Assessing the impact of HIV treatment interruptions using stochastic cellular Automata

Andreas Hillmann1, Martin Crane1, Heather J Ruskin1

  • 1Advanced Research Computing Centre for Complex Systems Modelling, School of Computing, Dublin City University, Dublin, Ireland.

Insights

Chronic HIV infection causes irreversible lymphatic tissue fibrosis, impairing T-cell regeneration. Treatment interruptions worsen this hidden damage, impacting immune function and T-cell levels.

Area of Science:

  • Immunology
  • Virology
  • Computational Biology

Background:

  • Chronic HIV infection progressively impairs immune homeostasis.
  • HIV-induced lymphatic tissue fibrosis, characterized by collagen accumulation, impedes T-cell regeneration.
  • Current antiretroviral therapy (cART) may mask underlying fibrosis and stromal cell damage.

Purpose of the Study:

  • To investigate the impact of treatment interruptions on lymphatic tissue structure and T-cell levels in HIV infection.
  • To explore the dynamics of HIV-induced fibrosis and T-cell loss, particularly concerning spatial collagen accumulation and repeated interruptions.
  • To model the consequences of cART interruption on T-cell homeostasis within fibrotic lymphatic tissues.

Main Methods:

  • Development and utilization of a novel Stochastic Cellular Automata model.
  • Parametrization of the model using available clinical data, including spatial aspects of collagen buildup.
  • Simulation of HIV-induced fibrosis and T-cell dynamics under various treatment interruption scenarios.

Main Results:

  • The study explores the spatial dynamics of collagen accumulation in lymphatic tissues.
  • It investigates how repeated treatment interruptions exacerbate fibrosis and T-cell loss.
  • The model quantifies the impact of interruptions on T-cell levels within the context of fibrosis.

Conclusions:

  • HIV-induced lymphatic tissue fibrosis is a significant, potentially irreversible complication.
  • Treatment interruptions can worsen fibrosis and T-cell depletion, with unclear quantification of effects.
  • Computer simulation provides a framework for understanding these complex dynamics and informing clinical management.

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