Transcript Genomics & Proteomics Based Drug DISCOVERY Dr. Basavaraj K. Nanjwade M.Pharm., Ph.

Genomics & Proteomics Based
Drug DISCOVERY
Dr. Basavaraj K. Nanjwade M.Pharm., Ph. D
Associate Professor
Department of Pharmaceutics
KLE University
BELGAUM – 590010
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Dept.of Pharmaceutics
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Genomics
Genetic scientist isolate individual
genes and determine their chemical
composition, and ultimately to sequence
entire genomes.
The sequencing of the human genome
with the human genome project
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Genome Sequencing

Gene number, exact locations, and functions

Gene regulation

DNA sequence organization

Chromosomal structure and organization

Noncoding DNA types, amount, distribution, information content, and
functions

Coordination of gene expression, protein synthesis, and posttranslational events

Interaction of proteins in complex molecular machines

Predicted vs experimentally determined gene function
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Genome Sequencing

Evolutionary conservation among organisms

Protein conservation (structure and function)

Proteomes (total protein content and function) in organisms

Correlation of SNPs (Single nucleotide polymorphisms ) with health and
disease

Disease-susceptibility prediction based on gene sequence variation

Genes involved in complex traits and multigene diseases

Complex systems biology including microbial consortia useful for
environmental restoration

Developmental genetics, genomics
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Genome Sequencing
C = Cytosine, G = Guanine, A = Adenine and T = Thymine
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Drug Discovery
SBI* can be used to examine:
• drug targets (usually proteins)
• binding of ligands
↓
“rational” drug design
(benefits = saved time and RsRsRs)
* SBI-Structural Bioinformatics
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Modern Drug Discovery
What’s different?
 Drug
discovery process begins
with a disease (rather than a treatment)
 Use
disease model to pinpoint relevant
genetic/biological components (i.e.
possible drug targets)
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Modern Drug Discovery
disease → genetic/biological target
↓
discovery of a “lead” molecule
- design assay to measure function of
target
- use assay to look for modulators of
target’s function
↓
high throughput screen (HTS)
- to identify “hits”
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Modern Drug Discovery
small molecule hits
↓
manipulate structure to increase potency
↓
*optimization of lead molecule into
candidate drug*
fulfillment of required pharmacological properties:
potency, absorption, bioavailability, metabolism, safety
↓
clinical trials
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Interesting facts...

Over 90% of drugs
entering clinical
trials fail to make
it to market

The average cost
to bring a new
drug to market is
estimated at $770
million
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Relating druggable targets
to disease...
Analysis of Pharm
industry reveals:
GPCR
Other 110
families
STY kinases
Cys proteases
Gated ionchannel
Zinc peptidases
Ion channels
Nuclear PDE
receptor
P450 enzymes
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Serine
proteases
• Over 400 proteins used
as drug targets
• Sequence analysis of
these proteins shows
that most targets fall
within a few major
gene families (GPCRs,
kinases, proteases and
peptidases)
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Assessing Target Druggability
 Once
a target is defined for your
disease of interest, SBI can help
answer the question:
Is this a “druggable” target?
• Does it have sequence/domains similar to
known targets?
• Does the target have a site where a drug
can bind, and with appropriate affinity?
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Genome Annotation and Analysis
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Impact of Structural Bioinformatics
on Drug Discovery
Genome
Gene
Protein
HTS
Hit
Lead
Candidate Drug
Genomics
Bioinformatics
Structural Bioinformatics
Chemoinformatics
Structure-based Drug Design
ADMET Modelling
. Speeds up key steps in
DD process by combining
aspects of bioinformatics,
structural biology, and
structure-based drug
design
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human genome
polysaccharides
lipids
nucleic acids
proteins
Problems with toxicity, specificity, and
difficulty in creating potent inhibitors
eliminate the first 3 categories...
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human genome
polysaccharides
lipids
nucleic acids
proteins
proteins with
binding site
“druggable genome” = subset of genes which
express proteins capable of binding small drug-like
molecules
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Proteomics

Proteomics studies networks of proteins by measuring,
among other things, protein expression.

Protein activity is regulated by post-translational
modification and degradation; these cannot yet be
predicted from DNA sequence.

