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ROLE OF BIOINFORMATICS IN DRUGDESIGNINGANDDEVELOPMENT
Division of Biochemistry,
Indian Veterinary Research institute,
Izatnagar, India-243122
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INTRODUCTION
The in silico identification of novel drug targets is now
feasible by systematically searching for paralogs (relatedproteins within an organism) of known drug targets (eg.
may be able to modify an existing drug to bind to the
paralog).
Can compare the entire genome of pathogenic andnonpathogenic strains of a microbe and identify
genes/proteins associated with pathogenism. Current Opin.
Microbiol 1:572-579 1998
Using gene expression microarrays and gene chiptechnologies, a single device can be used to evaluate and
compare the expression of up to 20000 genes of healthy and
diseased individuals at once. Trends Biotechnol 19:412-415
2001
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INFORMATICS
The ability to transform raw data into meaningful information by
applying computerized techniques for managing, analyzing, and
interpreting data.
The identification of new biological targets has benefited from thegenomics approach: eg. The sequencing of the human genome.
Nature 409:860-921 2001; Science 291:1304-1351 2001
Blueprint of all proteins
Bioinformatics methods are used to transform the raw sequence
into meaningful information (eg. genes and their encoded
proteins) and to compare whole
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IMPORTANT POINTSIN DRUG DESIGNBASED
ON BIOINFORMATICS TOOLS
Detect the Molecular Bases for Disease
Detection of drug binding site
Tailor drug to bind at that site
Protein modeling techniques Traditional Method (brute force testing)
Rational drug design techniques
Screen likely compounds built
Modeling large number of compounds (automated)
Application of Artificial intelligence
Limitation of known structures
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TECHNOLOGY
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 aday for activity against a target protein
Using a computer topredict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing
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DRUG DISCOVERY PROCESSWITHBIOINFORMATICS
Target Identification
Target validation and theidentification of ligandbinding regions
Lead optimization throughDocking
Clinical Trial
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DRUG TARGET IDENTIFICATION
The identification of new, clinically relevant, molecular targets isof utmost importance to the discovery of innovative drugs.
It has been estimated that up to 10 genes contribute to
multifactoral diseases. Science 287:1960-1964 (2000)
Typically these diseasegenes are linked to another 5 to 10 gene
products in physiological circuits which are also suitable for
pharmaceutical intervention.
If these numbers are multiplied with the number of diseases that
pose a major medical problem in the industrial world, then there
are ~5000 to 10000 potential drug targets
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DRUG TARGET IDENTIFICATION DATABASE
In the age of genomics, discovery of novel drug targets needsto incorporate and integrate different sources of data including
gene expression data, gene sequence data, gene polymorphism
data and so on.
Many public biological databases are warehousing andproviding a great amount of functional information for drug
discovery.
Databases to create systematic analysis architecture will be
helpful for inferring the underlying interaction of genes and
gaining insights about the pathway structures with which drug
targets interact
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LISTOFSOMERELEVANTDATABASESFORDRUG
TARGETIDENTIFICATION.
Database Access Contents
BIND http://bind.ca The biomolecular interactionnetwork database
KEGG http://www.genome.ad.jp/kegg/
Kyoto encyclopedia of genes andgenomes
OMIM ttp://ww3.ncbi.nlm.nih.gov/Omim/
Online mendelian inheritance inman
PIM http://proteome.wayne.edu/PIMdb.html
Protein interactions mapsdatabase
KinG http://hodgkin.mbu.iisc.ernet.in/~king
Protein kinases database
GPCRDB http://www.gpcr.org/7tm/ http://www.gpcr.org/7tm/
GEO http://www.ncbi.nlm.nih.g
ov/geo/
Gene expression omnibus
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THENETWORK-BASEDSTRATEGYFORDRUG
TARGETIDENTIFICATION
With the development of bioinformatics, a number of
computational techniques have been used to search for novel
drug targets from the information contained in genomics.
