Bayesian models to accelerate antibacterial drug discovery

贝叶斯模型加速抗菌药物的发现

基本信息

批准号：
9243961
负责人：
Joel Stephen Freundlich
金额：
$ 42.93万
依托单位：
RBHS-NEW JERSEY MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/9243961
关键词：
Accelerated Phase Address Animal Model Anti-Bacterial Agents Antimalarials Bacteria Bacterial Infections Bayesian Method Bayesian Modeling Biological Assay Cells Chemical Structure Chemicals Collection Communicable Diseases Computational Technique Computational algorithm Computer software Data Data Set Disincentive Drug Industry Drug resistance Drug-sensitive Evolution Failure Future Goals Growth In Vitro Infection Innovative Therapy Lead Learning Libraries Literature Machine Learning Medical Methodology Microbe Modeling Mycobacterium tuberculosis Natural Products Pharmaceutical Chemistry Pharmaceutical Preparations Publishing Quinolones Recording of previous events Research Resistance Safety Statistical Models Techniques Technology Testing Therapeutic Time Validation Wages base beta-Lactams clinical candidate comparative cost effective cytotoxicity drug discovery global health heuristics high throughput screening inhibitor/antagonist learning strategy next generation novel novel strategies novel therapeutics outcome forecast pathogen patient population pre-clinical predictive modeling process optimization prospective test resistance mechanism scaffold screening small molecule small molecule therapeutics success virtual

项目摘要

Infections caused by a range of bacteria represent a significant medical need that is not being sufficiently addressed by the pharmaceutical industry. M. tuberculosis, the ESKAPE bacteria, and Select Agent bacteria constitute three classes of microbes that are relevant to global health in large part because of their resistance to available therapeutics. Most new antibacterials are developed by classical discovery methodologies, such as randomly assaying small molecule collections for growth inhibition ofthe appropriate bacterium. We have chosen to look at antibacterial drug discovery differently and sought a novel strategy utilizing Bayesian models to discover and optimize small molecule antibacterials that is more efficient. For example, we viewed the M. tuberculosis data generated from these random "screens" as a computational learning opportunity. We have used computational algorithms to analyze what attributes ofthe molecules tested are consistent with activity and inactivity. Significantly, this approach yielded validated models for M. tuberculosis that have predicted actives with comparatively high rates of success. Thus, we propose two important extensions of this technology: 1) the optimization ofthe three most promising antitubercular actives arising from our models and 2) the creation and validation of this Bayesian methodology to uncover novel actives against each ofthe ESKAPE and Select Agent bacteria, which will be subsequently optimized. These optimization processes will afford molecules with significant potential as novel therapeutics.

由一系列细菌引起的感染代表了制药行业尚未充分解决的重要医疗需求。结核分枝杆菌、ESKAPE 细菌和 Select Agent 细菌构成了与全球健康相关的三类微生物，这在很大程度上是因为它们对现有治疗方法具有耐药性。大多数新的抗菌药物都是通过经典的发现方法开发的，例如随机分析小分子集合以抑制适当细菌的生长。我们选择以不同的方式看待抗菌药物的发现，并寻求一种利用贝叶斯模型来发现和优化更有效的小分子抗菌药物的新策略。例如，我们将这些随机“屏幕”生成的结核分枝杆菌数据视为计算学习机会。我们使用计算算法来分析测试分子的哪些属性与活性和非活性一致。值得注意的是，这种方法产生了经过验证的结核分枝杆菌模型，该模型以相对较高的成功率预测了活性物质。因此，我们提出了该技术的两个重要扩展：1）从我们的模型中优化三种最有前途的抗结核活性物质，2）创建和验证这种贝叶斯方法，以发现针对 ESKAPE 和 Select Agent 细菌的新活性物质，这后续会进行优化。这些优化过程将提供具有作为新型疗法的巨大潜力的分子。