CADD.pptx

COMPUTER AIDED
DRUG DESIGN –
A MODERN ERA
TOOL
Dr. Naga Prashant
Koppuravuri
M.Pharm, Ph.D, FAGE

RESEARCH IS TO SEE WHAT
EVERYBODY ELSE HAVE SEEN
AND TO THINK WHAT NOBODY
HAS THOUGHT
“
Albert Szent-Gyorgyi
”
2
youtube.com/KNPPharmaceuticalChemistry

OUTCOMES
• Explain the various stages of drug discovery
• Explain the various techniques in Virtual Screening
• Describe physicochemical Properties and the techniques
involved in QSAR
• Explain various structure based drug design methods
(Molecular docking, Denovo drug design)

In 1981, a cover article entitled "Next
Industrial Revolution: Designing Drugs by
Computer at Merck" was published by
Fortune magazine. This event may be
regarded as the beginning of the potential
interests among scientists towards
computer-added drug design (CADD).
4

WHAT IS CADD

 Drug design is the inventive process of finding new
medications based on the knowledge of a biological target.
 Computer-aided drug design uses computational
approaches to discover, develop, and analyze drugs and similar
biologically active molecules
6

7
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
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)
DRUG
DISCOVERY &
DEVELOPMENT

8
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
TECHNOLOGY
IMPACTING

WHY CADD

 Drug Discovery 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 rigorous competition
amongst different pharmaceutical companies.
10

Advantages of CADD
 Time
 Cost
 Accuracy
 Information about Disease
 Database screening
 Minimal Manpower
11

SUCCESS STORIES
OF CADD

Some of the earliest examples of approved drugs that owe their
discovery in large part to the tools of CADD include the following:
 Tirofiban
 Saquinavir
 Ritonavir
 Indinavir
 Dorzolamide
 Captopril
13

TYPES OF CADD
14

Target(Protein) Structure
Ligand
Structure
Known Unknown
Known
Unknown
Structure based drug design
Protein-Ligand Docking
De novo Design
Ligand Based Drug Design
Pharmacophore Modelling
Quantitative Structure
activity relation ship
(QSAR)
Need some
experimental data
CADD no use
15

WORK
FLOW
OF
CADD
16

LIGAND BASED
DRUG DESIGN
17

 Relies on knowledge of other molecules that bind to the
biological target of interest.
 Used to derive a pharmacophore model that defines the
minimum necessary structural characteristics a molecule
must possess in order to bind to the target.
 A model of the biological target may
be built based on the knowledge of
what binds to it, and this model in
turn may be used to design new
molecular entities that interact with
the target.
18

19

 Alternatively, a Quantitative Structure Activity Relationship
(QSAR), in which a correlation between calculated properties of
molecules and their experimentally determined biological
activity, may be derived. These QSAR relationships in turn may
be used to predict the activity of new analogs.
Example:
log 1/C = -a ( log P)2 + b. log P + ρ σ + δ ES + k
20

STRUCTURE
BASED DRUG
DESIGN
21

 Structure Based Drug Design relies on knowledge of the three
dimensional structure of the biological target obtained
through :
1. X-Ray Diffraction
2.Electron Microscopy
 If an experimental structure of a target is not available, it may
be possible to create a homology model of the target based on
the experimental structure of a related protein.
22

 Using the structure of the biological target, candidate drugs
that are predicted to bind with high affinity and selectivity to
the target may be designed using:
 Homology modeling, also known as comparative modeling of
protein, refers to constructing an atomic- resolution model of
the "target" and an experimental three-dimensional structure
of a related homologous protein (the "template").
• Interactive graphics
• Intelligence of a medicinal chemist.
• Various automated computational
procedures may be used to suggest new
drug candidates.
23

Binding site identification
 It is the first step in structure based
design.
 Relies on identification of concave
surfaces on the protein that can
accommodate drug sized molecules that
also possess appropriate "hot spots"
(hydrophobic surfaces, hydrogen
bonding sites, etc.) that drive ligand
binding.
24

Docking:
It involves identification of new ligands for a given receptor by
searching large databases of 3D structures of small molecules to
find those fitting the binding pocket of the receptor using an
appropriate docking programs.
25

Scoring & Ranking
 Docking attempts to find the “best”
matching between two molecules
 It includes finding the Right Key for the
Lock
 To place a ligand (small molecule) into the
binding site of a receptor in the manners
appropriate for optimal interactions with a
receptor.
26

 To evaluate the ligand-receptor interactions in a way that may
discriminate the experimentally observed mode from others and
estimate the binding affinity.
E bind = E intra + E nonpolar +
E polar
27

28

DE NOVO DRUG
DESIGN
29

De novo means from the beginning
De novo drug design is a process in which the 3D
structure of the receptor is used to design newer molecules.
It involves structure determination of the lead target
complexes and the design of lead modifications using
molecular modeling tools.
30

31

SUMMARY
Computer Aided Drug Design is an efficient way of
drug discovery through which one can save time
and money. Even though this method have some
limitations like some times repeatability of results
through invivo and availability of experimental
data.
32

THANK YOU
Dr. Naga Prashant Koppuravuri,
M.Pharm, Ph.D, FAGE,
Assistant Professor,
Sri Adichunchanagiri College Of Pharmacy,
Adichunchanagiri University,
B.G.Nagar, Mandya, Karnataka.
Contact: drknprashant@gmail.com
Follow: youtube.com/KNPPharmaceuticalChemistry

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