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Uncertainty quantification for the random HIV dynamical model driven by drug adherence.

Dingding Yan1, Mengqi He1, Sanyi Tang2

  • 1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, PR China.

Journal of Theoretical Biology
|July 5, 2024
PubMed
Summary

Understanding drug adherence is crucial for effective HIV treatment. This study models HIV pharmacokinetics to link drug adherence to therapeutic outcomes, improving personalized antiretroviral strategies.

Keywords:
Adaptive generalized polynomial chaosData fittingRandom differential equationsSobol indexUncertainty quantification

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Area of Science:

  • Mathematical Biology
  • Pharmacokinetics
  • Immunology

Background:

  • High variability in CD4+ T cell counts and viral loads in HIV patients complicates treatment efficacy assessment.
  • Poor drug adherence is a potential driver of this variability and treatment uncertainty.

Purpose of the Study:

  • To develop a dynamical HIV model incorporating pharmacokinetics and drug adherence as a random variable.
  • To quantify the relationship between drug adherence and therapeutic outcomes in HIV treatment.
  • To enhance the design of individualized antiretroviral therapy strategies.

Main Methods:

  • Developed a dynamical HIV model coupled with pharmacokinetic principles.
  • Employed adaptive generalized polynomial chaos for stochastic solution approximation.
  • Validated model accuracy using Monte Carlo sampling and clinical patient data.

Main Results:

  • The model accurately depicts HIV dynamics, fitting clinical data from four patients.
  • Sensitivity analyses (Sobol index) reveal drug effect randomness significantly impacts CD4+ T cells and viral loads.
  • Time-dependent probability density functions were calculated theoretically and numerically.

Conclusions:

  • Drug adherence significantly influences HIV treatment efficacy, impacting CD4+ T cells and viral loads.
  • The developed model provides a framework for interpreting clinical data fluctuations.
  • This research contributes to designing optimal, individual-based antiretroviral strategies.