- 2013/2014 - 3d structures of biological macromolecules part 5: drug research and design jürgen...
TRANSCRIPT
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- 2013/2014 -
3D Structures of Biological Macromolecules3D Structures of Biological Macromolecules
Part 5: Drug Research and DesignPart 5: Drug Research and Design
Jürgen SühnelJürgen Sü[email protected]@fli-leibniz.de
Supplementary Material: www.fli-leibniz.de/www_bioc/3D/
Leibniz Institute for Age Research, Fritz Lipmann Institute,Leibniz Institute for Age Research, Fritz Lipmann Institute,Jena Centre for BioinformaticsJena Centre for Bioinformatics
Jena / GermanyJena / Germany
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Example of Drug DiscoveryExample of Drug Discovery
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Example of Drug DiscoveryExample of Drug Discovery
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Phases of Clinical TrialsPhases of Clinical Trials
Phase I: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase II: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase III: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase IV: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.
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Example of Drug DiscoveryExample of Drug Discovery
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Pacific yew tree(Eibe)
Example of Drug DiscoveryExample of Drug Discovery
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Drug Research isDrug Research is
the Search for a Needle in a Haystack.the Search for a Needle in a Haystack.
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Development of Drug ResearchDevelopment of Drug Research
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Drug TimelineDrug Timeline
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Drug TimelineDrug Timeline
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• 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 ResearchCosts in Drug Research
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Global Company Sales 2006Global Company Sales 2006
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Top Ethical Drugs by Sales in 2006Top Ethical Drugs by Sales in 2006
http://www.p-d-r.com/ranking/Top_100_Ethical_Drugs_by_Sales.pdf
(Lowering blood cholesterol)(Asthma treatment)(Inhibits blood clots)(Proton pump inhibitor; treatment of dyspepsia, peptic ulcer disease, …)
(Calcium channel blocker; anti-hypertensive agent)
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New Products Marketed for the First TimeNew Products Marketed for the First Time
http://www.p-d-r.com/ranking/Prous_TYND_2005.pdf
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Molecular Conceptor
Disciplines Involved in Drug DevelopmentDisciplines Involved in Drug Development
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Molecular Conceptor
The Role of Molecular StructureThe Role of Molecular Structure
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Molecular Conceptor
The Pharmacophore ConceptThe Pharmacophore Concept
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Mechanisms of Drug Action – Definitions IMechanisms of Drug Action – Definitions I
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Mechanisms of Drug Action – Definitions IIMechanisms of Drug Action – Definitions II
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Molecular Conceptor
Serendipity - PenicillinSerendipity - Penicillin
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Serendipity - PenicillinSerendipity - Penicillin
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Serendipity - AspirinSerendipity - Aspirin
Molecular Conceptor
Serendipity - AspirinSerendipity - Aspirin
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Strategies in Drug DesignStrategies in Drug Design
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Target identification Lead discovery Lead optimization
Ligand-based design Receptor-based design (Docking)
Database screening (Virtual screening) Supporting combinatorial chemistry
Computational Approaches to Drug ResearchComputational Approaches to Drug Research
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3D Structures in Drug Design3D Structures in Drug Design
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Lead Structure IdentificationLead Structure Identification
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Lead Structure Search PipelineLead Structure Search Pipeline
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Lead Structures: Endogenous NeurotransmittersLead Structures: Endogenous Neurotransmitters
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Lead Structures: Endogenous NeurotransmittersLead Structures: Endogenous Neurotransmitters
Neurotransmitters are chemicals that are used to relay, amplify andmodulate electrical signals between a neuron and another cell.
Acetylcholine:Acetylcholine: voluntary movement of the musclesNoradrenaline:Noradrenaline: wakefulness or arousalDopamine:Dopamine: voluntary movement and emotional arousalSerotonin:Serotonin: sleep and temperature regulationGABA:GABA: (gamma aminobutryic acid) - motor behaviour
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Lead OptimizationLead Optimization
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Ligand-based Design: What is QSAR ?Ligand-based Design: What is QSAR ?
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Ligand-based Design:Ligand-based Design: Basic Requirements for QSAR StudiesBasic Requirements for QSAR Studies
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Ligand-based Design: QSARLigand-based Design: QSAR
Hansch analysis is the investigation of the quantitative relationship between thebiological activity of a series of compounds and their physicochemical substituentor global parameters representing hydrophobic, electronic, steric and other effectsusing multiple regression correlation methodology.
