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Session 1 part 3


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  • Untreated cortical neurons (overlay of green (Neurofilament), red (TUJ-1), and blue (Hoechst)). Cortical neurons treated with WAY-265920 .
  • mention hot spots, etc.)
  • Transcript

    • 1. An overview of the drug discovery process “ Hit to Lead” Nature Review Drug Discovery,8, 892 2009.
    • 2. From Hit to Lead
      • Hits from HTS screening- may have many potential scaffolds
        • Hit-to-lead involves synthesis of many compounds to determine what is important
        • Need to see if there is room to improve the compound
      Synthesis HTS HIT/Natural Product Essential scaffold Synthesis Potential lead compound
    • 3. Hit to lead – fragment evolution Nature Reviews Drug Discovery 3, 660-672 (August 2004) Fragment evolution – aided by structure of fragment in the protein Essential fragment Synthesis to increase potency Potential lead compound
      • Hits from Fragment based screening- may have many potential scaffolds
        • Hit-to-lead involves synthesis to expand the core to move from binding to activity
        • Most efficient when aided by structure-based methods
    • 4. From Hit to Lead
      • For a hit to become a lead it must:
        • Show structure-activity relationships (SAR)
          • Activity should be sensitive to structure
            • Losing activity is NOT a negative result!
        • The compound should have handles for reactivity
          • Able to modify
            • Most scaffolds are retained during optimization
          • Compounds should be simple
            • Stereocenters = cost
        • Should show activity in a cellular assay (or in vivo)
          • Can your hits get into a cell or a target tissue?
        • Should show lead-like molecular properties
          • Expedite and simplify further optimization
    • 5. Lead Optimization Nat Rev Drug Disc 2, 369-78, 2003 Medicinal chemist In vivo efficacy is key
    • 6. An overview of the drug discovery process
    • 7. Medicinal Chemistry Refinement Synthesis of compounds Screen for activity AND/OR Screen against activity AND/OR Screen for ADME Data Analysis (SAR trends) Refinement of criteria Planning Many compounds must be made! What are the strategies used for efficient synthesis? What tools are in the chemists’ synthetic toolbox?
    • 8. Approaches to synthesis - discovery
      • Compounds are made in bunches, not as single efforts
        • The more molecules made at once, the better to understand trends i n efficacy, physicochemical properties, etc.
        • If one compound fails to show the expected in vivo pharmacology , others are there to fall back on-
          • Is it the scaffold?
          • Is it the target?
            • Without a variety of lead compounds, you won’t know!
        • Compounds may show similar activity, but vary greatly in selectivity, or ADME properties
          • Making series of compounds helps to spot trends to guide future research
      • Parallel synthesis of groups of compounds made by facile reactions from a common intermediate
          • Allows response to biological data with the shortest turnaround time possible
    • 9. A case study for library design R. J. Gillespie et al. / Bioorg. Med. Chem. 17 (2009) 6590 – 6605 A diversifiable scaffold with three synthetic handles Facile coupling reactions with commercially available amines create a library to explore space around this position The more reactive chloride can be replaced with various groups through carbon-carbon bond formation The chloride can be substituted with various heteroatoms and groups Straightforward chemistries and commercial reagents allow for rapid diversification Prioritization is necessary
    • 10. An overview of the drug discovery process
    • 11. S ynthesis of an active pharmaceutical ingredient (API)
      • Syntheses that are scalable from gms to kgs
      • Syntheses that avoids metals, such as Pd
        • Metal impurities must be minimal in the final compound
        • Removal of metals can be very expensive
      • Syntheses that can be purified easily
      • Salt forms are often used as APIs due to their greater stability and solubility
      As the f ocus of chemistry efforts shift from making a library of many compounds to making large amounts of one compound , strategies change
    • 12. Discovery synthesis vs API synthesis: A case study The chosen compound 5 has a m ethyl group added in the last step via a Pd catalyzed reaction as part of a parallel chemistry scheme
    • 13. Synthetic scheme for compound 5 as an API W. Hu et al. / Bioorg. Med. Chem. Lett. 17 (2007) 414–418 Methyl group is set early in the synthesis via a cyclization reaction “ Green chemistry”
    • 14. Summary
      • The path to drug discovery begins with the selection of the library picked for screening
        • Libraries should be chosen for the same reasons that compounds are chosen later in development
      • There are a variety of complimentary ways to get hits
      • Optimization of hits toward clinical candidates
        • Increase of potency and selectivity
        • Increase of in vivo efficacy
          • Maintenance of potency and selectivity; optimization of other factors
            • Incorporation of drug-like molecular property filters in the front end of discovery facilitates this process
      • Chemists use standard tools in drug discovery regardless of the therapeutic area
        • Pattern recognition
        • Parallel chemistries
    • 15. Conclusions
      • Many factors influence all steps of drug discovery, from choosing how to find a hit to choosing a clinical candidate
      • Drug discovery chemistry works to find compounds that are potent and selective with ADME properties that forecast in vivo efficacy in the clinic
      • Discovery synthesis and design should be efficient and make the best compounds possible to guarantee success
      • Chemistry efforts are led by biological results
      • Constant communication and feedback between team members of different disciplines gives the best chance to overcome the many obstacles and to succeed in the discovery of an efficacious drug
    • 16. Thank you for your attention!
