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Critical Analysis of Library Types for High Throughput Screening

Critical Analysis of Library Types for High Throughput Screening. Monica Wirz. March 11, 2010. Drug Discovery. Drugs improve the quality of life for billions worldwide. Drug discovery is long, tedious, and unpredictable. Low hit ratio.

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Critical Analysis of Library Types for High Throughput Screening

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  1. Critical Analysis of Library Types for High Throughput Screening Monica Wirz March 11, 2010

  2. Drug Discovery • Drugs improve the quality of life for billions worldwide • Drug discovery is long, tedious, and unpredictable • Low hit ratio • Large libraries enable screening of many compounds B. Hughes; Nature Reviews Drug Discovery,2010, 9, 89-92

  3. Drug Pipeline 1 FDA approved drug 10 000 Compounds • How do we build libraries to increase hit ratio? J. A.DiMasi, R. W. Hansen and H. G. Grabowski; J. Health Econ.,2003, 835, 1-35

  4. Ideal Library Generation • Today’s drug discovery environment calls for: • Rapid screening • Rapid hit identification • Rapid hit-to-lead development • Traditional programs face a competitive disadvantage • Need high molecular diversity within the boundaries of reasonable drug- like properties • Natural product structures have many molecular properties that are favourable as lead structures F. E.Koehn and G. T. Carter; Nature, 2005, 4, 206-220

  5. Main Library Types • Natural product libraries • Combinatorial chemistry libraries • Combinatorial chemistry libraries • Diversity-oriented synthesis libraries • Diversity-oriented synthesis libraries

  6. Natural Products • Half of new drugs are natural products or derived from natural products • 60% of anticancer drugs are natural products • 75% of infectious disease drugs are natural products • Natural products generate such hits because of evolutionary pressure for biological properties • Millions of natural products have yet to be discovered • 0.1% of all bacterial strains have been cultured and analyzed G. M. Cragg, P. G. Grothaus, D. J. Newman; Chem. Rev.,2003, 109, 3012-3043 C. M. Dobson; Nature,2004, 432, 824-828

  7. Privileged Structures • Bioactivity is a rare property to possess • Natural products reside in biologically relevant chemical space • They are libraries of prevalidated, functionally diverse structures • Natural products have evolved to optimize interactions with biomolecules • They can bind to various receptors • Natural products already possess some necessary ADMET properties • They are often stable, soluble and can • cross membranes R. Balamurugan, F. J. Dekker, H. Waldmann; Mol. BioSyst.,2005, 1, 36-45

  8. Privileged Structures • Privileged in a chemical sense • Balance of necessary flexibility and rigidity • Present functional groups in a favourable spatial arrangement • Privileged in a biological sense • Core structure binds to multiple targets • Specific functional groups give high selectivity • Hit rates for natural product in screens are 10-100 fold higher than in libraries generated on the basis of chemical feasibility 8 R. Balamurugan, F. J. Dekker, H. Waldmann; Mol. BioSyst.,2005, 1, 36-45

  9. Examples of Natural Products G. M. Cragg, D. J. Newman; Phytochem. Rev.2009, 8, 313-331 F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220

  10. Drug Discovery Process for Natural Products F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220

  11. Different types of Natural Product Libraries • Crude extracts libraries • Prefractionated (semi-pure) extracts libraries • Pure extracts libraries

  12. Crude Libraries • Inexpensive to prepare and minimal sample preparation time • High degree of diversity • Many drugs were discovered using this method • Rediscovery of known structures • Too viscous • Minor metabolites may go undetected • False positives • Chemically unattractive compounds isolated • Time- and resource-intensive follow-up needed M. M. Wagenaar; Molecules2008, 13, 1406-1426 F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220

  13. Pre-fractionated Libraries • Pre-fractionation is the fractionation of a crude extract prior to biological testing • Each fraction may vary in complexity from a mixture of multiple compounds to a single major compound of >90% purity • Less complex and less viscous than crude extracts • Increase in number of active fractions detected • Structures from active fractions are elucidated M. M. Wagenaar; Molecules2008, 13, 1406-1426

  14. Pure Libraries • Address many of the disadvantages of other natural product libraries • Easier to identify hit • Time- and resource-intensive steps have not been eliminated • Hard to obtain large library size • Obtained by commercial source or through partnerships • Additional quantities of compound of interest may be difficult Crude Library Pure Library – 1700 compounds M. M. Wagenaar; Molecules2008, 13, 1406-1426

  15. Other Problems Associated with Natural Product Libraries • Access to natural products are limited • Time-consuming • Expensive • Requires extensive resources • Leads are hard to optimize • Supply can be a problem • Natural products hit might not have the optimal pharmacological properties J. W.-H. Li and J. C. Vederas; Science2009, 325, 161-165

