234
Views
3
CrossRef citations to date
0
Altmetric
Review

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

&
Pages 987-1005 | Received 01 Apr 2021, Accepted 01 Jun 2021, Published online: 21 Jun 2021

References

  • EFSA. Scientific Opinion on an update on the present knowledge on the occurrence and control of foodborne viruses. EFSA J. 2011;9(7):2379 -n/a..
  • European Medicines Agency. ICH guideline S2 (R1) on genotoxicity testing and data interpretation for pharmaceuticals intended for human use. 2012. EMA/CHMP/ICH/126642/200.
  • European Commission. 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), 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. The Official Journal of the European Union.. L396/1. 2006.
  • Food and Drug Administration. Use of International Standard ISO 10993–1, “Biological evaluation of medical devices - Part 1: Evaluation and testing within a risk management process.” 2016.
  • European Medicines Agency. ICH guideline M7(R1) on assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. 2017. EMA/CHMP/ICH/83812/2013
  • EFSA. Guidance on the establishment of the residue definition for dietary risk assessment. EFSA J. 2016;14(12): 4549.
  • ECHA. Read-Across Assessment Framework (RAAF): European Chemicals Agency; 2017. [cited 2021 Jun 21]. Available from: https://echa.europa.eu/documents/10162/13628/raaf_en.pdf.
  • ECHA. The use of alternatives to testing on animals for the REACH Regulation. European Chemicals Agency, 2017 June 2017. Report No.
  • ECHA. Practical Guide: how to use alternatives to animal testing to fulfil the information requirements for REACH registration: European Chemicals Agency; 2016. [cited 2021 Jun 21]. Available from: https://echa.europa.eu/documents/10162/13655/practical_guide_how_to_use_alternatives_en.pdf/148b30c7-c186-463c-a898-522a888a4404.
  • ECHA. Practical Guide – how to use and report (Q)SARs: European Chemicals Agency (ECHA); 2016. [cited 2021 Jun 21]. Available from: https://echa.europa.eu/documents/10162/13655/pg_report_qsars_en.pdf/407dff11-aa4a-4eef-a1ce-9300f8460099.
  • ECHA. Evaluation under REACH Progress Report 2016 – executive summary and recommendations to registrants. European Chemicals Agency, 2016 March 2017. Report No.
  • SCCS (Scientific Committee on Consumer Safety), SCCS Notes of Guidance for the Testing of Cosmetic Ingredients and their Safety Evaluation 10th revision. 24-25 October 2018.
  • OECD. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. 2007.
  • Younes M, Aquilina G, Castle L, et al. Scientific Guidance for the preparation of applications on smoke flavouring primary products. EFSA J. 2021;19(3):e06435.
  • Honma M, Kitazawa A, Cayley A, et al. Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project. Mutagenesis. 2019;34(1):3–16. Epub 2018/ 10/26.. PubMed PMID: 30357358; PubMed Central PMCID: PMCPMC6402315.
  • Hasselgren C, Ahlberg E, Akahori Y, et al. Genetic toxicology in silico protocol. Regul Toxicol Pharmacol. 2019;107:104403. Epub 2019/ 06/14.. PubMed PMID: 31195068; PubMed Central PMCID: PMCPMC7485926. .
  • Amberg A, Beilke L, Bercu J, et al. Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regul Toxicol Pharmacol. 2016;77:13–24. Epub 2016/ 02/16.. PubMed PMID: 26877192. .
  • Benigni R, Serafimova R, Parra Morte JM, et al. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: an EFSA funded project. Regul Toxicol Pharmacol. 2020;114:104658.
  • Morita T, Shigeta Y, Kawamura T, et al. In silico prediction of chromosome damage: comparison of three (Q)SAR models. Mutagenesis. 2019;34(1):91–100. Epub 2018/ 08/08.. PubMed PMID: 30085209.
  • Benigni R, Battistelli CL, Bossa C, et al. New perspectives in toxicological information management, and the role of ISSTOX databases in assessing chemical mutagenicity and carcinogenicity. PubMed PMID: 23470317 Mutagenesis. 2013;284:401–409.
  • Benigni R, Bossa C, Tcheremenskaia O, et al. The new ISSMIC database on in vivo micronucleus and its role in assessing genotoxicity testing strategies. PubMed PMID: 21965461 Mutagenesis. 2012;271:87–92.
  • Metruccio F, Castelli I, Civitella C, et al. Compilation of a database, specific for the pesticide active substance and their metabolites, comprising the main genotoxicity endpoints. EFSA Supporting Publications. 2017;14(5):1229E.
