Why Are We Still Doing Industrial
Age Drug Discovery For Neglected
Diseases in The Information Age?

Sean Ekins

Collabora...
Some Technologies change faster than we do
But Drug Discovery has not changed much in 40 years
Because change
happens slowly

Drug discovery is a very slow
race… that needs a kickstart
Still valuing the 70’s BLOCKBUSTER
model but its changing

And of course no treatments for neglected diseases are blockbus...
The Old School vs New School
screening

•

•
•

New School - Many hurdles before in vivo lots of data Yet HTS started in t...
Drug Discovery Archeology
• Still a heavy emphasis on
“testing” “doing “ rather
than ‘learning’
• Mining data and historic...
Now neglected diseases has big data too
A computational
window into data and
models
Should there be more ?
But what about small data?
• In some cases its all we have
• In vivo data is not high throughput

V

• Small data builds n...
Ponder et al., Pharm Res In Press 2013
Big Data: Screening for New Tuberculosis Treatments

Tested >300,000 molecules
>1500 active and non toxic

Tested ~2M
Publ...
Small data: Mouse In vivo model data

«Tuberculosis» 333 papers in PubMed
«Malaria» 301 papers in PubMed
Can combining Big and Small
data (in vitro, in vivo) help us
find better compounds,
faster ?
Avoid testing as
many molecul...
In vitro data

In vivo data

Target data

ADME/Tox data & Models

Connecting data/tools like a TB Spider

Drug-like scaffo...
Where are the New TB drugs to be found?

In vivo actives (yellow)
Optimal Mouse properties

Optimal TB entry properties

Optimal Human properties
Filling the toolbox
• Who has the data?
• Who has the models?
• Who has molecules?

Drug Discovery
Toolbox
Hunting for the in vivo data

It’s out there.. be patient
TB
30 years with little TB mouse in vivo data
MoDELS RESIDE IN PAPERS
NOT ACCESSIBLE…THIS IS
UNDESIRABLE
Hunting High and Low for new
molecules to test
We need to search
sources..
From the Oceans…

To the ground
To the trees
To...
Time for the New New School
Models replace testing
Testing = confirming
Predict in vivo and in vitro in parallel
MULTIDIME...
TO BE CONTINUED…
Joel S. Freundlich
Antony J. Williams
Alex M. Clark
Why are we still doing industrial age drug
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Why are we still doing industrial age drug

  1. 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. 2. Some Technologies change faster than we do
  3. 3. But Drug Discovery has not changed much in 40 years
  4. 4. Because change happens slowly Drug discovery is a very slow race… that needs a kickstart
  5. 5. Still valuing the 70’s BLOCKBUSTER model but its changing And of course no treatments for neglected diseases are blockbusters
  6. 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. 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. 8. Now neglected diseases has big data too
  9. 9. A computational window into data and models Should there be more ?
  10. 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/
  11. 11. Ponder et al., Pharm Res In Press 2013
  12. 12. 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?
  13. 13. Small data: Mouse In vivo model data «Tuberculosis» 333 papers in PubMed «Malaria» 301 papers in PubMed
  14. 14. Can combining Big and Small data (in vitro, in vivo) help us find better compounds, faster ? Avoid testing as many molecules
  15. 15. 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
  16. 16. Where are the New TB drugs to be found? In vivo actives (yellow)
  17. 17. Optimal Mouse properties Optimal TB entry properties Optimal Human properties
  18. 18. Filling the toolbox • Who has the data? • Who has the models? • Who has molecules? Drug Discovery Toolbox
  19. 19. Hunting for the in vivo data It’s out there.. be patient
  20. 20. TB 30 years with little TB mouse in vivo data
  21. 21. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE
  22. 22. 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
  23. 23. Time for the New New School Models replace testing Testing = confirming Predict in vivo and in vitro in parallel MULTIDIMENSIONAL Save resources
  24. 24. TO BE CONTINUED…
  25. 25. Joel S. Freundlich Antony J. Williams Alex M. Clark
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