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Design of Small Libraries for Lead Exploration

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Abstract

A combinatorial chemical library is a (usually large) set of compounds made to contain all possible structures of a certain type. The library is often made in order to find a lead compound for a specific drug action or for the optimisation of a lead. Because of the large number of synthesised compounds in the library, their biological activity is usually measured by rapid and simple tests, i.e. “high throughput screening” (HTS), giving crude answers, for instance “active” or “not”. Combinatorial chemistry (CombC) comprises a chain of parts linked by the objective of finding lead compounds for further development. Sometimes the objective is to optimise an existing lead compound, but this is not much discussed in this chapter. An analysis of this CombC chain indicates that the biological testing is the weakest part of the chain. This is due to the difficulty in performing an in-depth biological testing of any set of compounds exceeding a couple of hundred members. Hence there is a strong motivation to decrease the size of libraries to a size that allows in-depth biological testing.

We discuss how the size of a library can be drastically reduced without loss of information or decreases in the chances of finding a lead compound. The approach is based on the use of statistical molecular design (SMD) for the selection of library compounds to synthesise and test, followed by the use of quantitative structure activity relationships (QSARs) for the evaluation of the resulting test data.The use of SMD and QSAR is, in turn, critically dependent on an appropriate translation of the molecular structure to numerical descriptors, the recognition of inhomogeneities (clusters) in both the structural and biological data spaces, and the ability to analyse and interpret relationships between multidimensional data sets.

We present a strategy for constructing a library with optimal information while still taking synthetic feasibility into account. The objective is to provide optimal chemical diversity with a moderate number of compounds, plus adequate depth and width of the biological testing. The strategy is based on a multivariate characterisation of the synthesis starting materials (building blocks), Principal Component Analysis (PCA), multivariate design, and Multivariate Quantitative Structure-Activity Relationships (M-QSAR). The strategy applies both to solid phase synthesis as well as libraries in solution.

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References

  1. Broach, J.R., Thorner, J. High-throughput Screening for Drug Discovery, Nature, 1996, SUPPI., 384, 14–16.

    CAS  Google Scholar 

  2. Spilker, B. ‘Multinational Drug Companies’. Issues in Drug Discovery and Development. Raven Press, New York, 1989.

    Google Scholar 

  3. van de Waterbeemd H. (Ed.), QSAR: Chemometric Methods in Molecular Design. Methods and Principles in Medicinal Chemistry, 2, Verlag Chemie, Weinheim, 1995

    Google Scholar 

  4. Kubinyi, H. (Ed.), ‘3D QSAR in Drug Design; Theory, Methods and Applications’. ESCOM Science Publishers, Leiden, Holland, 1993.

    Google Scholar 

  5. Houghten, R.A., Pinilla, C., Bondelle, S.E., Appel, J.R., Dooley C.T. and Cuervo, J.H. Generation and Use of Synthetic Peptide Combinatorial Libraries for Basic Research and Drug Discovery. Nature, 1991, 354, 84–86.

    Article  CAS  Google Scholar 

  6. Geysen, H.M., Meloen, R.H. and Barteling, S.J. Use of Peptide Synthesis to Probe Viral Antigens for Epitopes to a Resolution of a Single Amino Acid. Proc. Natl. Acad. Sci. U.S.A., 1984, 81, 3998–4002.

    CAS  Google Scholar 

  7. Houghten R.A. General Method for the Rapid Solid-Phase Synthesis of Large Numbers of Peptides: Specificity of Antigen-Antibody Interaction at the Level of Individual Amino Acids. Proc. Natl. Acad. Sci. U.S.A., 1985, 82, 5131–5135.

    CAS  Google Scholar 

  8. Carell, T., Wintner, E.A., Bashir-Hashemi, A. and Rebeck, J.Jr. A Novel Procedure for the Synthesis of Libraries Containing Small Organic Molecules. Angew. Chem. Int. Ed. Eng., 1994,33, 2059–2061.

    Google Scholar 

  9. Personal communication, T. Olsson, Astra Hassle AB, Sweden

    Google Scholar 

  10. Martin, E.J., Blaney, J.M., Siani M.A., Spellmyer, D.C., Wong, A.K. and Moos W.H. Measuring Diversity: Experimental Design of Combinatorial Libraries for Drug Design. J. Med.Chem., 1995, 38, 1431–1436.

