1. Use of bioinformatics in drug development and diagnostics

2. 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 to 5,000 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. 2 4 6 8 10 12
Years 16
Source: Tufts Center for the Study of Drug Development

3. 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

4. Rational Approach to Drug Discovery
Identify target
Clone gene encoding target
Express target in recombinant form

5. Identify lead compounds
Crystal structures of target and target/inhibitor complexes
Screen recombinant target with available inhibitors
Synthesize modifications of lead compounds
Identify lead compounds

6. Toxicity & pharmacokinetic
Synthesize modifications of lead compounds
Identify lead
compounds
Toxicity & pharmacokinetic
studies
Preclinical trials

7. 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

8. 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.

9. 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

10. 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

11. Impact of Structural Genomics on Drug Discovery
Dry, S. et. al. (2000) Nat. Struc.Biol. 7:

12. 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

13. 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.

14. Sequence identities & Molecular Modeling methods
Methods Sequence Identity with known structures
ab initio %
Fold recognition %
Homology Modeling >35%

15. 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

16. 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.

17. Lead Optimization
Active site
Lead
Lead Optimization

18. 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 X N C H Y

19. Factors Affecting The Affinity Of A Small Molecule For A Target Protein
LIGAND.wat n +PROTEIN.wat n LIGAND.PROTEIN.watp+(n+m-p) wat
HYDROGEN BONDING
HYDROPHOBIC EFFECT
ELECTROSTATIC INTERACTIONS
VAN DER WAALS INTERACTIONS

20. Slow Or No Development Of Resistance
DIFFERENCE BETWEEN AN INHIBITOR AND DRUG
Extra requirement of a drug compared to an inhibitor
LIPINSKI’S 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 P>5)
-The sums of N’s and O’s 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

21. Mecanismo antibacteriano de la PZA: Pro-droga

22. 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.
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.

23. 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:

24. 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

25. 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.

26. GRID Based Docking Methods
Grid Based methods
GRID (Goodford, 1985, J. Med. Chem. 28:849)
GREEN (Tomioka & Itai, 1994, J. Comp. Aided. Mol. Des. 8:347)
MCSS (Mirankar & Karplus, 1991, Proteins, 11:29).
Functional groups are placed at regularly spaced ( A) lattice points in the active site and their interaction energies are evaluated.

27. Folate Biosynthetic
pathway
DHFR

28. Multiple alignment of DHFR of
Plasmodium species

29. Drug binding pocket of L. casei DHFR

30. Antifolate drugs in the active site of DHFR L
Antifolate drugs in the active site of DHFR L. casei to show hydrogen bonding with surrounding residues
MTX
PYR
SO3
TMP

31. 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.

32. Sitio Activo de la pirazinamidasa

33. Docking P. Horikoshii – PZA en presencia de Zn

34. Additional Drug Target: glutathione-GR

35. Additional Drug
Target:
Thioredoxin
reductase (TrxR)

36. How Bioinformatics Aids in Vaccine Development / Peptide Vaccine Development Using Bionformatics Approaches

37. 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 , UK/France
Dengue fever ,Africa/Asia/Latin America/USA
Ebola virus-1994,Gabon;1995,Zaire;1996,United States(monkey)
Hantavirus-1993,United States; 1997, Argentina
HIV subtype O-1994,Africa
Influenza A/Beijing/32/92, A/Wuhan/359/95, HS:N1-1993,United States; 1995,China; 1997, Hongkong
Japanese Encephalitis-1995, Australia
Lassa fever-1992,Nigeria
Measles-1997, Brazil
Monkey pox-1997,Congo
Morbillivirus – 1994, Australia
O’nyong-nyong fever-1996,Uganda
Polio-1996,Albania
Rift Valley fever-1993,Sudan
Venezuelan equine encephalitis ,Venezuela/Colombia
West Nile Virus-1996,Romania
Yellow fever-1993,Kenya;1995,Peru

