Chemogenomic approaches in mapping adverse events to a protein class Nuclear Hormone Receptors

1. Chemogenomic approaches in mapping adverse events to a protein classNuclear Hormone Receptors

2. Collaborators Johan van der Lei Marc Weeber Jordi Mestres Montserrat Cases Scott Boyer Kristina Hettne

3. Overview • NHRs in Drug Discovery • A double-edged sword • Can chemogenomics help ? • Current progress

4. 50 Nuclear Hormone Receptors

5. 50 Nuclear Hormone Receptors

6. NHRs: Ligands • Steroids • Thyroid hormone • Vitamin D • Retinoids

7. NHRs: Ligand-Activated DNA-Binding Transcription Factors

8. NHRs: General Organisation A/B C D E F DBD LBD AF-2 Folkertsma et al., J Mol Biol 2004, 341:321-335

9. NHRs: A rich source of drug targets • Diabetes/Dyslipidemias • Cancer • Inflammation • Osteoporosis/Connective Tissue Diseases

10. NHRs: Why Worry? • Control key cellular functions • Apparently a rich source of adverse effects • As therapeutic targets • As ’side pharmacologies’ • Bind a wide variety of ligands

11. NHRs: Adverse Events ? • Reproductive effects • Fertility changes • Teratogenic effects • Enzyme Induction • Drug-drug interactions • Changes in thyroid hormone levels • Dyslipidemias • Systemic • Organ-centred

12. NHRs: Part of a general chemogenomic strategy in Safety • NHRs:Repro/Endocrine/Metabolism • GPCRs:CNS/Peripheral/Malaise • Ion Channels:Arrythmias/Vascular Dystonia • Kinases:Hyperplasia/Dysplasia

13. Predictive Toxicology? • No ! • Method for hypothesis generation • Method for information assimilation and structuring

14. Overview • NHRs in Drug Discovery • A double-edged sword • Can chemogenomics help ? • Current progress

15. Clinical Practice Chemistry Biology Hypothesis Generation Using Informatics/Modelling Testicular Degeneration Candidate Compound

16. Hypothesis Generation Using Informatics/Modelling Testicular Degeneration Candidate Compound Clinical Practice Chemistry Biology

17. Ligand-Protein Association via Experimental & Virtual Methods Term Association via Text Mining Hypothesis Generation Using Informatics/Modelling Proteins Testicular Degeneration Candidate Compound

18. Signature Matches Activity RAR a, ß, g RAR a, ß, g RAR a, ß, g RAR a, ß, g Pharmacological Signature Searching For ’Secondary Pharmacologies’ Query Structure: Testicular Degeneration

19. Biological Context of Pharmacology Data: Links with Known Pathways Activity RAR a, ß, g

20. Text Mining: EMBASE RAR + Pathology Ontology ?

21. Nice Story • Cost? £40M

22. Cyclooxygenase 2 (Cox-2)A Pharmacologist’s View From: Warner & Mitchell 2004 FASEB J18:791

23. Traditional Drug Discovery

24. Cyclooxygenase 2 (Cox-2)An Informatician’s View

25. Traditional Drug Discovery

26. Can we anticipate interactions by identifying ’chemotypes’? Necessary Drug Discovery NHR Kinases Ion Channels GPCR Proteases Enzymes Compounds Targets Cannot make and measure everything

27. Overview • NHRs in Drug Discovery • A double-edged sword • Can chemogenomics help ? • Current progress

28. Where to start ? • Annotate compounds to targets • Annotate targets to function(s) (pathways) • Annotate targets/pathways to pathologies

29. Where to start ? • Annotate compounds to targets • Need a flexible definition of ‘compound’ • Exact • Several levels of detail = classification scheme

30. Classification Schemes The existence of classification schemes for both chemical and biological entities is a key prerequisite for storing data properly

31. Storing data: Classification schemes – Biological entities • Biological entities • Nomenclature committees for protein families • Lack of existence of a unified standard classification scheme for all existing proteins • Several classification schemes coexist currently for many protein families

32. Storing data: Classification schemes – Biological entities Enzymes A unified classification scheme for enzymes exists based on the type of reaction catalysed and consists of a four-digit code: • The first digit specifies the class of enzyme • The second digit specifies the enzyme subclass according to a compound or group involved in the reaction being catalysed • The third digit specifies the enzyme sub-subclass defining the type of reaction in a more concrete manner • The fourth digit specifies the individual enzyme within a sub-subclass Dihydrofolate reductase – EC.1.5.1.3