    CAS  Google Scholar 

  11. Young, S.S. and Hawkins, D.M. Analysis of a 29 Full Factorial Chemical Library. J. Med. Chem., 1995, 38, 2784–2788.

    Article  CAS  Google Scholar 

  12. van de Waterbeemd, H., Constantino, G., Clementi, S., Cruciani, G. and Valigi, R. Disjoint Principal Properties of Organic Substituents. In QSAR: Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. H. van de Waterbeemd, Verlag Chemie, Weinheim, Germany, 1995

    Google Scholar 

  13. Lundstedt, T., Andersson, P.M., Clementi, S., Cruciani, G., Kettaneh, N., Linusson, A., Nordén, B., Pastor, M., Sjöström, M. and Wold, S. Intelligent Combinatorial Libraries. In Computer-Assisted Lead Finding and Optimization Ed. H. van de Waterbeemd, Verlag Helvetica Chimica Acta. Basel, Switzerland, 1997, 191–208.

    Google Scholar 

  14. Hansch, C. and Leo, A.J. Substituent Constants for Correlation Analysis in Chemistry and Biology. Wiley, New York, 1979.

    Google Scholar 

  15. Hansch, C., Leo, A.J. and Hoekman. D. Exploring QSAR, Hydrophobic, Electronic and Steric Constants, ACS, Washington DC, 1995.

    Google Scholar 

  16. Skagerberg, B., Bonelli, D., Clementi, S., Cruciani, G. and Ebert, C. Principal Properties for Aromatic Substituents. A Multivariate Approach for Design in QSAR. QSAR, 1989, 8, 32–38.

    CAS  Google Scholar 

  17. Hellberg, S., Sjöström, M., Skagerberg, B., Wikström. C. and Wold, S. On the design of multipositionally varied test series for quantitative structure-activity relationships. Acta Pharm Jugosl., 1987, 37, 53–65.

    CAS  Google Scholar 

  18. Hellberg, S., Sjöström. M. and Wold. S. The Prediction of Bradykinin Potentiating Potency of Pentapeptides. An Example of a Peptide Quantitative Structure-Activity Relationship. Acta Chem. Scand. 1986, B40, 135–140.

    CAS  Google Scholar 

  19. Jonsson, J., Eriksson, L., Hellberg, S., Sjöström, M. and Wold, S. Multivariate Parametrization of 55 Coded and Non-Coded Amino Acids, QSAR, 1989, 8, 204–209.

    CAS  Google Scholar 

  20. Sandberg, M., Eriksson, L., Jonsson, J., Sjöström, M. and Wold, S. New Chemical Descriptors Relevant for the Design of Biologically Active Peptides. A Multivariate Characterisation of 87 Amino Acids. J. Med. Chem., 1998, 41, 2481–2491.

    Article  CAS  Google Scholar 

  21. Goodford, P.J. A computational procedure for determining energetically favourable binding sites on biologically important macromolecules. J.Med.Chem.,1985, 28, 849–857.

    Article  CAS  Google Scholar 

  22. Clementi, S., Cruciani, G., Fisi, P., Riganelli, D., Valigi, R. and Musumarra G. A New Set of Principal Properties for Heteroaromatics Obtained by GRID. QSAR, 1996, 15, 108–120.

    CAS  Google Scholar 

  23. Jackson J.E. A Users Guide to Principal Components, Wiley, New York, 1991.

    Google Scholar 

  24. Wold, S., Sjöström, M., Carlson, R., Lundstedt, T., Hellberg, S., Skagerberg, B., Wikström, C. and Öhman, J. Multivariate Design. Anal. Chim. Acta, 1986, 191, 17–32.

    Article  CAS  Google Scholar 

  25. Carlson, R. Design and Optimization in Organic Synthesis, Elsevier, Amsterdam, 1992.

    Google Scholar 

  26. Lundstedt, T. A QSAR Strategy for Screening of Drugs and Predicting Their Clinical Activity. Drug News Perspect., 1991, 4(8), 468–474.

