Identification of novel therapeutics for tuberculosis combining cheminformatics,

结合化学信息学鉴定结核病新疗法，

• 批准号: 8312280
• 负责人: SEAN EKINS
• 金额: $ 47.96万
• 依托单位: COLLABORATIVE DRUG DISCOVERY, INC.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8312280
• 关键词: Address; Alanine; Archives; Biological; Biological Assay; Businesses; Cell Wall; Cells; Cessation of life; Clinical; Clinical Research; Collaborations; Communicable Diseases; Communities; Complement; Computer Simulation; Computer software; Data; Data Set; Databases; Developed Countries; Developing Countries; Development; Dihydropteroate Synthase; Disease; Drug Delivery Systems; Drug resistance; Drug resistance in tuberculosis; Drug-sensitive; Enzymes; Equilibrium; Essential Genes; Evaluation; Foundations; Fructose; Goals; International; Intervention; Intuition; Knowledge; Laboratories; Lead; Link; Literature; Logic; Machine Learning; Malaria; Metabolic; Metabolic Pathway; Mining; Modeling; Molecular; Monobactams; Mycobacterium tuberculosis; Pathway Analysis; Pathway interactions; Peptidoglycan; Permeability; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Property; Publishing; Relative (related person); Research; Research Personnel; Resources; Route; Scientist; Screening procedure; Small Business Technology Transfer Research; Solubility; Stretching; Structure; Sulfonamides; System; Systems Biology; Techniques; Technology; Technology Transfer; Testing; Time; Toxic effect; Tuberculosis; United States National Institutes of Health; Validation; Vendor; Work; analog; antimicrobial; base; cheminformatics; combat; cost; data exchange; data mining; data sharing; drug candidate; drug discovery; enzyme substrate; experience; high throughput screening; in vitro activity; in vivo; interest; knowledge base; microbial alkaline proteinase inhibitor; neurotensin mimic 2; novel; novel strategies; novel therapeutics; p aminobenzoate; pharmacophore; programs; research and development; scaffold; software development; tool; tuberculosis drugs; tuberculosis treatment

DESCRIPTION (provided by applicant): We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis (Mtb) strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery. This Small Business Technology Transfer Phase II project entitled "Identification of novel therapeutics for tuberculosis combining cheminformatics, diverse databases and logic-based pathway analysis" describes the development of software that will facilitate new drug discovery efforts for Mycobacterium tuberculosis (TB) and the progression of molecules discovered with it as mimics for substrates of enzymes and their in vivo essential genes. In phase 1 we illustrated the concept of loosely marrying the cheminformatic and pathways database that resulted in two compounds as proposed mimics of 2 D-fructose 1,6 bisphosphate with activity against Mtb (MIC 20 and 40mg/ml). In phase II via an API we will link the knowledge in CDD, SRI and other databases and tools seamlessly. A researcher will be able to investigate molecules, targets, pathways and then select metabolites or other molecules for pharmacophore analysis, scoring with TB machine learning models and ADME and drug-likeness assessment from within one interface. This tool will be used to aid the identification of novel therapeutics for tuberculosis and be useful for hypotheses testing, knowledge sharing, data archiving, data mining and drug discovery. We will make CDD into a mobile application such that the generalized workflow in this project can be performed anywhere. We present promising preliminary work which resulted in two active compounds, that suggests phase II support of the mimic strategy to identify compounds of interest for TB would be a viable strategy. This proposal balances software development, database development and drug discovery activities in order to achieve our goals. We expect this product could be quickly applied to other infectious diseases which have a great societal impact and as a stretch goal we will endeavor to demonstrate this. PUBLIC HEALTH RELEVANCE: We propose to develop an integrated system to facilitate new drug discovery efforts for TB using novel logical inference techniques developed by scientists at SRI International, linked with knowledge which has been assembled by the curation of diverse biological data types and computational prediction by Collaborative Drug Discovery (CDD). This tool will be used to aid the identification of novel therapeutics for tuberculosis by combining cheminformatics, diverse databases and logic-based pathway analysis. We will demonstrate the utility of the tool (available also as a mobile application) and overall workflow ourselves by discovering and developing compounds for TB and other infectious diseases.

描述（由申请人提供）：我们目睹了药物敏感和耐药结核分枝杆菌（Mtb）菌株病例增加的日益严重的威胁，以及40多年来生产第一种新型结核病（TB）药物的挑战。结核病界已经投入了大量的高通量筛查工作，面临着如何最佳地利用这些数据，以便从一个打击到一个领导到临床的问题。 候选人，可能是一种新药。作为对这种方法的补充，化学信息学技术已经被利用，但规模要小得多。我们建议这些计算方法应该更优化地整合在一个工作流程中，实验方法，以加速结核病药物的发现。这个小型企业技术转让第二阶段项目题为“结合化学信息学、多样化数据库和基于逻辑的途径分析来识别结核病的新型疗法”，描述了软件的开发，该软件将促进结核分枝杆菌（TB）的新药发现工作以及分子的进展。用它发现的分子作为酶及其体内必需基因底物的模拟物。在第1阶段，我们说明了将化学信息学和途径数据库松散结合的概念，这导致两种化合物作为具有抗Mtb活性的2D-果糖1，6二磷酸的拟议模拟物（MIC 20和40 mg/ml）。在第二阶段，通过API，我们将把CDD、SRI和其他数据库和工具中的知识无缝地连接起来。研究人员将能够研究分子、靶标、途径，然后选择代谢物或其他分子进行药效团分析，使用TB机器学习模型和ADME进行评分，并从一个界面中进行药物相似性评估。该工具将用于帮助确定结核病的新疗法，并可用于假设检验、知识共享、数据存档、数据挖掘和药物发现。我们将把CDD变成一个移动的应用程序，这样在这个项目中的通用工作流程可以在任何地方执行。我们提出了有希望的初步工作，导致两个活性化合物，这表明第二阶段的模拟策略，以确定感兴趣的化合物结核病的支持将是一个可行的策略。该提案平衡了软件开发、数据库开发和药物发现活动，以实现我们的目标。我们希望该产品能够迅速应用于其他具有巨大社会影响的传染病，作为一个延伸目标，我们将奋进证明这一点。 公共卫生相关性：我们建议开发一个集成系统，以促进结核病新药发现工作，使用由SRI国际科学家开发的新型逻辑推理技术，与协作药物发现（CDD）通过管理不同的生物数据类型和计算预测而组装的知识相关联。该工具将用于通过结合化学信息学，不同的数据库和基于逻辑的途径分析来帮助识别结核病的新疗法。我们将通过发现和开发结核病和其他传染病的化合物来展示该工具的实用性（也可作为移动的应用程序）和整体工作流程。