World TB Day 2013
From the desk of Sean Ekins (VP Science)
It’s World TB day 2013 and each year we try to put a few words together to raise awareness and highlight recent development. In recent months we have seen a new therapy for TB-MDR approved in bedaquiline. And yet still we need faster cures to battle this scourge while there are many researchers globally trying to find better drugs. By working with some visionary collaborators we are continuing to do our bit to support the research side. Whether that is through providing the CDD Vault to the BMGF funded TB-accelerator project (TBDA), the MM4TB project, NIAID Grants, or with individual collaborations with academics and institutes, we are aware of the enormity of the challenge.
Here are just a few recent collaborations and insights from the CDD perspective:
Developing the TB Mobile app for assisting in predicting targets for molecules from phenotypic screening. This entailed a collaboration with Stanford Research Institute (SRI International) and Molecular Materials Informatics and lead to a paper (http://www.ncbi.nlm.nih.gov/pubmed/23497706). This work was funded by an STTR from NIAID.
Using dual event Bayesian models of Mtb bioactivity and cytotoxicity data to identify compounds with activity in vitro. This involved a multigroup collaboration with Dr. Joel Freundlich at UMDNJ (as well as many others at several institutes including the Souther Research Institute) and has lead to a paper (http://www.sciencedirect.com/science/article/pii/S1074552113000343) and press release (http://newsle.com/article/0/66085182/) describing novel molecules which are being optimized further. This work was funded by an SBIR from NLM.
We have also shown how potentially valuable prospective prediction with computational methods can be, for example identifying Mtb active compounds 3 years before they are screened (http://www.collabchem.com/2013/01/25/prospective-prediction-of-m-tuberculosis-inhibition-3-years-on/). Which begs the question why do we continue to screen millions of molecules when the computational models can predict the actives?
These are just a few of many TB projects we have supported and worked on over the past year. As these various projects progressed, we see some of the gaps in research or from the side of the researchers that are less accepting of new ideas from outside (for example use of computational predictions, or are less open to the efficiencies of collaboration). If we are to really find novel cures and approaches for TB we should look well outside our comfort zone. The drug discovery field is still conservative as a whole, and increased funding has not necessarily increased the diversity of ideas. Also, collaboration necessary for the optimal integration of diverse powerful ideas, technologies, and approaches is still in its infancy.
We have seen through collaborations and learning from the wealth of “public” data already available in CDD (and corresponding models), we can now do so much more to fight TB.