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3D Structures of Biological Macromolecules
Part 3
Drug Research and Design
Jürgen Sühnel
[email protected]
Institute of Molecular Biotechnology, Jena Centre for Bioinformatics
Jena / Germany
Supplementary Material: http://www.imb-jena.de/www_bioc/3D/
Example of Drug Discovery
Example of Drug Discovery
Example of Drug Discovery
Pacific yew tree
(Eibe)
Development of Drug Research
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Drug Timeline
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Drug Timeline
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Drug Discovery
 Cost for discovering and developing a new drug:
several € 100 million up to € 1000 million (average € 802 M)
 Time to market:
10 – 15 years
Costs in Drug Research
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Pharma Sales and Eearnings in 1999-2002
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The World´s Top-Selling Drugs in 2004
Disciplines Involved in Drug Development
Molecular Conceptor
The Role of Molecular Structure
Molecular Conceptor
The Pharmacophore Concept
Molecular Conceptor
Mechanisms of Drug Action – Definitions I
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Mechanisms of Drug Action – Definitions II
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Serendipity - Penicillin
Molecular Conceptor
Serendipity - Aspirin
Molecular Conceptor
Strategíes in Drug Design
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3D Structures In Drug Research
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Computational Approaches to Drug Discovery
 Target identification
 Lead discovery
 Lead optimization
 Ligand-based design
 Receptor-based design (Docking)
 Database screening (Virtual screening)
 Supporting combinatorial chemistry
Lead Structure Identification
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Lead Structure Search
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Lead Structures: Endogeneous Neurotransmitters
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Lead Optimization
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What is QSAR ?
Basic Requirements in QSAR Studies
QSAR
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QSAR Parameters
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QSAR Parameters
QSAR Parameters -Lipophilicity
QSAR Parameters
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QSAR Parameters
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QSAR Parameters
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QSAR Parameters
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QSAR Parameters
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QSAR Parameters
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A QSAR Success Story
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A QSAR Success Story
pI50 – concentration of test compound required to reduce the protein content of cell by 50%
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3D-QSAR - CoMFA
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Molecular Superposition of D Receptor Ligands
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The Future: Pharmagenomics and Personalized Medicine
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3D-QSAR - CoMFA
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3D-QSAR - CoMFA
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Electrostatic and Van-der-Waals Interactions
Drug Discovery – Ligand-based Design
Comparative
Molecular
Field
Analysis
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Hydrogen Bonds and Ligand Affinities
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Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Molecular Conceptor
Drug Discovery – Receptor-based Design (Structure-based Design)
Drug Discovery – Receptor-based Design (Structure-based Design)
Combinatorial Diversity in Nature
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Classical vs. Combinational Chemistry
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Combinatorial Library
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Combinatorial Library
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Types and Features of Combinatorial Libraries
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Virtual Screening
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
Docking of the 1.1 million data set
30 s
(SGI R10000 processor)
6 days
(64-processor SGI ORIGIN)
ACD-SC: Database from Molecular Design Ltd.
Agonists: Known active compounds
Docking of ligands to the estrogen receptor
(nuclear hormone receptor)
Virtual Screening
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“.
ADME
The Future: Pharmagenomics and Personalized Medicine
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Prediction Issues
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