1 / 26

Sean Ekins

Why Are We Still Doing Industrial Age Drug Discovery For Neglected Diseases in The Information Age?. Collaborations In Chemistry, Fuquay Varina , NC. Sean Ekins. Some Technologies change faster than we do. But Drug Discovery has not changed much in 40 years.

saskia
Download Presentation

Sean Ekins

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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 vsNew Schoolscreening • 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 V DATA http://smalldatagroup.com/

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

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

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

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

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

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

  17. Optimal Mouse properties Optimal TB entry properties Optimal Human properties

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

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

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

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

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

  24. TO BE CONTINUED…

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

More Related