Optimizing combination therapy for Hepatitis C virus with pharmacodynamic models

描述（由申请人提供）：丙型肝炎病毒（HCV）感染是影响全球近1.7亿人的重大公共卫生问题。由于目前还没有预防丙型肝炎病毒的疫苗或预防性治疗方案，感染者必须依靠抗病毒治疗来控制丙型肝炎病毒感染。目前HCV感染的治疗标准是聚乙二醇化干扰素和利巴韦林的联合治疗。不幸的是，这种治疗方案充满了副作用，并且对不到50%的基因型HCV感染患者有效，基因型HCV是美国最普遍的基因型。因此，对于治疗丙肝病毒的新治疗方案有很大的医学需求。为了满足这种对新型HCV治疗药物的需求，吉利德科学公司、默克公司和百时美施贵宝公司已经专注于开发抑制病毒复制周期中特定过程的新化合物，称为直接作用抗病毒药物（DAAs）。daa与目前的标准治疗方案相比有几个优势，包括提高疗效和改善耐受性。然而，耐药性一直是开发这些新化合物的主要挑战。联合治疗两种或多种作用于不同靶点的daa是一种很有前途的预防耐药性出现的策略。为了使联合治疗对人体有效，必须阐明每种化合物的最佳剂量（多少？）和给药间隔（多久？），以最大限度地抑制耐药性和病毒抑制。对于本提案，我们将评估共9个daa，分别代表四种抗hcv药物类别。我们将首先使用最先进的BelloCell药效学（PD）模型系统，模拟人类PK谱，确定9种化合物作为单一疗法（Aim #1）对抗基因型1b HCV复制子的最佳剂量和给药间隔。单药治疗研究的结果将用于设计最有希望的daa双药联合用药方案，这些方案将在BelloCell PD系统中进行评估（Aim #2）。一个新的和创新的数学混合模型的联合治疗将适合从这些实验产生的数据。该模型描述了联合治疗对易感复制子以及突变复制子的影响，这些复制子对联合治疗中的任何一种药物都具有抗性。将该模型与蒙特卡罗模拟结合使用，可以确定基于人群的最佳联合化疗方案。最后，三联疗法也将在BelloCell PD系统中进行评估。作为该应用程序的一部分，将开发新的计算机程序（具体目标#3）来改进数学模型的运行时间和效率。完成这项研究将导致智能设计的联合治疗方案，具有最大的临床成功的可能性。这些方案可直接应用于人体临床试验的设计方案。

DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) infection is a significant public health concern that affects nearly 170 million people worldwide. Since a vaccine or prophylactic therapeutic regimen to protect against HCV does not currently exist, infected individuals must rely on antiviral therapy to manage HCV infection. The current standard of care for HCV infection is a combination of pegylated interferon and ribavirin. Unfortunately, this therapeutic regimen is riddled with side effects and is effective in less than 50% of patients infected with genotype 1 HCV, the most prevalent genotype in the United States. Therefore, there is a great medical need for new therapeutic regimens for the treatment of HCV. In an attempt to fulfill this need for new HCV therapeutics, Gilead Sciences, Inc., Merck and Co., and Bristol-Myers Squibb have focused on developing new compounds that inhibit specific processes in the viral replication cycle, referred to as direct acting antiviral agents (DAAs). DAAs offer several advantages to the current standard of care regimen, including increased efficacy and improved tolerability. However, drug resistance has been a major challenge in the development of these new compounds. Combination therapy with two or more DAAs that act on different target sites is a promising strategy to prevent the emergence of resistance. In order for combination therapy to be effective in man, one must elucidate the optimal dose (how much?) and dosing interval (how often?) for each compound that will maximize resistance suppression and viral inhibition. For this proposal we will evaluate a total of 9 DAAs that represent each of the four anti-HCV drug classes. We will first determine the optimal dose and dosing interval for the 9 compounds as monotherapy (Aim #1) against a genotype 1b HCV replicon using the state-of-the-art BelloCell pharmacodynamic (PD) model system in which human PK profiles are simulated. The results from monotherapy studies will be applied to design dosage regimens for the most promising 2-drug combinations of DAAs and these regimens will be evaluated in the BelloCell PD system (Aim #2). A novel and innovative mathematical mixture model for combination therapy will be fit to the data generated from these experiments. This model delineates the impact of combination therapy upon susceptible replicons as well as mutant replicons resistant to either drug in the combination. The use of this model together with Monte Carlo simulation allows for the identification of population-based optimal regimens for combination chemotherapy. Finally, triple combination therapy will also be assessed in the BelloCell PD system. As part of this application, new computer programs will be developed (Specific Aim #3) to improve the run time and efficiency of the mathematical models. Completing this research will result in intelligently designed combination therapeutic regimens that have the greatest likelihood of clinical success. These regimens can be directly applied to the design protocol for human clinical trials.