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Making the best of a bad situation Using side effects to reveal new targets for old drugs Lars Juhl Jensen EMBL Heidelberg
the problem
two drugs
do they share a target?
chemical similarity
Tanimoto coefficients
 
 
similar drugs share targets
only trivial predictions
the idea
chemical perturbations
phenotypic readout
drug treatment
side effects
the implementation
information on side effects
package inserts
 
text mining
side-effect ontology
backtracking
 
side-effect correlations
 
GSC weighting
side-effect frequencies
 
raw similarity score
 
p-values
 
side-effect similarity
chemical similarity
 
reference set
drug–target pairs
 
drug–drug pairs
score bins
benchmark
 
fit calibration function
 
probabilistic scores
the results
drug–drug network
 
categorization
 
 
 
 
map onto score space
 
the experiments
20 drug–drug relations
in vitro  binding assays
 
 
 
K i <10 µM for 11 of 20
cell assays
9 of 9 showed activity
 
the future
drug–target relations
 
Acknowledgments <ul><li>Monica Campillos </li></ul><ul><li>Michael Kuhn </li></ul><ul><li>Anne-Claude Gavin </li></ul><ul>...
http://larsjuhljensen.wordpress.com
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Making the best of a bad situation: Using side effects to reveal new targets for old drugs

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Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Canada, July 25, 2008

Published in: Health & Medicine
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Making the best of a bad situation: Using side effects to reveal new targets for old drugs

  1. 1. Making the best of a bad situation Using side effects to reveal new targets for old drugs Lars Juhl Jensen EMBL Heidelberg
  2. 2. the problem
  3. 3. two drugs
  4. 4. do they share a target?
  5. 5. chemical similarity
  6. 6. Tanimoto coefficients
  7. 9. similar drugs share targets
  8. 10. only trivial predictions
  9. 11. the idea
  10. 12. chemical perturbations
  11. 13. phenotypic readout
  12. 14. drug treatment
  13. 15. side effects
  14. 16. the implementation
  15. 17. information on side effects
  16. 18. package inserts
  17. 20. text mining
  18. 21. side-effect ontology
  19. 22. backtracking
  20. 24. side-effect correlations
  21. 26. GSC weighting
  22. 27. side-effect frequencies
  23. 29. raw similarity score
  24. 31. p-values
  25. 33. side-effect similarity
  26. 34. chemical similarity
  27. 36. reference set
  28. 37. drug–target pairs
  29. 39. drug–drug pairs
  30. 40. score bins
  31. 41. benchmark
  32. 43. fit calibration function
  33. 45. probabilistic scores
  34. 46. the results
  35. 47. drug–drug network
  36. 49. categorization
  37. 54. map onto score space
  38. 56. the experiments
  39. 57. 20 drug–drug relations
  40. 58. in vitro binding assays
  41. 62. K i <10 µM for 11 of 20
  42. 63. cell assays
  43. 64. 9 of 9 showed activity
  44. 66. the future
  45. 67. drug–target relations
  46. 69. Acknowledgments <ul><li>Monica Campillos </li></ul><ul><li>Michael Kuhn </li></ul><ul><li>Anne-Claude Gavin </li></ul><ul><li>Peer Bork </li></ul>
  47. 70. http://larsjuhljensen.wordpress.com

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