Impact of high-throughput screening in biomedical research

Article metrics

Abstract

High-throughput screening (HTS) has been postulated in several quarters to be a contributory factor to the decline in productivity in the pharmaceutical industry. Moreover, it has been blamed for stifling the creativity that drug discovery demands. In this article, we aim to dispel these myths and present the case for the use of HTS as part of a proven scientific tool kit, the wider use of which is essential for the discovery of new chemotypes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Examples of high-throughput screening in drug discovery.
Figure 2: Size of corporate screening collections over time.
Figure 3: Comparison of average molecular mass and clogP of leads identified in 2009 by HTS or other strategies in three pharmaceutical companies.

References

  1. 1

    LaMattina, J. L. (ed.) in Drug Truths: Dispelling the Myths About Pharma R&D 114–115 (John Wiley and Sons, Inc., Hoboken, New Jersey, 2009).

  2. 2

    Graul, A. I., Revel, L., Tell, M., Rosa, E. & Cruces, E. Overcoming the obstacles in the pharma/biotech industry: 2009 update. Drug News Perspect. 23, 48–63 (2010).

  3. 3

    Lahana, R. Who wants to be irrational? Drug Discov. Today 8, 655–656 (2003).

  4. 4

    Landers, P. Human Element: drug industry's big push into technology falls short — testing machines were built to streamline research — but may be stifling it — officials see payoff after 2010. The Wall Street Journal 1 (24 Feb 2004).

  5. 5

    Ashburn, T. T. & Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nature Rev. Drug Discov. 3, 673–683 (2004).

  6. 6

    Garnier, J. Rebuilding the R&D engine in big pharma. Harvard Bus. Rev. 86, 68–76 (2008).

  7. 7

    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, 67–73 (1999).

  8. 8

    Makarenkov, V. et al. An efficient method for the detection and elimination of systematic error in high-throughput screening. Bioinformatics 23, 1648–1657 (2007).

  9. 9

    Coma, I. et al. Process validation and screen reproducibility in high-throughput screening. J. Biomol. Screen. 14, 66–76 (2009).

  10. 10

    Taylor, P. B. et al. A standard operation procedure for assessing liquid handler performance in high-throughput screening. J. Biomol. Screen. 7, 554–569 (2002).

  11. 11

    Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Rev. Drug Discov. 9, 203–214 (2010).

  12. 12

    Fox, S. et al. High-throughput screening: update on practices and success. J. Biomol. Screen. 11, 864–869 (2006).

  13. 13

    Bleicher, K. H., Bohm, H.-J., Muller, K. & Alanine, A. I. Hit and lead generation: beyond high-throughput screening, Nature Rev. Drug Discov. 2, 369–378 (2003).

  14. 14

    Mullin, R. As high-throughput screening draws fire, researchers leverage science to put automation into perspective. Chem. Eng. News 82, 23–32 (2004).

  15. 15

    Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nature Rev. Drug Discov. 3, 711–715 (2004).

  16. 16

    Perola, E. An analysis of the binding efficiencies of drugs and their leads in successful drug discovery programs. J. Med. Chem. 53, 2986–2997 (2010).

  17. 17

    Dorr, P. et al. Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunodeficiency virus type 1 activity. Antimicrob. Agents Chemother. 49, 4721–4732 (2005).

  18. 18

    Duffy, K. J. et al. Hydrazinonaphthalene and azonaphthalene thrombopoietin mimics are nonpeptidyl promoters of megakaryocytopoiesis. J. Med. Chem. 44, 3730–3745 (2001).

  19. 19

    Duffy, K. J. et al. Identification of a pharmacophore for thrombopoietic activity of small, non-peptidyl molecules. 1. Discovery and optimization of salicylaldehyde thiosemicarbazone thrombopoietin mimics. J. Med. Chem. 45, 3573–3575 (2002).

  20. 20

    Duffy, K. J. et al. Identification of a pharmacophore for thrombopoietic activity of small, non-peptidyl molecules. 2. Rational design of naphtho[1,2-d]imidazole thrombopoietin mimics. J. Med. Chem. 45, 3576–3578 (2002).

