2. DRUG DESIGN
It is the inventive process of finding new
medications based on the knowledge of a biological target.
The drug is most commonly an organic small molecule that
activates or inhibits the function of a biomolecule such as
a protein, which in turn results in a therapeutic benefit to
the patient.
In the most basic sense, drug design involves the design of
small molecules that are complementary in shape and charge
to the biomolecular target with which they interact and
therefore will bind to it.
3. TERMS TO BE KNOWN
LIGAND
PHARMACOPHORE
MOLECULAR DOCKING
LEAD IDENTIFICATION
4. Drug Discovery & Development
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation &
Scale-up
Human clinical trials
(2-10 years)
FDA approval
(2-3 years)
5. modern drug discovery process
Target
identification
Target
validation
Lead
identification
Lead
optimization
Preclinical
phase
Drug
discovery
2-5 years
• Drug discovery is an expensive process involving high R & D cost and extensive
clinical testing.
• A typical development time is estimated to be 10-15 years.
6-9 years
6. Design, development and commercialization of a
drug is a tedious, time-consuming and cost-intensive process
Timeline in a drug discovery project.
7. Medicinal chemists today are facing a serious challenge because of the
increased cost and enormous amount of time taken to discover a new drug,
and also because of fierce competition amongst different drug companies.
INTRODUCTION
It needs approximately 300 to 350 million US $ and 12-13 years
for a drug to reach the market.
Considering both the potential benefits to human health and the enormous
costs in time and money of drug discovery.
technique that increases the efficiency of any stage of the drug
discovery enterprise will be highly prized.
8. Computer-aided drug design (CADD) is one of these tools which can be used
to increase the efficiency of the drug discovery process. CADD cannot, however,
maximize its utility in isolation and will not do so. Rather, it can form a
valuable partnership with experiment by providing estimates when experiments
are difficult, expensive, or impossible, and by coordinating the experimental data
available.
A close coupling between computational chemists and experimentalists allows
information to flow immediately and directly between the two. This helps CADD
chemists to better understand the details of the problem and to refine their
approach. It also provides valuable information for the experimentalist, it helps
to guide further experimental planning and potentially makes this process more
efficient
CADD is, however, not a direct route to new drugs, but it provides a somewhat
more detailed map to the goal. The hope is that by providing bit and pieces of
information, and by helping to coordinate the information, CADD will help to
save days and money for drug discovery projects
9. • Random, trial and error
• Time consuming
• Very expensive
• Extremely low yield ( 1 in 100,000)
• Target specific and structure-based
• Fast and automatic
• Very low cost
• High success rate
Computer-based Design
Traditional Drug Screening
10. 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
11. Identify Target Disease
know all about the disease
A real drug needs to be developed
drug must influence the target protein
INSILICO methods
12. 1.One needs to identify and study the lead
compounds that have some activity against a disease.
2. These compounds provide a starting point for refinement of the
chemical structures.
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.
Study Interesting Compounds
13. 1. Traditionally, the primary way of determining what compounds
would be tested computationally was provided by the researchers'
understanding of molecular interactions.
2. A second method is the brute force testing of large numbers of
compounds from a database of available structures.
Detect the Molecular Bases for Disease
known actives structures founddatabase
14. 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.
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
15. Refinement of compounds
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.
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.
16. 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
17. 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.
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.
18. 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.
Drug Testing
19. Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
20.
21. Impact of new technology on drug discovery
• The last few years have seen a number of
“revolutionary” new technologies:
– Gene chips, genomics.
– Bioinformatics & Molecular biology
– protein structures
– High-throughput screening & assays
– Docking
– Combinatorial chemistry
– In-vitro ADME testing
• How do we make it all work for us?
22. Information generated at different points in the Drug Design process
Gene chip experiments
Project selection decisions
Assay protocols
Series selection decisions
SAR studies
Protein structures
Combinatorial Expts.
Pharmacophores
ADME studies
Toxicology studies
Scaleup reactions
Lead cmpd decisions
Clinical Trials data
Doctor/patient studies
Marketing, surveys, etc
23. For workstations, minicomputers, and supercomputers (SGI, Sun,
Cray, etc.)
AMBER — Peter Kollman and coworkers, UCSF
Computer assisted model building, energy minimization,
molecular dynamics, and free energy perturbation calculations.
Midas Plus — UCSF Computer Graphics Laboratory
CHARMM — Martin Karplus and cowrokers, Harvard
QUANTA/CHARMm — Molecular Simulations Inc. (MSI)
molecular/drug design, QSAR, quantum chemistry,
X-ray & NMR data analysis
Insight/DISCOVER — Biosym, Inc.
Now MSI and Biosym became Accelrys Inc.
SYBYL — Tripos, Inc.
ECEPP — (Harold Scheraga and coworkers, Cornell)
MM3 — (Norman Allinger and coworkers, Georgia)
Software for General Purpose Molecular Modeling
34. hydrogen bonds
(directed interactions
π interactions
hydrophobic
contacts
explicitly placed
water molecules
ligand and protein
flexibility
Terms Contributing to Ligand Binding
35.
36.
37. Pharmaceutical Research Software
Spartan is a powerful tool for computer aided drug design. The easy-
to-use interface delivers a new suite of molecular modelling features
as well as quantum calculation tools for chemists working in drug
discovery.
38. 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
Software developed
39. PHARMACO informatics 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
