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

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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. - PowerPoint PPT Presentation

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Page 1: Sean  Ekins

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

Information Age?

Sean Ekins

CollaborationsIn Chemistry,Fuquay Varina, NC

Page 2: Sean  Ekins

Some Technologies change faster than we do

Page 3: Sean  Ekins

But Drug Discovery has not changed much in 40 years

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Because change happens slowly

Drug discovery is a very slow race… that needs a kickstart

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And of course no treatments for neglected diseases are blockbusters

Still valuing the 70’s BLOCKBUSTER model but its changing

Produce few of …

Page 6: Sean  Ekins

The Old School vs New 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 ?

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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?

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Now neglected diseases has big data too

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A computational window into data and models

Should there be more ?

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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/

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Page 12: Sean  Ekins

Ponder et al., Pharm Res In Press 2013

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Tested >300,000 molecules Tested ~2M>1500 active and non toxic Published 177

Big Data: Screening for New Tuberculosis Treatments

How many will become a new drug?How do we learn from this big data?

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«Tuberculosis» 333 papers in PubMed«Malaria» 301 papers in PubMed

Small data: Mouse In vivo model data

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Can combining Big and Small data (in vitro, in vivo) help us find better compounds, faster ?

Avoid testing as many molecules

Page 16: Sean  Ekins

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

Page 17: Sean  Ekins

Where are the New TB drugs to be found?

In vivo actives (yellow)

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Optimal Human properties

Optimal Mouse properties

Optimal TB entry properties

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Filling the toolbox

• Who has the data?• Who has the models?• Who has molecules?

Drug Discovery Toolbox

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Hunting for the in vivo data

It’s out there.. be patient

Page 21: Sean  Ekins

30 years with little TB mouse in vivo data

TB

Page 22: Sean  Ekins

MoDELS RESIDE IN PAPERSNOT ACCESSIBLE…THIS IS UNDESIRABLE

Page 23: Sean  Ekins

Hunting High and Low for new molecules to test

We need to search sources..From the Oceans…

To the ground To the treesTo the air..And do it virtually

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Time for the New New School

Models replace testingTesting = confirmingPredict in vivo and in vitro in parallelMULTIDIMENSIONALSave resources

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TO BE CONTINUED…

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Joel S. FreundlichAntony J. WilliamsAlex M. Clark