Transcript applications of bioinformatics in drug discovery and process

APPLICATIONS OF BIOINFORMATICS
IN DRUG DISCOVERY AND PROCESS
RESEARCH
Dr. Basavaraj K. Nanjwade M.Pharm., Ph.D
Associate Professor
Department of Pharmaceutics
JN Medical College
KLE University,
Belgaum- 590010
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Bioinformatics
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Application of CS and informatics to biological and
Drug Development science

Bioinformatics is the field of science in which
biology, computer science, and information
technology merge to form a single discipline.

The ultimate goal of the field is to enable the
discovery of new biological insights as well as to
create a global perspective from which unifying
principles in biology can be discerned
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Bioinformatics Hub
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Bioinformatics Tools
The processes of designing a new drug using bioinformatics
tools have open a new area of research. However,
computational techniques assist one in searching drug target
and in designing drug in silco, but it takes long time and
money. In order to design a new drug one need to follow the
following path.
1.
2.
3.
4.
5.
6.
7.
8.
Identify target disease
Study Interesting Compounds
Detection the Molecular Bases for Disease
Rational Drug Design Techniques
Refinement of Compounds
Quantitative Structure Activity Relationships (QSAR)
Solubility of Molecule
Drug Testing
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Bioinformatics Tools

Identify Target Disease:-
1. One needs to know all about the disease and
existing or traditional remedies. It is also
important to look at very similar afflictions and
their known treatments.
2. Target identification alone is not sufficient in
order to achieve a successful treatment of a
disease. A real drug needs to be developed.
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Bioinformatics Tools
 Identify Target Disease:3. This drug must influence the target protein in
such a way that it does not interfere with normal
metabolism.
4. Bioinformatics methods have been developed to
virtually screen the target for compounds that
bind and inhibit the protein.
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Bioinformatics Tools
Study Interesting Compounds:1. One needs to identify and study the lead
compounds that have some activity
against a disease.
2. These may be only marginally useful and
may have severe side effects.
3. These compounds provide a starting point
for refinement of the chemical structures.

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Bioinformatics Tools
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Detect the Molecular Bases for Disease:-
1.
If it is known that a drug must bind to a
particular spot on a particular protein or
nucleotide then a drug can be tailor made to
bind at that site.
2.
This is often modeled computationally using
any of several different techniques.
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Bioinformatics Tools
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Detect the Molecular Bases for Disease:-
3. Traditionally, the primary way of
determining what compounds would be
tested computationally was provided by
the researchers' understanding of
molecular interactions.
4. A second method is the brute force
testing of large numbers of compounds
from a database of available structures.
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Bioinformatics Tools

Rational drug design techniques:-
1. These techniques attempt to reproduce the
researchers' understanding of how to choose likely
compounds built into a software package that is
capable of modeling a very large number of
compounds in an automated way.
2. Many different algorithms have been used for
this type of testing, many of which were adapted
from artificial intelligence applications.
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Bioinformatics Tools
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Rational drug design techniques:-
3. The complexity of biological systems makes it
very difficult to determine the structures of large
biomolecules.
4. Ideally experimentally determined (x-ray or
NMR) structure is desired, but biomolecules are
very difficult to crystallize
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Bioinformatics Tools
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Refinement of compounds:-
1. Once you got a number of lead
compounds have been found,
computational and laboratory techniques
have been very successful in refining the
molecular structures to give a greater drug
activity and fewer side effects.
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Bioinformatics Tools
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Refinement of compounds:-
2. Done both in the laboratory and
computationally by examining the
molecular structures to determine which
aspects are responsible for both the drug
activity and the side effects.
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Bioinformatics Tools
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Quantitative Structure Activity Relationships
(QSAR):-
1. Computational technique should be used to detect the
functional group in your compound in order to refine your
drug.
2. QSAR consists of computing every possible number that can
describe a molecule then doing an enormous curve fit to find
out which aspects of the molecule correlate well with the
drug activity or side effect severity.
3. This information can then be used to suggest new
chemical modifications for synthesis and testing.
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Bioinformatics Tools

