Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Opinion
  • Published:

Impact of high-throughput screening in biomedical research

Nature Reviews Drug Discovery volume 10, pages 188–195 (2011)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

References

  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).

    Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Google Scholar 

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

Author notes

  1. Jeff W. Paslay

    Present address: G.Sitta Sittampalam is at the Department of Pharmacology, Toxicology & Therapeutics, Institute for Advancing Medical Innovations, The University of Kansas Cancer Center, 3901 Rainbow Blvd., Kansas City, Kansas 66160, USA,

  2. G. Sitta Sittampalam

    Present address: Jeff W.Paslay was previously at the Department of Screening Sciences, Research Centers of Emphasis, Pfizer; Present address: 302 Old Barn Circle, Phoenixville, Pennsylvania 19460, USA,

Authors and Affiliations

  1. Ricardo Macarron is at the Department of Sample Management Technologies, GlaxoSmithKline, Mail Stop UP 12-200, 1250S Collegeville Rd, Collegeville, Pennsylvania 19426, USA,

    Ricardo Macarron

  2. Martyn N. Banks is at the Department of Applied Biotechnologies, Bristol-Myers Squibb Co., 5 Research Parkway, Wallingford, Connecticut 06492, USA,

    Martyn N. Banks

  3. Dejan Bojanic is at the Lead Finding Platform, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA,

    Dejan Bojanic

  4. David J.Burns is with the Early Pain Discovery Team, Abbott Laboratories, R4DI AP52N 200 Abbott Park Road, Abbott Park, Illinois 60064, USA,

    David J. Burns

  5. Dragan A.Cirovic is at the Department of Lead Generation and Candidate Realization, Sanofi-Aventis, 1041 Route 202-206, Mail Stop BRJR1-002A, Bridgewater, New Jersey 08807, USA,

    Dragan A. Cirovic

  6. Tina Garyantes is at the Department of Lead Generation and Candidate Realization, Sanofi-Aventis, 1041 Route 202-206, P.O. BOX 6800, JR-303D, Bridgewater, New Jersey 08807, USA,

    Tina Garyantes

  7. Darren V.S.Green is at the Department of Computational and Structural Chemistry, GlaxoSmithKline, Stevenage, Hertfordshire SG1 2NY, UK,

    Darren V. S. Green

  8. Robert P.Hertzberg is at the Department of Screening and Compound Profiling, GlaxoSmithKline, Mail Stop UP 12-L05, 1250 S Collegeville Rd, Collegeville, Pennsylvania 19426, USA,

    Robert P. Hertzberg

  9. William P.Janzen is at the Division of Medicinal Chemistry and Natural Products, Center for Integrative Chemical Biology and Drug Discovery, Eshelman School of Pharmacy, The University of North Carolina, Chapel Hill, North Carolina 27599-7363, USA,

    William P. Janzen

  10. Ulrich Schopfer is at the Lead Finding Platform, Novartis Institutes for BioMedical Research, Forum 1, Novartis Campus, CH-4056 Basel, Switzerland,

    Ulrich Schopfer

Authors
  1. Ricardo Macarron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martyn N. Banks
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dejan Bojanic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David J. Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dragan A. Cirovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tina Garyantes
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Darren V. S. Green
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Robert P. Hertzberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. William P. Janzen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jeff W. Paslay
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ulrich Schopfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. G. Sitta Sittampalam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

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). https://doi.org/10.1038/nrd3368

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd3368

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing