Innovative technologies to transform antibiotic discovery. Project 2 Engineering antibiotic sensitization therapies

ESKAPE病原体由于多药耐药的流行而继续构成重大的全球健康风险。 耐药性和广泛的感染率。因此，新的治疗方法是非常需要的，我们建议利用 抗生素的组合，以提高疗效和管理耐药性。最佳多药治疗方案 考虑每种药物如何影响其他药物的疗效。针对ESKAPE的协同多药治疗 病原体可以通过提供更有效的协同疗法来改变患者护理， 这降低了药物依赖的发病率，并迅速减少病原体种群，可能减缓 耐药性获得我们已经开发了实验和分析平台，以有效地测量， 分析和预测成对和高阶药物相互作用，使我们能够从一个大的优先组合， 毒品的数量。我们建议建立在我们的平台上， 针对三种重要的医院ESKAPE病原体的治疗：鲍曼不动杆菌（Ab）、克雷伯菌 肺炎（Kp）和铜绿假单胞菌（Pa）。目前对这些ESKAPE病原体的治疗有限 因为它们具有获得耐药性和“逃避”治疗的显著能力。有前途的组合 针对ESKAPE病原体的治疗方法目前正在开发中，这说明了系统性 采用这种方法的战略。 为了充分发挥新候选药物的潜力并优化其对ESKAPE病原体的使用，我们 建议在开发管道的早期系统地探索联合治疗。我们将利用 一个经过充分验证的微型筛选方法的规模和效率，以衡量疗效， 25种抗生素与小分子库和新化学品之间的成对组合的相互作用 包括项目1、3和4中发现的生物制剂和缀合物在内的实体。探索将包括筛选 对抗耐药临床分离株。我们将严格验证筛选命中，并按化学品对这些命中进行优先排序。 进展性，市场需求评估，以及针对临床分离物组的测试，扩展的抗生素组， 基本毒性评估，以及在更复杂的生长环境条件下（如宿主样 环境条件、生物膜和动物模型）。显示有利特征的组合 在初步分析中，将进行进一步深入的作用机制和抗性采集 问题研究基于这些数据，我们将预测与我们的命中集和测试中进一步可用的化合物的相互作用。 设计了更高级的联合疗法优先线索将系统地优化， 药物化学工作，旨在设计一个全面的产品特性曲线， 通过体内概念验证（In vivo Proof of Concept，简称ICPs）。我们预计这项工作将确定有效的候选药物方案 具有商业吸引力，并具有强大的科学基础，可用于临床。

The ESKAPE pathogens continue to pose a significant global health risk due to the prevalence of multidrug resistance and widespread rates of infection. New therapies are thus highly desired, and we propose leveraging combinations of antibiotics to both improve efficacy and manage drug resistance. Optimal multi-drug regimens consider how each drug affects the efficacy of others. Synergistic multi-drug treatments against the ESKAPE pathogens may transform patient care by providing more potent synergistic therapies, allowing dosing at levels that lower the rate of drug-dependent morbidity, and quickly shrinking pathogen populations, possibly slowing drug resistance acquisition. We have developed experimental and analytical platforms to efficiently measure, analyze, and predict pairwise and high-order drug interactions, allowing us to prioritize combinations from a large numbers of drugs. We propose to build upon our platforms to accelerate the development of combination therapies against three important nosocomial ESKAPE pathogens: Acinetobacter baumannii (Ab), Klebsiella pneumonia (Kp), and Pseudomonas aeruginosa (Pa). Treatment of these ESKAPE pathogens is currently limited because of their remarkable ability to acquire drug resistance and "escape" treatment. Promising combination therapies against ESKAPE pathogens are being developed ad hoc today, illustrating the need for systematic strategies that employ this approach. To fully realize the potential of new drug candidates and optimize their use against ESKAPE pathogens, we propose to systematically explore combination therapy early in the development pipeline. We will leverage the scale and efficiency of a well-validated micro-scale screening approach to measure the efficacies and interactions of pairwise combinations among 25 antibiotics and small molecule libraries and new chemical entities including biologics and conjugates discovered in projects 1, 3, and 4. Discovery will consist of screening against resistant clinical isolates. We will rigorously validate screening hits and prioritize these by chemical progressibility, evaluation of market need, and in tests against clinical isolate panels, expanded antibiotic sets, basic toxicity assessment, and efficacy in more complex growth-niche conditions (such as host-like environmental conditions, biofilms, and in animal models). Combinations that display favorable characteristics in preliminary analyses will be subjected to further intensive mechanism-of-action and resistance acquisition studies. Based on these data, we will predict interactions with further available compounds in our hit set and test engineered higher-order combination therapies. Priority leads will be systematically optimized in a substantial medicinal chemistry effort aimed at engineering a comprehensive product characteristic profile and extending through in vivo proof of concept (PoC). We anticipate that this work will identify potent candidate drug regimens that are commercially attractive and have a strong scientific basis for translation to the clinic.