Skip to main content

QSAR Models for Regulatory Purposes: Experiences and Perspectives

  • Chapter
  • First Online:
Practical Aspects of Computational Chemistry
  • 3050 Accesses

Abstract

Quantitative structure–activity relationships (QSARs) are more and more discussed and used in several situations. Their application to legislative purposes stimulated a large debate in Europe on the recent legislation on industrial chemicals. To correctly assess the suitability of QSAR, the discussion has to be done depending on the target. Different targets modify the model evaluation and use. The application of QSAR for legislative purposes requires keeping into account the use of the values obtained through the QSAR models. False negatives should be minimized. The model should be robust, verified, and validated. Reproducibility and transparency are other important characteristics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation Authorisation and Restriction of Chemicals (REACH). http://eur-lex.europa.eu/LexUriServ/site/en/oj/2006/l_396/l_39620061230en00010 849.pdf

  2. C. Hansch et al., The correlation of biological activity of plant growth-regulators and chloromycetin derivatives with hammett constants and partition coefficients. J. Am. Chem. Soc. 85, 2817–2824 (1963)

    Article  CAS  Google Scholar 

  3. J. Hermens, Quantitative Structure–Activity Relationships for Predicting Fish Toxicity, in Practical Applications of Quantitative Structure–Activity Relationships in Environmental Chemistry and Toxicology, ed. by W. Karcher, J. Devillers (Kluwer, Dordrecht, 1990), pp. 263–280

    Google Scholar 

  4. G.C. Gini, A.R. Katritzky (Eds), Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools. AAAI 1999 Spring Symposium Series. (AAAI Press, Menlo Park, 1999).

    Google Scholar 

  5. J. Devillers (ed), Neural Networks in QSAR and Drug Design (Academic Press, London, 1996)

    Google Scholar 

  6. J. Devillers (ed), Genetic Algorithms in Molecular Modeling (Academic Press, London, 1996)

    Google Scholar 

  7. R. Todeschini, V. Consonni, Handbook of Molecular Descriptors (Wiley-VCH, Weinheim, 2000)

    Book  Google Scholar 

  8. M. Karelson, Molecular Descriptors in QSAR/QSPR (Wiley, New York, 2000)

    Google Scholar 

  9. A.A. Toropov, E. Benfenati, SMILES in QSPR/QSAR modeling: Results and perspectives. Curr. Drug Dis. Technol. 4, 77–116 (2007)

    Article  CAS  Google Scholar 

  10. E. Benfenati, A. Roncaglioni, In silico-aided prediction of biological properties of chemicals: Oestrogen receptor-mediated effects. Chem. Soc. Rev. 37, 441 (2008)

    Article  Google Scholar 

  11. Estimation Programme Interface (EPI) Suite. US EPA. http://www.epa.gov/opptintr/exposure/pubs/episuite.htm

  12. J. Devillers, Application of QSARs in Aquatic Toxicology, in Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals, ed. by S. Ekins (Wiley, Hoboken, 2007), pp. 651–675

    Google Scholar 

  13. E. Benfenati et al., Validation of the Models, in Quantitative Structure–Activity Relationships (QSAR) for Pesticide Regulatory Purposes, ed. by E. Benfenati (Elsevier, Amsterdam, 2007), pp. 185–199

    Chapter  Google Scholar 

  14. L. Eriksson et al., Methods for reliability, uncertainty assessment, and applicability evaluations of regression based and classification QSARs. Environ. Health Perspect. 111, 1361–1375 (2003)

    Article  CAS  Google Scholar 

  15. A. Golbraikh, A. Tropsha, Beware of q2!. J. Mol. Graph Model. 20, 269–276 (2002)

    Article  CAS  Google Scholar 

  16. R. Benigni et al., The expanding role of predictive toxicology: An update on the (Q)SAR models for mutagens and carcinogens. J. Environ. Sci. Health C 25, 53–97 (2007)

    Article  CAS  Google Scholar 

  17. E. Benfenati, Predicting toxicity through computers: a changing world. Chem. Cent. J. 1, 32 (2007)

    Article  Google Scholar 

  18. OECD. OECD Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure–Activity Relationship Models. Paris, France. http://www.oecd.org/document/23/0,3343,en_2649_34379_33957015_1_1_1_1,00.html

  19. E. Benfenati, The specificity of the QSAR models for regulatory purposes: the example of the DEMETRA project. SAR QSAR Environ. Res. 18, 209–220 (2007)

    Article  CAS  Google Scholar 

  20. DEMETRA EC project. http://www.demetra-tox.net

  21. TOPKAT software. http://www.accelrys.com/products/topkat/

  22. MULTICASE software. http://multicase.com/products/prod09.htm

  23. HazardExpert software. http://www.compudrug.com/

  24. DEREK software. http://www.lhasalimited.org/index.php?cat = 2&sub_cat = 64

  25. OECD QSAR Toolbox. http://www.oecd.org/document/23 /0,3343,en_2649_37465_33957015_1_1_1_37465,00.html

  26. E. Benfenati (ed), Quantitative Structure–Activity Relationships (QSAR) for Pesticide Regulatory Purposes (Elsevier, Amsterdam, 2007)

    Google Scholar 

  27. C. Porcelli et al., Regulatory perspectives in the use and validation of QSAR. A case study: DEMETRA model for daphnia toxicity. Environ. Sci. Technol. 42, 491–496 (2008)

    Article  CAS  Google Scholar 

  28. CAESAR EC project. http://www.caesar-project.eu

  29. CHEMOMENTUM EC project. http://www.chemomentum.org

  30. OSIRIS project. http://www.osiris-reach.eu

Download references

Acknowledgements

We gratefully acknowledge the financial contribution of the European Commission’s CAESAR (Contract SSPI 022674), OSIRIS (Contract GOCE-CT-2007-037017), and CHEMOMENTUM projects (Contract MIF1-CT-2006-039036).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emilio Benfenati .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Benfenati, E. (2009). QSAR Models for Regulatory Purposes: Experiences and Perspectives. In: Leszczynski, J., Shukla, M. (eds) Practical Aspects of Computational Chemistry. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2687-3_8

Download citation

Publish with us

Policies and ethics