Skip to main content
SpringerLink
Log in
Book cover

Recent Advances in QSAR Studies pp 305–325Cite as

Prediction of Harmful Human Health Effects of Chemicals from Structure

  • Chapter
  • First Online:

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 8))

Abstract

There is a great need to assess the harmful effects of chemicals to which man is exposed. Various in silico techniques including chemical grouping and category formation, as well as the use of (Q)SARs can be applied to predict the toxicity of chemicals for a number of toxicological effects. This chapter provides an overview of the state of the art of the prediction of the harmful effects of chemicals to human health. A variety of existing data can be used to obtain information; many such data are formalized into freely available and commercial databases. (Q)SARs can be developed (as illustrated with reference to skin sensitization) for local and global data sets. In addition, chemical grouping techniques can be applied on “similar” chemicals to allow for read-across predictions. Many “expert systems” are now available that incorporate these approaches. With these in silico approaches available, the techniques to apply them successfully have become essential. Integration of different in silico approaches with each other, as well as with other alternative approaches, e.g., in vitro and -omics through the development of integrated testing strategies, will assist in the more efficient prediction of the harmful health effects of chemicals

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. van Leeuwen CJ, Vermeire T (eds) (2007) Risk assessment of chemicals: An introduction, 2nd edn. Springer, Dordrecht, The Netherlands

    Google Scholar 

  2. Hester RE, Harrison RM (eds) (2006) Alternatives to animal testing. RSC Publishing, Cambridge

    Google Scholar 

  3. van Leeuwen CJ, Bro-Rasmussen F, Feijtel TCJ et al. (1996) Risk assessment and management of new and existing chemicals. Environ Toxicol Pharmacol 2:243–299

    Article  Google Scholar 

  4. European Union (2007) Corrigendum to Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC (OJ L396, 30.12.2006). Off J Eur Union L 136:50

    Google Scholar 

  5. Schaafsma G, Kroese ED, Tielemans, ELJP et al. (2009) REACH, non-testing approaches and the urgent need for a change in mind set. Reg Tox Pharm 53:70–80

    Article  CAS  Google Scholar 

  6. van Leeuwen CJ, Hansen BG, de Bruijn JHM (2007) Management of industrial chemicals in the European Union (REACH). In: Van Leeuwen CJ and Vermeire TG (eds), Risk Assessment of Chemicals An Introduction. 2nd edn, Springer Publishers, Dordrecht, The Netherlands, pp. 511–551

    Google Scholar 

  7. Cronin MTD (2005) Toxicological information for use in predictive modeling: quality, sources and databases. In Helma C (ed) Predictive toxicology. Taylor and Francis, Boca Raton, FL, pp. 93–133

    Chapter  Google Scholar 

  8. Bassan A, Worth AP (2008) The integrated use of models for the properties and effects of chemicals by means of a structured workflow. QSAR Comb Sci 27:6–20

    Article  CAS  Google Scholar 

  9. Cronin MTD, Schultz TW (2003) Pitfalls in QSAR. J Mol Struct (Theochem) 622:39–51

    Article  CAS  Google Scholar 

  10. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–266

    Google Scholar 

  11. Young D, Martin T, Venkatapathy R et al. (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27:1337–1345

    Article  CAS  Google Scholar 

  12. Klimisch HJ, Andreae E, Tillmann U (1997) A systematic approach for evaluating the quality of experimental and ecotoxicological data. Reg Tox Pharm 25:1–5

    Article  CAS  Google Scholar 

  13. Cronin MTD, Basketter DA (1994) A multivariate QSAR analysis of a skin sensitization database. SAR QSAR Environ Res 2:159–179

    Article  CAS  Google Scholar 

  14. Gerberick GF, Ryan CA, Kern PS et al. (2005) Compilation of historical local lymph node data for evaluation of skin sensitization alternative methods. Dermatitis 16:157–202

    Google Scholar 

  15. Roberts DW, Patlewicz G, Dimitrov SD et al. (2007) TIMES-SS—A mechanistic evaluation of an external validation study using reaction chemistry principles. Chem Res Toxicol 20:1321–1330

    Article  CAS  Google Scholar 

  16. Patlewicz G, Aptula AO, Uriarte E et al. (2007) An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential. SAR QSAR Environ Res 18:515–541

    Article  CAS  Google Scholar 

  17. Patlewicz G, Aptula AO, Roberts DW et al. (2008) A minireview of available skin sensitization (Q)SARs/expert systems. QSAR Comb Sci 27:60–76

    Article  CAS  Google Scholar 

  18. Roberts DW, Aptula AO, Cronin MTD et al. (2007) Global (Q)SARs for skin sensitisation – assessment against OECD principles. SAR QSAR Environ Res 18:343–365

    Article  CAS  Google Scholar 

  19. Cronin MTD, Basketter DA (1993). A QSAR evaluation of an existing contact allergy database. In: Wermuth CG (ed) Trends in QSAR and molecular modelling 92. Escom, Leiden, pp. 297–298

    Google Scholar 

  20. Payne MP, Walsh PT (1994) Structure–activity relationships for skin sensitization potential: Development of structural alerts for use in knowledge-based toxicity prediction systems. J Chem Inf Comput Sci 34:154–161