  16. Main Library Types • Natural product libraries • Combinatorial chemistry libraries • Diversity-oriented synthesis libraries

  17. Combinatorial Chemistry • Combinatorial chemistry involves the rapid synthesis of a large number of different but structurally related molecules • First libraries « quantity over quality » • Generate libraries that are lead-like or drug-like • Different approach to generation of a combinatorial chemistry library: • Total synthesis • Natural-product template • Natural product “look-alike” • Totally novel molecules • Computational libraries J. Nielson; Current Opinions in Chemical Biology2002, 6, 297-305

  18. GDB Database • Computational library • Create all possible molecules with up to 11 atoms of C, N, O, F • 26.4 million molecules (110.9 million stereoisomers) • Follow Lipinski’s rule of five • Almost half follow Congreve’s rule of three • Over 99% of compounds are new 18 T. Fink, J-L Reymond; J. Chem. Inf. Comput. Sci.2007, 47, 342-353

  19. How to Generate Combinatorial Libraries The number of “drug-like” molecules is 1062 to 10200 • How to generate the most diverse chemical space? • How to more effectively fill the “holes” in an existing in-house database? • How to increase the chance for finding new hits? • How to generate more “drug-like” compounds and increase the probability for finding new screening hits? J. Sadowski, H. Kubinyi; J. Med. Chem. 1998, 41,3325-3329

  20. How to Generate Combinatorial Libraries • Compare natural products and synthetic drugs • Natural products: • Richer in oxygen • Higher molecular weight • Higher degree of complexity • Based on these observations, different rules were defined T. Henkel, R. M. Brunne, H. Müller, F. Reichel; Angew. Chem. Int. Ed.1999, 38(5), 643-647

  21. Lipinski’s Rule of Five • Used to generate drug-like molecules • Molecular weight smaller than 500 • Calculated logarithm of the octanol-water partition coefficient (clg P) is less than 5 • There are less than 5 hydrogen bond donors • There are less than 10 nitrogen or oxygen atoms • There are less than 10 hydrogen bond acceptors C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney; Adv. Drug Delivery Rev.1997, 23, 3-25

  22. Congreve’s Rule of Three • Used to generate lead-like molecules • Molecular weight smaller than 300 • Calculated logarithm of the octanol-water partition coefficient (clg P) is less than or equal to 3 • There are 3 or less hydrogen bond donor • There are 3 or less hydrogen bond acceptors M. Congreve, R. Carr, C. Murray, H. Jhoti; Drug Discovery Today2003, 8(19), 876-877

  23. Problems Associated with the Rule of Five G. M. Cragg, D. J. Newman; Phytochem. Rev.2009, 8, 313-331 F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220 C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney; Adv. Drug Delivery Rev.1997, 23, 3-25

  24. Problems Associated with the Rule of Five G. M. Cragg, D. J. Newman; Phytochem. Rev.2009, 8, 313-331 F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220 D. J. Payne, M. N. Gwynn, D. J. Holmes, D. L. Pompliano; Nature2007, 6, 29-40

  25. Problems Associated with the Rule of Five G. M. Cragg, D. J. Newman; Phytochem. Rev.2009, 8, 313-331 F. E. Koehn and G. T. Carter; Nature2005, 4, 206-220 M. Feher, J. M. Schmidt; J. Chem. Inf. Comput. Sci.2003, 43, 218-227

  26. How to Build Combinatorial Libraries • Methodology applicable to a broad range of functional groups • Rapid and efficient purification • Need high product purity • Solution-phase library synthesis • Solid-phase library synthesis A. Ganesan; Pure Appl. Chem.2001, 73(7), 1033-1039

  27. Solution Phase Libraries • Every organic reactions available • High-throughput workup more complicated • Parallel synthesis A. Ganesan; Pure Appl. Chem.2001, 73(7), 1033-1039 D. S. Tan; Nature Chemical Biology2005, 1(2), 74-84

  28. Synthesis of Distamycin A and Analogues D. L. Boger, B. E. Fink, M. P. Hedrick; J. Am. Chem. Soc.2000, 122, 6382-6394

  29. Synthesis of Distamycin A and Analogues D. L. Boger, B. E. Fink, M. P. Hedrick; J. Am. Chem. Soc.2000, 122, 6382-6394

  30. Synthesis of Distamycin A and Analogues D. L. Boger, B. E. Fink, M. P. Hedrick; J. Am. Chem. Soc.2000, 122, 6382-6394

  31. Solid Phase Libraries • Preferred approach for very large libraries • Need to rely on a few reliable carbon-heteroatom bond-forming reactions • Work-up is often simple filtration • Final product purification may be difficult • Spectroscopic characterization challenging • Split-pool synthesis • Ganesan; Pure Appl. Chem.2001, 73(7), 1033-1039 • D. S. Tan; Nature Chemical Biology2005, 1(2), 74-84

