Mathematical Modeling in Clinical Trials of Tuberculosis Therapeutics

描述(由申请人提供)：控制结核病伙伴关系已将开发较短的方案作为其消除结核病的全球计划的一部分。为了实现这一目标，许多实体进行了第二阶段的临床试验，使用2个月的培养转化作为替代终点，以测试新药方案的灭菌能力。然而，所有建议的方案的顺序试验都是耗时和昂贵的，因此需要更有效的策略来选择试验方案。此外，如果不事先评估新疗法的成本，就有可能开发一种在经济上不可行的结核病新疗法。数学建模可以通过合成先前人类和小鼠研究的数据来生成可用于临床试验设计的新方案的复发率估计，从而为这个问题提供解决方案。首先，将建立结核病治疗和复发的通用马尔可夫模型，在该模型中，结核病患者将被分配到几个治疗部门中的一个，然后按月循环，在1、2、3或4个月时进行培养阳性的中期评估。然后，他们将被认为完成了巩固阶段的治疗，在此之后，他们将被跟踪两年以防止复发。在每个阶段都会考虑毒性和耐受性。我们将对所有相关参数，特别是培养转化率和复发率使用适当的分布，并使用概率敏感性分析来产生复发率的点估计，其可信区间为95%，可用于确定哪些方案值得进一步测试。然后，该模型将用于实现3个具体目标：目标1将使用该模型来检查在给定标准和缩短的疗程的情况下，在结核病治疗的头两个月中用莫西沙星替代异烟肼对结核病复发率的潜在影响。目标2将确定大剂量利福喷丁(在结核病治疗的头2个月期间使用)对复发率和缩短结核病治疗持续时间的可能性的影响。目标3将模拟利奈唑胺治疗耐多药结核病的潜在效果，特别关注增加的有效性和毒性之间的权衡。将在每个模型中计算成本和成本效益。

DESCRIPTION (provided by applicant): The Stop TB Partnership has prioritized the development of shorter regimens as part of their global plan to eliminate TB. In pursuit of that goal, many entities have undertaken Phase II clinical trials using 2-month culture conversion as a surrogate endpoint to test the sterilizing ability of new drug regimens. However, the sequential testing of all proposed regimens is time-consuming and cost-prohibitive, so more efficient strategies for selecting regimens for testing are required. Furthermore, without prior evaluation of the costs of new therapies, there is a risk of developing a new treatment for TB that is not economically viable. Mathematical modeling can provide a solution to this problem by synthesizing data from prior human and mouse studies to generate estimates of relapse rates for new regimens that could be used in the design of clinical trials. First, a generic Markov model of TB treatment and relapse will be developed in which individuals with TB will be assigned to one of several treatment arms, then followed in monthly cycles through an interim evaluation of culture positivity at 1, 2, 3, or 4 months. They will then be assumed to complete a consolidation phase of therapy, after which they will be followed for two years for relapse. Toxicity and tolerability will be considered at each stage. We will use appropriate distributions for all relevant parameters, particularly culture conversion and relapse rates, and use probabilistic sensitivity analysis to produce point estimates of relapse rates with 95% confidence intervals that can be used to determine which regimens are worthy of further testing. This model will then be used to accomplish 3 specific aims: Aim 1 will be to use the model to examine the potential impact of substituting moxifloxacin for isoniazid during the first two months of TB treatment on TB relapse rates, given standard and abbreviated treatment durations. Aim 2 will be to determine the impact of high-dose rifapentine (administered during the first 2 months of TB treatment) on relapse rates and potential for reduction of the duration of TB treatment. Aim 3 will be to model the potential effect of linezolid on the treatment of MDR-TB, paying particular attention to the tradeoff between increased effectiveness and toxicity. Costs and cost-effectiveness will be calculated in each model.