  21. 21

    Erickson-Miller, C. L. et al. Discovery and characterization of a selective, nonpeptidyl thrombopoietin receptor agonist. Exp. Hematol. 33, 85–93 (2005).

  22. 22

    Cuatrecasas, P. Drug discovery in jeopardy. J. Clin. Invest. 116, 2837–2842 (2006).

  23. 23

    Lemm, J. A. et al. Identification of hepatitis C virus NS5A inhibitors. J. Virol. 84, 482–491 (2010).

  24. 24

    Gao, M. et al. Chemical genetics strategy identifies an HCV NS5A inhibitor with a potent clinical effect. Nature 456, 96–100 (2010).

  25. 25

    Keserü, G. M. & Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nature Rev. Drug Discov. 8, 203–212 (2009).

  26. 26

    Oprea, T. I., Davis, A. M., Teague, S. J. & Leeson, P. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315 (2001).

  27. 27

    Hann, M. M. & Oprea, T. I. Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol. 8, 255–263 (2004).

  28. 28

    Jacoby, E. et al. Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection. Curr. Top. Med. Chem. 5, 397–411 (2005).

  29. 29

    Lane, S. J. et al. Defining and maintaining a high quality screening collection: the GSK experience. Drug Discov. Today 11, 267–272 (2006).

  30. 30

    HTStec Limited. Cellular Assay Reagents Trends 2009 Report (Cambridge, UK, 2009).

  31. 31

    Drewry, D. H. & Macarron, R. Enhancements of screening collections to address areas of unmet medical need: an industry perspective. Curr. Opin. Chem. Biol. 14, 289–298 (2010).

  32. 32

    Fink, T. & Reymond, J.-L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 47, 342–353 (2007).

  33. 33

    Harper, G., Pickett, S. D. & Green, D. V. S. Design of a compound screening collection for use in high throughput screening. Comb. Chem. High Throughput Screen. 7, 63–70 (2004).

  34. 34

    Martin, Y. C., Kofron, J. L. & Traphagen, L. M. Do structurally similar molecules have similar biological activity? J. Med. Chem. 45, 4350–4358 (2002).

  35. 35

    Munos, B. Lessons from 60 years of pharmaceutical innovation. Nature Rev. Drug Discov. 8, 959–968 (2009).

  36. 36

    Austin, C. P. The completed human genome: implications in chemical biology. Curr. Opin. Chem. Biol. 7, 511–515 (2003).

  37. 37

    Society for Laboratory Automation and Screening. Screening facilities. SLAS [online], http://www.slas.org/screeningFacilities/facilityList.cfm, (2010).

  38. 38

    Molecular Libraries Program. MLPCN probes Web table. MLP [online], http://mli.nih.gov/mli/mlp-probes/, (2010).

  39. 39

    Kaiser, J. Industrial-style screening meets academic biology. Science 321, 764–766 (2008).

  40. 40

    Silber, B. M. Driving drug discovery: the fundamental role of academic labs. Sci. Transl. Med. 2, 30cm16 (2010).

  41. 41

    Frye, S. V. The art of the chemical probe. Nature Chem. Biol. 6, 159–161 (2010).

  42. 42

    Houston, J. G. et al. Case study: impact of technology investment on lead discovery at Bristol-Myers Squibb, 1998–2006. Drug Discov. Today 13, 44–51 (2008).

  43. 43

    Banks, M. N., Zhang, L. & Houston, J. G. in Exploiting Chemical Diversity for Drug Discovery (eds Bartlett, P. A. & Entzeroth, M.) 315–335 (Royal Chemical Society Publishing, London, 2006).

  44. 44

    Kunapuli, P. et al. Application of division arrest technology to cell-based HTS: comparison with frozen and fresh cells. Assay Drug Dev. Technol. 3, 17–26 (2005).

  45. 45

    Digan, M. E., Pou, C., Niu, H. & Zhang, J. H. Evaluation of division-arrested cells for cell-based high-throughput screening and profiling. J. Biomol. Screen. 10, 615–623 (2005).

  46. 46

    Cawkill, D. & Eaglestone S. S. Evolution of cell-based reagent provision. Drug Discov. Today 12, 820–825 (2007).

