ART initiation, periodic variation in efficacy, and progressive loss of treatment effect. Without therapy, the model approaches a state of persistent infection. ART reduces viral load and promotes recovery of the CD4+ T-cell population, while the latent reservoir persists under the parameter set considered. Periodic changes in treatment efficacy produce sustained forced oscillations. These results connect the analytical threshold conditions with the treatment-dependent dynamics of the model and provide a basis for future calibration, sensitivity analysis, uncertainty quantification, and extensions informed by clinical data.

Related

  1. Virtual Brain Modelling Reveals Altered Network Dynamics and Predicts Symptom Severity in Schizophrenia: Relevance to Antipsychotic Treatment Response and Resistance.
    M. Vitacca, D. Pischedda, M. Ciccarelli +10
    bioRxivPreprintCC BYFull textNeurosciencebioRxiv
  2. An eicosanoid-enriched follicular microenvironment shapes germinal centre immune dynamics in antiretroviral-treated PLWH
    S. Georgakis, H. Lindsay, M. Orfanakis +17
    bioRxivPreprintAll rights reservedFull textImmunologybioRxiv
  3. Mathematical modelling of interactive tapping reveals tap intervals as strongest predictor of subsequent tap timing
    Shane Mannion, Ivana Konvalinka
    PsyArXivPreprintCC BY 4.0Full textNeuroscienceOSF
  4. Predictors of immunological non-response despite virological suppression in people living with HIV on antiretroviral therapy: A systematic review and meta-analysis
    Brian Eka Rachman, Yehuda Tri Nugroho Supranoto, Fitri Revita Nurannisa +9
    PLOS OnePeer-reviewedCC BY 4.0Full textImmunologyPLOS
  5. A general mathematical framework for modelling subnetworks of the nuclear auxin pathway
    J. G. Shuttleworth, E. Chan, T. Welch +3
    bioRxivPreprintCC BYFull textPlant biologybioRxiv
BibTeX
@misc{polyakov2026mathematical,
  title = {{Mathematical Modelling of Within-Host HIV Dynamics with Cytotoxic Immune Response and Antiretroviral Therapy}},
  author = {Maxim Polyakov and Yulia Chernikova},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.29972},
  eprint = {2609.29972},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.29972},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Mathematical Modelling of Within-Host HIV Dynamics with Cytotoxic Immune Response and Antiretroviral Therapy
AU  - Maxim Polyakov
AU  - Yulia Chernikova
PY  - 2026
DA  - 2026/09/24
PB  - arXiv
DO  - 10.48550/arxiv.2609.29972
UR  - https://arxiv.org/abs/2609.29972
AB  - We develop and analyse a mechanistic ODE model of within-host HIV dynamics that includes uninfected CD4^+ T cells, latently and productively infected cells, free virions, and cytotoxic immune effectors. Antiretroviral therapy is described by two time-dependent efficacy functions that separately reduce new infections and virion production. We show that solutions remain non-negative and bounded, identify the infection-free and endemic equilibria, and derive the basic reproduction number together with the local stability condition for the infection-free state. We also obtain treatment-dependent suppression thresholds and an analytical estimate of when this threshold is crossed as treatment efficacy declines. Numerical simulations consider untreated infection, ART initiation, periodic variation in efficacy, and progressive loss of treatment effect. Without therapy, the model approaches a state of persistent infection. ART reduces viral load and promotes recovery of the CD4^+ T-cell population, while the latent reservoir persists under the parameter set considered. Periodic changes in treatment efficacy produce sustained forced oscillations. These results connect the analytical threshold conditions with the treatment-dependent dynamics of the model and provide a basis for future calibration, sensitivity analysis, uncertainty quantification, and extensions informed by clinical data.
ER  -