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Ligand-based Design: QSARLigand-based Design: QSAR
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters - LipophilicityLigand-based Design: QSAR Parameters - Lipophilicity
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
- reaction constant - substituent constant
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: QSAR ParametersLigand-based Design: QSAR Parameters
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Ligand-based Design: A QSAR Success StoryLigand-based Design: A QSAR Success Story
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pI50 – concentration of test compound required to reduce the protein content of cell by 50%
Ligand-based Design: A QSAR Success StoryLigand-based Design: A QSAR Success Story
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Ligand-based Design: 3D-QSAR CoMFALigand-based Design: 3D-QSAR CoMFA
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Molecular Superposition of Vitamin D Receptor LigandsMolecular Superposition of Vitamin D Receptor Ligands
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Ligand-based Design: 3D-QSAR CoMFALigand-based Design: 3D-QSAR CoMFA
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Ligand-based Design: 3D-QSAR CoMFALigand-based Design: 3D-QSAR CoMFA
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Ligand-based Design: 3D-QSAR CoMFALigand-based Design: 3D-QSAR CoMFA
Partial least squares regression (PLS regression) is a statistical method that finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models.
PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. PLS-regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases.
PLS regression is an important step in PLS path analysis, a multivariate data analysis technique that employs latent variables. This technique is often referred to as a form of variance-based or component-based structural equation modeling.
Partial least squares was introduced by the Swedish statistician Herman Wold, who then developed it with his son, Svante Wold, a professor of chemometrics at Umeå University. An alternative term for PLS (and more correct according to Svante Wold[3]) is projection to latent structures, but the term partial least squares is still dominant in many areas. It is widely applied in the field of chemometrics, in sensory evaluation, and more recently, in the analysis of functional brain imaging data.[4]
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Electrostatic and Van-der-Waals InteractionsElectrostatic and Van-der-Waals Interactions
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ComparativeMolecularFieldAnalysis
Ligand-based Design: 3D-QSAR CoMFALigand-based Design: 3D-QSAR CoMFA
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Molecular Conceptor
Receptor-based Design (Structure-based Design)Receptor-based Design (Structure-based Design)
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Molecular Conceptor
Receptor-based Design (Structure-based Design)Receptor-based Design (Structure-based Design)
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Molecular Conceptor
Receptor-based Design: DockingReceptor-based Design: Docking
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Molecular Conceptor
Receptor-based Design: DockingReceptor-based Design: Docking
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Molecular Conceptor
Receptor-based Design: DockingReceptor-based Design: Docking
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Molecular Conceptor
Hydrophobic Amino AcidsHydrophobic Amino Acids
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Molecular Conceptor
Receptor-based Design: DockingReceptor-based Design: Docking
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Molecular Conceptor
H-Bond Properties of Amino AcidsH-Bond Properties of Amino Acids
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Molecular Conceptor
Receptor-based Design: H-bond EffectReceptor-based Design: H-bond Effect
IC50 -Drug concentrationrequired for 50% inhibition of abiological effect
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Receptor-based Design: H-bond EffectReceptor-based Design: H-bond Effect
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Molecular Conceptor
Charge Properties of Amino AcidsCharge Properties of Amino Acids
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Molecular Conceptor
116.
Receptor-based Design: Salt BridgeReceptor-based Design: Salt Bridge
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Molecular Conceptor
Receptor-based Design: DockingReceptor-based Design: Docking
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Molecular Conceptor
Receptor-based Design: SAR (Pharmacophore Features)Receptor-based Design: SAR (Pharmacophore Features)
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Molecular Conceptor
Receptor-based Design: DNA ReceptorReceptor-based Design: DNA Receptor
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Molecular Conceptor
Receptor-based Design: DNA Intercalating AgentsReceptor-based Design: DNA Intercalating Agents
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Molecular Conceptor
Receptor-based Design: DNA Intercalating AgentsReceptor-based Design: DNA Intercalating Agents
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Receptor-based Design: AIDS DrugsReceptor-based Design: AIDS Drugs
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Receptor-based Design: AIDS DrugsReceptor-based Design: AIDS Drugs
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Combinatorial Diversity in NatureCombinatorial Diversity in Nature
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Classical vs. Combinatorial ChemistryClassical vs. Combinatorial Chemistry
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Combinatorial LibraryCombinatorial Library
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Combinatorial LibraryCombinatorial Library
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Types and Features of Combinatorial LibrariesTypes and Features of Combinatorial Libraries
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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 set 6 days
(64-processor SGI ORIGIN)
ACD-SC: Database from Molecular Design Ltd.Agonists: Known active compoundsDocking of ligands to the estrogen receptor(nuclear hormone receptor)
Receptor-based Design: Virtual ScreeningReceptor-based Design: Virtual Screening
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Receptor-based Design: Virtual ScreeningReceptor-based Design: Virtual Screening
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Compounds are likely to have a good absorption and permeationin biological systems and are thus more likely to be successful drug candidatesif 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 daltons•Calculated log P less than or equal to 5
•„Compound classes that are substrates for biological transporters are exceptions to the rule“.
Lipinski‘s „Rule of Five“Lipinski‘s „Rule of Five“
Druggable compounds
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ADMEADME
ADMEADME
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The Future: Pharmacogenomics and Personalized MedicineThe Future: Pharmacogenomics and Personalized Medicine
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Prediction IssuesPrediction Issues