The network-based strategy for drug target identification
attempts to reconstruct endogenous metabolic, regulatory and
signaling networks with which potential drug targets interact
Development of microarray technology, large volume of gene
expression or protein expression data have been produced, and
there have been considerable models proposed to infer gene
networks or protein networks from these data. Microarray data, such as drug response expression data, time-
course expression data and steady-state expression data of gene
knockout, could be used
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LIST OF SOME RELEVANT COMPUTATIONAL TOOLS
FORGENENETWORKIDENTIFICATION
Tools Access Contents
GNA http://wwwhelix.inrialpes.fr/gna
Tool for the modeling and simulation ofgenetic regulatory networks
BioMiner http://www.zbi.uni-saarland.de
System for analyzing and visualizingbiochemical pathways and networks
GenePath http://genepath.org Tool for automated construction ofgenetic networks from mutant data
PathFinder
http://bibiserv.techfak.unibielefeld.de/pathfinder/
Tool for biochemical pathwaysreconstruction and dynamicvisualization
ToPNet http://www.biosolveit.de/ToPNet/
Tool for joint analysis of biologicalnetworks and expression data
VisANT http://visant.bu.edu Integrative platform fornetwork/pathway analysis
Pathway
Miner
http://www.biorag.org/pat
hway.html
Extracting gene association networks
from molecularpathways
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TARGET VALIDATION
Involves demonstrating the relevance of the target protein in a
disease process/ pathogenicity and ideally requires both gain
and loss of function studies.
This is accomplished primarily with knock-out or knock-in
animal models, small molecule inhibitors/agonists/antagonists,
antisense nucleic acid constructs, ribozymes, and neutralizing
antibodies.
In silico characterization can be carried by using approaches
such as genetic-network mapping, protein-pathway mapping,
proteinprotein interactions, disease-locus mapping, andsubcellular localization predictions
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Bioinformatics is being increasingly used to support target
validation by providing functionally predictive information
mined from databases and experimental datasets using a variety
of computational tools. Sequence-based approaches-The most commonly used
approach to assign function to proteins is by sequence similarity.
The Eukaryotic Linear Motif (ELM) server (http://elm.eu.org/) is
a resource for investigating short peptide linear motifs which areused for cell compartment targeting, proteinprotein interaction,
regulation by phosphorylation, acetylation, glycosylation and a
range of other post-translational modifications.
Structure-based approaches- homology modelling (e.g.http://swissmodel.expasy.org/)produces the most accurate
models, it does require homologous proteins with a structure and
a high percentage sequence identity with the target protein.
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LEAD COMPOUND IDENTIFICATION
The identification of a small molecule hit as a startingpoint for the hit-to lead process. The identification of
small molecule modulators of protein function and the
process of transforming these into high-content lead
series are key activities in modern drug discovery
(Robert AG 2006).
Hits can be identified by one or more of severaltechnology-based approaches like high throughputbiochemical and cellular assays, assay of naturalproducts, structure-based design
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HIGH-THROUGHPUT SCREENING
Used to test large numbers of compounds for their ability to
affect the activity of target proteins. Natural product and synthetic compound libraries with millions
of compounds are screened using a test assay. Curr Opin Chem
Biol 4:445-451 2000
There are concerns with the numbers approach to screeningfor a lead molecule. In theory generating the entire chemical
space for drug molecules and testing them would be an elegant
approach to drug discovery.
One solution may be to accumulate as much knowledge aspossible on biological targets (eg. structure, function,
interactions, ligands) and choose targeted approaches to
chemical synthesis.
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VIRTUALSCREENING
It is a computational technique used in drug discovery research.
It involves the rapid in silico assessment of large libraries ofchemical structures in order to identify those structures which
are most likely to bind to a drug target, typically a protein
receptor or enzyme.
The aim of virtual screening is to identify molecules of novelchemical structure that bind to the macromolecular target of
interest
There are two broad categories of screening techniques:
ligand-based structure-based
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STRUCTURE BASED SCREENING
Three dimensional structures of compounds from virtual or
physically existing libraries are docked into binding sites oftarget proteins with known or predicted structure.
Scoring functions evaluate the steric and electrostatic
complementarity between compounds and the target protein.
The highest ranked compounds are then suggested forbiological testing.
