1 / 36

Drug design overview. Introduction of methodology. Examples: drug resistance, toxicity prediction.

SMA5422: Special Topics in Biotechnology Lecture 10: Computer aided drug design: structure-based approach. Chen Yu Zong Department of Computational Science National University of Singapore. Drug design overview. Introduction of methodology. Examples: drug resistance, toxicity prediction.

eketner
Download Presentation

Drug design overview. Introduction of methodology. Examples: drug resistance, toxicity prediction.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SMA5422: Special Topics in Biotechnology Lecture 10: Computer aided drug design: structure-based approach.Chen Yu ZongDepartment of Computational ScienceNational University of Singapore • Drug design overview. • Introduction of methodology. • Examples: drug resistance, toxicity prediction.

  2. Traditional Drug Design Methods: Random screening • Long design cycle: 7-12 years. • High cost: $350 million USD per marketed drug. Drug Discovery Today 2, 72-78 (1997) Too slow and costly to meet demand.

  3. Strategies for improving design cycle: • Smart screening: • High-throughput robotic screening. • Diversity of chemical compounds: • Combinatorial chemistry. Nature 384 Suppl., 2-7 (1996) High expectation.

  4. Alternative approach? • Current situation: • Molecular mechanism of disease processes, structural biology. • Rising cost of experimental equipment and resources. • Computer revolution (low cost, high power). • Software development. Computer approach?

  5. Strategies for improving design cycle: • Computer-aided drug design: • Receptor 3D structure unknown: • QSAR. Pharm. Res. 10, 475-486 (1993). • Receptor 3D structure known: • Ligand-protein docking. Science 257, 1078-1082 (1992)

  6. Is ligand-protein docking practical? • 3D structure of proteins and small molecules: • 15,000 protein entries in PDB, growth rate: ~100-200 per month. • 250,000 small molecules in ACD. • Computation time: • 100,000 small molecules per week. Nature 384 Suppl., 23-26 (1996) • Computer cost: • Decreasing dramatically.

  7. Success Stories: • HIV-1 Protease Inhibitors: • Inverase (Hoffman-LaRoche, 1995) • Norvir (Abbot, 1996) • Crixivan (Merck, 1996) • Viracept (Agouron, 1997) Drug discovery today 2, 261-272 (1997)

  8. Examples of Other Drugs Designed by Structure-Based Methods: • Human renin inhibitor Antihypertension. • Collagenase and stromelysin inhibitor Anticancer and antiarthritis. • Purine nucleotide phosphorylase inhibitor Antidepressant. • Thymidylate synthase inhibitor Antiproliferation. Nature 384 suppl, 23-26 (1996)

  9. Human Genome Project Protein Crystallography Functional Genomics Pharmacogenomics Molecular Biology Modeling Technology Information Technology Ligand-Protein Docking Favourable Conditions forApplication of Ligand-Protein Docking

  10. Computer-aided drug design in Industry andPremier Universities • Pharmaceutical Giants: • Merck, Abbott, Bristol-Myers Squibb, Pfizer, Glaxo-Welcome. • Biotech New and Emerging Stars: • Agouron, Arris, Chiron, ISIS, MetaXen, Vertex. • Major Universities: • Harvard, UCSF, UC Berkeley, Washington U, Cambridge, Columbia.

  11. Computer-aided drug design in Industry • Structure-based design viewed as having competitive edge: • An indication: Companies are withholding 3D structures of key proteins. • Modeling group viewed as a key component in drug discovery team: • Many companies have setup modeling group. • Investment in computer equipment: • An indication: Glaxo-welcome bought 100 SGI workstations in 1996.

  12. Ligand-Protein Docking is the Most Rational Approach:Reason:Based on receptor structure • Mechanism of drug action:

  13. Mechanism of drug action:

  14. Mechanism of drug binding:

  15. Ligand binding mechanism

  16. .

  17. .

  18. .

  19. .

  20. Scoring Functions in Ligand-Protein Docking Potential Energy Description:

  21. Scoring Functions in Ligand-Protein Docking Potential Energy Description:

  22. Scoring Functions in Ligand-Protein Docking • Potential Energy Description: • van der Waals interactions • Electrostatic interactions V = ligand atoms [ Aij1/2 Arec- Bij1/2 Brec+ qiQrec ]

  23. Modelling Strategy for Ligand-Protein Docking Average CPU time: 5,000 small molecules per week

  24. The Use of Molecular Mechanics Energy Functions in Docking Evaluation • Potential Energy Description: • Hydrogen bonding • van der Waals interactions • Electrostatic interactions • Empirical solvation free energy (energy evaluation only) V = H bonds [ V0 (1-e-a(r-r0) )2 - V0 ] + non bonded [ Aij/rij12 - Bij/rij6 + qiqj /r rij] + atoms iDsi Ai