    • 17.
    • 18. A structure – toxicity study - A 2A antagonists A2A binding: 2.8 nm A1 binding: 601 nm 3mg/kg p . o . efficacious in vivo for anti-cataleptic activity Molecular Weight: 449.51 log P: 3.33 tPSA: 100.51 hERG inhibition of 81% Maintain potency and selectivity while decreasing hERG % inhibition J. J. Matasi et al. / Bioorg. Med. Chem. Lett. 15 (2005) 3670–3674 J. J. Matasi et al. / Bioorg. Med. Chem. Lett. 15 (2005) 3675–3678
    • 19. Natural Products as Drug Starting Points Frank E. Koehn 6 th Drug Discovery for Neurodegeneration February 13 th , 2012 New York, NY
    • 20. Just What in Fact, is a Natural Product?
      • ~ 300,000 distinct compounds from microbes, plants, and other organisms
      FK-506- fujimycin Streptomyces tsukubaensis palytoxin Palythoa tuberculosum aureomycin Streptomyces sp. nicotine Nicotiana tabacum
    • 21. Natural products- A major impact on drug discovery
      • Liberal analysis - 47% of New Chemical Entities 1940-2006 are “ Natural Product Derived ”
      • Conservative analysis - 1970-2012 - 58 approved NCE’s came directly from natural products
          • 10% of all drugs over last 10 years (19 of 200)
          • Native molecules- 27, analogues- 31
          • Sources: microorganisms> Plants>> marine sources
      • Unique Challenges with NPs.
          • Accessibility - synthetic manipulation
          • NP extracts- Isolation is slow, resource-intensive
          • Pure NP libraries- difficult to enable
      J. Med Chem. 2009, 52 1953-1962, Curr. Opin. in Chem. Biol, 2008, 12 :306-317
    • 22. Targets, Libraries and Screening Strategies
      • Chemical Space - Exceeds 10 60 compounds with less than 500 MW
      • Not all chemical space is biologically relevant!
      • To screen effectively- screen the biologically relevant part of chemical space
      • Natural products are privileged (biased to occupy biologically relevant chemical space)
      Predicted score plot of NP and medicinally active WOMBAT compounds. Rosen, et. al., J. Med. Chem. 2009, 52, 1953–1962
    • 23. Screening for Lead Generation Target Compounds Biochemical HTS (Single target) Target-compound binding Phenotypic Screening (many targets) NP chemical Library Phenotypic response New target & mechanism Cell
    • 24. Screening and Natural Products Library Design minutes ABSORBANCE 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 . Media components polar metabolites & biopolymers Lipids, fatty acids non-polar biopolymers Crude Extract Library Fractions/extract Library size per culture Low Assay interferences High Sample prep Low Redundancy High Hit identification Slow Sensitivity 10X Pre-fractionated Library 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 0 Moderate Moderate Moderate Moderate Moderate 100X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 Pure Compound Library Moderate Low High Very Low Rapid 10 Liter Fermentation 100 Liter Fermentation optimized
    • 25. The Challenge- Tougher Targets
      • The Rule of Five (Ro5) has guided the design of compounds into privileged ADME space
      MW < 500 Da ClogP < 5 HBD < 5 HBA (N, O) < 10 Good Fraction Absorbed (Solubility, Permeability) Low Clearance Oral Bioavailability Excellent strategy for many targets….. But not for targets involving protein-protein interactions
    • 26. The “Druggable” Genome - Hopkins Highly “Druggable” targets, Ro5 leads Disease relevant “Undruggable” biological targets, Beyond Ro5 leads Very Limited Overlap Hopkins, A.L., Groom, C.R. “The druggable genome” Nat. Rev. Drug Discov., 2002, 1(9) 727-30.