Once hits (compounds that elicit a positive response in an
assay) have been identified via the screening approach, these
are validated by re-testing them and checking the purity andstructure of the compounds
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STRUCTURE-BASED DRUG DESIGN
Compounddatabases,
Microbial broths,Plants extracts,Combinatorial
Libraries
3-D ligandDatabases
DockingLinking or
Binding
Receptor-LigandComplex
Randomscreening
synthesis
Lead molecule
3-D QSAR
Target EnzymeOR Receptor
3-D structure byCrystallography,NMR, electronmicroscopy OR
Homology Modeling
Redesignto improve
affinity,specificity etc.
Testing
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LIGAND-BASED SCREENING
Given a set of structurally diverse ligands that binds to a receptor, a
model of the receptor can be built by exploiting the collectiveinformation contained in such set of ligands.
A candidate ligand can then be compared to the pharmacophore
model to determine whether it is compatible with it and thereforelikely to bind.
Another approach to ligand-based virtual screening is to use 2D
chemical similarity analysis to scan a database of molecules against
one or more active ligand structure.
A popular approach to ligand-based virtual screening is based on
searching molecules with shape similar to that of known actives, as
such molecules will fit the target's binding site and hence will be
likely to bind the target
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LEAD OPTIMIZATION
Molecules are chemically modified and subsequentlycharacterized in order to obtain compounds with suitable
properties to become a drug.
Leads are characterized with respect to pharmacodynamic
properties such as efficacy and potency in vitro and in vivo,
physiochemical properties, pharmacokinetic properties, and
toxicological aspects.
Lead structures are optimized for target affinity andselectivity.
Docking techniques are currently applied
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CONT..
Only if the hits fulfill certain criteria are they regarded as
leads. The criteria can originate from: Pharmacodynamic properties - efficacy, potency, selectivity
Physiochemical properties - water solubility, chemical
stability, Lipinskisrule-of-five.
Pharmacokinetic properties - metabolic stability andtoxological aspects.
Chemical optimization potential - ease of chemical synthesis
and derivatization.
5) Patentability
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DOCKING METHODS
Docking of ligands to
proteins is a formidableproblem since it entailsoptimization of the 6positional degrees of
freedom.
Rigid vs Flexible
Speed vs Reliability
Manual InteractiveDocking
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DOCKING TERMINOLOGY
Receptor or host or lock The "receiving" molecule, most
commonly a protein or other biopolymer. Ligand or guest or keyThe complementary partner molecule
which binds to the receptor.
Binding mode The orientation of the ligand relative to the
receptor as well as the conformation of the ligand and receptorwhen bound to each other.
PoseA candidate binding mode.
Scoring The process of evaluating a particular pose by
counting the number of favorable intermolecular interactionssuch as hydrogen bonds and hydrophobic contacts.
Ranking The process of classifying which ligands are most
likely to interact favorably to a particular receptor based on the
predicted free-energy of binding.
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ACTIVESITEIDENTIFICATION
Active site identification is the first step in this program.
It analyzes the protein to find the binding pocket, interaction
sites within the binding pocket, and then prepares the necessary
data for Ligand fragment link.
The basic inputs for this step are the 3D structure of the protein
and a pre-docked ligand in PDB format, as well as their atomicproperties
The space inside the ligand binding region would be studied
with virtual probe atoms of the four types above so the chemical
environment of all spots in the ligand binding region can beknown.
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AUTOMATED DOCKING METHODS
Basic Idea is to fill the active site of the Target proteinwith a set of spheres.
Match the centre of these spheres as good as possiblewith the atoms in the database of small molecules with
known 3-D structures. Examples:
DOCK, CAVEAT, AUTODOCK, LEGEND, ADAM,LINKOR, LUDI.
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THERMODYNAMICS OF RECEPTOR LIGAND BINDING
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THERMODYNAMICS OF RECEPTOR-LIGAND BINDING
Proteins that interact with drugs are typically enzymes or
receptors
Drug may be classified as: substrates/inhibitors (for enzymes)
agonists/antagonists (for receptors)
Ligands for receptors normally bind via a non-covalent
reversible binding.
Enzyme inhibitors have a wide range of modes:non-covalent
reversible, covalent reversible/irreversible or suicide inhibition
Enzymes prefer to bind transition states (reaction
intermediates) and may not optimally bind substrates as part ofenergy used for catalysis.
