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Drug Discovery Today: Fighting TB with Technology

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Drug Discovery Today: Fighting TB with Technology

  1. 1. Desktop Drug Discovery and Development rational drug discovery computer-aided drug design (CADD) computational drug design computer-aided molecular design (CAMD) computer-aided molecular modeling (CAMM) in silico drug design computer-aided rational drug design ! jbbillones KeyNotes Junie B. Billones, Ph.D. Department of Physical Sciences and Mathematics College of Arts and Sciences and Institute of Pharmaceutical Sciences National Institutes of Health University of the Philippines Manila The Health Sciences Center AKA
  2. 2. ! jbbillones KeyNotes Discovery by ‘trial and error’ mold Alexander Fleming (1928) Penicillium notatum Penicillin - first miracle drug Amoxicillin (1972)
  3. 3. Bovet (1937) conducted over 1000 expts to come up with first antihistamine. Laboratory Chemicals Histamine ! jbbillones KeyNotes Discovery by ‘trial and error’ The Antihistamines Diphenhydramine (1943) Chlorpheniramine (1950) an SSRI too! (1969) Promethazine (1940s)
  4. 4. ! jbbillones KeyNotes Drug Discovery and Development http://thirusaba.blogspot.com 5000 workers, USD 800 M, 12 years
  5. 5. Our Approach: Rational Drug Discovery ! jbbillones KeyNotes Rational Drug Discovery Kapetanovic, IM. Chemico-Biological Interactions 171 (2008) 165–176
  6. 6. ! jbbillones KeyNotes Rational Drug Discovery http://thirusaba.blogspot.com
  7. 7. ! jbbillones KeyNotes Rational Drug Discovery Tang et al. (2006) Drug Discovery Today: Technologies, 3(3), 307. Disease-related genomics Target identification Target validation Lead discovery Lead optimization Preclinical tests Clinical trials Computer-Aided Drug Discovery - Reverse docking - Bioinformatics - Protein structure prediction - Target druggability - Library design - Docking Scoring - De novo design - Pharmacophore - Target flexibiity - QSAR - Structure-based optimization - In silico ADMET prediction - Physiologically-based pharmacokinetic (PBPK) simulations
  8. 8. ! jbbillones KeyNotes Target Identification and Validation
  9. 9. Li et al, PLoS One, 5(7) 2010 ! jbbillones KeyNotes Protein Target Prediction DrugCIPHER For a query chemical, each protein in the PPI network (genome-wide) is assigned three concordance scores based on the different regression models. The protein with large concordance scores is hypothesized to be the target proteins.
  10. 10. ! jbbillones KeyNotes Lead Discovery
  11. 11. ! http://www.proxychem.com jbbillones KeyNotes Lead Optimization
  12. 12. ! (cell/enzyme) jbbillones KeyNotes Preclinical Tests
  13. 13. Protein Structure Known Unknown ! jbbillones KeyNotes Strategies in Lead Discovery http://thirusaba.blogspot.com Structure- Based Design Ligand- Based Design De Novo Design Library Design HTS Unknown Known Ligand Structure
  14. 14. ! jbbillones KeyNotes Protein Structure-Based Drug Design
  15. 15. ! jbbillones KeyNotes Protein Structure Prediction
  16. 16. Example of a Forcefield How do we calculate the energy of a ! http://alexandrutantar.wordpress.com jbbillones KeyNotes conformation?
  17. 17. ! jbbillones KeyNotes Ligand Structure Optimization
  18. 18. ! jbbillones KeyNotes Pharmacophore Generation Receptor-based Pharmacophore Pharmacophore - the spat ial arrangement of chemical groups that determine its activity
  19. 19. Pharmacophore Generation ! jbbillones KeyNotes Ligand-based Pharmacophore Niu et al. (2012) Chemical Biology and Drug Design, 79(6), 972.
  20. 20. ! jbbillones KeyNotes Virtual Screening
  21. 21. Energy component methods - based on the assumption that the free energy of binding interaction can be decomposed into a sum of individual contributions: (e.g., LUDI,ChemScore, GOLD, AutoDock) ! jbbillones KeyNotes Knowledge-based scoring functions - using statistics for observed interatomic contact frequencies and or distances in a large database of structures (e.g., PMF, DrugScore, SmoG, Bleep) Example: Molecular Docking
  22. 22. Virtual Screening Results ! jbbillones KeyNotes Rank-ordered list of hits #1 #2 #3 #4
  23. 23. Product of Structure-based RDD ! jbbillones KeyNotes The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Nelfinavir in the active site of HIV-1 protease: AIDS drug nelfinavir (brand name Viracept) is one of the drugs on the market that can be traced directly to computer-aided structure-based methods.
