Presentation is loading. Please wait.

Presentation is loading. Please wait.

Jürgen Sühnel Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material:

Similar presentations


Presentation on theme: "Jürgen Sühnel Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material:"— Presentation transcript:

1 Jürgen Sühnel Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material: 3D Structures of Biological Macromolecules Part 3 Drug Research and Design

2 Example of Drug Discovery

3 Drug discovery Example of Drug Discovery

4 Pacific yew tree (Eibe) Example of Drug Discovery

5 Development of Drug Research

6 Drug Timeline

7 Drug Timeline

8 Drug Discovery 4 Cost for discovering and developing a new drug: several € 100 million up to € 1000 million (average € 802 M ) 4 Time to market: 10 – 15 years

9 Costs in Drug Research ww.kubinyi.de

10 Pharma Sales and Eearnings in ww.kubinyi.de

11 The World´s Top-Selling Drugs in 2004

12 Disciplines Involved in Drug Development Molecular Conceptor

13 The Role of Molecular Structure Molecular Conceptor

14 The Pharmacophore Concept Molecular Conceptor

15 Mechanisms of Drug Action – Definitions I

16 Mechanisms of Drug Action – Definitions II

17 Serendipity - Penicillin Molecular Conceptor

18 Serendipity - Aspirin Molecular Conceptor

19 Strategíes in Drug Design

20 3D Structures In Drug Research

21 Computational Approaches to Drug Discovery 4 Target identification 4 Lead discovery 4 Lead optimization 4 Ligand-based design 4 Receptor-based design (Docking) 4 Database screening (Virtual screening) 4 Supporting combinatorial chemistry

22 Lead Structure Identification

23 Lead Structure Search

24 Lead Structures: Endogeneous Neurotransmitters

25 Lead Optimization

26 What is QSAR ?

27 Basic Requirements in QSAR Studies

28 QSAR

29 QSAR Parameters

30 QSAR Parameters

31 QSAR Parameters -Lipophilicity

32 QSAR Parameters

33 QSAR Parameters

34 QSAR Parameters

35 QSAR Parameters

36 QSAR Parameters

37 QSAR Parameters

38 A QSAR Success Story

39 A QSAR Success Story pI 50 – concentration of test compound required to reduce the protein content of cell by 50%

40 3D-QSAR - CoMFA

41 Molecular Superposition of D Receptor Ligands

42 The Future: Pharmagenomics and Personalized Medicine

43 3D-QSAR - CoMFA

44 3D-QSAR - CoMFA

45 Electrostatic and Van-der-Waals Interactions

46 Drug Discovery – Ligand-based Design Comparative Molecular Field Analysis

47 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

48 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

49 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

50 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

51 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

52 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

53 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

54 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

55 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

56 Hydrogen Bonds and Ligand Affinities

57 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

58 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

59 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

60 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

61 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

62 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

63 Drug Discovery – Receptor-based Design (Structure-based Design) Molecular Conceptor

64 Drug Discovery – Receptor-based Design (Structure-based Design)

65

66 Combinatorial Diversity in Nature

67 Classical vs. Combinational Chemistry ww.kubinyi.de

68 Combinatorial Library ww.kubinyi.de

69 Combinatorial Library ww.kubinyi.de

70 Types and Features of Combinatorial Libraries ww.kubinyi.de

71 Virtual Screening: Select subsets of compounds for assay that are more likely to contain active hits than a sample chosen at random Time Scales: Docking of 1 compound 30 s (SGI R10000 processor) Docking of the 1.1 million data set6 days (64-processor SGI ORIGIN) Virtual Screening ACD-SC: Database from Molecular Design Ltd. Agonists: Known active compounds Docking of ligands to the estrogen receptor (nuclear hormone receptor)

72 Virtual Screening

73 Lipinski‘s „Rule of Five“ Compounds are likely to have a good absorption and permeation in biological systems and are thus more likely to be successful drug candidates if they meet the following criteria: 5 or fewer H-bond donors 10 or fewer H-bond acceptors Molecular weight less than or equal to 500 Calculated log P less than or equal to 5 „Compound classes that are substrates for biological transporters are exceptions to the rule“.

74 ADME

75 The Future: Pharmagenomics and Personalized Medicine

76 Prediction Issues


Download ppt "Jürgen Sühnel Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material:"

Similar presentations


Ads by Google