Proteomics measures protein expression directly, not via
gene expression, thus achieving better accuracy. Current
work uses 2-dimensional polyacrylamide gel
electrophoresis (2D-PAGE) and mass spectrometry.

New separation and characterization technologies, such
as protein microarrays and high-throughput
chromatography, are being developed
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Proteomics
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Process Flow Chart of Proteomics
Two dimensional – gel electrophoresis
(Image) analysis
(Data massage, Evaluation)
Spot identification
(Mass spectrometry)
Biomarkers
(Principal compound analysis)
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Proteomics in Drug Discovery

As we have seen genomics has dramatically altered the
way drug discovery is now being viewed.

However, there may not be a good correlation between
gene expression and protein expression as most disease
processes and treatments are manifest at the protein
level.

It is believed that gene-based expression analysis alone
will be totally inadequate for drug discovery.
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Proteomics is Drug Discovery

Proteomics has unique and significant
advantages as an important complement
to a genomics approach.
1.
Target/marker identification
2.
Target validation/toxicology
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Target/marker identification
This application of proteomics provides a
protein profile of a cell, tissue and/or bodily
fluids that can be used to compare a
healthy with a diseased state for protein
differences in the search for drugs or drug
targets.
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Target validation/toxicology

Proteomics can be applied as an assay procedure for
the potential utility of drug candidates.

This can be achieved by a comparative analysis of
reference protein profiles from normal or diseased states
with profiles after drug treatment (Wang 1999).

Proteomics technology can also be integrated with
combinatorial chemistry to evaluate comparative
structure-activity relationships of drug analogs.
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Protein-Ligand Docking
Starting orientation of the program with 2 water molecules as the “Protein”
and “Ligand” (a useful setup for testing the application). The energy of the
system is in J/mol.
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Protein-Ligand Docking
Independent control of both molecules is allowed. The leftmost
molecule is rotated using a trackball style rotation, while the second
molecule remains fixed.
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Protein-Ligand Docking
From the previous figure, the second molecule has been
independently translated up and away from the first molecule.
Molecules can be arbitrarily positioned and oriented in 3D
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Protein-Ligand Docking
This is the same setup as the previous figure, except the viewpoint has been
rotated, translated and zoomed to a different location. The energy of the system
remains the same as the molecules are physically unmoved relative to each other
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Protein-Ligand Docking
The two oxygen atoms are just overlapping and consequently the energy of the
system takes on a large negative value indicating a VERY high energy (the energy
well is reversed for the purpose of the program, so large positive values indicate a
favourable conformation, and large negative values indicate unfavourable
conformations
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Protein-Ligand Docking
Here the atoms are at an optimum distance for the van der Waals
Forces to hit the minimum of the well potential. However, the atoms
are not aligned for any dipole-dipole interaction or hydrogen bonding
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Protein-Ligand Docking
The energy of the system attains a maximum with the following
orientation. This is the orientation that occurs between water
molecules when ice forms. This puts the hydrogen bond in its
optimum orientation, and this changes makes another order of
magnitude difference in the energy of the system
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Protein-Ligand Docking

Structure being the key to function, determining a
protein’s structure is a key step toward elucidating its
role.

The subfield of protein-ligand docking is useful in rational
drug design.

Laboratory prediction is time consuming and expensive,
so researchers have been working on computerized
prediction for several decades.

Exact computational prediction is difficult but
sophisticated algorithms to find approximate solutions
continue to be developed.
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Critical Assessment of Methods of Protein
Structure Prediction
Computational groups predict structures of
proteins whose structures have been found in the
laboratory before the latter results are released.
Tools are Classified
1.
Comparative modeling looks for amino acid similarity to
proteins of known structure.
2.
Fold recognition predicts folds in regions that do not
share amino acid similarity with known structures
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Advantages

More Powerful Medicines

Better, Safer Drugs the First Time

More Accurate Methods of Determining Appropriate Drug
Dosages

Advanced Screening for Disease

Better Vaccines

Improvements in the Drug Discovery and Approval Process

Decrease in the Overall Cost of Health Care
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Disadvantages

Complexity of finding gene variations that
affect drug response

Limited drug alternatives

Disincentives for drug companies to make
multiple pharmacogenomic products

Educating healthcare providers
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THANK YOU
E-mail: [email protected]
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