Despite millions of people being infected by M. tuberculosis annually, the pipeline of tuberculosis drug discovery still suffers from a lack of novel chemical entities that inhibit validated biological targets and lack cross-resistance to current frontline therapies. Our efforts with machine learning models to discover novel chemotypes with promising growth inhibitory activity versus in vitro cultured M. tuberculosis have led to the discovery of diaminoaryl-triazine nitrofurylhydrazones. Optimization efforts of the triazine series have led to a focused set of pyrimidines. Compounds were evaluated for in vitro activity and cytotoxicity, physiochemical and absorption-distribution-metabolism-excretion (ADME) properties to select candidates for pharmacokinetic profiling in mice. The findings in this program will be disclosed and suggest a potential of these pyrimidine antituberculars for further optimization to develop novel antitubercular agents.

Novel pyrimidine antituberculars discovered through machine-learning Bayesian method

254th National Meeting (2017)

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