Representative projects leverage the following techniques: computational modeling and drug design, chemical synthesis, biochemistry, metabolomics, microbiology, and molecular biology.
A. Novel chemical probes of M. tuberculosis discovered through innovative methods
We assert that the research community can more optimally leverage the large data sets created over the last ca. 70 years of exploring small molecule inhibition of the growth and/or killing of M. tuberculosis. In vitro and in vivo data may be utilized to create machine-learning models. These models provide highly valuable characterizations of the physiochemical properties and structural features of both active and inactive molecules. They also enable the successful prediction (hit rates often ~ 10 – 20%) of novel antituberculars that have significantly different structures than the training set used to educate the model. Thus, they enable exploration of new chemical space more efficiently. We believe that such molecules, given their novel chemotypes amongst antituberculars, have a heightened probability of modulating novel biological targets (See B). Thus, novel computational methods are being developed and then implemented to uncover new chemical probes and drug discovery hits/leads.
These novel antituberculars are then optimized with respect to their efficacy, in vitro Absorption-Distribution-Metabolism-Excretion, in vivo pharmacokinetic, and toxicity profiles to obtain high-value chemical probes and drug discovery hits/leads.
B. Exploration of the polypharmacology of novel chemical probes and what it reveals about new strategies to pursue synergistic targets
Given our unique mixture of experience with both academic tuberculosis research and industrial drug discovery, we are particularly fascinated by polypharmacology. Polypharmacology may be defined as the ability of a single small molecule to modulate multiple drug targets within a pathogen to elicit a favorable therapeutic effect. We assert that this is not rampant promiscuity, but it can instead represent the beneficial perturbation of targets within the same pathway or disparate pathways. Utilizing a range of biological techniques, including transcriptomics, metabolomics, and drug resistance studies, we aim to elucidate how a select set of novel antituberculars utilize polypharmacology to achieve significant cidal activity versus M. tuberculosis. These multidisciplinary studies can lead to the identification of novel combinations of targets that can be pursued to develop new multi-target inhibitors or new synergistic combination therapies.
C. Extension of our computational and chemical discovery platforms to other infectious diseases
The Freundlich lab also seeks to extend and apply our computational and chemical techniques to enhance the efficiency of drug discovery research against other infectious diseases. We view these methodologies as being critical to furthering our basic understanding of the biology of bacteria and viruses of global health relevance, while also seeding drug discovery efforts. Through our NIH/NIAID-funded U19 program, we are working on the ESKAPE bacteria. In 2016, we launched a new collaboration with IBM, Dr. Carolina Horta Andrade, and Dr. Sean Ekins that is focused on jump-starting drug discovery efforts against the Zika virus, by performing massive virtual screens on the World Community Grid. We have also published on computational approaches, which have suggested potential starting points for an Ebola virus therapy.
To learn more, please visit the web site for our NIH CETR grant, where we are instrumental to two projects and one core.
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