iDRUG - intelligent drug discovery


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

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iDRUG - intelligent drug discovery

  1. 1. iDrug -The Age of Intelligent Drug Discovery Sean Ekins
  2. 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. 3. 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 Bayesian models identified GSK TB hits 3 years earlier
  4. 4. Predicted targets of GSK TB hits months earlier using TB Mobile GSK report hits Dec 2012 24th Jan 2013 GSK predict targets Oct 2013
  5. 5. The Internet of Things – your house is under control from anywhere – but what about your lab?
  6. 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. 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. 8. Transporters are the next big thing
  9. 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. 10. Clark et al.,Submitted App workflows
  11. 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. 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. 13. A computational window into data and models Should there be more ? Make them accessible to anyone
  14. 14. Optimal Human properties Optimal Mouse properties Optimal TB entry properties
  15. 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. 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. 17. iDrug Discovery