Skip to main content
SpringerLink
Log in
Book cover

Regulatory Toxicology pp 1–13Cite as

Computer-Based Prediction Models in Regulatory Toxicology

  • Living reference work entry
  • First Online:

  • 351 Accesses

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.

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

References

  • 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 55:1316–1322

    Article  CAS  Google Scholar 

  • 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 97–120

    Google Scholar 

  • 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 49–62

    Google Scholar 

  • 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 2017

    Google Scholar 

  • 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 88:227–237

    Article  Google Scholar 

  • Klimisch HJ, Andreae M, Tillmann U (1997) A systematic approach for evaluating the quality of experimental toxicological and ecotoxicological data. Regul Toxicol Pharmacol 25:1–5

    Article  CAS  Google Scholar 

  • 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 33:95–109

    PubMed  PubMed Central  Google Scholar 

  • 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/s00204-017-2024-8

    Article  CAS  PubMed  Google Scholar 

  • 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 134:1–47

    Article  CAS  Google Scholar 

  • 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

  • OECD (2013) The OECD QSAR toolbox. http://www.oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm

  • 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

  • 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 32:56–67

    Article  CAS  Google Scholar 

  • Simon-Hettich B, Rothfuss A, Steger-Hartmann T (2006) Use of computer-assisted prediction of toxic effects of chemical substances. Toxicology 224:156–162

    Article  CAS  Google Scholar 

  • Steger-Hartmann T, Pognan F (2017) The eTOX consortium: to improve the safety assessment of new drug candidates. Pharmazeutische Medizin 19(1):4–12

    Google Scholar 

  • 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):2310–2316

    Article  CAS  Google Scholar 

  • 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 67:39–52

    Article  CAS  Google Scholar 

  • Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29:476–488

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Head of Investigational Toxicology, Bayer AG, Pharmaceuticals, Berlin, Germany

    Thomas Steger-Hartmann

  2. Synapse Managers, Barcelona, Spain

    Scott Boyer

Authors
  1. Thomas Steger-Hartmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott Boyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Steger-Hartmann .

Editor information

Editors and Affiliations

  1. Universität München Inst. Pharmakologie und Toxikologie, München, Germany

    Franz-Xaver Reichl

  2. Tübingen, Germany

    Michael Schwenk

Appendices

Resources

List of noncommercial toxicological internet data sources including a short description:

  • CTD (Comparative Toxicogenomics database; a publicly available database focusing on gene expression data that aims to advance understanding about how environmental exposures affect human health. Structure-based searches are not possible): http://ctdbase.org/

  • COSMOS (toxicological data and information from regulatory submissions and the literature, focusing on chronic toxicity assessment. The database was developed in the framework of the European SEURAT project. Structure-based searches are possible): http://www.cosmostox.eu/what/COSMOSdb/

  • DrugBank (a chem- and bioinformatics resource supported by the Canadian Institutes of Health Research that combines drug (i.e., chemical, pharmacological and pharmaceutical. Structure-based searches are not possible) data with drug target information): https://www.drugbank.ca/

  • DSSTox (Distributed Structure-Searchable Toxicity Database; a resource for public chemistry data, including bioassay and physicochemical data maintained by US EPA. Structure-based searches are not possible): https://www.epa.gov/chemical-research/distributed-structure-searchable-toxicity-dsstox-database Chemicals from the DSSTox can be either downloaded as sd files or accessed via the two dashboards:

  • eTOXsys (user interface of the IMI eTOX project containing a sample set of systemic toxicity studies and predictive tools developed in the project): https://etoxsys.eu/etoxsys.v3-demo-bk/dashboard/

  • IUCLID (REACH study results; a collection of nonconfidential substance data that was submitted to ECHA under the REACH regulation): https://iuclid6.echa.europa.eu/reach-study-results

  • LiverTox (a database hosted by the US National Library of Medicine providing information about drug-induced liver injury caused by prescription and nonprescription drugs, herbals, and dietary supplements): https://livertox.nih.gov/

  • Liver Toxicity Knowledge Base (LTKB) (a database hosted by the FDA containing drugs whose potential to cause DILI (Drug-Induced Liver Injury) in humans has been established using the FDA-approved prescription drug labels): https://www.fda.gov/ScienceResearch/BioinformaticsTools/LiverToxicityKnowledgeBase/default.htm

National Toxicology Program

Selection of Freely Available Software and Tools

Rights and permissions

Reprints and permissions

Copyright information

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

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Steger-Hartmann, T., Boyer, S. (2020). Computer-Based Prediction Models in Regulatory Toxicology. In: Reichl, FX., Schwenk, M. (eds) Regulatory Toxicology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36206-4_36-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-36206-4_36-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36206-4

  • Online ISBN: 978-3-642-36206-4

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics

Search

Navigation