38. Emerging and re-emerging infectious diseases threats contd.,
Parasitic
African trypanosomiasis-1997,Sudan
Ancylcostoma caninum(eosinophilic enteritis)-1990s,Australia
Cryptosporiadiasis-1993+,United States
Malaria ,Africa/Asia/Latin America/United states
Metorchis-1996,Canada
Microsporidiosis-Worldwide
Fungal
Coccidiodomycosis-1993,United States
Penicillium marneffi

39. Emerging and re-emerging infectious diseases threats contd.
Bacterial
Anthrax-1993,Caribbean
Cat scratch disease/Bacillary angiomatosis(Bartonella 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 O ,United States;1996,Japan
Gonorrhea(drug resistant)-1995,United States
Helicobacter pylori(ulcers/cancer_-worldwide(discovered 1983)
Leptospirosis-195,Nicaragun
Lyme disease(Borrelia burgdorferi)-1990s,United states
Meningococcal meningitis(serogroup A) ,West Africa
Pertussis-1994,UK/Netherlands;1996,USA
Plague-1994,India
Salmonella typhimurium DT104(drug resistant)-1995,USA
Staphylococcus aureus(drug resistant)-1997,United States/Japan
Toxic strep-United States
Trench fever(Barnionella quintana)-1990s,United States
Tuberculosis(highly transmissible)-1995,United states
Vibrio cholerae ,Southern Asia

40. Types of Vaccines Killed virus vaccines Live-attenuated vaccines
Recombinant DNA vaccines
Genetic vaccines
Subunit vaccines
Polytope/multi-epitope vaccines
Synthetic peptide vaccines

41. 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 ISCOMs Recombinant 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-MHC class 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.

42. 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.

45. Epitopes …
B-cell epitopes
Th-cell epitopes

47. 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

48. Antigenic determinants of Egp of JEV Kolaskar & Tongaonkar approach

49. 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 *

50. Various transformations on side-chain orientation in a model tetrapeptide

51. Reverse Vaccinology Advantages Disadvantages
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.

52. Rappuoli 2001
Curr. Opin. Microbiol.

53. Rappuoli 2001
Curr. Opin. Microbiol.

54. Vaccine development
In Post-genomic era:
Reverse Vaccinology
Approach.

55. Global genomic approach to identify new vaccine candidates
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

56. Disease related protein DB Gene/Protein Sequence Database
In Silico Analysis
Peptide
Multitope
vaccines
VACCINOME
Candidate Epitope DB
Epitope prediction
Disease related protein DB
Gene/Protein Sequence Database

57. Synthetic Peptide Vaccine Design and Development of Synthetic Peptide vaccine against Japanese encephalitis virus

58. 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.

59. Model Refinement PARAMETERS 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 kcal/mol/A for CG
Biosym from InsightII, MSI and modules therein

60. 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.

62. Model Evaluation II: Ramachandran Plot

65. 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

66. 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 4*rij
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

67. Chimeric B+Th Cell Epitope With Spacer: SENHGNYSAQVGASQAAKFTSIGKAVHQVF
MD simulations of following peptides were carried out
B Cell Epitopes:
SENHGNYSAQVGASQ
NHGNYSAQVGASQ
YSAQVGASQ
YSAQVGASQAAKFT
NHGNYSAQVGASQAAKFT
SENHGNYSAQVGASQAAKFT
T-helper Cell Epitope:
SIGKAVHQVF
Chimeric B+Th Cell Epitope With Spacer:
SENHGNYSAQVGASQAAKFTSIGKAVHQVF

70. Single amino acid differences are highlighted.
Structural comparison of Egps of Nakayama and Sri Lanka strains of JEV.
Single amino acid differences are highlighted.

71. Ts18 epitope mapping
13-mers window skipping 3 aminoacids

72. Ts18 MHC II epitope profiles for different alleles

73. Ts18 MHC I and MHC II consensus profile

74. Ts18 modeled 3D structure