Computational modeling for HCV vaccine trial design and optimal vaccine-based combination interventions

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PROJECT SUMMARY/ABSTRACT Despite remarkable progress with direct-acting antivirals (DAAs), hepatitis C virus (HCV) infection remains a serious global public health problem with over 1% of the world’s population and about 3 million in the United States (U.S.) infected. The U.S. Department of Health & Human Services recently renewed the Action Plan to Prevent, Care and Treat Viral Hepatitis to eliminate viral hepatitis infection as a public health threat and the WHO introduced global targets for the care and management of HCV. While the highest uptake of HCV treatment occurred in 2017 and 1.5 million people were cured, 1.6 million new infections occurred. Due to many treatment barriers, only an estimated 21% of infected patients are diagnosed and only 2% of total infected patients are being treated for the disease annually. DAAs alone are unlikely to achieve HCV elimination; as such, the development of a vaccine to prevent HCV infection is an important focus of ongoing research. Vaccine clinical trials for HCV infection will need to recruit from high-risk populations, such as persons who inject drugs (PWID), who contribute an estimated 60% of all new HCV infections in the U.S. and have an increasing incidence of HCV, especially among young PWID. Additionally, limited access to DAA treatment, syringe service programs (SSP), and continued injection drug use poses challenges among PWID even as treatment with medication for opioid use disorder (MOUD) is expanding. Factors contributing to transmission and successful intervention (DAAs, MOUD, SSP) among PWID are dynamic and complex and occur at the individual (e.g., pathogen-host interplay, risk behaviors), social (e.g., injection network, social norms), structural (e.g., access to SSP and MOUD), and geographic (e.g., interaction locations) levels. As such, performing HCV vaccine randomized clinical trials (RCT) in the PWID population presents major challenges. We propose to develop an integrated comprehensive computational modeling approach to examine these challenges systematically and assess the impact of specific RCT modifications on clinical trial success. To explore vaccine trial design and outcomes, our interdisciplinary team will: (1) design and evaluate clinical trials in low incidence PWID sites, using metropolitan Chicago as the model, which reduces the chance of someone becoming exposed before being fully protected; (2) design and evaluate clinical trials in high incidence PWID sites, using San Francisco as the model, which increases the chance of someone becoming exposed before being fully protected; and (3) discover effective HCV vaccine-based intervention strategies to achieve WHO elimination goals in the context of a licensed vaccine. The literature supports that non-sterilizing vaccines are expected to be the focus of future trials, reminiscent of recent COVID-19 vaccines, therefore we will simulate their effect on transmission to reach elimination. We will account for SSP and MOUD and their effect on outcomes in two cities with disparate HCV epidemic profile among PWID—Chicago and San Francisco.