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Title: APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY AND PROCESS RESEARCH

1
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

2
Bioinformatics

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
4
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.
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

5
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.

6
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.

7
Bioinformatics Tools

Study Interesting Compounds-
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.

8
Bioinformatics Tools

Detect the Molecular Bases for Disease-
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.
This is often modeled computationally using any
of several different techniques.

9
Bioinformatics Tools

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.

10
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.

11
Bioinformatics Tools

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

12
Bioinformatics Tools

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.

13
Bioinformatics Tools

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.

14
Bioinformatics Tools

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.

15
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.

16
Bioinformatics Tools

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.

17
Bioinformatics Tools

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.
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

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.

21
Bioinformatics Supports CADD Research

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 todays 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.

22
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.

23
Bioinformatics Supports CADD Research

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.

24
Bioinformatics Supports CADD Research

Homology Modeling-
Another common challenge in CADD research is
determining the 3-D structure of proteins.
2. Most drug targets are proteins, so its
important to know their 3-D structure in detail.
Its 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.

25
Bioinformatics Supports CADD Research

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.

26
Bioinformatics Supports CADD Research

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

27
Bioinformatics Supports CADD Research

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.

28
Bioinformatics Supports CADD Research

Drug Lead Optimization-
3. This process can be enhanced using software
tools that explore related compounds
(bioisosteres) to the lead candidate. OpenEyes
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.

29
Bioinformatics Supports CADD Research

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.

30
Bioinformatics Supports CADD Research

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 efficacyin 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.

31
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.

32
Benefits of CADD

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
dead-ends can be avoided early based on the
results of CADD simulations.

33
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.

34
Benefits of CADD

Insight-
1. One of the non-quantifiable benefits of CADD
and the use of bioinformatics tools is the deep
insight that researchers acquire about
drug-receptor interactions.
2. Molecular models of drug compounds can reveal
intricate, atomic scale binding properties that
are difficult to envision in any other way.

35
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.

36
CADD

CADD and bioinformatics together are a powerful
combination in drug research and development.
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.

37
Research Achievements