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A guide to drug discovery

Designing screens: how to make your hits a hit

Nature Reviews Drug Discovery volume 2, pages 259–266 (2003)Cite this article

Key Points

  • In recent years, the emphasis in high-throughput screening has shifted from increasing screening capacity to improving the quality of the resulting data.

  • This article discusses the issues that are crucial to successful screening, including:

  • What makes a good assay?

  • Types of screen and readouts

  • Compound selection by filtering screening libraries to remove compounds with undesirable properties, and focusing libraries to target types to enhance hit rates.

Abstract

The basic goal of small-molecule screening is the identification of chemically 'interesting' starting points for elaboration towards a drug. A number of innovative approaches for pursuing this goal have evolved, and the right approach is dictated by the target class being pursued and the capabilities of the organization involved. A recent trend in high-throughput screening has been to place less emphasis on the number of data points that can be produced, and to focus instead on the quality of the data obtained. Several computational and technological advances have aided in the selection of compounds for screening and widened the variety of assay formats available for screening. The effect on the efficiency of the screening process is discussed.

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Figure 1: Screening at higher concentration leads to greater structural diversity.
Figure 2: Examples of screening assays.
Figure 3
Figure 4: Examples of titration curves for compounds obtained from screening.
Figure 5: The effect of REOS (Rapid Elimination Of Swill) on screening efficiency.

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References

  1. Kapur, R. Fluorescence imaging and engineered biosensors functional and activity-based sensing using high content screening. Ann. NY Acad. Sci. 961, 196–197 (2002).

    Article  Google Scholar 

  2. Bleicher, K. H. Chemogenomics: bridging a drug discovery gap. Curr. Med. Chem. 23, 2077–2084 (2002).

    Article  Google Scholar 

  3. Namchuk, M. Finding the molecules to fuel chemogenomics. Targets 1, 125–129 (2002).

    Article  CAS  Google Scholar 

  4. Zhang, J. H., Chung, T. D. & Oldenburg, K. R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen. 4, 27–32 (1999). Describes a widely used metric for defining the quality of high-throughput screening assays.

    Article  Google Scholar 

  5. Miller, A. Discovery of cytokine mimics using cell-based systems. Drug Discov. Today 5, 77–83 (2000).

    Article  Google Scholar 

  6. Selvin, P. R. Principles and biophysical applications of lanthanide-based probes. Annu. Rev. Biophys. Biomol. Struct. 31, 275–302 (2002).

    Article  CAS  Google Scholar 

  7. Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeablity in drug discovery. Adv. Drug Deliv. Rev. 23, 3–25 (1997). Introduces Lipinski's rule-of-five.

    Article  CAS  Google Scholar 

  8. Ghose, A. K., Viswanadhan, V. N. & Wendelowski, J. J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative characterization of known drug databases. J. Comb. Chem. 1, 55–67 (1999).

    Article  CAS  Google Scholar 

  9. Oprea, T. I. Property distribution of drug-related chemical databases. J. Comput. Aided Mol. Des. 14, 251–264. (2000).

    Article  CAS  Google Scholar 

  10. Walters, W. P., Ajay, A. & Murcko, M. A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 3, 384–387 (1999).

    Article  CAS  Google Scholar 

  11. Egan, W. J. & Lauri, G. Prediction of intestinal permeability. Adv. Drug Deliv. Rev. 54, 273–289 (2002).

    Article  CAS  Google Scholar 

  12. Clark, D. E. & Grootenhuis, P. D. J. Progress in computational methods for the predicition of ADMET properties. Curr. Opin. Drug. Discov. Devel. 5, 382–390 (2002).

    CAS  PubMed  Google Scholar 

  13. Hann, M. M., Leach, A. R. & Harper G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J. Chem. Inf. Comput. Sci. 41, 856–864 (2001).

    Article  CAS  Google Scholar 

  14. Oprea, T. I. Current trends in lead discovery: are we looking for the appropriate properties? J. Comput. Aided Mol. Des. 16, 325–34 (2002).

