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）感染是一个重大的公共卫生问题，影响着全球近1.7亿人。由于目前还没有针对HCV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HCV感染。目前HCV感染的标准治疗是聚乙二醇干扰素和利巴韦林的组合。不幸的是，这种治疗方案充满了副作用，并且在不到50%的感染基因型1 HCV的患者中有效，基因型1 HCV是美国最流行的基因型。因此，对于治疗HCV的新治疗方案存在巨大的医学需求。 为了满足对新的HCV治疗剂的需求，吉利德科学公司，默克公司，和Bristol-Myers Squibb专注于开发新的化合物，抑制病毒复制周期中的特定过程，称为直接作用的抗病毒剂（DAA）。DAA为当前的标准治疗方案提供了几个优势，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。与作用于不同靶位点的两种或更多种DAA的联合治疗是预防耐药性出现的有希望的策略。为了使联合治疗对人体有效，必须阐明最佳剂量（多少？）和给药间隔（多久一次？）每种化合物都能最大限度地抑制耐药性和病毒。 对于本提案，我们将评估代表四种抗HCV药物类别的共9种DAA。我们将首先使用最先进的BelloCell药效学（PD）模型系统（其中模拟人PK曲线）确定9种化合物作为针对基因型1b HCV复制子的单药治疗（目标#1）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 2种药物组合的剂量方案，并将在BelloCell PD系统中评价这些方案（目标#2）。一个新颖的和创新的数学混合物模型的组合治疗将适合从这些实验中产生的数据。该模型描述了联合治疗对易感复制子以及对联合治疗中任一药物具有抗性的突变复制子的影响。使用该模型与蒙特卡罗模拟允许识别基于人群的联合化疗的最佳方案。最后，还将在BelloCell PD系统中评估三联疗法。作为这一应用的一部分，将开发新的计算机程序（具体目标#3），以提高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组合治疗方案，具有最大的临床成功的可能性。这些方案可以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.