  • Sasaki JC, Allemang A, Bryce SM, et al. Application of the adverse outcome pathway framework to genotoxic modes of action. Environ Mol Mutagen. 2020;61(1):114–134.
  • Sakuratani Y, Horie M, Leinala E. Integrated Approaches to Testing and Assessment: OECD activities on the development and use of adverse outcome pathways and case studies. Basic Clin Pharmacol Toxicol. 2018; 123 Suppl 123: 20–28. Epub 2018/ 01/10.. PubMed PMID: 29316278.
  • Barber C, Amberg A, Custer L, et al. Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol. 2015;73(1):367–377. Epub 2015/ 08/08. PubMed PMID: 26248005.
  • Committee ES, Benford D, Halldorsson T, et al. Guidance on uncertainty analysis in scientific assessments. EFSA J. 2018;16(1):e05123.
  • Cronin MTD, Richarz A-N, Schultz TW. Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction. Regul Toxicol Pharmacol. 2019;106:90–104..
  • Schultz TW, Richarz A-N, Cronin MTD. Assessing uncertainty in read-across: questions to evaluate toxicity predictions based on knowledge gained from case studies. Comput Toxicol. 2019;9:1–11.
  • Hardy A, Benford D, Halldorsson T, et al. EFSA Scientific Committee. Guidance on the use of the weight of evidence approach in scientific assessments. EFSA J. 2017;15(8):e04971.
  • SCCS (Scientific Committee on Consumer Safety), Memorandum on the use ofIn silico Methodsfor Assessment of Chemical Hazard. 2016 Contract No.: SCCS/1578/16.
  • ECHA. Guidance on information requirements and chemical safety assessment, Chapter R.6: qSARs and grouping of chemicals May 2008. [cited 2021 Jun 21]. Available from: https://echa.europa.eu/documents/10162/13632/information_requirements_r6_en.pdf.
  • Schultz TW, Diderich R, Kuseva CD, et al. The OECD QSAR Toolbox starts its second decade. Methods Mol Biol Epub 2018/ 06/24. PubMed PMID: 29934887. 2018;1800:55–77.
  • Kuseva C, Schultz TW, Yordanova D, et al. The implementation of RAAF in the OECD QSAR Toolbox. Regul Toxicol Pharmacol. 2019;105:51–61. Epub 2019/ 04/11.. PubMed PMID: 30970268. .
  • Paternò A, Bocci G, Goracci L, et al. Modelling the aquatic toxicity of ionic liquids by means of VolSurf in silico descriptors. SAR QSAR Environ Res. 2016;27(1):1–17. PubMed PMID: 26892800.
  • The European Commission. Setting out the data requirements for active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market. Official Journal of European Union. (COMMISSION REGULATION (EU) No 283/2013).
  • Scientific EFSA-P-PR. Opinion on Evaluation of the Toxicological Relevance of Pesticide Metabolites for Dietary Risk Assessment. EFSA J. 2012;10(7):2799.
  • EU-JRC. Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment EFSA Supporting Publications. 2010;7(5): 50E-n/a. doi: 10.2903/sp.efsa.2010.EN-50.
  • Hasselgren C, Bercu J, Cayley A, et al. Management of pharmaceutical ICH M7 (Q)SAR predictions – the impact of model updates. Regul Toxicol Pharmacol. 2020;118:104807.
  • Barber C, Cayley A, Hanser T, et al. Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained. Regul Toxicol Pharmacol. 2016;76:7–20. SupplementC.
  • Barber C, Hanser T, Judson P, et al. Distinguishing between expert and statistical systems for application under ICH M7. Regul Toxicol Pharmacol. 2017;84:124–130. SupplementC.
  • Cartus A, Schrenk D. Current methods in risk assessment of genotoxic chemicals. Food Chem Toxicol. 2017;106:574–582. PartB.
  • Greene N, Dobo KL, Kenyon MO, et al. A practical application of two in silico systems for identification of potentially mutagenic impurities. Regul Toxicol Pharmacol. 2015;72(2):335–349. Epub 2015/ 05/20. PubMed PMID: 25980641.
  • Powley MW. (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission. Regul Toxicol Pharmacol. 2015;71(2):295–300.
  • Sutter A, Amberg A, Boyer S, et al. Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol. 2013;67(1):39–52.
  • Teasdale A. Regulatory Highlights. Org Process Res Dev. 2017;21(9):1209–1212.
