tics in Drug Designing and Development New

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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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tics in Drug Designing and Development New