1. Why Are We Still Doing Industrial Age Drug Discovery For Neglected Diseases in The Information Age? Sean Ekins Collaborations In Chemistry, Fuquay Varina, NC

2. Some Technologies change faster than we do

3. But Drug Discovery has not changed much in 40 years

4. Because change happens slowly Drug discovery is a very slow race… that needs a kickstart

5. And of course no treatments for neglected diseases are blockbusters Still valuing the 70’s BLOCKBUSTER model but its changing Produce few of …

6. The Old School vs New School screening New School - Many hurdles before in vivo - lots of data Yet HTS started in the 1980’s!! Old school – go in vivo at outset – little data New database technologies work well for New school but..Old School type data ?

7. Drug Discovery Archeology Still a heavy emphasis on “testing” “doing “ rather than ‘learning’ Mining data and historic data will increase in value Data becomes a repurposing opportunity How do we position databases for this? What about neglected diseases?

8. Now neglected diseases has big data too

9. A computational window into data and models Should there be more ?

10. But what about small data? In some cases its all we have In vivo data is not high throughput Small data builds networks DATA V http://smalldatagroup.com/

12. Ponder et al., Pharm Res In Press 2013

13. Tested >300,000 moleculesTested ~2M >1500 active and non toxicPublished 177 Big Data: Screening for New Tuberculosis Treatments How many will become a new drug? How do we learn from this big data?

14. «Tuberculosis» 333 papers in PubMed «Malaria» 301 papers in PubMed Small data: Mouse In vivo model data

15. Can combining Big and Small data (in vitro, in vivo) help us find better compounds, faster ? Avoid testing as many molecules

16. Connecting data/tools like a TB Spider In vitro data In vivo data Target data ADME/Tox data & Models Drug-like scaffold creation TB Prediction Tools TB Publications

17. Where are the New TB drugs to be found? In vivo actives (yellow)

18. Optimal Human properties Optimal Mouse properties Optimal TB entry properties

19. Filling the toolbox Who has the data? Who has the models? Who has molecules? Drug Discovery Toolbox

20. Hunting for the in vivo data It’s out there.. be patient

21. 30 years with little TB mouse in vivo data TB

22. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE

23. Hunting High and Low for new molecules to test We need to search sources.. From the Oceans… To the ground To the trees To the air.. And do it virtually

24. Time for the New New School Models replace testing Testing = confirming Predict in vivo and in vitro in parallel MULTIDIMENSIONAL Save resources

25. TO BE CONTINUED…

26. Joel S. Freundlich Antony J. Williams Alex M. Clark