Presentation on rational drug design converted

PRESENTATION ON
Rational Drug Design
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Department of Pharmacology BVVS COP
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Definition
➢ Development of medications based on the study of structure and
function of target molecules.
➢ Identify a receptor or enzyme that is relevent to a disease they are going
to design a drug.
➢ Elucidation the structure and function these receptor or enzyme.
➢ Design a drug molecule that interact with the receptor or enzyme in a
therapeutically beneficial way.
➢ Drug: Compounds used for the prevention and treatment of diseases like
cancer, etc.
➢ Ideal drug:
1) Target: Bio-molecule, involved in signaling or metabolic pathways, that
are specific to disease process by either protein-protein or protein-
nucleic acid interactions.
2) Antagonist action-inhibiting functions of the disease causing proteins.
3) Inhibiting interactions of the proteins.
4) Activates other proteins, that are deregulated in such disease like cancer
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BACKGROUND
➢ Bimolecular target (proteins or nucleic acids) is a key
molecule involved in a particular metabolic or signaling
pathway that is leading to a specific disease condition or
pathology or to the infectivity or survival of a microbial
pathogen.
➢ In Some cases, small molecules will be designed to
inhibit the target function in the specific pathway
(diseased state)
➢ Small molecules (inhibitors or modulators) will be
designed that are complementary to the active
site/allosteric site of target.
➢ In some other cases, small molecules will be designed or
developed to enhance the normal pathway by promoting
specific bimolecular molecules in the normal pathways
that may have been affected in the diseased state.
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➢ Small molecules (drugs) can be designed so as not to
affect any other important “off-target” molecules or anti
targets, since drug interactions with off-target molecules
may lead to undesirable side effects.
➢ Sequence homology is often used to identify such risks.
➢ Most commonly, drugs are organic small molecules
produced through chemical synthesis, but biopolymer-
based drugs (also known as biologics) produced through
biological processes are becoming increasingly more
common
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Drug Discovery
➢ Protein targets for drugs can be known by 3D X-ray
crystallography, NMR, docking tools, Computer aided
drug designing.
➢ PDB: 71550 proteins structure available.
➢ X-ray crystallography: 63124
➢ NMR: 7985
➢ Cryo-electron microscopy: 266
➢ Hybrid: 42
➢ Other: 133
➢ Still, insignificant so many lead drugs are unknown.
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Drug designing is:
➢The inventive process of finding new medications based on the
knowledge of a biological target.
1)challenging
2)Expensive
3)Time consuming
➢So, Multidisciplinary approach:
➢Computational tools, methodologies for structure guided
approach + Global gene expression data analysis by software's.
➢Hence,
1) Efficiency increased
2) Cost effectiveness
3) Time saved
4) Strategies to overcome toxic side effects
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Challenges
➢ Researchers have worked for a long time to overcome
the limitations of screening by designing molecules to
perform specific therapeutic tasks.
➢ Rational drug design was made more plausible by
improvements in our understanding of the similarities
between the actions of different biologically active
compounds.
➢ Almost all drug molecules achieve a biological response
through interaction with a target or receptor
biomolecule.
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❖3 Basic task
➢ Appropriate protein target for a given therapeutic need
must be identified.
➢ Distinguishing structure of the target protein must be
determined.
➢ The structure of a drug must be designed to interact
with the target protein.
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Drug Design
❖2 ways:
A) Development of ligands with desired properties for
targets having known structure and functions.
B) Development of ligands with predefined properties
for targets whose structural information may be or
may not be known.
➢ This, unknown target information can be found by
global gene expression data.
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RATIONAL DRUG DESIGN
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Pharmacophore Based Approaches
➢ Pharmacophore was first defined by Paul Ehrlich in
1909 as “a molecular framework that carries (phoros)
the essential features responsible for a drug’s
(pharmacon’s) biological activity” .
➢In 1977, this definition was updated by Peter Gund to
“a set of structural features in a molecule that is
recognized at a receptor site and is responsible for
that molecule's biological activity” .
➢The IUPAC definition of a pharmacophore is “an
ensemble of steric and electronic features that is
necessary to ensure the optimal supramolecular
interactions with a specific biological target and to
trigger (or block) its biological response”. 12
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Pharmacophore Based Approaches
➢ Pharmacophore is the pattern of features of molecule that is responsible for
biological effect.
