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优先考虑开发较短的治疗方案，作为其全球消除结核病计划的一部分。为了实现这一目标，许多实体已经开展了II期临床试验，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，对所有建议的方案进行顺序测试既耗时又成本高昂，因此需要更有效的选择方案进行测试的策略。此外，如果没有事先对新疗法的成本进行评估，就有可能开发出一种经济上不可行的结核病新疗法。数学建模可以通过综合先前人类和小鼠研究的数据来产生可用于临床试验设计的新方案的复发率估计，从而为这一问题提供解决方案。首先，将开发一种结核病治疗和复发的通用马尔可夫模型，其中结核病患者将被分配到几个治疗组中的一个，然后通过在1、2、3或4个月对培养阳性进行中期评估，按月进行随访。然后假设他们完成了一个巩固治疗阶段，之后他们将被随访两年，以防复发。毒性和耐受性将在每个阶段进行考虑。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，以95%的置信区间产生复发率的点估计，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将使用该模型检查在结核病治疗的前两个月以莫西沙星代替异烟肼对结核病复发率的潜在影响，给定标准和缩短的治疗时间。目标2将确定高剂量利福喷丁（在结核病治疗的前2个月给予）对复发率的影响以及缩短结核病治疗持续时间的潜力。目标3将是模拟利奈唑胺对耐多药结核病治疗的潜在影响，特别注意提高有效性和毒性之间的权衡。成本和成本效益将在每个模型中计算。

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.