    CAS  Google Scholar 

  21. Barratt MD, Basketter DA, Chamberlain M et al. (1994) An expert system rulebase for identifying contact allergens. Toxicol In Vitro 8:1053–1060

    Article  CAS  Google Scholar 

  22. Aptula AO, Roberts DW (2006) Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: General principles and application to reactive toxicity. Chem Res Toxicol 19:1097–1105

    Article  CAS  Google Scholar 

  23. Roberts DW, Aptula AO, Patlewicz G (2007) Electrophilic chemistry related to skin sensitization. Reaction mechanistic applicability domain classification for a published data set of 106 chemicals tested in the mouse local lymph node assay. Chem Res Toxicol 20:44–60

    Article  CAS  Google Scholar 

  24. Enoch SJ, Madden JC, Cronin MTD (2008) Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach. SAR QSAR Environ Res 19:555–578

    Article  CAS  Google Scholar 

  25. Fedorowicz A, Singh H, Soderholm S et al. (2005) Structure–activity models for contact sensitization. Chem Res Toxicol 18:954–969

    Article  CAS  Google Scholar 

  26. Li S, Adam Fedorowicz A, Singh H et al. (2005) Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Model 45:952–964

    Article  CAS  Google Scholar 

  27. Devillers J (2000) A neural network SAR model for allergic contact dermatitis. Toxicol Mech Meth 10:181–193

    Article  CAS  Google Scholar 

  28. Ren Y, Liu H, Xue C et al. (2006) Classification study of skin sensitizers based on support vector machine and linear discriminant analysis. Anal Chim Acta 572:272–282

    Article  CAS  Google Scholar 

  29. Basketter D, Darlenski R, Fluhr JW (2008) Skin irritation and sensitization: mechanisms and new approaches for risk assessment. 2. Skin sensitization. Skin Pharmacol Physiol 21:191–202

    Article  CAS  Google Scholar 

  30. Basketter DA, Roberts DW, Cronin M et al. (1992) The value of the local lymph node assay in quantitative structure activity investigations. Contact Derm 27:137–142

    Article  CAS  Google Scholar 

  31. Patlewicz G, Roberts DW, Walker JD (2003) QSARs for the skin sensitization potential of aldehydes and related compounds. QSAR Comb Sci 22:196–203

    Article  Google Scholar 

  32. Patlewicz GY, Basketter DA, Pease CKS et al. (2004) Further evaluation of quantitative structure–activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens. Contact Derm 50:91–97

    Article  CAS  Google Scholar 

  33. Enoch SJ, Cronin MTD, Schultz TW et al. (2008) Quantitative and mechanistic read across for predicting the skin sensitization potential of alkenes acting via Michael addition. Chem Res Toxicol 21:513–520

    Article  CAS  Google Scholar 

  34. Miller MD, Yourtee DM, Glaros AG et al. (2005) Quantum mechanical structure−activity relationship analyses for skin sensitization. J Chem Inf Model 45:924–929

    Article  CAS  Google Scholar 

  35. Estrada E, Patlewicz G, Chamberlain M et al. (2003) Computer-aided knowledge generation for understanding skin sensitization mechanisms: The TOPS-MODE approach. Chem Res Toxicol 16:1226–1235

    Article  CAS  Google Scholar 

  36. Dearden JC, Barratt MD, Benigni R et al. (1997) The development and validation of expert systems for predicting toxicity. Altern Lab Anim 25:223–252

    Google Scholar 

  37. Helma C (ed) (2005) Predictive toxicology. CRC Press, Boca Raton, FL

    Google Scholar 

  38. Mohan CG, Gandhi T, Garg D (2007) Computer-assisted methods in chemical toxicity prediction. Mini-Rev Med Chem 7:499–507

    Article  CAS  Google Scholar 

  39. Muster W, Breidenbach A, Fischer H et al. (2008) Computational toxicology in drug development. Drug Disc Today 13:303–310

    Article  CAS  Google Scholar 

  40. Nigsch F, Macaluso NJM, Mitchell JBO et al. (2009) Computational toxicology: an overview of the sources of data and of modelling methods. Exp Opin Drug Metab Toxicol 5:1–14

    Article  CAS  Google Scholar 

  41. Gallegos-Saliner A, Patlewicz G, Worth AP (2008) A review of (Q)SAR models for skin and eye irritation and corrosion. QSAR Comb Sci 27:49–59

    Article  Google Scholar 

  42. Cronin MTD, Worth AP (2008) (Q)SARs for predicting effects relating to reproductive toxicity. QSAR Comb Sci 27:91–100

    Article  CAS  Google Scholar 

  43. Organization for Economic Cooperation and Development (OECD) (2007) Guidance on grouping of chemicals. OECD Environment Health and Safety Publications Series on Testing and Assessment. No. 80. OECD, Paris, France. ENV/JM/MONO(2007)28 available from http://www.oecd.org.