  47. 47

    Zaman, G. J. et al. Cryopreserved cells facilitate cell-based drug discovery. Drug Discov. Today 12, 521–526 (2007).

  48. 48

    Leifert, W. R., Aloia, A. L., Bucco, O., Glatz, R. V. & McMurchie, E. J. G-protein coupled receptors in drug-discovery: nano-sizing using cell-free technologies and molecular biology approaches. J. Biomol. Screen. 10, 765–779 (2005).

  49. 49

    Heilker, R., Zemanova, L., Valler, M. J. & Niehaus, G. U. Confocal fluorescence microscopy for high-throughput screening of G-protein coupled receptors. Curr. Med. Chem. 12, 2551–2255 (2005).

  50. 50

    Hovius, R., Vallotton, P., Wohland, T. & Vogel, H. Fluorescence techniques: shedding light on ligand-receptor interactions. Trends Pharmacol. Sci. 21, 266–273 (2000).

  51. 51

    Jia, Y., Quinn, C. M., Kwak, S. & Talanian, R. V. Current in vitro kinase assay technologies: the quest for a universal format. Curr. Drug Discov. Technol. 5, 59–69 (2008).

  52. 52

    Gasparri, F., Sola, F., Bandiera, T., Moll, J. & Galvani, A. High content analysis of kinase activity in cells. Comb. Chem. High Throughput Screen. 11, 523–536 (2008).

  53. 53

    McLoughlin, D. J., Bertelli, F. & Williams, C. The A, B, Cs of G-protein-coupled receptor pharmacology in assay development for HTS. Expert Opin. Drug Discov. 2, 1–17 (2007).

  54. 54

    Williams, C. & Sewing, A. G-protein-coupled receptor assays: to measure affinity or efficacy that is the question. Comb. Chem. High Throughput Screen. 8, 285–292 (2005).

  55. 55

    Wunder, F., Kalthof, B., Müller, T. & Hüser, J. Functional cell-based assays in microliter volumes for ultra-high throughput screening. Comb. Chem. High Throughput Screen. 11, 495–504 (2008).

  56. 56

    Kenakin, T. P. Cellular assays as portals to seven-transmembrane receptor-based drug discovery. Nature Rev. Drug Discov. 8, 617–626 (2009).

  57. 57

    Fry, D. W. et al. A specific inhibitor of the epidermal growth factor receptor tyrosine kinase. Science 265, 1093–1095 (1994).

  58. 58

    Wilhelm, S. et al. Discovery and development of sorafenib: a multikinase inhibitor for treating cancer. Nature Rev. Drug Discov. 5, 835–844 (2006).

  59. 59

    Thaisrivongs, S. et al. Structure-based design of HIV protease inhibitors: 4-hydroxycoumarins and 4-hydroxy-2-pyrones as nonpeptidic inhibitors. J. Med. Chem. 37, 3200–3204 (1994).

  60. 60

    Thornberry, N. A. & Weber, A. E. Discovery of JANUVIA (Sitagliptin), a selective dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Curr. Top. Med. Chem. 7, 557–568 (2007).

  61. 61

    Das, J. et al. 2-aminothiazole as a novel kinase inhibitor template. Structure–activity relationship studies toward the discovery of N-(2-chloro-6-methylphenyl)-2-[[6-[4-(2-hydroxyethyl)- 1-piperazinyl)]-2-methyl-4- pyrimidinyl]amino)]-1, 3-thiazole-5-carboxamide (Dasatinib, BMS-354825) as a potent pan-Src kinase inhibitor. J. Med. Chem. 49, 6819–6832 (2006).

  62. 62

    LaMattina, J. L. (ed.) in Drug Truths: Dispelling the Myths About Pharma R&D 65–66 (John Wiley and Sons, Inc., Hoboken, New Jersey, 2009).

  63. 63

    Riechers, H. et al. Discovery and optimization of a novel class of orally active nonpeptidic endothelin-A receptor antagonists. J. Med. Chem. 39, 2123–2128 (1996).