33. Storing data: Classification schemes – Biological entities Making associations Trypsin Thrombin Factor Xa EC.3.4.21.4 EC.3.4.21.5 EC.3.4.21.6 EC.3.4.21 Serine proteases EC.3.4 Peptide hydrolases EC.3.4.22 Cysteine proteases Papain Cathepsin L Cathepsin S EC.3.4.22.2 EC.3.4.22.15 EC.3.4.22.27

34. Storing data: Classification schemes – Biological entities Solving ambiguities • PPARg • PPARg • PPARgamma • PPAR gamma • PPAR-gamma • Peroxisome Proliferator Activated Receptor gamma

35. Storing data: Classification schemes – Biological entities Nuclear Receptors A unified classification scheme for nuclear receptors exists and consists of a three-character code: • The first character is a number that designates the subfamily • The second character is a capital letter specifying the group within the subfamily • The third character is a number identifying the individual nuclear receptor within the group PPARg – NR.1.C.3

36. Storing data: Classification schemes – Chemical entities HO00924 LO01325 SC12121 2-Acetoxybenzoic acid Acetylsalicylic acid Aspirin Hierarchical Classification Scheme for Chemical Structures

37. Storing data: Classification schemes – Chemical entities • Chemical entities • CAS number: Chemical Abstracts Service (not open) • INChI: IUPAC/NIST Chemical Identifier (open) • CACTVS hash codes (Ihlenfeldt & Gasteiger. J.Comput.Chem. 1994;15:793) • MEQNUM: Molecular EQuivalence NUMber (Xu & Johnson. J.Chem.Inf.Comput.Sci 2001;41:181)

38. Storing data: Classification schemes – Chemical entities Chemical Graph Identifier 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

39. Hierarchical Classification Scheme for Molecules Chemical Structure Code Level 1 No. of rings in core ring system 2 Level 2 No. of ring systems 4 Level 3 Framework ID 4KS69A6 Level 4 Scaffold ID 24GGV3N7 Level 5 Molecule ID 3A5J8HY 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

40. Hierarchical Classification Scheme for Molecules Chemical Structure Code Level 1 No. of rings in core ring system 2 Level 2 No. of ring systems 4 Level 3 Framework ID 4KS69A6 Level 4 Scaffold ID 24GGV3N7 Level 5 Molecule ID 3A5J8HY 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

41. Hierarchical Classification Scheme for Molecules Chemical Structure Code Level 1 No. of rings in core ring system 2 Level 2 No. of ring systems 4 Level 3 Framework CGI 4KS69A6 Level 4 Scaffold ID 24GGV3N7 Level 5 Molecule ID 3A5J8HY 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

42. Hierarchical Classification Scheme for Molecules Chemical Structure Code Level 1 No. of rings in core ring system 2 Level 2 No. of ring systems 4 Level 3 Framework CGI 4KS69A6 Level 4 Scaffold CGI 24GGV3N7 Level 5 Molecule CGI 3A5J8HY 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

43. Hierarchical Classification Scheme for Molecules Chemical Structure Code Level 1 No. of rings in core ring system 2 Level 2 No. of ring systems 4 Level 3 Framework CGI 4KS69A6 Level 4 Scaffold CGI 24GGV3N7 Level 5 Molecule CGI 3A5J8HY 2 . 4 . 4KS69A6 . 24GGV3N7 . 3A5J8HY

44. 1.2.070EIJ2.1UWWM4I Storing data: Classification schemes – Chemical entities Classifying families of related chemical structures

45. What about NHRs? • Raw Materials: • Ligand database from literature • 1426 unique compounds • Annotated to 28 NHRs

46. Annotated Compound Library NRacl 1426 Ligands 554 Scaffolds 302 Frameworks 1A1 1A2 1B1 1B2 1B3 1C1 1C2 1C3 1H1 1H2 1H3 1H4 1H5 1I1 1I2 1I3 2B1 2B2 2B3 3A1 3A2 3B1 3B2 3B3 3C1 3C2 3C3 3C4 Targets

47. Annotated Compound Library

48. Ligands Scaffolds Frameworks Annotated Compound Library

49. Ligand Specificity Scale Specificity High Low * * * *Low molecule and scaffold count

50. NRacl – NR Similarity based on Scaffold Profiling VDR, PXR, CAR, LXR, FXR, PPAR RAR, RXR