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Computer-Based Prediction Models in Regulatory Toxicology

  • Thomas Steger-HartmannEmail author
  • Scott Boyer
Living reference work entry
  • 8 Downloads

Abstract

The increasing regulatory safety demands for the submission and registration of chemicals, pesticides, or pharmaceuticals as well as tightening animal protection legislation have exacerbated the dilemma of regulatory toxicology, where on the one hand the required scientific contributions for the protection of workers, consumers, or patients are constantly augmented while on the other hand the number of experimental animal studies should be reduced.

One way to resolve this dilemma could be the use of computer-assisted systems to predict toxic effects. These so-called “in silico” tools have experienced improvements in their performance and predictive power over the past three decades. They are therefore able to contribute to hazard identification and risk assessment at least for some toxicological endpoints. However, knowledge of how these systems work, the importance of the underlying data quality, and their respective limitations are prerequisites for a sensible application.

References

  1. Alexander DLJ, Tropsha A, Winkler DA (2015) Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55:1316–1322CrossRefGoogle Scholar
  2. Glowienke S, Hasselgren C (2010) Use of structure activity relationship (SAR) evaluations as a critical tool in the evaluation of the genotoxic potential of impurities. In: Teasdale A (ed) Genotoxic impurities: strategies for identification and control. Wiley, New York, pp 97–120Google Scholar
  3. Hasselgren C, Muthas D, Ahlberg E, Andersson S, Carlsson L, Noeske T, Stålring J, Boyer S (2013) Chemoinformatics and beyond – moving from simple models to complex relationships in pharmaceutical computational toxicology. In: Bajorath J (ed) Chemoinformatics: case studies and pharmaceutical applications. Wiley, New York, pp 49–62Google Scholar
  4. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (2017) Assessment and control of DA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk M7(R1). 31 March 2017Google Scholar
  5. Kaltenhäuser J, Kneuer C, Marx-Stoelting P, Niemann L, Schubert J, Stein B, Solecki R (2017) Relevance and reliability of experimental data in human health risk assessment of pesticides. Regul Toxicol Pharmacol 88:227–237CrossRefGoogle Scholar
  6. Klimisch HJ, Andreae M, Tillmann U (1997) A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25:1–5CrossRefGoogle Scholar
  7. Luechtefeld T, Maertens A, Russo DP, Rovida R, Zhu H, Hartung T (2016) Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008–2014. ALTEX 33:95–109PubMedPubMedCentralGoogle Scholar
  8. Luo G, Shen Y, Yang L, Lu A, Xiang Z (2017) A review of drug-induced liver injury databases. Arch Toxicol 91:3039.  https://doi.org/10.1007/s00204-017-2024-8CrossRefPubMedGoogle Scholar
  9. McCann J, Horn L, Kaldor J (1984) An evaluation of Salmonella (Ames) test data in the published literature: application of statistical procedures and analysis of mutagenic potency. Mut Res 134:1–47CrossRefGoogle Scholar
  10. OECD (2004) OECD principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models. https://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf
  11. OECD (2017) Revised guidance document on developing and assessing adverse outcome pathways. Series on testing and assessment no 184. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2013)6&doclanguage=en
  12. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G, Bracken W, Dorato M, Van Deun K, Smith P, Berger B, Heller A (2000) Concordance of the toxicity of pharmaceuticals in humans and animals. Regul Toxicol Pharmacol 32:56–67CrossRefGoogle Scholar
  13. Simon-Hettich B, Rothfuss A, Steger-Hartmann T (2006) Use of computer-assisted prediction of toxic effects of chemical substances. Toxicology 224:156–162CrossRefGoogle Scholar
  14. Steger-Hartmann T, Pognan F (2017) The eTOX consortium: to improve the safety assessment of new drug candidates. Pharmazeutische Medizin 19(1):4–12Google Scholar
  15. Sushko I, Salmina E, Potemkin VA, Poda G, Tetko IV (2012) ToxAlerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. J Chem Inf Model 52(8):2310–2316CrossRefGoogle Scholar
  16. Sutter A, Amberg A, Boyer S, Brigo A, Contrera JF, Custer LL, Dobo KL, Gervais V, Glowienke S, van Gompel J, Greene N, Muster W, Nicolette J, Reddy MV, Thybaud V, Vock E, White AT, Müller L (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol 67:39–52CrossRefGoogle Scholar
  17. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29:476–488CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Head of Investigational ToxicologyBayer AG, PharmaceuticalsBerlinGermany
  2. 2.Synapse ManagersBarcelonaSpain

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