    Article  CAS  Google Scholar 

  15. Muegge, I., Heald, S. L. & Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 44, 1841–1846 (2001).

    Article  CAS  Google Scholar 

  16. Mitchell, T. M. Machine Learning (McGraw-Hill, New York, NY, 1997).

    Google Scholar 

  17. Zupan, J. & Gasteiger, J. Neural Networks for Chemists (VCH, Weinheim, 1993).

    Google Scholar 

  18. Ajay, A., Walters, W. P. & Murcko, M. A. Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules? J. Med. Chem. 41, 3314–3324 (1998).

    Article  CAS  Google Scholar 

  19. Sadowski, J. & Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329 (1998). References 18 and 19 describe the application of machine-learning techniques to the prediction of drug-likeness.

    Article  CAS  Google Scholar 

  20. Aronov, A. M., Munagala, N. R., Kuntz, I. D. & Wang, C. C. Virtual screening of combinatorial libraries accross a gene family: in search of inhibitors of Giardia lamblia guanine phosphorribosyltransferase. Antimicrob. Agents Chemother. 45, 2571–2576 (2001).

    Article  CAS  Google Scholar 

  21. Diller, D. J. & Merz, K. M. High throughput docking for library design and library prioritization. Proteins 43, 113–124 (2001).

    Article  CAS  Google Scholar 

  22. Stahura, F. L., Xue, L., Gooden, J. W. & Bajorath, J. Molecular scaffold-based design and comparison of combinatorial libraries focused on the ATP-binding site of protein kinases. J. Mol. Graph. Model. 17, 1–9 (1999).

    Article  CAS  Google Scholar 

  23. Balakin, K. V. et al. Property-based design of GPCR-targeted library. J. Chem. Inf. Comput. Sci. 42, 1332–1342 (2002).

    Article  CAS  Google Scholar 

  24. Muegge, I. & Rarey, M. in Reviews in Computational Chemistry Volume 17 (eds Lipkowitz, K. B. & Boyd, D. B.) 1–60 (Wiley–VCH, New York, 2001).

    Book  Google Scholar 

  25. Charifson, P. S., Corkery, J. J., Murcko, M. A. & Walters, W. P. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 42, 5100–5109 (1999).

    Article  CAS  Google Scholar 

  26. Ajay, A. & Murcko, M. A. Computational methods to predict binding free energy in ligand-receptor complexes. J. Med. Chem. 38, 4953–4967 (1995).

    Article  CAS  Google Scholar 

  27. Hull, R. D. et al. Chemical similarity searches using latent semantic structural indexing (LaSSI) and comparison to TOPOSIM. J. Med. Chem. 44, 1185–1191 (2001).

    Article  CAS  Google Scholar 

  28. Kearsley, S. K, & Sheridan, R. P. Why do we need so many chemical similarity search methods? Drug Discov. Today 7, 903–911 (2002).

    Article  Google Scholar 

  29. Fox, T. et al. A single amino acid substitution makes ERK2 susceptible to pyridinyl imidazole inhibitors of p38 MAP kinase. Protein Sci. 7, 2249–2255 (1998).

    Article  CAS  Google Scholar 

  30. Stills, M. et al. Comparison of assay technologies for tyrosine kinase assay generates different results in high throughput screening. J. Biomol. Screen. 7, 191–214 (2002).

    Article  Google Scholar 

  31. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol. Toxicol. Methods 44, 235–249 (2000).

    Article  CAS  Google Scholar 

  32. Segal, I. Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems. 465–504 (John Wiley and Sons, New York, 1993).

    Google Scholar 

  33. Motalsky, H. Analysing Data with Graphpad Prism, 173–174 (1998).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vertex Pharmaceuticals, 130 Waverley Street, Cambridge, 02139, Massachusetts, USA

    W. Patrick Walters & Mark Namchuk

Authors
  1. W. Patrick Walters
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  2. Mark Namchuk
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Corresponding author

Correspondence to Mark Namchuk.

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FURTHER INFORMATION

Society for Biomolecular Screening

Glossary

HILL SLOPE

In the program Prism, the curves are fitted to the equation:

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Walters, W., Namchuk, M. Designing screens: how to make your hits a hit. Nat Rev Drug Discov 2, 259–266 (2003). https://doi.org/10.1038/nrd1063

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