  44. Cunningham AR, Moss ST, Lype SA et al. (2008) Structure−activity relationship analysis of rat mammary carcinogens. Chem Res Toxicol 21:1970–1982

    Article  CAS  Google Scholar 

  45. Koleva YK, Madden JC, Cronin MTD (2008) Formation of categories from structure–activity relationships to allow read-across for risk assessment: toxicity of an unsaturated carbonyl compounds. Chem Res Toxicol 21:2300–2312

    Article  CAS  Google Scholar 

  46. Enoch SJ, Cronin MTD, Hewitt M (2009) Formation of structural categories to allow for read-across for teratogenicity. QSAR Comb Sci 28:696–708

    Article  CAS  Google Scholar 

  47. Patlewicz G, Jeliazkova N, Gallegos-Saliner A et al. (2008) Toxmatch – A new software tool to aid in the development and evaluation of chemically similar groups. SAR QSAR Environ Res 19:397–412

    Article  CAS  Google Scholar 

  48. Gramatica P, Pilutti P, Papa E et al. (2004) Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modelling. J Chem Inf Comput Sci 44:1794–1802

    CAS  Google Scholar 

  49. Hewitt M, Cronin MTD, Madden JC et al. (2007) Consensus QSAR Models: Do the benefits outweigh the complexity? J Chem Inf Model 47:1460–1468

    Article  CAS  Google Scholar 

  50. Matthews EJ, Kruhlak NL, Benz RD et al. (2008) Combined use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows software to achieve high-performance, high-confidence, mode of action-based predictions of chemical carcinogenesis in rodents. Toxicol Mech Meth 18:189–206

    Article  CAS  Google Scholar 

  51. Votano JR, Parham M, Hall LH et al. (2004) Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 19:365–377

    Article  CAS  Google Scholar 

  52. Abshear T, Banik GM, D’Souza ML et al. (2006) A model validation and consensus building environment. SAR QSAR Environ Res 17:311–321

    Article  CAS  Google Scholar 

  53. Grindon C, Combes R, Cronin MTD et al. (2006) Integrated testing strategies for use in the EU REACH system. Altern Lab Anim 34:407–427

    CAS  Google Scholar 

  54. Combes R, Grindon C, Cronin MTD et al. (2008) Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation. Altern Lab Anim 36:45–63

    CAS  Google Scholar 

  55. Combes R, Grindon C, Cronin MTD et al. (2008) An integrated decision-tree testing strategy for eye irritation with respect to the requirements of the EU REACH legislation. Altern Lab Anim 36:81–92

    Google Scholar 

  56. Combes R, Grindon C, Cronin MTD et al. (2008) Integrated decision-tree testing strategies for developmental and reproductive toxicity with respect to the requirements of the EU REACH legislation. Altern Lab Anim 36:65–80

    Google Scholar 

  57. Combes R, Grindon C, Cronin MTD et al. (2008) An integrated decision-tree testing strategy for repeat dose toxicity with respect to the requirements of the EU REACH legislation. Altern Lab Anim 36:93–101

    Google Scholar 

  58. Grindon C, Combes R, Cronin MTD et al. (2007) Integrated decision-tree testing strategies for skin sensitisation with respect to the requirements of the EU REACH Legislation. Altern Lab Anim 35:683–697

    CAS  Google Scholar 

  59. Grindon C, Combes R, Cronin MTD et al. (2007) Integrated decision-tree testing strategies for skin corrosion and irritation with respect to the requirements of the EU REACH Legislation. Altern Lab Anim 35:673–682

    CAS  Google Scholar 

  60. Combes R, Grindon C, Cronin MTD et al. (2007) Proposed integrated decision-tree testing strategies for mutagenicity and carcinogenicity in relation to the EU REACH legislation. Altern Lab Anim 35:267–287

    CAS  Google Scholar 

  61. European Chemicals Agency (2008) Guidance on information requirements and chemical safety assessment, EChA, Helsinki. Available from http://guidance.echa.europa.eu/docs/guidance_document/information_requirements_en.htm?time=1238407373

Download references

Acknowledgement

This project was sponsored by Defra through the Sustainable Arable Link Programme. The funding of the European Union 6th Framework OSIRIS Integrated Project (GOCE-037017-OSIRIS) is also gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, England

    Mark T. D. Cronin

Authors
  1. Mark T. D. Cronin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mark T. D. Cronin .

Editor information

Editors and Affiliations

  1. Dept. Chemistry, University of Gdansk, ul. Jana Sobieskiego 18, Gdansk, 80-952, Poland

    Tomasz Puzyn

  2. Dept. Chemistry, Jackson State University, J. R. Lynch St. 1325, Jackson, 39217, U.S.A.

    Jerzy Leszczynski

  3. Dept. Chemistry, John Moores University, Byrom St., Liverpool, L3 3AF, United Kingdom

    Mark T. Cronin

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Cronin, M.T.D. (2010). Prediction of Harmful Human Health Effects of Chemicals from Structure. In: Puzyn, T., Leszczynski, J., Cronin, M. (eds) Recent Advances in QSAR Studies. Challenges and Advances in Computational Chemistry and Physics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9783-6_11

Download citation

Publish with us

Policies and ethics

Search

Navigation