Solubility of Molecule:-
1. One need to check whether the target molecule
is water soluble or readily soluble in fatty tissue
will affect what part of the body it becomes
concentrated in.
2. The ability to get a drug to the correct part of
the body is an important factor in its potency.
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Bioinformatics Tools
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Solubility of Molecule:-
3. Ideally there is a continual exchange of
information between the researchers doing
QSAR studies, synthesis and testing.
4. These techniques are frequently used and often
very successful since they do not rely on
knowing the biological basis of the disease
which can be very difficult to determine.
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Bioinformatics Tools
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Drug Testing:-
1. Once a drug has been shown to be effective by an initial
assay technique, much more testing must be done before
it can be given to human patients.
2. Animal testing is the primary type of testing at this stage.
Eventually, the compounds, which are deemed suitable at
this stage, are sent on to clinical trials.
3. In the clinical trials, additional side effects may be found
and human dosages are determined.
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Structure Prediction flow chart
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Computer-Aided Drug Design (CADD)

Computer-Aided Drug Design (CADD) is a
specialized discipline that uses computational
methods to simulate drug-receptor interactions.
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CADD methods are heavily dependent on
bioinformatics tools, applications and databases.
As such, there is considerable overlap in CADD
research and bioinformatics.
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Bioinformatics Supports CADD Research
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Virtual High-Throughput Screening
(vHTS):-
1. Pharmaceutical companies are always searching
for new leads to develop into drug compounds.
2. One search method is virtual high-throughput
screening. In vHTS, protein targets are screened
against databases of small-molecule compounds
to see which molecules bind strongly to the
target.
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Bioinformatics Supports CADD Research
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Virtual High-Throughput Screening (vHTS):-
3. If there is a “hit” with a particular compound, it can be
extracted from the database for further testing.
4. With today’s computational resources, several million
compounds can be screened in a few days on sufficiently
large clustered computers.
5. Pursuing a handful of promising leads for further
development can save researchers considerable time and
expense.
e.g.. ZINC is a good example of a vHTS compound
library.
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Bioinformatics Supports CADD Research

Sequence Analysis:-
1. In CADD research, one often knows the genetic
sequence of multiple organisms or the amino acid
sequence of proteins from several species.
2. It is very useful to determine how similar or
dissimilar the organisms are based on gene or
protein sequences.
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Bioinformatics Supports CADD Research
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Sequence Analysis:-
3. With this information one can infer the
evolutionary relationships of the organisms,
search for similar sequences in bioinformatic
databases and find related species to those
under investigation.
4. There are many bioinformatic sequence
analysis tools that can be used to determine the
level of sequence similarity.
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Bioinformatics Supports CADD Research
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Homology Modeling:-
1.
Another common challenge in CADD research is
determining the 3-D structure of proteins.
2. Most drug targets are proteins, so it’s important to
know their 3-D structure in detail. It’s estimated
that the human body has 500,000 to 1 million
proteins.
3. However, the 3-D structure is known for only a
small fraction of these. Homology modeling is one
method used to predict 3-D structure.
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Bioinformatics Supports CADD Research
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Homology Modeling:-
4. In homology modeling, the amino acid sequence of a
specific protein (target) is known, and the 3-D structures
of proteins related to the target (templates) are known.
5. Bioinformatics software tools are then used to predict
the 3-D structure of the target based on the known 3-D
structures of the templates.
6. MODELLER is a well-known tool in homology modeling,
and the SWISS-MODEL Repository is a database of
protein structures created with homology modeling.
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Bioinformatics Supports CADD Research
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Similarity Searches:-
1.
A common activity in biopharmaceutical companies is the
search for drug analogues.
2. Starting with a promising drug molecule, one can search
for chemical compounds with similar structure or
properties to a known compound.
3. There are a variety of methods used in these searches,
including sequence similarity, 2D and 3D shape similarity,
substructure similarity, electrostatic similarity and others.
4.
A variety of bioinformatic tools and search engines are
available for this work
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Bioinformatics Supports CADD Research
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Drug Lead Optimization:-
1. When a promising lead candidate has been found in a
drug discovery program, the next step (a very long and
expensive step!) is to optimize the structure and
properties of the potential drug.
2. This usually involves a series of modifications to the
primary structure (scaffold) and secondary structure
(moieties) of the compound.
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Bioinformatics Supports CADD Research
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Drug Lead Optimization:-
3. This process can be enhanced using software
tools that explore related compounds
(bioisosteres) to the lead candidate. OpenEye’s
WABE is one such tool.
4. Lead optimization tools such as WABE offer a
rational approach to drug design that can reduce
the time and expense of searching for related
compounds.
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Bioinformatics Supports CADD Research
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Physicochemical Modeling:-
1. Drug-receptor interactions occur on atomic scales.
2. To form a deep understanding of how and why drug
compounds bind to protein targets, we must consider
the biochemical and biophysical properties of both the
drug itself and its target at an atomic level.
3. Swiss-PDB is an excellent tool for doing this. Swiss-PDB
can predict key physicochemical properties, such as
hydrophobicity and polarity that have a profound
influence on how drugs bind to proteins.
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Bioinformatics Supports CADD Research
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Drug Bioavailability and Bioactivity:-
1. Most drug candidates fail in Phase III clinical trials after
many years of research and millions of dollars have been
spent on them. And most fail because of toxicity or
problems with metabolism.
2. The key characteristics for drugs are Absorption,
Distribution, Metabolism, Excretion, Toxicity (ADMET)
and efficacy—in other words bioavailability and
bioactivity.
3. Although these properties are usually measured in the
lab, they can also be predicted in advance with
bioinformatics software.
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Benefits of CADD

