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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.
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Why Are We Still Doing Industrial Age Drug Discovery For Neglected Diseases in The Information Age? Collaborations In Chemistry, Fuquay Varina, NC Sean Ekins
Because change happens slowly Drug discovery is a very slow race… that needs a kickstart
And of course no treatments for neglected diseases are blockbusters Still valuing the 70’s BLOCKBUSTER model but its changing Produce few of …
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 ?
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?
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 • Small data builds networks V DATA http://smalldatagroup.com/
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?
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 molecules
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
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 the air.. And do it virtually
Time for the New New School Models replace testing Testing = confirming Predict in vivo and in vitro in parallel MULTIDIMENSIONAL Save resources
Joel S. Freundlich Antony J. Williams Alex M. Clark