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:
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: moc.serotlusnocogixe@xilef.egroj
More at: https://twitter.com/hiv insight
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