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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：
8841308
负责人：
Joel Stephen Freundlich
金额：
$ 38.39万
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/8841308
关键词：
Accelerated Phase Address Animal Model Anti-Bacterial Agents Antimalarials Bacteria Bacterial Infections Bayesian Method Bayesian Modeling Biological Assay Biological Factors 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 Lactams Lead Learning Libraries Literature Machine Learning Medical Methodology Methods Microbe Modeling Mycobacterium tuberculosis Pharmaceutical Chemistry Pharmaceutical Preparations Publishing Quinolones Recording of previous events Research Resistance Safety Statistical Models Techniques Technology Testing Therapeutic Time Validation Wages base cost effective cytotoxicity drug discovery global health heuristics high throughput screening inhibitor/antagonist meetings next generation novel novel strategies novel therapeutics outcome forecast pathogen patient population pre-clinical predictive modeling process optimization prospective resistance mechanism scaffold screening small molecule success

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是一项重大的医疗需求，而制药业尚未充分解决这一问题。结核分枝杆菌、ESKAPE细菌和选择剂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。大多数新的抗菌剂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“筛选”产生的结核分枝杆菌数据视为计算学习的机会。我们使用计算算法来分析被测分子的哪些属性与活性和非活性相一致。值得注意的是，这种方法产生了有效的结核分枝杆菌模型，预测活性的成功率相对较高。因此，我们提出了这项技术的两个重要扩展：1)优化从我们的模型中产生的三种最有希望的抗结核活性；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，这些活性随后将被优化。这些优化过程将提供具有重要潜力的分子作为新型治疗药物。