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

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
V
• Small data builds networks
http://smalldatagroup.com/

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

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

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

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

16. In vitro data
In vivo data
Target data
ADME/Tox data & Models
Connecting data/tools like a TB Spider
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 Mouse properties
Optimal TB entry properties
Optimal Human 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. TB
30 years with little TB mouse in vivo data

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