Use of bioinformatics in drug development and diagnostics

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Use of bioinformatics in drug development and
diagnostics
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Bringing a New Drug to Market
1 compound approved
Review and approval by Food Drug Administration
Phase III Confirms effectiveness and monitors
adverse reactions from long-term use in 1,000
to5,000 patient volunteers.
Phase II Assesses effectiveness and looks for
side effects in 100 to 500 patient volunteers.
5 compounds enter clinical trials
Phase I Evaluates safety and dosage in 20 to
100 healthy human volunteers.
5,000 compounds evaluated
Discovery and preclininal testing Compounds are
identified and evaluated in laboratory and animal
studies for safety, biological activity, and
formulation.
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14 Years
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Source Tufts Center for the Study of Drug
Development

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Biological Research in 21st Century

• The new paradigm, now emerging is that all the
  'genes' will be known (in the sense of being
  resident in databases available electronically),
  and that the starting "point of a biological
  investigation will be theoretical.
• - Walter Gilbert

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Rational Approach to Drug Discovery
Identify target
Clone gene encoding target
Express target in recombinant form
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Crystal structures of target and target/inhibitor
complexes
Screen recombinant target with available
inhibitors
Synthesize modifications of lead compounds
Identify lead compounds
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Synthesize modifications of lead compounds
Identify lead compounds
Toxicity pharmacokinetic studies
Preclinical trials
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An Ideal Target

• Is generally an enzyme/receptor in a pathway and
  its inhibition leads to either killing a
  pathogenic organism (Malarial Parasite) or to
  modify some aspects of metabolism of body that is
  functioning dormally.
• An ideal target
• Is essential for the survival of the organism.
• Located at a critical step in the metabolic
  pathway.
• Makes the organism vulnerable.
• Concentration of target gene product is low.
• The enzyme amenable for simple HTS assays

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How Bioinformatics can help in Target
Identification?

• Homologous Orthologous genes
• Gene Order
• Gene Clusters
• Molecular Pathways Wire diagrams
• Gene Ontology
• Identification of Unique Genes of Parasite as
  potential drug target.

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Comparative Genomics Malarial Parasites Source
for identification of new target molecules.

• Genome comparisons of malarial parasites of
  human.
• Genome comparisons of malarial parasites of human
  and rodent.
• Comparison of genomes of
• Human
• Malarial parasite
• Mosquito

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What one should look for?
Human P.f Mosquito

• Proteins that are shared by
• All genomes
• Exclusively by Human P.f.
• Exclusively by Human Mosquito
• Exclusively by P.f. Mosquito

Unique proteins in Human P.f. Targets
for anti-malarial drugs
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Impact of Structural Genomics on Drug Discovery

• Dry, S. et. al. (2000) Nat. Struc.Biol. 7976-949.

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Drug Development Flowchart

• Check if structure is known
• If unknown, model it using KNOWLEDGE-BASED
  HOMOLOGY MODELING APPROACH.
• Search for small molecules/ inhibitors
• Structure-based Drug Design
• Drug-Protein Interactions
• Docking

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Why Modeling?

• Experimental determination of structure is still
  a time consuming and expensive process.
• Number of known sequences are more than number of
  known structures.
• Structure information is essential in
  understanding function.

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Sequence identities Molecular Modeling methods

• Methods Sequence Identity with known
  structures
• ab initio 0-20
• Fold recognition 20-35
• Homology Modeling gt35

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STRUCTURE-BASED DRUG DESIGN
Target Enzyme OR Receptor
Compound databases, Microbial broths, Plants
extracts, Combinatorial Libraries
3-D ligand Databases
3-D structure by Crystallography, NMR, electron
microscopy OR Homology Modeling
Docking Linking or Binding
Random screening synthesis
Receptor-Ligand Complex
Testing
Redesign to improve affinity, specificity etc.
Lead molecule
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Binding Site Analysis

• In the absence of a structure of Target-ligand
  complex, it is not a trivial exercise to locate
  the binding site!!!
• This is followed by Lead optimization.

