Evaluation of HIV-1 kinetic models using quantitative discrimination analysis

Area of Science:

• Mathematical modeling
• Virology
• Immunology

Background:

• Numerous mathematical models of human immunodeficiency virus (HIV) dynamics have been developed since its identification.
• Evaluating these models is crucial for understanding disease progression and treatment efficacy.

Purpose of the Study:

• To assess intracellular and intercellular scale HIV models for their ability to describe viral and cell titer dynamics.
• To determine the best-fitting model using typically available patient data and Bayesian analysis.

Main Methods:

• Initial evaluation of twenty HIV-1 viral dynamics models for parameter identifiability from clinical data.
• Selection of three models for further comparison, with parameter estimation using data from 338 patients over 2484 days.
• Bayesian model discrimination analysis to identify the most probable model, including a newly developed hybrid model.

Main Results:

• A single model was overwhelmingly favored in Bayesian discrimination analysis, accurately accounting for uninfected cells, infected cells, and cytotoxic T lymphocyte dynamics.
• This favored model remained the most probable even after comparison with a hybrid model combining features of the initial three.
• Parameters for all models were successfully estimated using clinical data from a large patient cohort.

Conclusions:

• The selected mathematical model provides a robust framework for describing HIV-1 dynamics in patients.
• This model's ability to integrate viral, cellular, and immune components offers a more comprehensive understanding of HIV pathogenesis.
• The study demonstrates the utility of Bayesian model discrimination for selecting appropriate mathematical models in virology research.