    • 27. Natural Products are Successful Therapeutics in the Beyond Ro5 Space Selected Orally Active BRo5 Natural Product Drugs NP Lead, year NCEs Indication/MOA MW ClogP HBD HBA Oral Bioavailability Dose Validamycin, 1970 Acarbose, 1990 Voglibose, 1994 Anti-diabetic/glucosidase inhibitor 498 -6.2 13 14 25 mg Midecamycin, 1971 Miocamycin, 1985 Antibacterial/protein synthesis inhibitors 815 3.5 4 16 100% 600 mg Rapamycin, 1974 Sirolimus, 1999 Everolimus, 2004 Zotarolimus, 2005 Temsirolimus, 2007 Immune suppression/mTOR 914 7.0 3 14 20% 2 mg Cyclosporine A, 1975 Cyclosporine, 1983 Immune suppression /IL-2 inhibitor 1203 14.4 5 23 30% 25 mg Lipstatin, 1975 Orlistat, 1987 Obesity/Lipase inhibitor 492 7.6 1 6 120 mg Avermectin B1a, 1979 Ivermectin, 1987 Antiparasitic/Glutamate-gated chloride channel 873 5.1 3 14 100% 3 mg FK506, 1984 Tacrolimus, 1993 Immune suppression/T-lymphocyte activation inhibitor 804 5.8 3 13 20% 1 mg Myriocin Gilenya, 2010 Multiple sclerosis/S1P1 inhibitor 402 2.8 6 7 93% 0.5 mg
    • 28. Recent Synthetic Natural Product Derived Drugs Myriocin Mycelia sterilia Fingolimod Halichondrin B Halichondria okadai Eribulin
    • 29. PKS Engineering of Rapamycin 1) Gregory, M.A. and Leadlay, P.F. et al., Angew. Chem. Int. Ed. 2005, 44, 4757-4760. 2) Gregory, M. A. and Leadlay, P.F. et al., Org. & Biomol. Chem. 2006, 4, 3565-3568. rapamycin X X methylation and oxidation Pipecolate Incorporating Enzyme
    • 30. Rationale for NP Biological Bias is Based on Protein Fold Space Properties
      • Protein sequence space is essentially infinite- at 300 aa, possible sequences = 20 300 >>> than particles in known universe (10 80 )
      • Total complement of estimated world proteome 10 10
      • Most proteins resemble other proteins - built by amplification, recombination, divergence from a basic set of folding units- domains
      • Around 100 domain families have been recognized by sequence
      • Only ca. 1000 folds are populated in nature
      • Subdomain level - recurrent local arrangements of secondary structures
      • Biophysical constraints limit the number of folded conformations
    • 31. Characteristics of Protein folds
      • Distinct sequences often adopt very similar folds
      • Highly similar sequences can adopt very different folds
      • Identical peptide sequences can have different conformations in different proteins
      • A single protein chain may encode for more than one structural domain.
      • Similar domains are formed via different “methods”
      • Structure is conserved far more than sequence .
    • 32. Distinct Sequences Often Adopt Very Similar Folds Superposition of 3 proteins of similar structure but distinct sequences. 1 -Isomerase from Rhodopseudomonas palustris 2 - B chain of limonene-1,2-epoxide hydrolase from Rhodococcus erythropolis 3 - Putative polyketide cyclase from Acidithiobacillus ferrooxidans a) 1 and 2 b) 2 and 3 c) 1 and 3 <20% sequence identity in aligned regions Regions of overlap in protein 1 Regions of overlap in protein 2 A- Proteins with virtually identical structure and little or no sequence similarity Current Opinion in Structural Biology 2009, 19:312–320, J Biol Chem 2009, 284:992-999 B- Proteins with high sequence similarity and no structure similarity Arl2 (BART) from Homo sapiens and ADP-ribosylation factor-like protein 2-binding protein from Danio rerio – 72%
    • 33. Domains in Related Enzymes can be Formed in Distinctly Different Ways
      • Dimerization domain of GDP-mannose dehydogenase from P. aeruginosa
      • (b) Central dimerization domain of UDP-glucose dehydrogenase from S. pyogenes
      • (c) Single chain domain of ovine 6-phosphogluconate dehydrogenase The blue and yellow fragments highlight the correspondence with the chains shown in (b).
      Current Opinion in Structural Biology 2009, 19:312–320
    • 34. Natural Products Bind Proteins
      • As substrates for via PKS, NRPS, tailoring enzymes, etc.
      • Outcome of selective pressure to binding protein and cellular targets
      • Domains of these fold targets are conserved in the “protein foldome”
      • Natural product ligands leverage these properties in their mechanism and properties
      Natural products, by virtue their origin, are within or at least proximal to, biologically relevant chemical space.
    • 35. Polyketide Immunophilin Ligand Family Salituro, G. et. al., Tet. Lett., 1995 , 36(7), 997-1000 Summers, M.Y.; Leighton, M.; Liu, D.; Pong, K.; Graziani, E.I., J. Antibiot., 2006 , 59(3), 184-189.
    • 36. Natural Products lead to Unanticipated Drug Targets and Mechanisms FKBP binding domain mTOR effector domain Sehgal, S.N.; Baker, H.; Vézina, C., J. Antibiot., 1975, 28(10), 721-726. Choi, J.; Chen, J.; Schrieber, S.L.; Clardy, J., Science, 1996, 273, 239-241.
      • Rapamycin binds tightly to FKPB12 via FKBP binding domain
      • Rapa-FKBP12 complex binds mTOR, disrupting TORC1 complex
      mTOR FKBP-12 RAPAMYCIN Natural products, by virtue their origin, are within or at least proximal to, biologically relevant chemical space!