Strengthening evidence on optimal multidrug-resistant tuberculosis treatment regimens through improved epidemiologic methods

项目概要/摘要 2017年，超过55万人感染了对利福平具有耐药性的结核病（TB）菌株 和异烟肼，这是标准一线结核病治疗方案中最有效的两种药物。从历史上看， 耐多药（MDR）结核病的治疗时间长，毒性大，无效：平均只有55%的治疗患者 在常规的18至24个月的治疗方案结束时治愈，其中包括每天注射 大约八个月2012年和2013年，监管机构有条件地批准了第一个新的结核病 贝达喹啉（BDQ）和德拉马尼（DLM），为更有效、毒性更小的药物提供了希望。 耐多药结核病治疗。2019年初，根据现有最佳证据，世界卫生组织（WHO） 发布了新的耐多药结核病治疗指南。这些指南包括一个新的优先药物排名， MDR-TB方案的组成，该方案将BDQ确立为18至24个月无注射剂治疗的锚， 方案，并批准了一个标准化的九个月的方案，其中含有注射剂。 尽管传统的无注射剂和缩短的含注射剂的耐多药结核病方案代表了 这些新的指导方针推迟了对一些关键问题的指导， 由于缺乏证据，许多国家结核病规划对如何实施耐多药结核病治疗不确定。 例如，尽管建议指导治疗的总体持续时间，但没有建议 每种药物的最佳使用时间。此外，没有关于安全性或有效性的数据， 包括五种最优先药物的方案。缺乏高质量的耐多药结核病队列数据也阻碍了 关于其他紧急问题的指南，包括（1）BDQ常见标签外使用的安全性和有效性 和DLM，（例如，联合给药和药物的延长使用）;和（2）BDQ或DLM是否可以 按照推荐的九个月疗程进行治疗在缺乏临床试验数据来解决这些问题的情况下， 问题，从观察性耐多药结核病队列数据中进行可靠、有效的因果推断， 保证耐多药结核病的最佳治疗，并将死亡、发病率和疾病传播降至最低 美国和世界各地的患者。 由我们小组实施的endTB倡议为生成高质量证据提供了独特的机会 为耐多药结核病的治疗和护理提供信息。2，600例接受以下治疗的耐多药结核病患者的终末期结核病观察队列 17个国家的BDQ和/或DLM-最大的，系统收集的前瞻性数据集。详细 纵向数据将允许进行强有力的分析，为世界卫生组织优先考虑的挥之不去的关键问题提供信息 Organization.我们将利用这一独特的资源，在耐多药结核病负担高的国家建立强有力的伙伴关系， 流行病学方法方面的先进专业知识，以确定最佳的耐多药结核病治疗方案; BDQ和DLM的不良事件监测，并说明如何将稳健的纵向数据和 新的方法可以减少这些偏差。

PROJECT SUMMARY/ABSTRACT In 2017, more than 550,000 people became sick with a strain of tuberculosis (TB) that was resistant to rifampin and isoniazid, the two most potent drugs in the standard first-line TB regimen. Historically, treatment for patients with multidrug-resistant (MDR) TB has been long, toxic, and ineffective: on average, only 55% of treated patients were cured at the end of the conventional 18- to 24-month regimen, which included a daily injection for approximately eight months. In 2012 and 2013 regulatory authorities conditionally approved the first new TB drugs in fifty years, bedaquiline (BDQ) and delamanid (DLM), offering hope for more effective and less toxic MDR-TB treatment. In early 2019, based on the best available evidence, the World Health Organization (WHO) issued new guidelines for MDR-TB treatment. These guidelines included a new priority drug ranking for the composition of MDR-TB regimens, which established BDQ as an anchor of an injectable-free 18- to 24-month regimen and endorsed a standardized nine-month regimen containing an injectable. Although conventional injectable-free and shortened injectable-containing MDR-TB regimens represent enormous improvements, these new guidelines deferred guidance on a number of key questions for which evidence is lacking, leaving many national TB programs uncertain about how to implement MDR-TB treatment. For example, although recommendations guide the overall duration of treatment, there was no recommendation on the optimal duration of use of each drug. Moreover, there is no data on the safety or effectiveness of the regimen comprising the five top priority drugs. A lack of high quality MDR-TB cohort data also prevented guidance on other urgent questions, including (1) the safety and effectiveness of common off-label uses of BDQ and DLM, (e.g., co-administration and extended use of the drugs); and (2) whether BDQ or DLM could be substituted into the recommended nine-month regimen. In the absence of clinical trial data to address these questions, robust, valid causal inference from observational MDR-TB cohort data is of paramount importance to guaranteeing optimal treatment—and minimizing death, morbidity, and disease transmission—for MDR-TB patients throughout the United States and the world. The endTB initiative, implemented by our group, offers a unique opportunity to generate high-quality evidence to inform MDR-TB treatment and care. The endTB observational cohort of 2,600 MDR-TB patients treated with BDQ and/or DLM in 17 countries—the largest, systematically-collected prospective dataset of its kind. Detailed longitudinal data will permit robust analyses to inform lingering, critical questions, prioritized by the World Health Organization. We will exploit this unique resource, strong partnerships in high-MDR-TB-burden countries, and advanced expertise in epidemiologic methods to identify optimal MDR-TB regimens; inform optimal use of and adverse event monitoring for BDQ and DLM, and illustrate how combinations of robust, longitudinal data and novel methods can reduce these biases.