An urgent need exists to find new therapeutic options to address drug resistance and shorten therapy for those infected with Mycobacterium tuberculosis (Mtb). Whole-cell phenotypic screens have yielded well over 1500 in vitro inhibitors of Mtb which must be prioritized for in vivo testing. The mouse in vivo model for Mtb infection represents a validated model that is commonly utilized to select compounds for later studies. We have examined the data from the 1940's to the present day on this in vivo model which has not previously been curated. We have performed extensive structure-activity analysis and machine learning modeling that highlight new insights that could aid Mtb drug discovery. For example a Bayesian model (N = 338) had similar statistics to previously published in vitro models (ROC Score = 0.77, concordance = 70.57, specificity = 68.81 and sensitivity = 72.14). SVM and tree based models were also generated. We also describe how the SAR Table mobile app can be employed to mine local structure activity relationships. We will also describe the steps needed to make this open data.

Looking back at <i>Mycobacterium tuberculosis</i> mouse efficacy testing to move new drugs forward

247th National Meeting (2014)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.

Looking back at Mycobacterium tuberculosis mouse efficacy testing to move new drugs forward