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.

  • Letter
  • Published:

High-throughput assays for promiscuous inhibitors

Nature Chemical Biology volume 1, pages 146–148 (2005)Cite this article

Abstract

High-throughput screening (HTS) searches large libraries of chemical compounds for those that can modulate the activity of a particular biological target; it is the dominant technique used in early-stage drug discovery. A key problem in HTS is the prevalence of nonspecific or 'promiscuous' inhibitors. These molecules have peculiar properties, act on unrelated targets and can dominate the results from screening campaigns1. Several explanations have been proposed to account for promiscuous inhibitors, including chemical reactivity1,2, interference in assay read-out2, high molecular flexibility3 and hydrophobicity2,4. The diversity of these models reflects the apparently unrelated molecules whose behaviors they seek to explain. However, a single mechanism may explain the effects of many promiscuous inhibitors: some organic molecules form large colloid-like aggregates that sequester and thereby inhibit enzymes5. Hits from HTS, leads for drug discovery and even several drugs appear to act through this mechanism at micromolar concentrations5,6,7,8,9. Here, we report two rapid assays for detecting promiscuous aggregates that we tested against 1,030 'drug-like' molecules. The results from these assays were used to test two preliminary computational models of this phenomenon and as benchmarks to develop new models.

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: Results of the high-throughput assays applied to random drug-like molecules.
Figure 2
Figure 3: Accuracy of preliminary predictive models.

Similar content being viewed by others

Accession codes

Accessions

BINDPlus

References

  1. Rishton, G.M. Reactive compounds and in vitro false positives in HTS. Drug Discov. Today 2, 382–384 (1997).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Veber, D.F. et al. Molecular Properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).

    Article  CAS  Google Scholar 

  4. Roche, O. et al. Development of a virtual screening method for identification of 'frequent hitters' in compound libraries. J. Med. Chem. 45, 137–142 (2002).

    Article  CAS  Google Scholar 

  5. McGovern, S.L., Helfand, B.T., Feng, B.Y. & Shoichet, B.K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 46, 4265–4272 (2003).

    Article  CAS  Google Scholar 

  6. Frenkel, Y.V. et al. Concentration and pH dependent aggregation of hydrophobic drug molecules and relevance to oral bioavailability. J. Med. Chem. 48, 1974–1983 (2005).

    Article  CAS  Google Scholar 

  7. McGovern, S.L., Caselli, E., Grigorieff, N. & Shoichet, B.K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).

    Article  CAS  Google Scholar 

  8. McGovern, S.L. & Shoichet, B.K. Kinase inhibitors: not just for kinases anymore. J. Med. Chem. 46, 1478–1483 (2003).

    Article  CAS  Google Scholar 

  9. Seidler, J., McGovern, S.L., Doman, T. & Shoichet, B.K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 4477–4486 (2003).

    Article  CAS  Google Scholar 

  10. Ryan, A.J., Gray, N.M., Lowe, P.N. & Chung, C-W. Effect of detergent on 'promiscuous' inhibitors. J. Med. Chem. 46, 3448–3451 (2003).

    Article  CAS  Google Scholar 

  11. Lipinski, C.A., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).

    Article  CAS  Google Scholar 

  12. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  13. Weston, G.S., Blazquez, J., Baquero, F. & Shoichet, B.K. Structure-based enhancement of boronic acid-based inhibitors of AmpC β-lactamase. J. Med. Chem. 41, 4577–4586 (1998).

    Article  CAS  Google Scholar 

  14. Ghose, A.K., Viswanadham, V.M. & Wendoloski, J.J.A. Knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1, 55–68 (1999).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Supported by GM71630, the QB3 fund and the Burroughs-Wellcome Fund (A.S.). We thank J. Weisman and members of the Shoichet laboratory for reading this manuscript.

Author information

Author notes

  1. Brian Y Feng and Anang Shelat: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry & Graduate Group in Chemistry and Chemical Biology, 1700 4th St., University of California San Francisco, San Francisco, 94143-2550, California, USA

    Brian Y Feng, Anang Shelat, R Kip Guy & Brian K Shoichet

  2. Eli Lilly & Company Corporate Center, Drop Code 1523, Indianapolis, 46285, Indiana, USA

    Thompson N Doman

Authors
  1. Brian Y Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anang Shelat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thompson N Doman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R Kip Guy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian K Shoichet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to R Kip Guy or Brian K Shoichet.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

The probability-based classifier for DLS data. (PDF 247 kb)

Supplementary Fig. 2

Control data from high-throughput enzyme assay. (PDF 102 kb)

Supplementary Table 1

Interquartile ranges for common physical properties from the CMC*, and prediction and random sets selected from Chemical Diversity, Inc. (PDF 58 kb)

Supplementary Table 2

Results from low-throughput enzyme assays. (PDF 142 kb)

Supplementary Table 3

Results of low-throughput DLS testing to verify HT-DLS results. (PDF 74 kb)

Supplementary Table 4

Summary of predictive model performance. (PDF 64 kb)

Supplementary Table 5

Combined assay results for all compounds. (PDF 273 kb)

Supplementary Methods (PDF 122 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Feng, B., Shelat, A., Doman, T. et al. High-throughput assays for promiscuous inhibitors. Nat Chem Biol 1, 146–148 (2005). https://doi.org/10.1038/nchembio718

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio718

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