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Batke M, Gütlein M, Partosch F, et al. Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties. Front Pharmacol. 2016;7:321doi: 10.3389/fphar.2016.00321.
Batke, M., Gütlein, M., Partosch, F., Gundert-Remy, U., Helma, C., Kramer, S., Maunz, A., Seeland, M., & Bitsch, A. (2016). Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties. Frontiers in pharmacology, 7321. https://doi.org/10.3389/fphar.2016.00321
Batke, Monika, et al. "Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties." Frontiers in pharmacology vol. 7 (2016): 321. doi: https://doi.org/10.3389/fphar.2016.00321
Batke M, Gütlein M, Partosch F, Gundert-Remy U, Helma C, Kramer S, Maunz A, Seeland M, Bitsch A. Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties. Front Pharmacol. 2016 Sep 21;7:321. doi: 10.3389/fphar.2016.00321. eCollection 2016. PMID: 27708580; PMCID: PMC5030828.
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Front Pharmacol. 2016 Sep 21;7:321. doi: 10.3389/fphar.2016.00321. eCollection 2016.

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties.

Frontiers in pharmacology

Monika Batke, Martin Gütlein, Falko Partosch, Ursula Gundert-Remy, Christoph Helma, Stefan Kramer, Andreas Maunz, Madeleine Seeland, Annette Bitsch

Affiliations

  1. Department Chemikalienbeureilung, Dantenbanken und Expertensysteme, Fraunhofer Institut für Toxikologie und Experimentelle Medizin Hannover, Germany.
  2. Institut für Informatik, Johannes Gutenberg-Universität Mainz Mainz, Germany.
  3. Institut für Arbeits-, Sozial- und Umweltmedizin, Universitätsmedizin Göttingen Göttingen, Germany.
  4. Institut für Klinische Pharmakologie und Toxikologie, Charité Universitätsmedizin Berlin Berlin, Germany.
  5. In Silico Toxicology GmbH Basel, Switzerland.
  6. Oncotest GmbH Freiburg, Germany.
  7. Institut für Informatik, Technische Universität München München, Germany.

PMID: 27708580 PMCID: PMC5030828 DOI: 10.3389/fphar.2016.00321

Abstract

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

Keywords: Predictive Clustering Tree (PCT) method; QSAR; non-animal methods; read across; toxicological and structural similarity

References

  1. Artif Intell Med. 2010 Oct;50(2):105-15 - PubMed
  2. Fundam Appl Toxicol. 1990 Jan;14 (1):199-207 - PubMed
  3. Exp Clin Endocrinol Diabetes. 2010 Nov;118(10):678-84 - PubMed
  4. Bioorg Med Chem Lett. 2008 Sep 1;18(17):4872-5 - PubMed
  5. Front Pharmacol. 2013 Apr 09;4:38 - PubMed
  6. Regul Toxicol Pharmacol. 2014 Mar;68(2):275-96 - PubMed
  7. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2014;32(3):273-98 - PubMed
  8. Molecules. 2010 Jul 27;15(8):5079-92 - PubMed
  9. J Med Chem. 2014 Jun 26;57(12):4977-5010 - PubMed
  10. Regul Toxicol Pharmacol. 2013 Mar;65(2):189-95 - PubMed
  11. Arch Toxicol. 2010 Sep;84(9):681-8 - PubMed
  12. Chem Res Toxicol. 2013 Aug 19;26(8):1199-208 - PubMed
  13. Clin Toxicol (Phila). 2009 Jul;47(6):525-35 - PubMed
  14. J Toxicol Sci. 2012;37(3):503-15 - PubMed
  15. Environ Health Perspect. 1984 Aug;57:233-9 - PubMed
  16. ALTEX. 2014;31(4):387-96 - PubMed
  17. Clin J Am Soc Nephrol. 2008 Jan;3(1):208-25 - PubMed
  18. Bioorg Med Chem Lett. 2010 Sep 1;20(17):5308-12 - PubMed
  19. J Chem Inf Comput Sci. 2002 Nov-Dec;42(6):1273-80 - PubMed
  20. Regul Toxicol Pharmacol. 2010 Feb;56(1):67-81 - PubMed
  21. J Chem Inf Comput Sci. 2004 Sep-Oct;44(5):1623-9 - PubMed
  22. Toxicol In Vitro. 2012 Jun;26(4):613-20 - PubMed
  23. SAR QSAR Environ Res. 2013;24(5):351-63 - PubMed
  24. Regul Toxicol Pharmacol. 2014 Dec;70(3):711-9 - PubMed
  25. Regul Toxicol Pharmacol. 2006 Dec;46(3):202-10 - PubMed
  26. Eur J Endocrinol. 2006 Jul;155(1):17-25 - PubMed
  27. Arch Toxicol. 2012 Jan;86(1):17-25 - PubMed
  28. Regul Toxicol Pharmacol. 2013 Oct;67(1):1-12 - PubMed
  29. J Anal Toxicol. 2015 Jul-Aug;39(6):481-5 - PubMed
  30. J Cell Biol. 1976 Jul;70(1):247-51 - PubMed
  31. J Cheminform. 2012 Mar 17;4(1):7 - PubMed
  32. SAR QSAR Environ Res. 2013 Jan;24(1):35-46 - PubMed
  33. SAR QSAR Environ Res. 2008;19(5-6):413-31 - PubMed
  34. J Toxicol Sci. 2013;38(2):291-9 - PubMed
  35. J Anal Toxicol. 2003 Nov-Dec;27(8):569-73 - PubMed
  36. J Cheminform. 2011 Oct 07;3:33 - PubMed
  37. Endocrinology. 1997 Jul;138(7):2871-8 - PubMed
  38. Regul Toxicol Pharmacol. 2003 Jun;37(3):356-69 - PubMed
  39. Regul Toxicol Pharmacol. 2015 Apr;71(3):388-97 - PubMed
  40. J Toxicol Sci. 2015 Feb;40(1):77-98 - PubMed

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