Efforts to eliminate chronic hepatitis C virus face ongoing challenges, including continued transmission among people who inject drugs (PWID), high reinfection rates, and gaps in care. While direct-acting antivirals are effective, they don’t prevent reinfection and aren’t enough on their own. A prophylactic vaccine could be key to prevention. To inform trial design, researchers used an agent-based model simulating randomized controlled trials among PWID in Chicago. They tested assumed vaccine efficacy (aVE) levels of 50% and 75% across 500 simulations, analyzing the effects of testing frequency and post-randomization imbalances. Most outcomes fell within one standard deviation of the aVE, though some varied due to infection rate differences between groups.

Simulations showed that higher infection rates in the vaccine arm could underestimate efficacy, while more infections in the placebo arm could inflate it. Even small, undetected imbalances affected results, underscoring the need for strong trial design and statistical corrections like Cox regression. Testing more frequently (biweekly vs monthly) had little impact on efficacy estimates. While in silico models can’t fully reflect real-world complexity, they offer valuable guidance for designing HCV vaccine trials and improving accuracy—especially in high-risk groups like PWID.

Reference: Mackesy-Amiti ME, Gutfraind A, Tatara E, et al. Modeling of randomized hepatitis C vaccine trials: Bridging the gap between controlled human infection models and real-word testing. PNAS Nexus. 2024;4(1):pgae564. doi: 10.1093/pnasnexus/pgae564.