Optimizing Multi-drug Mycobacterium tuberculosis Therapy for Rapid Sterilization and Resistance Suppression

项目摘要/摘要 在P01 AI123036中，我们能够生成对分枝杆菌的单个制剂进行排序的算法 结核病(MTB)，确定了有希望的2种药物组合，并用一种全新的数学方法 方法，确定3种用药方案预测明显优于2种用药方案。这些预测 在BALB/c模型(H37Rv)和非人类灵长类结核模型(Erdman)中进行了前瞻性验证 菌株)。在这份提案中，我们将扩展我们之前的工作。 出现了大量新的结核分枝杆菌制剂，其中许多具有新的作用机制。我们有四个具体目标 (SA)完成后，将使我们能够确定多种药物组合，以优化对 生物处于3种不同的代谢状态，并将抑制抗药性的出现。 在中空纤维感染模型[HFIM](SA#1)中，我们将能够根据效力对新药物进行排名 和物理化学性质。HFIM提供了对药物暴露-杀死和反应的洞察 阻力抑制。我们确定了一种近乎最佳的3种药物方案(PMD/MFX/BDQ)。有了新的单一代理， 我们可以研究用一种新的药物来替代一种旧的药物，我们可以扩大治疗方案，以确定一种近乎- 最佳四联用药方案。这对细菌负荷高的患者尤其重要。 在SA#2中，我们将从SA#1开始在两个小鼠模型(BALB/c和C3HeB/FEJ小鼠)中测试方案。这些将会给你 有些不同的信息。两者都提供了关于杀戮和抵抗抑制的信息。克拉姆尼克小鼠 具有与人类更相似的病理学。我们将使用基质辅助激光解吸 电离-MS成像和激光捕获显微切割LC MS。这允许识别空间分布 以及药品的量化。关于治愈的一个问题是，要等多久才能把动物作为祭品来记录 根除。一些药物(BDQ)有很长的组织半衰期。我们将记录毒品进出的比率 进入感染部位，以便确定何时可以牺牲动物队列来记录根除情况。 在SA#3中，我们将定量记录抗菌效果的机制。我们产生了史无前例的- 一种结核分枝杆菌中PBP结合的动态模型，并将其与细胞杀伤率联系起来。我们还开发了 AMP/ADP/ATP细胞内测定法。这些药物将用于二芳基喹啉类药物(如BDQ)和PMD 作为能量毒药(对于PMD，这发生在厌氧/非复制条件下)。我们将衡量 细胞内(MTB)药物浓度，将它们与单独和联合治疗实验中的效果联系起来。 提案的成功取决于对数据的建模。在SA#4中，我们编写了扩展早期分析的代码， 从3到4种药物治疗方案。对于这些高维模型，我们开发了几种方法来 加快分析速度，使它们在计算上易于处理。在提案结束时，我们将开发一个4-药物算法 允许快速确定对敏感和较不敏感的方案都有效的近乎最佳的方案 有机体。该算法将是通用的。它将很好地适用于今天的代理，也适用于已发现的代理。

Project Summary/Abstract In P01 AI123036, we were able to generate an algorithm that ranked single agents for Mycobacterium tuberculosis (MTB), identified promising 2-drug combinations and, with a completely novel mathematical approach, identified 3-drug regimens predicted to be significantly better than 2-drug regimens. These predictions were prospectively validated in a BALB/c model (H37Rv) and in a Non-Human Primate model of MTB (Erdman strain). In this proposal, we will extend our previous work. There is a large number of new MTB agents, many with novel mechanisms of action. We have 4 Specific Aims (SA) that, when complete, will allow us to identify multi-drug combinations that will optimize rate of kill for organisms in 3 different metabolic states and will suppress resistance emergence. In the Hollow Fiber Infection Model [HFIM] (SA#1), we will be able to rank new agents on the bases of potency and physicochemical properties. The HFIM provides insight into the drug’s exposure-response for kill and resistance suppression. We identified a near optimal 3-drug regimen (PMD/MFX/BDQ). With new single agents, we can examine substituting a new agent for an older agent AND we can expand the regimens to identify a near- optimal 4-drug regimen. This will be particularly important for patients with high bacterial burdens. In SA #2, we will test regimens from SA#1 in two murine models (BALB/c & C3HeB/FeJ mice). These will give somewhat different information. Both give information regarding kill and resistance suppression. Kramnik mice have pathology more closely resembling that in humans. We will use Matrix-Assisted Laser Desorption Ionization-MS Imaging and Laser Capture Microdissection LCMS. This allows identification of spatial distribution and quantification of drugs. A question regarding cure is how long to wait to sacrifice animals to document eradication. Some agents (BDQ) have long tissue half-lives. We will document rates of ingress/egress of drugs into the infection site, allowing determination when animal cohorts may be sacrificed to document eradication. In SA #3, we will document mechanisms of antimicrobial effect quantitatively. We have generated a first-of-a- kind dynamic model for PBP-binding in MTB, and will link this to rates of cell kill. We have also developed AMP/ADP/ATP intracellular assays. These will be employed for agents like diarylquinolines (e.g. BDQ) and PMD that act as energy poisons (for PMD, this occurs under anaerobic/non-replicative conditions. We will measure intracellular (MTB) drug concentrations, linking them to effect alone and in combination therapy experiments. Proposal success rests on modeling of the data. In SA #4, we have written code to extend earlier analyses, going from 3- to 4-drug regimens. For these high dimensional models, we developed several approaches to speed up analysis making them computationally tractable. At proposal end, we shall develop a 4-drug algorithm allowing rapid identification of near optimal regimens that work for both susceptible and less-susceptible organisms. The algorithm will be general. It will work well for today’s agents but also for agents as discovered.