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Lead Optimization
Lead
Lead Optimization
Active site
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Compounds which are weak inhibitors may be
modified by combinatorial chemistry in silico if
the target structure (3-dimensional!) is known,
minimizing the number of potential test compounds
Target structure
Z
N
C
X
Y
H
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Factors Affecting The Affinity Of A Small
Molecule For A Target Protein

• LIGAND.wat n PROTEIN.wat n
  LIGAND.PROTEIN.watp(nm-p) wat
• HYDROGEN BONDING
• HYDROPHOBIC EFFECT
• ELECTROSTATIC INTERACTIONS
• VAN DER WAALS INTERACTIONS

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DIFFERENCE BETWEEN AN INHIBITOR AND DRUG Extra
requirement of a drug compared to an inhibitor
LIPINSKIS RULE OF FIVE Poor absorption or
permeation are more likely when -There are
more than five H-bond donors -The mol.wt is over
500 Da -The MlogP is over 4.15(or CLOG Pgt5) -The
sums of Ns and Os is over 10

• Selectivity
• Less Toxicity
• Bioavailability
• Slow Clearance
• Reach The Target
• Ease Of Synthesis
• Low Price
• Slow Or No Development Of Resistance
• Stability Upon Storage As Tablet Or Solution
• Pharmacokinetic Parameters
• No Allergies

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Mecanismo antibacteriano de la PZA Pro-droga
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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
  modesnon-covalent reversible,covalent
  reversible/irreversible or suicide inhibition.
• Inhibitors are designed to bind with higher
  affinity their affinities 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.

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• To understand what forces are responsible for
  ligands binding to Receptors/Enzymes,
• The observed structure of Protein is generally a
  consequence of the hydrophobic effect!
• Proteins generally bury hydrophobic residues
  inside the core,while exposing hydrophilic
  residues to the exterior Salt-bridges
  inside
• Ligand building clefts in proteins often expose
  hydrophobic residues to solvent and may contain
  partially desolvated hydrophilic groups that are
  not paired

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Docking Methods

• Docking of ligands to proteins is a formidable
  problem since it entails optimization of the 6
  positional degrees of freedom.
• Rigid vs Flexible
• Manual Interactive Docking

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Automated Docking Methods

• Speed vs Reliability
• Basic Idea is to fill the active site of the
  Target protein with a set of spheres.
• Match the centre of these spheres as good as
  possible with 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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GRID Based Docking Methods

• Grid Based methods
• GRID (Goodford, 1985, J. Med. Chem. 28849)
• GREEN (Tomioka Itai, 1994, J. Comp. Aided. Mol.
  Des. 8347)
• MCSS (Mirankar Karplus, 1991, Proteins, 1129).
• Functional groups are placed at regularly spaced
  (0.3-0.5A) lattice points in the active site and
  their interaction energies are evaluated.

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Folate Biosynthetic pathway
DHFR
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Multiple alignment of DHFR of Plasmodium species
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Drug binding pocket of L. casei DHFR
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Antifolate drugs in the active site of DHFR L.
casei to show hydrogen bonding with surrounding
residues
MTX
PYR
SO3
TMP
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How molecular modeling could be used in
identifying new leads

• These two compounds
• a triazinobenzimidazole
• a pyridoindole were found to be active with high
  Ki against recombinant wild type DHFR.
• Thus demonstrate use of molecular modeling in
  malarial drug design.

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Sitio Activo de la pirazinamidasa
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Docking P. Horikoshii PZA en presencia de Zn
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Additional Drug Target glutathione-GR
Glutathione-GR
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Additional Drug Target Thioredoxin reductase
(TrxR)
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How Bioinformatics Aids in Vaccine Development /
Peptide Vaccine Development Using Bionformatics
Approaches
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Emerging and re-emerging infectious diseases
threats, 1980-2001