  64. 64

    De Corte, B. L. From 4,5,6,7-tetrahydro-5-methylimidazo[4,5,1-jk](1,4)benzodiazepin-2(1H)-one (TIBO) to etravirine (TMC125): fifteen years of research on non-nucleoside inhibitors of HIV-1 reverse transcriptase. J. Med. Chem. 48, 1689–1696 (2005).

  65. 65

    Yamamura, Y. et al. OPC-21268, an orally effective, nonpeptide vasopressin VI receptor antagonist. Science 252, 572–574 (1991).

  66. 66

    Inglese, J. in Ask the Expert Forum, ACS Chemical Biology. ACS Publications [online] http://community.acs.org/ChemBiol/AsktheExpert/AsktheExpertArchive/tabid/67/Default.aspx (2008).

Download references

Acknowledgements

The authors are grateful to the many colleagues in the HTS community who have contributed data and opinions presented in this article.

Author information

Correspondence to Ricardo Macarron.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information Table S1

Molecular Libraries HTS Project Success- Key Indicators and Metrics 2004-2009* (PDF 242 kb)

Glossary

Cell-based luciferase reporter screen

A popular reporter gene assay that uses a luciferase gene to detect metabolites (for example, cyclic AMP levels) or changes in expression of a gene of interest.

Chemical space

The space spanned by all energetically stable stoichiometric combinations of electrons, atomic nuclei and topologies in molecules. It is calculated to contain up to 1 × 1060 distinct molecules. Drug-like space may contain up to 1 × 1030 molecules.

clogP

The calculated logarithm of the partition coefficient between n-octanol and water for a given compound. This parameter is an estimation of the lipophilicity of the compound.

Combinatorial chemistry

Rapid and parallel synthesis of large collections of compounds to facilitate the identification of new active compounds for drug targets by high-throughput screening techniques.

Constrained optimization

The process of finding the most favourable condition that satisfies all conditions (or constraints) that frame the problem.

Drug-like properties

Sharing certain characteristics — such as size, shape and solubility in water and organic solvents — with other molecules that act in the same way as drugs. Lipinski's rule of five provides a commonly used definition of these properties for oral drugs.

eADMET

Computational models designed to predict the ADMET (absorption, distribution, metabolism, excretion and toxicity) of molecules.

Fragment screening

The identification of bioactive substances by screening small-molecule fragments (<300 Da). It requires high-resolution structural techniques to guide the optimization of weak efficient hits into leads.

Lead-like properties

Sharing certain characteristics — such as size, shape and solubility in water and organic solvents — with other molecules that act as precursors of drugs (leads). Lead-likeness is typically associated with small size (molecular mass <400 Da) and low lipophilicity (clogP <4).

Plate pattern recognition algorithms

Microtitre plates may suffer from heterogeneous temperature, air flow, reader and liquid handler bias, and so on, leading to systematic assay errors that need to be detected and corrected by ad hoc algorithms.

Phenotypic cell-based screen

A screen based on whole cells that measures an observable change in cell physiology or morphology in the presence of active compounds. Phenotypic assays cannot distinguish direct compound interactions with the specific targets or signalling pathways in the cell.

Structure–activity relationship

Correlations that are constructed between the features of chemical structures in a set of candidate compounds and parameters of biological activity, such as potency, selectivity and toxicity.

Structure-based design

The use of three-dimensional structural information and molecular-modelling techniques to design a series of possible pharmacological modulators that can, for example, block an active site of an enzyme.

Target focused selection

The selection of chemical compounds that are related to either known ligands of a target or to the target class of interest.

Virtual screening

The selection of potential bioactive substances from a much larger list of candidate molecules using in silico models typically based on known structures and/or ligands of the target of interest.

Z′ trend monitoring

Z′ is a relative indication of the separation of the signal and background controls and is widely used in high-throughput screening (HTS) to assess the quality of an assay. Every microtitre plate in a run will exhibit a distinct Z′ value and monitoring its trends in a campaign is a standard quality control practice.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Macarron, R., Banks, M., Bojanic, D. et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10, 188–195 (2011) doi:10.1038/nrd3368

Download citation

Further reading