Cost Savings:-
1. The Tufts Report suggests that the cost of drug
discovery and development has reached $800
million for each drug successfully brought to
market.
2. Many biopharmaceutical companies now use
computational methods and bioinformatics tools
to reduce this cost burden.
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Benefits of CADD
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Cost Savings:-
3. Virtual screening, lead optimization and
predictions of bioavailability and bioactivity can
help guide experimental research.
4. Only the most promising experimental lines of
inquiry can be followed and experimental deadends can be avoided early based on the results
of CADD simulations.
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Benefits of CADD
Time-to-Market:-
1. The predictive power of CADD can help drug
research programs choose only the most
promising drug candidates.
2. By focusing drug research on specific lead
candidates and avoiding potential “dead-end”
compounds, biopharmaceutical companies can
get drugs to market more quickly.
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Benefits of CADD
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Insight:-
1. One of the non-quantifiable benefits of CADD
and the use of bioinformatics tools is the deep
insight that researchers acquire about drugreceptor interactions.
2. Molecular models of drug compounds can reveal
intricate, atomic scale binding properties that
are difficult to envision in any other way.
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Benefits of CADD

Insight:-
1. When we show researchers new molecular
models of their putative drug compounds, their
protein targets and how the two bind together,
they often come up with new ideas on how to
modify the drug compounds for improved fit.
2. This is an intangible benefit that can help design
research programs.
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CADD
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CADD and bioinformatics together are a
powerful combination in drug research
and development.
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An important challenge for us going
forward is finding skilled, experienced
people to manage all the bioinformatics
tools available to us, which will be a topic
for a future article.
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Research Achievements
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Software developed
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Bioinformatics data base developed
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Traditional medicine research tools
developed
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Software developed
1. SVMProt: Protein function prediction software
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
2. INVDOCK: Drug target prediction software
3. MoViES: Molecular vibrations evaluation
server
http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl
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Bioinformatics database developed
1. Therapeutic target database
http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp
2. Drug adverse reaction target database
http://xin.cz3.nus.edu.sg/group/drt/dart.asp
3. Drug ADME associated protein database
http://xin.cz3.nus.edu.sg/group/admeap/admeap.asp
4. Kinetic data of biomolecular interactions database
http://xin.cz3.nus.edu.sg/group/kdbi.asp
5. Computed ligand binding energy database
http://xin.cz3.nus.edu.sg/group/CLiBE/CLiBE.asp
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Traditional medicine research tools developed
1. Traditional medicine information database
2. Herbal ingredient and content database
3. Natural product effect and consumption
info system
4. Traditional medicine recipe prediction and
validation system
5. Herbal target identification system
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THANK YOU
E-mail: [email protected]
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