• Viral
• Bolivian hemorrhagic fever-1994,Latin America
• Bovine spongiform encephalopathy-1986,United
  Kingdom
• Creulzfeldt-Jackob disease(a new variant
  V-CID)/mad cow disease-1995-96, UK/France
• Dengue fever-1994-97,Africa/Asia/Latin
  America/USA
• Ebola virus-1994,Gabon1995,Zaire1996,United
  States(monkey)
• Hantavirus-1993,United States 1997, Argentina
• HIV subtype O-1994,Africa
• Influenza A/Beijing/32/92, A/Wuhan/359/95,
  HSN1-1993,United States 1995,China 1997,
  Hongkong
• Japanese Encephalitis-1995, Australia
• Lassa fever-1992,Nigeria
• Measles-1997, Brazil
• Monkey pox-1997,Congo
• Morbillivirus 1994, Australia
• Onyong-nyong fever-1996,Uganda
• Polio-1996,Albania
• Rift Valley fever-1993,Sudan
• Venezuelan equine encephalitis-1995-96,Venezuela/C
  olombia
• West Nile Virus-1996,Romania

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Emerging and re-emerging infectious diseases
threats contd.,

• Parasitic
• African trypanosomiasis-1997,Sudan
• Ancylcostoma caninum(eosinophilic
  enteritis)-1990s,Australia
• Cryptosporiadiasis-1993,United States
• Malaria-1995-97,Africa/Asia/Latin America/United
  states
• Metorchis-1996,Canada
• Microsporidiosis-Worldwide
• Fungal
• Coccidiodomycosis-1993,United States
• Penicillium marneffi

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Emerging and re-emerging infectious diseases
threats contd.

• Bacterial
• Anthrax-1993,Caribbean
• Cat scratch disease/Bacillary angiomatosis(Bartone
  lla henseiae)-1900s, USA
• Chlamydia pneumoniae(Pneumonia/Coronary artery
  disease?)-1990s, USA(discovered 1983)
• Cholera-1991,Latin America
• Diphtheria-1993,Former Soviet Union
• Ehrlichia chaffeensis,Human monocytic
  ahrlichiosis(HME)-United States
• Ehrlichia phagocytophilia,Human Granulocytic
  ehrlichis(HGE)-United States
• Escherichia coli O157-1982-1997,United
  States1996,Japan
• Gonorrhea(drug resistant)-1995,United States
• Helicobacter pylori(ulcers/cancer_-worldwide(disco
  vered 1983)
• Leptospirosis-195,Nicaragun
• Lyme disease(Borrelia burgdorferi)-1990s,United
  states
• Meningococcal meningitis(serogroup
  A)-1995-1997,West Africa
• Pertussis-1994,UK/Netherlands1996,USA
• Plague-1994,India
• Salmonella typhimurium DT104(drug
  resistant)-1995,USA
• Staphylococcus aureus(drug resistant)-1997,United
  States/Japan
• Toxic strep-United States

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Types of Vaccines

• Killed virus vaccines
• Live-attenuated vaccines
• Recombinant DNA vaccines
• Genetic vaccines
• Subunit vaccines
• Polytope/multi-epitope vaccines
• Synthetic peptide vaccines

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Systems with potential use as T-cell vaccines
CD4 T-cell vaccines CD8 T-cell
vaccines Killed microbe Live attenuated
microbe Live attenuated microbe - Synthetic
peptide coupled Synthetic peptide to
protein delivered in liposomes or
ISCOMsRecombinant microbial protein -bearing
CD4 T-cell epitope Chimeric virus
expressing Chimeric virus expressing CD4
T-cell epitope CD8 T-cell epitope Chimeric
Ig Self-molecule expressing CD8 T-cell
epitope Chimeric-peptide-MHC Chimeric
peptide-MHCclass II complex Class I
complex Receptor-linked peptide - Naked DNA
expressing Naked DNA expressing CD4 T-cell
epitope CD8 T-cell epitope Abbreviations Ig,
Immunoglobulin, ISCOM, immune-stimulating
complex MHC,Major histocompability complex.
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Why Synthetic Peptide Vaccines?

• Chemically well defined, selective and safe.
• Stable at ambient temperature.
• No cold chain requirement hence cost effective in
  tropical countries.
• Simple and standardised production facility.