  • Williams RV, Amberg A, Brigo A, et al. It’s difficult, but important, to make negative predictions. Regul Toxicol Pharmacol. 2016;76:79–86. SupplementC.
  • Fioravanzo E, Bassan A, Pavan M, et al. Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities. SAR QSAR Environ Res. 2012;23(3–4):257–277.
  • Serafimova R, Gatnik MF, Worth A. Review of QSAR Models and Software Tools for Predicting Genotoxicity and Carcinogenicity. JRC Technical Report EUR 24427 EN, Luxenbourg, 2010.
  • Benigni R, Battistelli CL, Bossa C, et al. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across. EFSA Supporting Publications. 2019;16(3):1598E.
  • Ashby J, Tennant RW. Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutation Research/Genetic Toxicology. 1988;204(1):17–115. Epub 1988/ 01/01. PubMed PMID: 3277047.
  • Honma M An assessment of mutagenicity of chemical substances by (quantitative) structure-activity relationship. Genes and environment: the official journal of the Japanese Environmental Mutagen Society. 2020;42:23. Epub 2020/ 07/07. doi: 10.1186/s41021-020-00163-1. PubMed PMID: 32626544; PubMed Central PMCID: PMCPmc7330942.
  • Benigni R, Bossa C. Data-based review of QSARs for predicting genotoxicity: the state of the art. Mutagenesis. 2019;34(1):17–23. Epub 2018/ 09/28. PubMed PMID: 30260416. .
  • Jolly R, Ahmed KB, Zwickl C, et al. An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment. Regul Toxicol Pharmacol. 2015;71(3):388–397. Epub 2015/ 02/07. PubMed PMID: 25656493.
  • Lhasa. Vitic Nexus. [cited 2021 Jun 21]. Available from: http://www.lhasalimited.org/products/vitic-nexus.htm 2014.
  • Hansen K, Mika S, Schroeter T, et al. Benchmark data set for in silico prediction of Ames mutagenicity. J Chem Inf Model. 2009;49(9):2077–2081. Epub 2009/ 08/26. PubMed PMID: 19702240.
  • Kazius J, McGuire R, Derivation BR. Validation of toxicophores for mutagenicity prediction. J Med Chem. 2005;48:312–320.
  • Feng J, Lurati L, Ouyang H, et al. Predictive Toxicology:  benchmarking Molecular Descriptors and Statistical Methods. J Chem Inf Comput Sci. 2003;43(5):1463–1470. Epub 2003/ 09/23. PubMed PMID: 14502479.
  • Gold LS. Carcinogenic Potency Database. [cited 2021 Jun 21]. Available from: https://www.nlm.nih.gov/databases/download/cpdb.html presently hosted at: https://www.lhasalimited.org/products/lhasa-carcinogenicity-database.htm
  • Chemical Carcinogenesis Research Information System (CCRIS). [cited 2021 Jun 21]. Available from: https://www.ncbi.nlm.nih.gov/pcsubstance?term=%22Chemical%20Carcinogenesis%20Research%20Information%20System%20(CCRIS)%22%5BSourceName%5D%20AND%20hasnohold%5Bfilt%5D.
  • Helma C, Cramer T, Kramer S, et al. Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. JChemInfCompSci. 2004;44:1402–1411.
  • Judson PN, Cooke PA, Doerrer NG, et al. Towards the creation of an international toxicology information centre. Toxicology. 2005;213(1–2):117–128..
  • Genetic Toxicology Data Bank (GENE-TOX). [cited 2021 Jun 21]. Available from: https://www.ncbi.nlm.nih.gov/pcsubstance?term=%22Genetic%20Toxicology%20Data%20Bank%20(GENE-TOX)%22%5BSourceName%5D%20AND%20hasnohold%5Bfilt%5D.
  • Bakhtyari NG, Raitano G, Benfenati E, et al. Comparison of in silico models for prediction of mutagenicity. J Environ Sci Health Part C, Environmental Carcinogenesis & Ecotoxicology reviews. 2013;31(1):45–66. Epub 2013/ 03/29. PubMed PMID: 23534394.;
  • Valencia A, Prous J, Mora O, et al. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol Appl Pharmacol. 2013;273:427–4324.
  • Tcheremenskaia O, Battistelli CL, Giuliani A, et al. In silico approaches for prediction of genotoxic and carcinogenic potential of cosmetic ingredients. Comput Toxicol. 2019;11:91–100.
  • Nexus D [ cited 2021]. [cited 2021 Jun 21]. