Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

丙型肝炎病毒（HCV）是一种嗜肝病毒，在约70%的暴露者中建立慢性感染。作为 因此，目前全世界有超过7100万人受到感染，患肝病的风险增加。 疾病和肝细胞癌。虽然有效的干扰素（IFN）免费直接作用的抗病毒药（DAA） 虽然治疗组合是非常有效的，但DAA的承诺尚未使我们走上实现WHO目标的轨道。 到2030年消除的目标。实现这一目标将需要扩大HCV筛查、与护理的联系， 降低治疗成本。此外，这将需要了解HCV的传播，特别是如何预防 病毒抗病毒抗性变异体在个体内的传播，使得这些抗药性病毒不被 传播给人口。重要的是，病毒细胞间传播与抗病毒逃逸有关， 免疫逃逸和一般病毒的持久性。因此，通过研究HCV获得的见解 应该广泛地为未来的抗病毒策略提供信息。 治疗期间感染患者血清中HCV的数学建模推动了我们对 HCV感染动力学、IFN治疗的效果，以及导致HCV定量评价的方法 治疗效果自FDA批准HCV DAA以来，我们率先开发了多尺度模型 DAA治疗反应在体外和患者。这项工作揭示了NS 5A的双重作用机制 抑制剂，并已证明，病毒动力学建模可能会减少DAA的持续时间 治疗大多数患者。我们对HCV感染的建模为这些临床见解提供了信息 在细胞培养物中，我们能够直接测量细胞内和细胞外病毒和细胞 参数应用细胞培养实验和体内患者数据，我们最近获得了 有证据表明，病毒进入/传播在维持稳态感染中起主要作用， 病毒传播作为抗病毒药物靶点的意义以及传播如何影响抗病毒治疗 在药物功效、病毒逃逸和药物协同作用方面的反应。 因为这些新的模型提出了关于HCV传播和抗病毒药物的重要生物学问题 战略，这一跨学科R 01更新的目标是使治疗的改善 通过制定和测试HCV感染和治疗反应的数学模型， 在体内和体外。具体目标是：1）使用体内和体内模型优化和验证治愈预测时间 计算机模拟试验; 2）阐明HCV生命周期的定量细节和肝细胞在HCV中的作用 清除率; 3）扩展和优化HCV细胞间传播的数学模型;以及4）确定 细胞间扩散作为抗病毒药物靶点重要性。

Hepatitis C virus (HCV) is a hepatotropic virus that establishes chronic infection in ~70% of those exposed. As a result, currently more than 71 million people worldwide are infected and at increased risk of developing liver disease and hepatocellular carcinoma. While effective interferon (IFN)-free direct acting antiviral (DAA) therapeutic combinations are highly potent, the promise of DAAs has not yet put us on track to achieve the WHO goal of elimination by 2030. Reaching this goal will require a scaling-up of HCV screening, linkage-to-care, and reduction of treatment cost. Additionally, this will require understanding HCV spread, specifically how to prevent the spread of viral antiviral resistance variants within individuals such that these drug resistance viruses are not transmitted to the population. Importantly, viral cell-to-cell spread has been implicated in antiviral escape, immune escape, and persistence of viruses in general. Thus, the insights gained through the study of HCV should broadly inform future antiviral strategies. Mathematical modeling of HCV in the serum of infected patients during therapy has driven our understanding of HCV infection dynamics, the effect of IFN treatment, and led to methods for the quantitative evaluation of HCV treatment efficacy. Since FDA-approval of HCV DAAs, we have pioneered the development of multiscale models of DAA treatment response in vitro and in patients. This work revealed the dual mechanism of action of NS5A inhibitors and has demonstrated that viral kinetic modeling might allow for a reduction in the duration of DAA therapy in the majority of patients. These clinical insights have been informed by our modeling of HCV infection in cell culture where we are able to directly measure both intracellular and extracellular viral and cellular parameters. Applying both cell culture experimentation and in vivo patient data, we have recently obtained evidence that viral entry/spread plays a major role in the maintenance of steady state infection having broad implications regarding viral spread as an antiviral drug target and how spread impacts antiviral treatment response in terms of drug efficacy, viral escape, and drug synergy. Because these new models have raised important biological questions about HCV spread and antiviral drug strategies, the objective of this cross disciplinary R01 renewal is to enable the improvement of the treatment for HCV and other viruses by formulating and testing mathematical models of HCV infection and treatment response in vivo and in vitro. The specific aims are: 1) Refine and validate time to cure predictions using in vivo and in silico trials; 2) Elucidate quantitative details about the HCV life cycle and the role of hepatocytes in HCV clearance; 3) Expand and optimize mathematical models of HCV cell-to-cell spread; and 4) Determine the importance of cell-to-cell spread as an antiviral drug target.