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Epitopes
B-cell epitopes
Th-cell epitopes
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Identified antigens must be checked for strain
varying polymorphisms, these polymorphism must be
represented in a anti-blood stage vaccine
Protective epitope
Variants in strains A B
C D
Candidate protein X
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Antigenic determinants of Egp of JEV Kolaskar
Tongaonkar approach
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Peptide vaccines to be launched in near future

• Foot Mouth Disease Virus (FMDV)
• Human Immuno Deficiency Virus (HIV)
• Metastatic Breast Cancer
• Pancreatic Cancer
• Melanoma
• Malaria
• T.solium cysticercosis

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Various transformations on side-chain orientation
in a model tetrapeptide
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Reverse Vaccinology

• Advantages
• Fast access to virtually every antigen
• Non-cultivable can be approached
• Non abundant antigens can be identified
• Antigens not expressed in vitro can be
  identified.
• Non-structural proteins can be used
• Disadvantages
• Non proteinous antigens like polysaccharides,
  glycolipids cannot be used.

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Rappuoli 2001 Curr. Opin. Microbiol.
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Rappuoli 2001 Curr. Opin. Microbiol.
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• Vaccine development
• In Post-genomic era
• Reverse Vaccinology
• Approach.

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Genome Sequence
Proteomics Technologies
In silico analysis
IVET, STM, DNA microarrays
High throughput Cloning and expression
In vitro and in vivo assays for Vaccine candidate
identification
Global genomic approach to identify new vaccine
candidates
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In Silico Analysis
Peptide Multitope vaccines
VACCINOME
Candidate Epitope DB
Epitope prediction
Disease related protein DB
Gene/Protein Sequence Database
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Synthetic Peptide Vaccine Design and
Development of Synthetic Peptide vaccine against
Japanese encephalitis virus
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Egp of JEV as an Antigen

• Is a major structural antigen.
• Responsible for viral haemagglutination.
• Elicits neutralising antibodies.
• 500 amino acids long.
• Structure of extra-cellular domain (399) was
  predicted using knowledge-based homology modeling
  approach.

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Model RefinementPARAMETERS USED

• force field AMBER all atom
• Dielectric const Distance dependent
• Optimisation Steepest Descents
• Conjugate Gradients.
• rms derivative 0.1 kcal/mol/A for SD
• rms derivative 0.001 kcal/mol/A for CG
• Biosym from InsightII, MSI and modules therein

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Model For Solvated Protein

• Egp of JEV molecule was soaked in the water layer
  of 10A?.
• 4867 water molecules were added.
• The system size was increased to 20,648 atoms
  from 6047.

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Model Evaluation II Ramachandran Plot
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Peptide Modeling
Initial random conformation Force field
Amber Distance dependent dielectric constant
4rij Geometry optimization Steepest descents
Conjugate gradients Molecular dynamics at 400 K
for 1ns Peptides are SENHGNYSAQVGASQ
NHGNYSAQVGASQ YSAQVGASQ YSAQVGASQAAKFT
NHGNYSAQVGASQAAKFT SENHGNYSAQVGASQAAKFT 149
168
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Prediction of conformations of the antigenic
peptides

• Lowest energy Allowed conformations were
  obtained using multiple MD simulations
• Initial conformation random, allowed
• Amber force field with distance dependent
  dielectric constant of 4rij
• Geometry optimization using Steepest descents
  Conjugate gradient
• 10 cycles of molecular dynamics at 400 K each of
  1ns duration, with an equilibration for 500 ps
• Conformations captured at 10ps intervals,
  followed by energy minimization of each
• Analysis of resulting conformations to identify
  the lowest energy, geometrically and
  stereochemically allowed conformations

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MD simulations of following peptides were carried
out

• B Cell Epitopes
• SENHGNYSAQVGASQ
• NHGNYSAQVGASQ
• YSAQVGASQ
• YSAQVGASQAAKFT
• NHGNYSAQVGASQAAKFT
• 149 168
• SENHGNYSAQVGASQAAKFT

T-helper Cell Epitope 436 445 SIGKAVHQVF

• Chimeric BTh Cell Epitope With Spacer
• SENHGNYSAQVGASQAAKFTSIGKAVHQVF

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Structural comparison of Egps of Nakayama and Sri
Lanka strains of JEV. Single amino acid
differences are highlighted.
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Ts18 epitope mapping 13-mers window skipping 3
aminoacids
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Ts18 MHC II epitope profiles for different alleles
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Ts18 MHC I and MHC II consensus profile
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Ts18 modeled 3D structure