Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

描述(申请人提供)：丙型肝炎病毒(丙型肝炎病毒)是一种嗜肝病毒，在约70%的接触者中建立慢性化。因此，目前全球有超过1.7亿人受到感染，罹患肝脏脂肪变性、胰岛素抵抗、慢性炎症、纤维化、肝硬变和肝细胞癌的风险增加。虽然最近出现了有效的不含干扰素(干扰素)的直接作用抗病毒(DAA)治疗组合，但病毒逃逸的风险尚未在不理想的依从性人群中确定。同样有问题的是，这些药物的天文数字成本使它们对世界上大多数丙型肝炎病毒阳性人群来说贵得令人望而却步，包括美国，在美国，AASLD指南建议仅对病情最严重的患者进行治疗。因此，迫切需要优化治疗(即加快病毒下降和防止逃逸)，同时还需要缩短治疗持续时间(即成本)。对治疗过程中感染患者血清中的丙型肝炎病毒RNA水平进行数学建模，可以加深我们对丙型肝炎病毒感染动态、干扰素治疗效果的了解，并为定量评估丙型肝炎病毒的治疗效果和达到治愈的疗程提供方法。我们率先在体外和患者中开发了DAA治疗反应的多尺度模型，以揭示NS5A抑制剂的双重作用机制，这可以解释病毒的快速下降，并建议接受治疗结束时HCVRNA阳性的NS5A抑制剂治疗的患者如何继续实现SVR。此外，我们和其他人积累的证据表明，病毒进入/传播在维持稳定感染方面发挥着非常大的作用，这一点以前认为具有广泛的影响，与病毒进入/传播作为抗病毒药物靶点的潜在有效性有关，并影响重要的抗病毒治疗考虑因素，如药物疗效、病毒逃逸和药物协同作用。然而，重要的是，需要对这些预测进行验证/测试，并将感染的这些额外方面的动态纳入能够准确预测治愈感染所需的最短治疗时间的模型中。为此，这一交叉学科R01的目标是通过在分子水平上建立和测试丙型肝炎病毒感染和治疗反应的数学模型来增加我们对丙型肝炎病毒的了解。具体地说，我们假设，对丙型肝炎病毒感染动态和治疗反应的更定量了解将有助于优化丙型肝炎病毒DAA疗法(例如，增强药物协同作用和增加病毒逃逸的屏障)，预测实现病毒清除所需的治疗持续时间(即确定治愈边界)，并最终允许个体化治疗，从而实现亟需的成本降低。因此，具体目标是：1)了解和确定丙型肝炎病毒治愈的标准；2)确定丙型肝炎病毒的生命周期、DAA作用模式和肝细胞在丙型肝炎病毒清除中的作用；3)建立和利用丙型肝炎病毒细胞间传播的数学模型。

DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) is a hepatotropic virus that establishes chronicity in ~70% of those exposed. As a result, currently more than 170 million people worldwide are infected and at increased risk of developing liver steatosis, insulin resistance, chronic inflammation, fibrosis, cirrhosis, and hepatocellular carcinoma. While effective interferon (IFN)-free direct acting antiviral (DAA) therapeutic combinations have recently become available, the risk of viral escape has not been determined in less than ideal compliance populations. Equally problematic, the astronomical cost of these drugs makes them prohibitively expensive for the majority of the world's HCV-positive populations including the US where AASLD guidelines recommending treatment for only the sickest patients. Hence, there is an immediate need to optimize therapy (i.e. speed viral decline and prevent escape) while also reducing treatment duration of therapy (i.e. cost ). Mathematical modeling of HCV RNA levels in the serum of infected patients during therapy has increased our understanding of HCV infection dynamics, the effects of treatment with IFN, and led to methods for the quantitative evaluation of HCV treatment efficacy and duration of therapy to achieve cure. We have pioneered the development of multiscale models of DAA treatment response in vitro and in patients to reveal a dual mechanism of action of NS5a inhibitors that accounts for rapid viral decline and suggests how patients treated with NS5a inhibitors that are HCV RNA positive at the end of treatment can go on to achieve SVR. Additionally, we and others have accumulated evidence that viral entry/spread plays a much large role in the maintenance of steady state infection that previously assumed having broad implications related to the potential effectiveness of viral entry/spread as an antiviral drug target and impacts important antiviral therapy considerations such as drug efficacy, viral escape, and drug synergy. Importantly however, these predictions need to be validated/tested and the dynamics of these additional aspects of infection incorporated into models that can accurately predict the minimum duration of treatment needed to cure the infection. Towards this end, the objective of this cross disciplinary R01 is to increase our knowledge of HCV by formulating and testing mathematical models of HCV infection and treatment response at the molecular level. Specifically, we hypothesize that a more quantitative understanding HCV infection dynamics and treatment response will help optimize HCV DAA therapy (e.g. enhance drug synergy and increase the barrier to viral escape), predict the duration of therapy needed to achieve viral clearance (i.e. define cure boundaries), and ultimately allow for individualize therapy enabling a desperately needed reduction in cost. Accordingly, the specific aims are: 1) Understand and determine criteria for HCV Cure; 2) Define the HCV life cycle, DAA mode of action and hepatocyte role in HCV clearance; 3) Develop and utilize mathematical models of HCV cell-to-cell spread.