Identifying Optimal Treatment Strategies for Tuberculosis Treatment

项目总结/摘要 目前对药物敏感结核病的护理标准是“一刀切”的方法， 复发风险较高的患者和获得耐药性风险较高的分枝杆菌。3期 治疗缩短研究TBTC/ACTG（研究31/A5349）正在评估两种新的 含有高剂量利福喷丁的短程方案。我们的建议的主要目的是全面嵌入 本临床试验中的药理学和微生物学分析（PK/PD），以提供详细的药物药代动力学， MIC反应和安全性数据-包括超过2,000例患者的新数据（持续性标志物）。我们 目的是了解和量化个体药物PK/PD、MIC、新的 基因组载量、持续感染者的新标记物、活动性疾病严重程度和早期治疗反应， 患者人群，并认识到它们与临床结局和安全性事件的关系。通过这样做，我们将 能够理解和量化药理学（多药药代动力学）和非 治疗反应的药理学（宿主、疾病严重程度）成分，并了解表型 很难治疗的病人，使我们能够为所有药物治疗的病人得出最佳的治疗策略， 敏感结核病，包括方案的选择，治疗持续时间和剂量。 我们提出了一个创新的假设，即感染细菌和宿主都可以被视为“低” 和“高”风险，这是这两个风险的组合，共同决定治疗 结果和所需的治疗持续时间，无论使用的药物。我们的方法将分层 细菌风险的负担，MIC -甚至在药物敏感的结核分枝杆菌-和耐药的存在 亚群宿主风险将按疾病严重程度、艾滋病毒状况和吸收能力进行分层， 代谢药物（PK）。然后，我们将使用先进的分析和建模策略来开发工具， 识别感染低风险细菌的低风险患者的算法，这些患者可以接受超短疗程治疗 （<=四个月）和感染高危细菌的高危患者，他们需要治疗的时间超过 六个月通过我们的分析，我们将能够为每个患者选择导致最高疗效的方案。 治愈的可能性。我们的发现将彻底改变全球结核病临床试验和护理的未来。 本研究将解决一些基本问题，例如， 和有利的AUC/MIC目标是所有一线结核病药物使用的主要临床结果（复发）， 对治疗的早期反应与大量不同患者群体的临床结果有关。该项目 TBTC/ACTG领导层和我们的行业合作伙伴（赛诺菲安万特）提供了前所未有的支持。内的资金 本R 01要求完成研究31中未包括的药物措施和MIC所需的预算（即，所有 利福喷丁和利福沙星以外的药物）以及全套PK/PD建模和学习， 试验的主要目的是测试四个月实验方案的非劣效性。

Project Summary/Abstract The current standard of care for drug-sensitive TB is a “one-size-fits-all” approach, putting hard-to-treat patients at higher risk of relapse and mycobacteria at higher risk of acquiring drug resistance. The Phase 3 treatment-shortening study TBTC/ACTG (Study 31/A5349) is evaluating the efficacy and safety of two new short-course regimens containing high-dose rifapentine. The primary aim of our proposal is to embed full pharmacology and microbiology analyses (PK/PD) in this clinical trial to provide detailed drug pharmacokinetic, MIC response and safety data - including novel data (markers of persisters) for more than 2,000 patients. Our goal is to understand and quantify the interactions among individual drug PK/PD, MICs, new markers of genome load, new markers for persisters, active disease severity and early treatment response in a diverse patient population and recognize how they relate to clinical outcome and safety events. By doing so, we will be able to understand and quantify the contributions of pharmacological (multidrug pharmacokinetic) and non- pharmacological (host, disease severity) components of treatment response and to understand the phenotypes of patients who are hard to treat, allowing us to derive optimal treatment strategies for all patients with drug- sensitive TB, including choice of regimen, treatment duration, and dose. We propose the innovative hypothesis that both the infecting bacteria and the host can be seen as “low” and “high” risk and that it is the combination of these two risks that together determine treatment outcome and the required duration of treatment, regardless of the drugs used. Our approach will stratify bacterial risk by burden, MIC - even among drug-susceptible Mtb - and the presence of drug-tolerant subpopulations. The host risk will be stratified by disease severity, HIV status and ability to absorb and metabolize drugs (PK). We will then use advanced analytic and modeling strategies to develop tools and algorithms to identify low-risk patients infected with low-risk bacteria who can be treated with ultra-short treatment (<=four months) and high-risk patients infected with high-risk bacteria who will need treatment for longer than six months. Through our analyses, we will be able to select for each patient the regimen that results in the highest likelihood of cure. Our findings will completely change the future of TB clinical trials and care worldwide. This study will address fundamental questions, such as what the exposure-response/safety relationships and favorable AUC/MIC targets are for all first-line TB drugs using a major clinical outcome (relapse) and how early response to treatment relates to clinical outcome in a large and diverse patient population. The project has unprecedented support from the TBTC/ACTG leadership and our industry partner (Sanofi Aventis). The funds in this R01 requests the budget needed to complete drug measures and MIC not included in Study 31 (i.e., all drugs other than rifapentine and moxifloxacin) and the full suite of PK/PD modeling and learnings that go beyond the trial's primary goal of testing the non-inferiority of the experimental four-month regimens.