  25. Example 1: Study of Drug Resistant Mutations by Ligand-Protein Docking Enzyme-inhibitor PDB Id Mutation introduced HIV-1 protease + MK 639 1HSG V82A, V82F, V82I, I84V, V82f/I84V, M46I/L63P, V82T/I84V, M46I/L63P/V82T/I84V HIV-1 protease + Saquinavir 1HXB V82F, V82I, I84V, G48V, V82F/I84V, V82T/I84V HIV-1 protease + SB 203386 1SBG I32V/V47I/I82V HIV-1 protease + VX 478 1HPV M46I/L63P, V82T/I84V, M46I/L63P/V82T/I84V HIV-1 protease + U89360e 1GNO V82D, V82N, V82Q, D30F HIV-1 RT + Nevirapine 1VRT L100I, K103N, V106A, E138K, Y181C, Y188H HIV-1 RT + TIBO R82913 1TVR L100I, K103N, V106A, E138K, Y181C, Y188H J. Mol. Graph. Mod. 19, 560-570 (2001).

  26. Quality of Modelled Structures Wild type X-ray structure: Blue Modelled mutant: Red Mutant X-ray structure: Green

  27. Mutation induced energy change compared with observed drug resistance data J. Bio. Chem.271, 31947 (1996) AIDS 12: 453 (1998) Biochemistry 37, 8735 (1998)

  28. Modelling Strategy for Ligand-Protein Induced Fit: Generation of multiple conformations

  29. Example 2: Prediction of toxicity, side effect, pharmacokinetics and pharmacogenetics by a receptor-based approach Annu. Rev. Pharmacol Toxicol 2000, 40:353-388 1997, 37:269-296 Pharmacological Rev. 2000, 52:207-236

  30. Importance of prediction of side effect, toxicity, pharmacokinetics in early stages of drug discovery • Most drug candidates fail to reach market • Pharmacokinetics (60%), side-effect and toxicity (40%) are the main reason. • Large portion of money (USD$350 million) and time (6-12 years) spent on a clinical drug has been wasted on failed drugs. Drug Discov Today 1997; 2:72 Drug Candidates in Different Stages of Development Majority of Candidates Fail to Reach Market Clin Pharmacol Ther. 1991; 50:471

  31. Strategy Proteins 2001;43:217 Science 1992;257: 1078

  32. Feasibility Proteins • Database: >14,400 3D structures in PDB. • Protein diversity: 17% in PDB with unique sequence. • Advance in structural genomics: 10,000 unique proteins within 5 years. Ann. Rev. Biophys. Biomol. Struct. 1996; 25:113 Nature Struct. Biol. 1998; 5:1029 Method • Ligand-protein docking docking algorithms capable of finding binding conformations. Proteins. 1999; 36:1 Proteins 2001; 43:217 Additional information • Rapid accumulation of knowledge in proteomics, pathways, protein functions. Computer resources • Increasing power and decreasing cost (Linux PC, Multi-processor machines)

  33. Automated Protein Targets IdentificationSoftware INVDOCK

  34. Compound Number of experimentally confirmed or implicated toxicity targets Number of toxicity targets predicted by INVDOCK Number of toxicity targets missed by INVDOCK Number of toxicity targets without 3D structure or involving covalent bond Number of INVDOCK predicted toxicity targets without experimental finding Aspirin 15 9 2 4 2 Gentamicin 17 5 2 10 2 Ibuprofen 5 3 0 2 2 Indinavir 6 4 0 2 2 Neomycin 14 7 1 6 6 Penicillin G 7 6 0 1 8 Tamoxifen 2 2 0 0 4 Vitamin C 2 2 0 0 3 Total 68 38 5 25 29 INVDOCK Testing on Toxicity Targets J. Mol. Graph. Mod., 20, 199-218 (2001).

  35. PDB Protein Experimental Finding Target Status Toxicity/Side Effect Ref 1a42 Carbonic anhydrase II Activate enzyme activity that may lead to increase in plasma bicarbonate concentration. Implicated Metabolic alkalosis (hypoventilation). Puscas I 1a6a HLA-DR3 Change in HLA level Implicated Aspirin-induced asthma Dekker JW 1a7c Plasminogen activator inhibitor Tissue-dependent response of protein. Implicated Hypertension, thrombolysis Smokovitis A 1d6n Hypoxanthine-guanine phosphoribosyltransferase Excess uric acid in serum* 1hdy Alcohol dehydrogenase Inhibition of activity Confirmed Increased blood alcohol level Gentry RT Toxicity and side effect targets of Aspirin identified from INVDOCK search of protein database J. Mol. Graph. Mod., 20, 199-218 (2001).

  36. Conclusion: • Structure-based computer aided drug design is a promising approach. • Revolution in molecular biology and computer technology sets the stage for this approach. • Much remains to be done.

More Related