Improving combination chemotherapy of tuberculosis: a computational approach

项目摘要/摘要 结核病(TB)是一种广泛传播的细菌传染病，每年导致近150万人死亡。而当 有效的药物治疗结核病已经有50多年的历史了，有相当数量的药物 严重影响公众健康的耐药临床病例fi。治疗结核病的药物方案旨在限制 多种药物同时使用(联合治疗)出现耐药性，大大增加 它们开发的时间和成本。而美国食品和药物管理局(FDA)与 最近成立的新结核病药物方案的关键路径(CPTR)倡议，现在提供了监管指导 作为一个单位开发新的药物组合，同时几种新的抗结核病方案正在临床上 在FDA的指导下进行测试，如何确定最佳剂量存在关键问题 这些新的联合疗法中的每一种药物。 新的抗结核药物组合的给药方案通常以fi为基础，并提供最佳剂量 每一种药物都处于临床前阶段，并通过第二阶段剂量范围的临床试验。在裁剪 一种药物组合中每种药物的剂量可能会产生更有效和更好的 耐受治疗方案，体外方法学的指数增长，动物EFfi研究，以及 为确定结核病所需的三种或三种以上药物组合的这种剂量所需的临床测试将是 贵得离谱。为了解决结核病药物开发中的这一差距，我们提出了一种新的剂量方法 联合药物疗法的方案设计，包括(1)使用传统的临床前和 临床测量为结核病特定药物组合的数学剂量-反应模型提供依据 患者，(2)将此数学模型与生物启发的遗传算法相结合来设计 以类似于自然选择的方式给药方案，以及(3)对这些优化方案的经验评估 实验性结核病感染模型中的治疗方案。 为了建立我们的方法与临床相关的例子，我们将设计优化的剂量方案。 对于新的抗结核组合，前列环素+莫西fl，奥沙星+吡嗪酰胺(PAMZ)；一个有希望和迫切的 多药耐药(MDR)结核病患者所需的治疗方案，目前正在进行II期临床评估 审判。这种结核病药物组合有大量高质量的临床前和临床数据，将 为开发我们的计算框架和检验我们的结论提供可靠的证据基础。成功 拟议目标的完成将建立新的方法和工具，以更好地翻译临床前研究 为未来抗结核联合用药的临床用药方案设计。虽然受到结核病药物需求的推动 开发，该项目包括适用于治疗其他疾病的创新，如癌症， 人类免疫缺陷病毒(fi)感染和疟疾。

Project Summary/Abstract Tuberculosis (TB) is a widespread bacterial infectious disease that kills nearly 1.5 million people annually. While effective drug therapy for TB has been available for more than 50 years, there is a substantial number of drug resistant clinical cases that are significantly impacting public health. Drug regimens for TB are designed to limit the emergence of resistance by using multiple drugs concurrently (combination therapy) which greatly increases the time and cost of their development. While the U.S. Food and Drug Administration (FDA), in partnership with the recently formed Critical Path to New TB Drug Regimens (CPTR) initiative, now provides regulatory guidance for developing new drug combinations as a single unit, and while several new anti-TB regimens are in clinical testing under this FDA guidance, there are critical questions about how to establish the optimal dose of each individual drug within these new combination regimens. Dosage regimens for new anti-TB drug combinations are generally based on finding an optimal dose for every single drug in the preclinical stage, and through Phase II dose-ranging clinical trials. While tailoring the doses of each individual drug within a drug combination could potentially yield a more effective and better tolerated treatment regimen, the exponential increase in the in vitro methodologies, animal efficacy studies, and clinical testing required to identify such doses for combinations of three or more drugs needed for TB would be prohibitively expensive. To address this gap in TB drug development we propose a new approach to dosage regimen design of combination drug therapies that consists of (1) the use of conventional preclinical and clinical measurements to inform a mathematical dose-response model for a specified drug combination in TB patients, (2) the integration of this mathematical model with a biologically inspired genetic algorithm to design dosage regimens in a manner analogous to natural selection, and (3) the empirical evaluation of these optimized regimens in experimental TB-infection models. To establish our approach with a clinically relevant example, we will design optimized dosage regimens for the new anti-TB combination pretomanid + moxifloxacin + pyrazinamide (PaMZ); a promising and urgently needed treatment option for patients with multidrug resistant (MDR) TB, currently assessed in a Phase II clinical trial. There is a large amount of high quality preclinical and clinical data for this TB drug combination that will provide a sound evidence base to develop our computational framework and to test our conclusions. Successful completion of the proposed aims will establish new methods and tools to better translate preclinical studies to clinical dosage regimen design for future anti-TB combinations. While motivated by the needs of TB drug development, this project includes innovations that apply to the treatment of other diseases such as cancer, human immunodeficiency virus (HIV) infection, and malaria.