The goal of this research was to establish a new and innovative framework for cost-effectiveness modeling of HIV-1 treatment, simultaneously considering both clinical and epidemiological outcomes.
EPICE-HIV is a multi-paradigm model based on a within-host micro-simulation model for the disease progression of HIV-1 infected individuals and an agent-based sexual contact network (SCN) model for the transmission of HIV-1 infection. It includes HIV-1 viral dynamics, CD4+ T cell infection rates, and pharmacokinetics/pharmacodynamics modeling. Disease progression of HIV-1 infected individuals is driven by the interdependent changes in CD4+ T cell count, changes in plasma HIV-1 RNA, accumulation of resistance mutations and adherence to treatment. The two parts of the model are joined through a per-sexual-act and viral load dependent probability of disease transmission in HIV-discordant couples. Internal validity of the disease progression part of the model is assessed and external validity is demonstrated in comparison to the outcomes observed in the STaR randomized controlled clinical trial.
We found that overall adherence to treatment and the resulting pattern of treatment interruptions are key drivers of HIV-1 treatment outcomes. Our model, though largely independent of efficacy data from RCT, was accurate in producing 96-week outcomes, qualitatively and quantitatively comparable to the ones observed in the STaR trial.
We demonstrate that multi-paradigm micro-simulation modeling is a promising tool to generate evidence about optimal policy strategies in HIV-1 treatment, including treatment efficacy, HIV-1 transmission, and cost-effectiveness analysis.
Below: Evolution of plasma HIV-1 RNA levels and CD4+ T cell counts in an untreated subject.
Left: Plasma HIV-1 RNA levels as a function of time, simulated by the HIV-D model accounting for disease progression. Right: corresponding CD4+ T cell counts. The model simulates the 3 typical stages of HIV-1 infection: acute infection, clinical latency and AIDS phase. Dashed grey lines represent the HIV-D model without disease progression, the stable-state solution of which is exploited to introduce between-patient variability in plasma cell concentrations.
Below: Evolution of plasma HIV-1 RNA levels and CD4+ T cell counts in a treated subject.
Left: plasma HIV-1 RNA levels as a function of time, simulated by the HIV-D model with drug effect, when persistent (black line) and non-persistent (grey dashed-dotted line) low level viremia are considered. The horizontal grey dashed line corresponds to the common viral load detection threshold of 50 plasma HIV-1 RNA copies/mL, whereas the horizontal grey dotted line corresponds to a theoretically imputed persistent low level viremia of 2 plasma HIV-1 RNA copies/mL (considered for this particular example). Right: corresponding CD4+ T cell counts. Simulated treatment with hypothetical continuous drug potency ε = 100% starting at 8 years after seroconversion.
Full article at: http://goo.gl/yzCh1w
By: Björn Vandewalle,1 Josep M. Llibre,2 Jean-Jacques Parienti,3 Andrew Ustianowski,4 Ricardo Camacho,5 Colette Smith,6Alec Miners,7 Diana Ferreira,1 and Jorge Félix1,*
1Exigo Consultores, Lisbon, Portugal
2Fundació Lluita contra la SIDA, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain
3Department of Clinical Research and Biostatistics, Côte de Nacre University Hospital, Caen, France
4Regional Infectious Disease Unit, North Manchester General Hospital, Manchester, United Kingdom
5Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium
6Department of Infection and Population Health, University College London, London, United Kingdom
7Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
University of Athens, Medical School, GREECE
Competing Interests: The authors have the following interests: Gilead contracted with Exigo for the development of the model and its validation using STaR clinical trial data. Gilead also made STaR study data available for external model validation. Björn Vandewalle, Diana Ferreira and Jorge Félix are employed by Exigo Consultores. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
Conceived and designed the experiments: BV JML JJP AU RC CS AM JF. Performed the experiments: BV. Analyzed the data: BV JML JJP AU RC CS AM. Contributed reagents/materials/analysis tools: BV DF JF. Wrote the paper: BV JML JJP AU RC CS AM DF JF.
* E-mail: firstname.lastname@example.org
PLoS One. 2016; 11(2): e0149007. Published online 2016 Feb 12. doi: 10.1371/journal.pone.0149007
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