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CONT
In contrast, inhibitors are designed to bind with higher
affinity: their affi nities often exceed the corresponding
substrate affinities by several orders of magnitude!
Agonists are analogous to enzyme substrates: part of the
binding energy may be used for signal transduction,
inducing a conformation or aggregation shift.
To understand what forces are responsible for ligands
binding to Receptors/Enzymes,
It is worthwhile considering what forces drive protein
foldingthey share many common features.
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CONT
The observed structure of Protein is generally aconsequence of the hydrophobic effect!
Secondary amides form much stronger H-bonds to water
than to other sec. Amides hydrophobic collapse
Proteins generally bury hydrophobic residues inside the core,
Exposing hydrophilic residues to the exterior Salt-
bridges inside
Ligand building clefts in proteins often exposehydrophobic residues to solvent and may containpartially desolvated hydrophilic groups that are notpaired:
SCORING METHOD
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SCORINGMETHOD
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CLINICALTRIALS
The NIH organizes clinical trials into 5 different types:
Treatment trials: test experimental treatments or a newcombination of drugs.
Prevention trials: look for ways to prevent a disease or
prevent it from returning.
Diagnostic trials: find better tests or procedures fordiagnosing a disease.
Screening trials: test methods of detecting diseases.
Quality of Life trials: explore ways to improve comfort
and quality of life for individuals with a chronic illness.
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CONT..
Pharmaceutical clinical trials are commonly classified into 4phases: (as of 2006, there are now 5)
Phase 0 - a recent designation for exploratory, first-in-humantrials.
Designed to expedite the development of promising therapeutic
agents by establishing early on whether the agent behaves inhuman subjects as was anticipated from preclinical studiesNew Scientist, March 2006,Catastrophic immune responsemay have caused drug trial horror
Phase I - a small group of healthy volunteers (20-80) areselected to assess the safety, tolerability, pharmacokinetics, andpharmacodynamics of a therapy. - normally include doseranging studies so that doses for clinical use can beset/adjusted.
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CONT..
Phase I - there are 3 common kinds of phase I trials: Single Ascending Dose (SAD) studies- a small group of
patients are given a single dose of the drug and then aremonitored over a period of time. If they do not exhibitany adverse side effects, the dose is escalated and a new
group of patients is given the higher dose. Multiple Ascending Dose (MAD) studies- a group of
patients receives multiple low doses of the drug, whileblood (and other fluids) are collected at various timepoints and analyzed to understand how the drug isprocessed within the body. The dose is subsequentlyescalated for further groups.
Food effect- designed to investigate any differences inabsorption caused by eating before the dose is given.
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CONT..
Phase II - performed on larger groups (20-300) and aredesigned to assess the activity of the therapy, and continuePhase I safety assessments.
Phase III - randomized controlled trials on large patientgroups (hundreds to thousands) aimed at being the definitive
assessment of the efficacy of the new therapy, in comparisonwith standard therapy. Side effects are also monitored.
It is typically expected that there be at least two successfulphase III clinical trials to obtain approval from the FDA.
Once a drug has proven acceptable, the trial results arecombined into a large document which includes a
comprehensive description of manufacturing procedures,formulation details, shelf life, etc.
This document is submitted to the FDA for review.
C
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CONT..
Phase IV - post-launch safety monitoring and ongoingtechnical support of a drug.
- may be mandated or initiated by the pharmaceutical
company.
- designed to detect rare or long term adverse effects over alarge patient population and timescale than was possible
during clinical trials.
SIGNIFICANCE
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SIGNIFICANCE
As structures of more and more protein targets become available
through crystallography, NMR and bioinformatics methods.
There is an increasing demand for computational tools that can
identify and analyze active sites and suggest potential drug molecules
that can bind to these sites specifically.
Time and cost required for designing a new drug are immense and
at an unacceptable level. According to some estimates it costs about
$880 million and 14 years of research to develop a new drug before it
is introduced in the market.
Intervention of computers at some plausible steps is imperative to
bring down the cost and time required in the drug discovery process.
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