Identification of novel therapeutics for tuberculosis combining cheminformatics,

描述(申请人提供)：我们目睹了越来越多的对药物敏感和耐药的结核分枝杆菌(Mtb)菌株病例的威胁，以及40多年来生产第一种结核病新药的挑战。在投入大量的高通量筛查工作后，结核病社区面临着一个问题，即如何最佳地利用这些数据，以便从热门到领先，再到临床 候选人，并有可能成为一种新药。作为对这一方法的补充，但规模要小得多，还利用了化学信息学技术。我们建议，这些计算方法应该更好地与加速结核病药物发现的实验方法结合在一起。这个名为“结合化学信息学、多样化数据库和基于逻辑的途径分析的结核病新疗法的鉴定”的小企业技术转移第二阶段项目描述了软件的开发，该软件将促进结核分枝杆菌(TB)的新药发现工作，以及利用它发现的作为酶底物及其体内基本基因模拟的分子的进展。在第一阶段，我们说明了松散地结合化学信息学和通路数据库的概念，从而产生了两个化合物，如所建议的具有抗结核杆菌活性的2 D-果糖1，6二磷酸的模拟化合物(MIC20和40 mg/ml)。在第二阶段，我们将通过API将CDD、SRI和其他数据库和工具中的知识无缝地联系起来。研究人员将能够研究分子、靶点、途径，然后选择代谢物或其他分子进行药效团分析，使用结核病机器学习模型和ADME进行评分，并在一个界面内进行药物相似性评估。这一工具将被用来帮助确定结核病的新疗法，并有助于假说检验、知识共享、数据存档、数据挖掘和药物发现。我们将把CDD变成一个移动应用程序，这样这个项目中的通用工作流就可以在任何地方执行。我们提出了有希望的初步工作，产生了两种活性化合物，这表明第二阶段支持模拟策略以确定结核病感兴趣的化合物将是一个可行的策略。这项建议平衡了软件开发、数据库开发和药物发现活动，以实现我们的目标。我们预计该产品可以迅速应用于其他具有重大社会影响的传染病，作为一个延伸目标，我们将努力证明这一点。

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.