  24. 24. Drugs derived from structure-based approaches Capoten Captopril ACE Hypertension 1981 Bristol- ! Myers Squibb jbbillones KeyNotes Trusopt Dorzolamide Carbonic anhydrase Glaucoma 1995 Merck Viracept Nelfinavir HIV protease HIV/ AIDS 1999 Agouron (Pfizer) and Lilly Tamiflu Oseltamivir Neuraminidase Influenza 1999 Gilead and Roche Gleevec Imatinib BCR- Abl Chronic myelogenous leukaemia 2001 Novartis
  25. 25. ! jbbillones KeyNotes De Novo Drug Design A. Binding site comprising three binding pockets B. Crystallographic screening locates molecular fragments that bind to one, two or all three pockets C. A lead compound is designed by organizing all three fragments around a core template D. Growing out of a single fragment
  26. 26. ! jbbillones KeyNotes De Novo Drug Design Growing Linking
  27. 27. ! jbbillones KeyNotes Quantitative Structure-Activity Relationship QSAR Biological activity = (0D + 1D + 2D + 3D + 4D) (IC50, Ki, MIC) molecular properties
  28. 28. ! jbbillones KeyNotes Quantitative Structure-Activity Relationship 0D 1D 2D 3D 4D atom count molecular weight sum of atomic properties fragment counts topological descriptors geometrical atomic coordinates energy grid combination of atomic coordinates and sampling of conformations e.g. # of OH # of NH e.g. Weiner index Harrary index Over 4000 descriptors can be calculated by Dragon software
  29. 29. ! jbbillones KeyNotes Quantitative Structure-Activity Relationship
  30. 30. ! jbbillones KeyNotes QSAR Study of Curcuminoids
  31. 31. Current Rational Drug Discovery Efforts in UP Computer-Aided Discovery of Compounds for the Treatment of Tuberculosis Billones, JB* et al. (EIDR 2012-2016) ! jbbillones KeyNotes in the Philippines 5 million compounds Vistual Screening Molecular Docking De Novo elaboration Chemical synthesis Bioassay Pantothenate synthetase (involved in synthesis of Vit B5 for growth) FtsZ (involved in bacterial cell division) lipB (involved in cofactor synthesis, Essential for growth) menB (involved in synthesis of Vit K2 for growth)
  32. 32. MTB PutativeDrug Targets Mtb Target Enzymes LipB BioA Ldt
  33. 33. Lipoate Protein Ligase B (LipB) catalyzes the biosynthesis of lipoate, a cofactor responsible for the activation of key enzymes in the Mtb metabolic pathway (Spalding et al. 2010) Mtb has no known back-up mechanism that can take over the role of LipB in its metabolic machinery (Rawal et al. 2010) lipB knockout model fails to grow significantly up-regulated in MDR-TB patients (Rachmann et al. 2005)
  34. 34. Structure-based Screening (A) Defined binding sphere (red) on the binding site of LipB. (B) Structure-based pharmacophore model based on the defined binding site of LipB. (A) Three dimensional structure of lipoate protein ligase B (LipB). (B) Molecular overlay of downloaded protein structure (blue) and prepared protein structure (pink); (RMSD = 0.71 Å). Billones et al. Orient. J. Chem., 29(4), 1457-1468 (2013)
  35. 35. Virtual Screening against LipB In silico ADMET filters 19 compounds Virtual Screening (rigid > flexible > docking) 131 compounds 5,347,140 compounds For cytotoxicity assay
  36. 36. Compound 5 Database I Natural Compounds Compound 1 Database I Compound 2 Database I The structures are concealed in accordance with patent rules. Compound 3 Database A Compound 4 Database A
  37. 37. Semi-Synthetic Compounds Compound 6 Database A Compound 7 Database A Compound 8 Databse A Compound 9 Database A The structures are concealed in accordance with patent rules.
  38. 38. Synthetic Compounds Compound 10 Database Z Compound 11 Database D Compound 12 Database D Compound 13 Database E The structures are concealed in accordance with patent rules.
  39. 39. In Silico ADMET Evaluation • Absorption • Distribution • Metabolism • Excretion • Hepatotoxicity ADMET Cheng Susnow and Dixon, 2003, and Dixon, 2003) • Carcinogenicity • Mutagenicity • Developmental Toxicity • Irritancy • Skin sensitivity • Aerobic Biodegradability • etc. TOPKAT Enslein K, Gombar V, Blake B, 1994
  40. 40. ADMET Properties Compound Carcinogenicity Mutagenicity Developmental Toxicity Potential Absorption Solubility CYP2D6 Inhibition Plasma Protein Binding Hepatotoxicity NSC68342 1.000 0 1.000* Low absorption Optimum solubility Inhibitor Binding is >90% Toxic NSC96317 1.000* 0 0 Very low absorption Good solubility Non-inhibitor Binding is <90% Toxic NSC118483 1.000* 0 0.998 Very low absorption Yes, optimal solubility Non-inhibitor Binding is >90% Non-toxic NSC118476 1.000 0 1.000 Very low absorption Yes, optimal solubility Non-inhibitor Binding is <90% Toxic NSC118473 0 0 0.959* Very low absorption Yes, optimal solubility Non-inhibitor Binding is >95% Toxic NSC164080 0 0 0.204 Good absorption Yes, good solubility Non-inhibitor Binding is >90% Toxic NSC211851 0 0 0.001 Very low absorption No, too soluble Non-inhibitor Binding is <90% Toxic NSC227190 0.999 0.265 1.000+ Very low absorption Yes, good solubility Non-inhibitor Binding is >95% Toxic NSC245342 0.001 1.000 1.000+ Very low absorption Yes, good solubility Non-inhibitor Binding is >95% Toxic TOPKAT VALUES: 0 – 0.29: Low probability; 0.30 – 0.69: Indeterminate; 0.70 – 1.00: High Probability; *Within Optimum Prediction Space (OPS) and OPS limit, and the probability value can be accepted with confidence; +Outside of OPS but within OPS limit
  41. 41. Next Step: Cytoxicity Assay
  42. 42. Next Step: Synthesis of Lead Variants
  43. 43. ! jbbillones KeyNotes Logout For queries: jbbillones@up.edu.ph