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.
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.
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|Ekins, Sean; Freundlich, Joel S; Reynolds, Robert C (2013) Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation. J Chem Inf Model 53:3054-63|