Drug Design

BIOCHEMICAL APPROACHES FOR

DRUG DESIGN

SEMINAR BY:

T.SRAVYA

Drug design

Drug designing is an process of finding new medications based on the knowledge of the biological target.

TWO APPROACHES

1. RECEPTOR BASED APPROACH

2. GENE BASED APPRAOCH

Receptor Structure

Known Unknown

Structure BasedDrug Design

Analog BasedDrug Design

Docking

Homology ModelingReceptor Mapping

REQUIREMENTLead Compound and

derivatives with biologicaldata

REQUIREMENTA Model Receptor

Molecular DynamicsSimulations

Rigid Docking

FlexiDock

Monte CarloSimulations

Simulated Annealing

Quantum Mechanical(BRABO) ANN

GA

PCA

CoMFACoMSIA

QuantumMechanicalDescriptors

QuantumMechanics forAlignment

SYBYL, INSIGHT II, CERIUS2, MOE, AMBER (CDAC), DOCK, AUTODOCK

SINGLE MOLECULE

QSAR

Receptor based Drug Design

Structure based Ligand based

What is Docking?

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Docking attempts to find the “best” matching between two molecules

Docking of Ligand to the Active site of Protein

3D Structure of the Complex

Experimental Information: The active site can be identified based on the position of the ligand in the crystal structures of the protein-ligand complexes

If Active Site is not KNOWN?????

Building Molecules at the Binding Site

Identify the binding regions Evaluate their disposition in space

Search for molecules in the library of ligands for similarity

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Put a compound in the approximate area where binding occurs and evaluate the following:

Do the molecules bind to each other?If yes, how strong is the binding?How does the molecule (or) the protein-ligand complex

look like. (understand the intermolecular interactions)Quantify the extent of binding.

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• Computationally predict the structures of protein-ligand complexes from their conformations and orientations.

• The orientation that maximizes the interaction reveals the most accurate structure of the complex.

• The first approximation is to allow the substrate to do a random walk in the space around the protein to find the lowest energy.

Ligand in Active Site Region

Ligand

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Examples of Docked structures

HIV protease inhibitors COX2 inhibitors

Exact Receptor Structure is not always known

• Receptor Mapping

The volume of the binding cavity is felt from the ligands. This receptor map is derived by looking at the localized charges on the active ligands and hence assigning the active site.

Receptor Map Proposed for Opiate

Narcotics(Morphine, Codeine, Heroin, etc.)

*6.5Å

7.5-8.5Å

Flat surface for aromatic ring

Cavity for part of piperidine ring

Focus of charge

Anionic site

R1R2

R3

Homology modeling

Predicting the tertiary structure of an unknown protein using a known 3D structure of a homologous protein(s) (i.e. same family).Assumption that structure is more conserved than sequence

How to construct homologousmodel?

• Find homologous sequence• Select the template sequence of known• structure• Align the template and the target structure• Build the model

•And finally… Build the model .It is the moment to use a MODELLER program

•In input: target, template sequence and theiralignment

•In output: the 3D structure responding ofthe constraints

Quantative Structure-Quantative Structure-Activity RelationshipsActivity Relationships

What is QSAR?

A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.

Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002

Why QSAR?

• QSAR models are derived from a series of (similar) molecules with known activity (training set)

• If a statistically relevant QSAR model has been found, it can be applied to new molecules in this series (test set) in order to predict their activity before biological testing (or even before synthesis!)

Introduction to QSAR

Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002

Why QSAR?

• QSAR models are derived from a series of (similar) molecules with known activity (training set)

• If a statistically relevant QSAR model has been found, it can be applied to new molecules in this series (test set) in order to predict their activity before biological testing (or even before synthesis!)

Introduction to QSAR

QSAR and Drug Design

Compounds + biological activity

New compounds with improved biological activity

QSAR

Statistical Concept

Pi = k(d1, d2, d3,…. Dn) • pi= biological

activity• d1, d2 =

descriptors( calculated structural properties)

QSAR MODELLING APPROACHES

3D-QSAR A.CoMFA

VARIABLE QSAR SELECTION APPROACHES A.LINEAR MODEL B.NON LINEAR

Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002

1. Superimpose 3D models of molecules("Alignment")

2. Generate a regular grid around the molecules

3. Calculate and tabulate steric and electrostatic interaction energy of each grid point and each molecule

Compound Number Biol. Activity Steric Interaction

S001

ElectrostaticInteraction

E001

Steric Interaction

S002

ElectrostaticInteraction

E002

Steric Interaction

S003

ElectrostaticInteraction

E003

Steric Interaction

S004

ElectrostaticInteraction

S004...

1 1.07

2 0.09

3 0.66

4 1.42

5 -0.62

6 0.64

7 -0.46

3D QSAR - The CoMFA Approach

Dr. Hans Briem Introduction to Drug Discovery - Summer Semester 2002

Compound Number Biol. Activity Steric Interaction

S001

ElectrostaticInteraction

E001

Steric Interaction

S002

ElectrostaticInteraction

E002

Steric Interaction

S003

ElectrostaticInteraction

E003

Steric Interaction

S004

ElectrostaticInteraction

S004...

1 1.07

2 0.09

3 0.66

4 1.42

5 -0.62

6 0.64

7 -0.46

3D QSAR - The CoMFA Approach

4. Derive a QSAR equation (typically by PLS analysis)

Biol. Activity = Const. + a( S001) + b( E001) + c( S002) + d( E002) + ...

5. Apply equation to test setor contour fields of same coefficients

For the capsaicin example, CoMFA predicted Log EC50=-0.21!

CoMFA 3D-QSAR

Problems:The molecules must be optimally aligned.Flexibility of the molecules.

3D-QSAR of CYP450cam with CoMFA

Maps of electrostatic fields: BLUE - positive chargesRED - negative charges

Maps of steric fields:GREEN - space filling areas YELLOW - space conflicting areas

ACHIEVEMENTS

• Forecasting of biological activity•Selection of proper substituents •Drug receptor interactions•Pharmacokinetic information•Bioisosterism

GENE BASED APPROACH

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Gene-based Approach

Gene Therapy

Transfer of a therapeutic gene into the target tissue and maintenance of the gene function for an acceptable time

Potential Target Diseases for Gene Therapy

Disease Deficient Gene Affected Tissue

Cystic Fibrosis CFTR Lung, intestine Familial

hypercholesterolemia LDL Receptor Liver

Emphysema 1-AT Liver Hemophilia A and B Factor VIII and IX Blood plasma Duchenne muscular

dystrophy Dystrophin Muscles

-thalassaemia -globin Erythrocytes Phenylketonuria PAH Liver

Cancer Various Various Parkinsons Dopamine synthesis Brain Alzheimers Apo E/amyloid inhibition Brain

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Gene-based Approach

Gene Therapy – Basic Steps

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Gene-based Approach

Gene Therapy – Basic Steps

• Discover Genes

• Design Replacements

• Deliver to cell / body

• Ensure Incorporation

• Detect Function

• Ensure Stability

• Test Toxicity

• Test long-term effects

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Gene-based Approach

Gene Therapy – Delivering Genes

Basic Methods for Gene Transfer in Cell Culture

Method Efficacy Stability

Physical Methods Electroporation Moderate Short/long Microinjection High Short/Long

Particle Bombardment High Short

Chemical Methods Calcium phosphate Low/moderate Short/long

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Delivering Genes – Cells

THANK YOU

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