  32. Nicolaou’s 10 000-Membered Benzopyran Library K. C. Nicolaou, J. A. Pfefferkorn, H. J. Mitchell, A. J. Roecker, S. Barluenga, G.-Q. Cao, R. L. Affleck, J. E. Lillig; J. Am. Chem. Soc.2000, 122, 9954-9967

  33. Nicolaou’s 10 000-Membered Benzopyran Library K. C. Nicolaou, J. A. Pfefferkorn, H. J. Mitchell, A. J. Roecker, S. Barluenga, G.-Q. Cao, R. L. Affleck, J. E. Lillig; J. Am. Chem. Soc.2000, 122, 9954-9967

  34. Nicolaou’s 10 000-Membered Benzopyran Library K. C. Nicolaou, J. A. Pfefferkorn, H. J. Mitchell, A. J. Roecker, S. Barluenga, G.-Q. Cao, R. L. Affleck, J. E. Lillig; J. Am. Chem. Soc.2000, 122, 9954-9967

  35. Problems Associated with Combinatorial Chemistry • Limited structural diversity • Chemical space diversity is limited M. D. Burke, S. L. Schreiber; Science2003, 302(24), 613-618

  36. Main Library Types • Natural product libraries • Libraries synthesized by combinatorial chemistry • Diversity-oriented synthesis libraries

  37. Diversity Oriented Synthesis (DOS) • Aims to create a broad distribution of compounds in chemical space • Populates new regions of chemical space • Identifies correlation for specific properties in chemical space • While natural product-like, not based on known natural product M. D. Burke, S. L. Schreiber; Angew. Chem. Int. Ed.2004, 43, 46-58

  38. Chemical Space Target-Oriented Synthesis Combinatorial Chemistry DOS M. D. Burke, S. L. Schreiber; Angew. Chem. Int. Ed.2004, 43, 46-58

  39. How to Build DOS Libraries D. Lee, J. K. Sello, S. L. Schreiber; Org. Lett.2000, 2(5), 709-712

  40. How to Build DOS Libraries • Synthesis in a reasonable time scale (3-5 steps) • Easy structural modifications, i.e. medicinal chemistry • High-throughput, i.e. solid phase organic synthesis/library generation • Appendage diversity • Simplest diversity generating process • Attach different appendage to common molecular skeleton • Stereochemical diversity • Skeletal diversity M. D. Burke, S. L. Schreiber; Angew. Chem. Int. Ed.2004, 43, 46-58

  41. Skeletal Diversity • Reagent-based approach • use of different reagents to transform a substrate into a collection of products • Substrate-based approach • use of different substrates with a common reaction condition into a collection of products M. D. Burke, S. L. Schreiber; Angew. Chem. Int. Ed.2004, 43, 46-58

  42. Morton’s Build Couple Pair DOS Library D. Morton, S. Leach, C. Cordier, S. Warriner, A. Nelson; Angew. Chem. Int. Ed.2009, 48, 104-109 S. L. Schreiber; Nature2009, 457(8), 153-154

  43. Morton’s Build Couple Pair DOS Library D. Morton, S. Leach, C. Cordier, S. Warriner, A. Nelson; Angew. Chem. Int. Ed.2009, 48, 104-109

  44. Morton’s Build Couple Pair DOS Library D. Morton, S. Leach, C. Cordier, S. Warriner, A. Nelson; Angew. Chem. Int. Ed.2009, 48, 104-109

  45. Morton’s Build Couple Pair DOS Library D. Morton, S. Leach, C. Cordier, S. Warriner, A. Nelson; Angew. Chem. Int. Ed.2009, 48, 104-109

  46. Morton’s Build Couple Pair DOS Library D. Morton, S. Leach, C. Cordier, S. Warriner, A. Nelson; Angew. Chem. Int. Ed.2009, 48, 104-109

  47. Conclusion DOS Libraries Combinatorial Libraries Natural Products Pros: Pros: Pros: • Generate hits • Privileged structures • Lead identification • Drug development • Drug development • Easy synthesis • Compatible with HTS • Large library size • Lead optimization • Better SAR • Chemical space • Lead discovery • Short synthesis • High complexity • Large library size • Compatible with HTS Cons: Cons: Cons: • Limited access • Time-consuming • Expensive • Optimization • Complex • Library size • Methodology • Structural diversity • Chemical space • Failure to produce hits

  48. Acknowledgments • Dr. Christopher Boddy • Ben Lundgren • Meng Wang • Burk Wilke • Ata Pinto • Cole Stevens • Taylor Hari • Pat Hill • Nelson Pearce • Kyle Conway • Ben Chung • Hakim Gol • Mark Horsman • Touria El Aamri

  49. Points for Discussion • Is a universal library realistic? • What library type should we most focus our limited resources for drug discovery? • Which is the biggest limitation in each library?

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