➢ The first decision point is determined by the availability of the 3-dimensional
structure of the enzyme or complex.
➢ If the structure of the biological target is unknown, various methods that utilize
active (and inactive) analogs can be used to develop a working model of the
requirements for biological activity.
➢ There are several evolving quantitative methods that utilize active compounds
such as 2DQ SAR , 3 D - QSAR and neural network.
➢ Comparative Molecular Field Analysis (CoMFA) is a very widely used 3D-
QSAR technique.
➢ CoMFA represents a significant achievement due to its ability to develop a
three-dimensional quantitative model that relates steric and electrostatic fields
to biological activity.
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➢ A n initial problem with the method was the need to select both
conformations and alignments of the molecules to be modeled.
Due to this problem, many initial uses of the CoMF A method
involved molecules with rigid ring systems.
➢ For example, Allen et. al. predicted the binding affinities of six
analogs of beta-carbolines for the benzodiazepine receptor
(BzR) prior to synthesis, using a previously published CoMFA
model.
➢ The standard error of prediction for these six analogs is
significantly lower than the standard error estimate of the
cross-validation runs on the training set, hence the predictions
made using this model are much better than expected.
➢ In addition to such three-dimensional models, pharmacophore
hypotheses may also be developed by more qualitative
methods. Using any of these methods one could propose new
analogs of a lead compound based on the pharmacophore
model.
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Structure Based Approaches
➢ The process typically begins by generating a working computational model
from crystallographic data, but methods to develop models of the binding site
from active ligands are becoming more prevalent.
➢ Development of the working model may include developing molecular
mechanics force field parameters for non-standard residues consistent with the
force field for standard residues, modelling any missing segments, assigning
the protonation states of histidines, and orienting carbonyl and amide groups of
asparagine and glutamine residues based upon neighboring donor and acceptor
groups. Characterization of the active site is then aided by a variety of
visualization tools.
➢ For example, hydrophobic and hydrophilic regions of the active site are readily
identified by calculating the electrostatic potential at different surface grid
points, and hydrogen bond donor and acceptor groups can be highlighted in the
active site. The information gained by the characterization of the active-site is
very important for proposing new lead compounds or analogs of a known leads.
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New Lead Generation
➢ Generation of new lead compounds can be accomplished using
de novo design methods to design new structures or by
searching databases of known chemicals for particular structural
features.
➢ De novo molecular design methods may design structures by
sequentially adding or joining molecular fragments to a growing
structure, by adding functionality to an appropriately-sized
molecular scaffold, or by evolving complete structures.
➢ Some de novo design methods have concentrated on the design
of diverse molecular scaffolds or on the development of diverse
substituents to place on a single scaffold. Database search
methods have been developed that search based on separation of
molecular functionality by a particular number of bonds or
distance ranges.
➢ More chemically intuitive database search methods seek for
chemicals with particular steric and electrostatic fields.
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➢ A growing number of drug leads are being generated by
combinatorial methods in combination with high-
throughput screening.
➢ This ensures that a diverse set of lead compounds can be
found and optimized at much lower cost than i f the
entire library of possible structures were synthesized and
tested.
➢ The diverse set of leads that can be found by
combinatorial chemistry can give important insight into
the requirements for biological activity.
➢ This is particularly valuable for relatively new drug
targets for which insufficient information is available for
application of the structure-based or pharmacophore
based approaches.
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Structure Evaluation
➢ The challenge within this limitation is to develop evaluation methods that
rapidly and accurately predict absolute binding affinities of the aforementioned
large, diverse set of potential ligands.
➢ Currently available evaluation methods can either provide qualitative rank
ordering of a large number of molecules in a relatively short period of timeor
generate quantitatively accurate predictions of relative binding affinities for
structurally related molecules using substantial computing power.
➢ Consequently, biological activity evaluation techniques that increase speed
without greatly compromising accuracy (orvice versa) are of value to drug
discovery programs.
➢ Methods of ligand evaluation include graphical visualization of the ligand in
the binding site, substitution of parameters from the new ligand into SAR
models, and calculation of relative binding affinities.
➢ Usually about 50% of proposed new leads or optimized analogs can be
eliminated by evaluating their expected binding affinities based on docking,
visualization, conformational analysis and desolvation costs.
