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

HCV 疫苗试验设计和基于疫苗的最佳组合干预措施的计算模型

基本信息

批准号：
10367717
负责人：
Basmattee Boodram
金额：
$ 75.97万
依托单位：
LOYOLA UNIVERSITY CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-11-01 至 2026-10-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10367717
关键词：
Advisory Committees Affect Antiviral Agents Antiviral Therapy Area Behavioral COVID-19 COVID-19 vaccine Caring Chicago Chronic Chronic Hepatitis C Cities Clinical Trials Clinical Trials Design Communities Complex Computer Models Country Data Diagnosis Disease Drug usage Epidemic Epidemiologist Future Geographic Factor Geography Goals Harm Reduction Health Hepatitis C Hepatitis C Incidence Hepatitis C Therapy Hepatitis C Transmission Hepatitis C Vaccine Hepatitis C virus Illinois Immunity Immunologist Incidence Individual Infection Injecting drug user Injections Intervention Kinetics Literature Location Modeling Modification Needle-Exchange Programs Outcome Patients Persons Pharmaceutical Preparations Policy Maker Population Prevalence Public Health Randomized Clinical Trials Research Research Personnel Risk Risk Behaviors SARS-CoV-2 transmission San Francisco Site Social Network Statistical Data Interpretation United States United States Dept. of Health and Human Services Vaccine Clinical Trial Vaccine Design Vaccines Viral Viral Load result Viral hepatitis Work World Health attenuation base chronic infection design follow-up global health high risk population in silico injection drug use interdisciplinary collaboration mathematical model metropolitan opioid use disorder pathogen placebo group prevent recruit social social norm success successful intervention transmission process trial design uptake vaccination outcome vaccine development vaccine efficacy vaccine trial viral RNA

项目摘要

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.

项目概要/摘要尽管直接作用抗病毒药物 (DAA) 取得了显着进展，但丙型肝炎病毒 (HCV) 感染仍然是一个严重的全球公共卫生问题，影响到世界1%以上的人口，其中美国约有300万人口美国卫生与公众服务部最近更新了行动计划预防、护理和治疗病毒性肝炎，消除病毒性肝炎感染对公共卫生的威胁和世界卫生组织制定了 HCV 护理和管理的全球目标，同时 HCV 治疗的采用率最高。 2017年发生了150万人治愈事件，由于多次治疗，出现了160万新感染病例。障碍，估计只有 21% 的感染患者得到诊断，只有 2% 的感染患者总数得到确诊每年单独接受 DAA 治疗不太可能实现 HCV 消除；开发预防丙型肝炎病毒感染的疫苗是正在进行的研究的一个重要重点。 HCV 感染的疫苗临床试验需要从高危人群中招募，例如个人注射吸毒者 (PWID)，估计占美国所有新发 HCV 感染的 60%，并且有 HCV 发病率增加，特别是年轻注射吸毒者，此外，获得 DAA 治疗的机会有限，注射器服务计划 (SSP) 和持续注射吸毒给注射吸毒者带来了挑战，尽管阿片类药物使用障碍 (MOUD) 的药物治疗正在扩大，导致传播和传播的因素正在扩大。吸毒者中的成功干预（DAA、MOUD、SSP）是动态且复杂的，并且发生在个人身上（例如，病原体与宿主的相互作用、危险行为）、社会性（例如，注射网络、社会规范）、结构性（例如，访问 SSP 和 MOUD）和地理（例如，互动地点）水平，因此，进行 HCV 疫苗。针对吸毒者人群的随机临床试验（RCT）提出了重大挑战。综合综合计算建模方法来系统地研究这些挑战评估特定 RCT 修改对临床试验成功的影响。为了探索疫苗试验设计和结果，我们的跨学科团队将：(1) 设计和评估以芝加哥大都市为模型，在低吸毒者发生率地点进行临床试验，降低了吸毒者发生率某人在受到充分保护之前就暴露了；(2) 设计和评估高水平的临床试验；发生率 PWID 网站，以旧金山为模型，这增加了某人成为的机会 (3) 发现有效的基于 HCV 疫苗的干预策略在获得许可的疫苗的背景下实现世卫组织消除目标文献支持非绝育疫苗。疫苗预计将成为未来试验的重点，让人想起最近的 COVID-19 疫苗，因此我们将模拟它们对传输的影响以达到消除的目的。我们将考虑 SSP 和 MOUD 及其影响。关于注射吸毒者中 HCV 流行情况不同的两个城市——芝加哥和旧金山的结果。