Some simple slides looking back and forwards to imagine how drug discovery might benefit from a bit of intelligence

1. iDrug -The Age of Intelligent Drug
Discovery
Sean Ekins
collabchem.com

2. • Being ahead of the curve
• Internet of Things– lab is wireless, control
everything from office
• Personalized drug discovery from home
• Beyond Apps – drug discovery workflows
• One drug – many diseases = new blockbuster
• Portals – data and predictions together
• Who needs robots – can we automate more
• Anyone can do a this – even you!

3. http://goo.gl/UujRX
Ballel et al., Fueling Open-Source drug discovery: 177 small-
molecule leads against tuberculosis ChemMedChem 2013.
GSK screened 2M compounds – 3 yrs before
Bayesian predictions for 14,000 cpds exposed 11 / 15 (73%)
correct when paper was published
Further prospective validation example
http://goo.gl/UujRX
http://goo.gl/UujRX
Bayesian models identified GSK TB hits 3 years earlier

4. Predicted targets of GSK TB hits months
earlier using TB Mobile
GSK report hits Dec 2012
24th Jan 2013 http://goo.gl/9LKrPZ
GSK predict targets Oct 2013

5. The Internet of Things – your house is under control
from anywhere – but what about your lab?

6. What if our databases did more than house
data – controlling its creation
• Enable connections to vendors
– Assay Depot, CROs etc
• Facilitate outsourcing, insourcing data, cpds
• Control lab equipment remotely, data upload
• Control lab staffing, resources, plan useage
• Why own the lab when someone else can –
but you control it (wherever it is)

7. DYOD – design your own drugs
• Thanks to our genome screening all will have
an idea of what enzymes, transporters we are
deficient in
• We will know which drugs are metabolized by
which enzymes and which transporters and
involved
• Why not tailor drugs
• What tools do we need?
• How to predict enzymes, transporters?

8. Transporters are the next big thing

9. All Apped out?Prepare for apps to be around for a long time
– Its what we do with them that matters
• Drug discovery Workflows
– Connectivity
– Shareability
– Ease
– Use across devices
• New drug discovery tools may go straight to apps
and ignore desktop
– E.g. green solvents app
• Desktop – diminishing importance
– We better prepare for that

10. Clark et al.,Submitted
App workflows

11. One drug– many diseases –
repurposing
• Disease A may have a market of $400M
• Disease B may have a market of $600M
• Alone they may not be big enough to entice a
pharma
• Together it’s a $1bn dollar drug
• Can we find examples 1 drug – many diseases

12. TB and malaria
• 2 different diseases – combined deaths ~3-4M/yr
R&D budget ~$1Bn
• 1 mycobacteria
• 1 parasite
• Shared targets
• Screening data in TB
• Actives vs different diseases
• What if people are unaware of compounds with
dual activity?

13. A computational
window into data and models
Should there be more ?
Make them accessible to anyone

14. Optimal Human properties
Optimal Mouse properties
Optimal TB entry properties

15. Drug discovery is repetitive and there are 1000s of diseases
Drug discovery is high risk
Do we need robots or just smarter programs that discover the ideas we test?

16. • Imagine having time / resources to
mine datasets
• Imagine having time / resources to
keep repeating vs different diseases
• Challenge is not creating the data but
finding hidden value
• Anyone can do this, if the tools are
available – experience not necessary
En route to treasure?

17. iDrug Discovery