EPICE-HIV: An Epidemiologic Cost-Effectiveness Model for HIV Treatment

Abstract

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.

Fig 1. Diagram of the within-host model for disease progression of HIV-1 infected individuals.

For the treatment regimen of primary interest (blue diagram), the adherence model dictates the daily intake of ART. Through a pharmacokinetic (PK) and pharmacodynamic (PD) model, the plasma drug concentration and drug effect are determined. Drug effect influences the inter-dependent and time-evolving plasma HIV-1 RNA level and CD4+ T cell count in the basic HIV-1 dynamics model (HIV-D). For treatment regimens of secondary interest (orange diagram), a discrete event simulation model (DES) determines the plasma HIV-1 RNA level over time. Using the dynamics of the HIV-D model, CD4+ T cell counts are updated accordingly.

Fig 2. Stock and flow diagram of the basic HIV-1 dynamics model, modified from Rong et al [35].

Uninfected CD4+ T cells (T₁) are produced at a rate ${\mathrm{f}}_{\mathrm{s}}{\mathrm{s}}_{{\mathrm{T}}_1}$. Infected CD4+ T cells are produced at a rate ${\mathrm{k}}_{{\mathrm{T}}_1}\mathrm{V}{\mathrm{T}}_1$, following mass-action kinetics between free virions (V) and uninfected CD4+ T cells. A small fraction α_L of infections results in latently infected resting CD4+ T cells (L). The remaining fraction results in productively infected activated CD4+ T cells (T₂). Latently infected CD4+ T cells are activated into productively infected cells at a constant rate a_L. Free virions are produced by productively infected CD4+ T cells at a rate f_pp_vT₂.

Fig 3. 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.

Fig 4. 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.

Fig 5. Simulated average adherence and treatment interruption duration.

Left: Correlation between the theoretical average adherence and simulated observed average adherence, obtained with the first-order binary autoregressive adherence model, for a random sample of 100 hypothetical individuals followed for a 1-year time-period. Theoretical average adherence distribution: 30% with adherence below 80%; 20% with adherence between 80% and 90%; 50% with adherence above 90%. Right: Relationship between simulated observed average adherence and corresponding longer treatment interruption, for the same random sample.

Fig 6. Drug inhibition as a function of drug concentration and time.

Left: Logarithmic measure of inhibition (F_wt) as a function of drug concentration (percentage of maximum concentration, log₁₀-scale), for drugs RPV, FTC and TDF alone (green lines) and predictions of the combined effect of RPV+FTC+TDF by the Bliss, Loewe and DI model. For the DI model, a hypothetical index 0.5 of degree of independence was assumed. The vertical grey dashed line represents the inhibition potential at maximum concentration for all three drugs. Right: Logarithmic measure of inhibition (F_wt) as a function of days, based on the Bliss, Loewe and DI model combined effect predictions. Simulations are representative of triple combination ART initiation with RPV+FTC+TDF, for a patient 100% adherent to therapy, except for a treatment interruption of 15 days, starting at day 50. After 15 days, a level of only 50% inhibition, represented by the horizontal grey dashed line, starts being approached.

Fig 7. Treatment interruptions and viral response.

Left: Plasma HIV-1 RNA levels as a function of time, simulated by the HIV-D model with drug effect modeled by the primary treatment regimen module of EPICE-HIV. The treatment considered is RPV/FTC/TDF, starting at 8 years after seroconversion with a theoretically imputed persistent low viremia level of 10 plasma HIV-1 RNA copies/mL. For comparability with Parienti et al. [45, 54] during the first year of therapy, 100% adherence was assumed, allowing for plasma HIV-1 RNA levels to be controlled (below 50 copies/mL). After that, adherence to therapy is driven by the first-order binary autoregressive adherence model. In the second year of therapy, the patient demonstrated 76% of adherence. Right: Close-up of the last half year of therapy, demonstrating two large treatment interruptions, represented by black squares: one of 15 days (around 9.6 years) and another of 24 days (around 9.9 years). The horizontal grey dashed and dotted lines correspond to viral load detection thresholds of 50 and 400 plasma HIV-1 RNA copies/mL, respectively. Though the 15 day interruption leads to drug concentrations that allow for viral replication, it is not long enough for plasma HIV-1 RNA levels to rise above the detection thresholds. The 24 day interruption leads to plasma HIV-1 RNA levels above 400 copies/mL. Several days after reintroduction of treatment, levels remain detectable by the 50 copies/mL threshold.