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Hypothesis-Driven Screening

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Chemogenomics

Summary

Phenotypic chemogenomics studies require screening strategies that account for the complex nature of the experimental system. Unknown mechanism of action and high frequency of false positives and false negatives necessitate iterative experiments based on hypotheses formed on the basis of results from the previous step. Process-driven High Throughput Screening (HTS), aiming to “industrialize” lead finding and developed to maximize throughput, is rarely affording sufficient flexibility to design hypothesis-based experiments.

In this contribution, we describe a High Throughput Cherry Picking (HTCP) system based on acoustic dispensing technology that was developed to support a new screening paradigm. We demonstrate the power of hypothesis-based screening in three chemogenomics studies that were recently conducted.

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References

  1. Booth, B. and Zemmel, R. (2004) Prospects for productivity. Nat. Rev. Drug Discov. 3, 451–456.

    Article  PubMed  CAS  Google Scholar 

  2. Mitchison, T. J. (1994) Towards a pharmacological genetics. Chem. Biol. 1, 3–6.

    Article  PubMed  CAS  Google Scholar 

  3. Schreiber, S. L. (1998) Chemical genetics resulting from a passion for synthetic organic chemistry. Bioorg. Med. Chem. 6, 1127–1152.

    Article  PubMed  CAS  Google Scholar 

  4. Chiang, S. L. (2006) Chemical genetics: use of high-throughput screening to identify small-molecule modulators of proteins involved in cellular pathways with the aim of uncovering protein function. In: J. Hüser (ed.) High Throughput-Screening in Drug Discovery. Wiley-VCH, Weinheim, pp. 1–13.

    Chapter  Google Scholar 

  5. Hertzberg, R. P. and Pope, A. J. (2000) High-throughput screening: new technologies for the 21st century. Curr. Opin. Chem. Biol. 4, 445–451.

    Article  PubMed  CAS  Google Scholar 

  6. Macarron, R. (2006) Critical review of the role of HTS in drug discovery. Drug Discov. Today 11, 277–279.

    Article  PubMed  Google Scholar 

  7. http://www.sbsonline.org

  8. Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26.

    Article  PubMed  CAS  Google Scholar 

  9. Zhang, J. H., Chung, T. D. Y., and Oldenburg, K. R. (2000) Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations. J. Comb. Chem. 2, 258–265.

    Article  PubMed  CAS  Google Scholar 

  10. Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J., and Nadon, R. (2006) Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24, 167–175.

    Article  PubMed  CAS  Google Scholar 

  11. Padmanabha, R., Cook, L., and Gill, J. (2005) HTS quality control and data analysis: a process to maximize information from a high-throughput screen. Comb. Chem. High Throughput Screen. 8, 512–527.

    Article  Google Scholar 

  12. Alanine, A., Nettekoven, M., Roberts, E., and Thomas, A. W. (2003) Lead generation – enhancing the success of drug discovery by investing in the hit to lead process. Comb. Chem. High Throughput Screen. 6, 51–66.

    PubMed  CAS  Google Scholar 

  13. Schopfer, U., Engeloch, C., Stanek, J., Girod, M., Schuffenhauer, A., Jacoby, E., and Acklin, P. (2005) The Novartis compound archive - from concept to reality. Comb. Chem. High Throughput Screen. 8, 513–519.

    Article  PubMed  CAS  Google Scholar 

  14. http://www.velocity11.com.

  15. Scheel, G., Pfeiffer, M. J. (2009) Long-Term Storage of Compound Solutions for High Throughput Screening by Using a Novel 1536-Well Microplate. J. Biomol. Screen. 14, 492–498.

    Article  PubMed  Google Scholar 

  16. http://www.thermo.com.

  17. Engeloch, C., Schopfer, U., Muckenschnabel, I., Le Goff, F., Mees, H., Boesch, K., Hueber, M., and Popov, M. (2008) Stability of screening compounds in wet DMSO. J. Biomol. Screen. 13, 999–1006.

    Article  PubMed  CAS  Google Scholar 

  18. http://las.perkinelmer.com.

  19. http://www.labcyte.com.

  20. Slee, A. M., Wuonola, M. A., McRipley, R. J., Zajac, I., Zawada, M. J., Bartholomew, P. T., Gregory, W. A., and Forbes, M. (1987) Oxazolidinones, a new class of synthetic antibacterial agents: invitro and invivo activities of DuP 105 and DuP 721. Antimicrob. Agents Chemother. 31, 1791–1797.

    Article  PubMed  CAS  Google Scholar 

  21. Projan, S. J. and Bradford, P. A. (2007) Late stage antibacterial drugs in the clinical pipeline. Curr. Opin. Microbiol. 10, 441–446.

    Article  PubMed  CAS  Google Scholar 

  22. Payne, D. J., Gwynn, M. N., Holmes, D. J., and Pompliano, D. L. (2007) Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug Discov. 6, 29–40.

    Article  PubMed  CAS  Google Scholar 

  23. O’Brien, J., Wilson, I., Orton, T., and Pognan, F. (2000) Investigation of the Alamar Blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity. Eur. J. Biochem. 267, 5421–5426.

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  25. Egan, W. J., Merz, K. M. Jr., and Baldwin, J. J. (2000) Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43, 3867–3877.

    Article  PubMed  CAS  Google Scholar 

  26. National Committee for Clinical Laboratory Standards (2003) Methods for Dilution Antimicrobial Susceptibility Test for Bacteria That Grow Aerobically; Approved Standard – Sixth Edition. NCCLS document M7-A6. NCCLS, Wayne, PA.

    Google Scholar 

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Acknowledgments

Drs E. Jacoby and P. Fürst (both NIBR associates) are acknowledged for support and discussions.

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© 2009 Humana Press, a part of Springer Science+Business Media, LLC

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Schopfer, U. et al. (2009). Hypothesis-Driven Screening. In: Jacoby, E. (eds) Chemogenomics. Methods in Molecular Biology, vol 575. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-274-2_13

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  • DOI: https://doi.org/10.1007/978-1-60761-274-2_13

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-273-5

  • Online ISBN: 978-1-60761-274-2

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