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➢ The remaining analogs will be ranked for synthesis using one or
all of the following methods, depending on computational
power, time and resources, namely;
1) Free Energy Perturbation (FEP) calculations, which give
very accurate quantitative predictions, but are computationally
very expensive
2) molecular mechanics calculations, which will give only
qualitative predictions, but these calculations are very fast,
3)regression methods that incorporate interaction variables
(intra and inter molecular interaction energies, hydrophobic
interactions) and ligand properties (desolvation, logP etc.),
which w i l l give semi-quantitative predictions, and are much
faster than FEP calculations. relative hydration free energies.
4) Calculation of relative hydration free energies is important
in the design and optimization of molecules that act as enzyme
inhibitors only after undergoing covalent hydration.
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Structure of few anti-cancer drugs
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BCR-ABL kinase domain: showing the binding pocket of nilotinib
(purple) bound to the active site of the target BCR-ABL in Chain C.
In this figure, BCR-ABL is the cluster of four chains (chain A , chain
B [purple], chain C [blue], and chain D [red]).
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Adjacent surface for nilotinib: shows the adjacent surface pocket that
is the surface within 3 Å of the drug to the active site of the target
(BCR-ABL). This surface provides guidance in the process of drug
development.
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- This adjacent surface provides information necessary for
modification of bound drug.
- Red= where protein needs H-acceptors
- Cream= Hydrophobic surface
- Pocket surface= active site
➢ Ligands that bind well, should have H-bond acceptor
groups touching red surface.
➢ Reactivity: Nucleophilic, electrophilic or radical frontier
density measures susceptibility of substrate by attack.
- It reveals reactive sites based on electron configuration.
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Known targets for Cancer therapy
➢ EGFR shows aberrant expression in cancers like lung cancer,
breast cancer.
➢ It interacts with other 151 proteins.
➢ It is involved in 19 signaling pathways.
➢ EGF is a natural ligand and binds to EGFR and mediates
signaling events.
➢ Another drug with the EGFR as a target is gefitinib.
➢ It attaches to protein kinase domain in EGFR, which is an
important target involved in many malignancy promoting
processes.
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The tyrosine kinase domain of the EGF receptor: showing
the binding of gefitinib
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Combination Therapy
➢ Ancient Asian herbal medicine with combination of
many herbs produces no side effects
➢ Same approach is applied to modern medicine
➢ We can use combination of different drugs to combat a
specific disease like cancer.
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Multidisciplinary Approach
A) Global gene expression profiling
➢ It reveals insights into pathogenosis of diseases including cancer.
➢ Methods such as SAGE or Microarray analysis(MA) are used.
➢ To access SAGE or MA, bioinformatics tools are used.
B) Global Gene Expression Analysis
➢ Databases like GEO (for microarray data) or SAGE data depository, Stanford
MA Database etc. are available.
➢ We can ‘group the genes’ based on the data from above mentioned tools for
gene annotation.
➢ DAVID tool- gene annotation, gene ontology, protein pathways and protein
domains finding.
➢ GSEA (Gene Set Enrichment Analysis)- defines whether set of genes shows
difference between normal and abnormal states of the genes or not.
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➢ All these tools are free tools but many commercial tools
are available to carry out specific experiments.
➢ Gene up/down/unchanged expression is required for
target finding for lead molecules.
➢ Modification of the drug itself or applying different
strategies is necessary for combination therapy.
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Conclusion
➢ Cancer is common in all the ages and it shows metastasis,
angiogenesis and cell death, etc.
➢ So, drugs to cure cancer are necessary.
➢ Except toxicity, drugs are quite tolerable so they are
combined with other drugs.
➢ Comparative analysis of gene expression levels between
drug treated and non-treated condition should be studied.
➢ If getting superior quality drug than the available ones,
clinical trial phases can be performed on them.
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REFERENCE
➢Rational drug design novel methodology and
practical application by Abbyl. Parrill, M.Rami
Reddy.
➢European Journal of Pharmacology, "Rational drug
design", Soma Mandal, 625(2009)90-100.
➢Drug Discovery Today, “Pharmacophore modelling
and applications in drug discovery", 15(2010)11-
12.Current computer-Aided Drug Design,
“Pharmacophore based drug design approaches a
practical process in drug discovery", 6(2010) 37-49.
➢Journal of receptor, ligand and channel research,
“Pharmacophore modelling: Advances, limitations
and current utility in drug discovery" 7(2014) 81.
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THANK U
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Presentation on rational drug design converted