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Use of bioinformatics in drug development and diagnostics

Use of bioinformatics in drug development and diagnostics

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Use of bioinformatics in drug development and diagnostics

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  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 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. 0 2 4 6 8 10 12 14 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. Crystal structures of target and target/inhibitor complexes Screen recombinant target with available inhibitors Synthesize modifications of lead compounds Identify lead compounds

  6. 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 UniqueGenes 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. 7:976-949.

  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 MethodsSequence Identity with known structures • ab initio 0-20% • Fold recognition 20-35% • 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 Lead Lead Optimization Active site

  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 N C X Y H

  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. 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 (0.3-0.5A) 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. 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 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-1995-96, UK/France • Dengue fever-1994-97,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-1995-96,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-1995-97,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 O157-1982-1997,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)-1995-1997,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 0139-1992,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 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.

  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.

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

  44. 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

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

  46. 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 *

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