    Google Scholar 

  27. Sjöström, M. and Eriksson, L. Application of Statistical Experimental Design and PLS Modelling in QSAR. In QSAR: Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. H. van de Waterbeemd. Verlag Chemie, Weinheim, Germany, 1995

    Google Scholar 

  28. Box G.E.P. and Draper, N.R. Empirical Model-building and Response Surfaces, Wiley, Chichester, 1987.

    Google Scholar 

  29. Baroni, M., Clementi, S., Cruciani, G., Kettaneh-Wold, N. and Wold, S. D-Optimal Designs in QSAR. QSAR, 1993, 12, 225–231.

    CAS  Google Scholar 

  30. A. Linusson, F. Lindgren, J. Gottfries, S. Wold, in manuscript

    Google Scholar 

  31. Carlson. R., Prochazka. M.P. and Lundstedt. T. Principal Properties for Synthetic Screening: Ketones and Aldehydes. Acta Chem. Scand., 1988, B42, 145–156.

    CAS  Google Scholar 

  32. Lundstedt, T., Carlson, R. and Shabana, R.. Optimum Conditions for the Willgerodt-Kindler Reaction. 3. Amine Variation. Acta Chem. Scand., 1987, B41, 157–163.

    CAS  Google Scholar 

  33. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.

    Google Scholar 

  34. Wold, S. and Andersson, K. Major Components Influencing Retention Indices in Gas Chromatography. J. Chromatogr., 1973, 80, 43.

    Article  CAS  Google Scholar 

  35. Dunn III, W.J. and Wold. S. SIMCA Pattern Recognition and Classification. In QSAR: Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. van de Waterbeemd, H., Verlag Chemie, Weinheim, Germany, 1995.

    Google Scholar 

  36. Linusson A., Wold, S. and Nordén, B. Chemometrics and Intell. Lab. Syst., 1998, in press.

    Google Scholar 

  37. Carlson, R., Prochazka, M.P. and Lundstedt, T. Principal Properties for Synthetic Screening: Amines. Acta Chem. Scand., 1988, B42, 157–165.

    CAS  Google Scholar 

  38. Tsar 3.11, Oxford Molecular Group, http://www.oxmol.co.uk/"

    Google Scholar 

  39. Simca-P 3.01, Umetri AB, Umeå, http://www.umetri.se/

    Google Scholar 

  40. Carlson, R. and Lundstedt, T. Scope of Organic Synthetic Reactions. Multivariate Methods for Exploring the Reaction Space. An example by the Willgerodt-Kindler Reaction. Acta Chem. Scand., 1987, B41, 164–173.

    CAS  Google Scholar 

  41. Scitec Laboratory Automation SA, Av. de Provence 18, CH-1007 Lausanne, Switzerland.

    Google Scholar 

  42. Wold, S., Johansson, E. and Cocchi, M. PLS—Partial Least-Squares Projections to Latent Structures. In 3D QSAR in Drug Design; Theory, Methods and Applications, Ed. Kubinyi, H., ESCOM Science Publishers, Leiden, Holland. 1993, pp523–550.

    Google Scholar 

  43. Wold, S. PLS for Multivariate Linear Modeling. In QSAR: Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry, 2, Ed. van de Waterbeemd, H., Verlag Chemie, Weinheim, Germany, 1995, pp195–218.

    Google Scholar 

  44. Eriksson, L., Berglind, R. and Sjöström, M. A Multivariate Quantitative Structure-Activity Relationship for Corrosive Carboxylic Acids. Chemometrics and Intell. Lab. Syst., 1994, 23, 235–245.

    CAS  Google Scholar 

  45. Box, G.E.P., Hunter, W.G. and Hunter, J.S. Statistics for Experimenters, Wiley, New York, 1978.

    Google Scholar 

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Philip M. Dean Richard A. Lewis

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© 2002 Kluwer Academic Publishers

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Andersson, P.M., Linusson, A., Wold, S., Sjöström, M., Lundstedt, T., Nordén, B. (2002). Design of Small Libraries for Lead Exploration. In: Dean, P.M., Lewis, R.A. (eds) Molecular Diversity in Drug Design. Springer, Dordrecht. https://doi.org/10.1007/0-306-46873-5_9

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  • DOI: https://doi.org/10.1007/0-306-46873-5_9

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-5980-7

  • Online ISBN: 978-0-306-46873-5

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