- 2013-
3D Structures of Biological Macromolecules
Part 5: Drug Research and Design
Jürgen Sühnel
jsuehnel@fli-leibniz.de
Leibniz Institute for Age Research, Fritz Lipmann Institute,
Jena Centre for Bioinformatics
Jena / Germany
Supplementary Material: www.fli-leibniz.de/www_bioc/3D/
Example of Drug Discovery
Example of Drug Discovery
Phases 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.
Example of Drug Discovery
Example of Drug Discovery
Pacific yew tree
(Eibe)
Drug Research is
the Search for a Needle in a Haystack.
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Development of Drug Research
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Drug Timeline
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Drug Timeline
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Costs in Drug Research
• Cost for discovering and developing a new drug:
several € 100 million up to € 1000 million (average € 802 M)
• Time to market:
10 – 15 years
Global Company Sales 2006
Top Ethical Drugs by Sales in 2006
(Lowering blood cholesterol)
(Asthma treatment)
(Inhibits blood clots)
(Proton pump inhibitor; treatment of dyspepsia, peptic ulcer disease, …)
(Calcium channel blocker; anti-hypertensive agent)
http://www.p-d-r.com/ranking/Top_100_Ethical_Drugs_by_Sales.pdf
New Products Marketed for the First Time
http://www.p-d-r.com/ranking/Prous_TYND_2005.pdf
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 - Penicillin
Serendipity - Aspirin
Serendipity - Aspirin
Molecular Conceptor
Strategies in Drug Design
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Computational Approaches to Drug Research
Target identification
Lead discovery
Lead optimization
Ligand-based design
Receptor-based design (Docking)
Database screening (Virtual screening)
Supporting combinatorial chemistry
3D Structures in Drug Design
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Lead Structure Identification
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Lead Structure Search Pipeline
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Lead Structures: Endogenous Neurotransmitters
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Lead Structures: Endogenous Neurotransmitters
Neurotransmitters are chemicals that are used to relay, amplify and
modulate electrical signals between a neuron and another cell.
Acetylcholine:
Noradrenaline:
Dopamine:
Serotonin:
GABA:
voluntary movement of the muscles
wakefulness or arousal
voluntary movement and emotional arousal
sleep and temperature regulation
(gamma aminobutryic acid) - motor behaviour
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Lead Optimization
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Ligand-based Design: What is QSAR ?
Ligand-based Design: Basic Requirements for QSAR Studies
Ligand-based Design: QSAR
Hansch analysis is the investigation of the quantitative relationship between the
biological activity of a series of compounds and their physicochemical substituent
or global parameters representing hydrophobic, electronic, steric and other effects
using multiple regression correlation methodology.
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Ligand-based Design: QSAR
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Ligand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters
Ligand-based Design: QSAR Parameters - Lipophilicity
Ligand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters
s - reaction constant
r - substituent constant
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Ligand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters
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Ligand-based Design: QSAR Parameters
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Ligand-based Design: A QSAR Success Story
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Ligand-based Design: A QSAR Success Story
pI50 – concentration of test compound required to reduce the protein content of cell by 50%
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Ligand-based Design: 3D-QSAR CoMFA
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Molecular Superposition of Vitamin D Receptor Ligands
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Ligand-based Design: 3D-QSAR CoMFA
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Ligand-based Design: 3D-QSAR CoMFA
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Ligand-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]
Electrostatic and Van-der-Waals Interactions
Ligand-based Design: 3D-QSAR CoMFA
Comparative
Molecular
Field
Analysis
Receptor-based Design (Structure-based Design)
Molecular Conceptor
Receptor-based Design (Structure-based Design)
Molecular Conceptor
Receptor-based Design: Docking
Molecular Conceptor
Receptor-based Design: Docking
Molecular Conceptor
Receptor-based Design: Docking
Molecular Conceptor
Hydrophobic Amino Acids
Molecular Conceptor
Receptor-based Design: Docking
Molecular Conceptor
H-Bond Properties of Amino Acids
Molecular Conceptor
Receptor-based Design: H-bond Effect
IC50 Drug concentration
required for 50% inhibition of a
biological effect
Molecular Conceptor
Receptor-based Design: H-bond Effect
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Charge Properties of Amino Acids
Molecular Conceptor
Receptor-based Design: Salt Bridge
116.
Molecular Conceptor
Receptor-based Design: Docking
Molecular Conceptor
Receptor-based Design: SAR (Pharmacophore Features)
Molecular Conceptor
Receptor-based Design: DNA Receptor
Molecular Conceptor
Receptor-based Design: DNA Intercalating Agents
Molecular Conceptor
Receptor-based Design: DNA Intercalating Agents
Molecular Conceptor
Receptor-based Design: AIDS Drugs
Receptor-based Design: AIDS Drugs
Combinatorial Diversity in Nature
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Classical vs. Combinatorial 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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Receptor-based Design: 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)
Receptor-based Design: 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 daltons
•Calculated log P less than or equal to 5
•„Compound classes that are substrates for biological transporters are exceptions to the rule“.
Druggable compounds
ADME
ADME
The Future: Pharmacogenomics and Personalized Medicine
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Prediction Issues
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