Identification of drug targets from side-effect similarity

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Lars Juhl Jensen

Gesundheit & Medizin Technologie

Identification of drug targets
from side-effect similarity
Lars Juhl Jensen

the problem

new uses for old drugs

drug–drug network

shared target(s)

chemical similarity

Campillos & Kuhn et al., Science, 2008

Campillos & Kuhn et al., Science, 2008

similar drugs share targets

only trivial predictions

the idea

chemical perturbations

phenotypic readouts

drug treatment

side effects

the hard work

information on side effects

no database

package inserts

Campillos & Kuhn et al., Science, 2008

text mining

side-effect ontology

backtracking

Campillos & Kuhn et al., Science, 2008

manual validation

SIDER
Kuhn et al., Molecular Systems Biology, 2010

side-effect correlations

Campillos & Kuhn et al., Science, 2008

GSC weighting

side-effect frequencies

Campillos & Kuhn et al., Science, 2008

raw similarity score

Campillos & Kuhn et al., Science, 2008

p-values

Campillos & Kuhn et al., Science, 2008

side-effect similarity

chemical similarity

Campillos & Kuhn et al., Science, 2008

confidence scores

reference set

incomplete databases

text mining

manual validation

MATADOR
Günther et al., Nucleic Acids Research, 2008

Campillos & Kuhn et al., Science, 2008

text mining

reflect.ws

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009

collaborate with industry

the results

drug–drug network

Campillos & Kuhn et al., Science, 2008

categorization

Campillos & Kuhn et al., Science, 2008

20 drug–drug pairs

in vitro binding assays

Ki<10 µM for 11 of 20

cell assays

9 of 9 showed activity

the future

link side-effects to targets

direct target prediction

Acknowledgments
Side effects
– Monica Campillos
– Michael Kuhn
– Anne-Claude Gavin
– Peer Bork
Reflect.ws
– Heiko Horn
– Sune Frankild
– Evangelos Pafilis
– Reinhardt Schneider
– Sean O’Donoghue

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Cardiac Output, Venous Return, and Their Regulation

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Identification of drug targets from side-effect similarity

1. Identification of drug targets from side-effect similarity Lars Juhl Jensen

3. new uses for old drugs

4. drug–drug network

5. shared target(s)

6. chemical similarity

7. Campillos & Kuhn et al., Science, 2008

8. Campillos & Kuhn et al., Science, 2008

9. similar drugs share targets

10. only trivial predictions

12. chemical perturbations

13. phenotypic readouts

14. drug treatment

15. side effects

16. the hard work

17. information on side effects

18. no database

19. package inserts

20. Campillos & Kuhn et al., Science, 2008

21. text mining

22. side-effect ontology

23. backtracking

24. Campillos & Kuhn et al., Science, 2008

25. manual validation

26. SIDER Kuhn et al., Molecular Systems Biology, 2010

27. side-effect correlations

28. Campillos & Kuhn et al., Science, 2008

29. GSC weighting

30. side-effect frequencies

31. Campillos & Kuhn et al., Science, 2008

32. raw similarity score

33. Campillos & Kuhn et al., Science, 2008

35. Campillos & Kuhn et al., Science, 2008

36. side-effect similarity

37. chemical similarity

38. Campillos & Kuhn et al., Science, 2008

39. confidence scores

40. reference set

41. incomplete databases

42. text mining

43. manual validation

44. MATADOR Günther et al., Nucleic Acids Research, 2008

45. Campillos & Kuhn et al., Science, 2008

46. text mining

48. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009

49. collaborate with industry

50.

51. the results

52. drug–drug network

53. Campillos & Kuhn et al., Science, 2008

54. categorization

55. Campillos & Kuhn et al., Science, 2008

56. 20 drug–drug pairs

57. in vitro binding assays

58. Ki<10 µM for 11 of 20

59. cell assays

60. 9 of 9 showed activity

62. link side-effects to targets

63. direct target prediction

64. Acknowledgments Side effects – Monica Campillos – Michael Kuhn – Anne-Claude Gavin – Peer Bork Reflect.ws – Heiko Horn – Sune Frankild – Evangelos Pafilis – Reinhardt Schneider – Sean O’Donoghue

65. larsjuhljensen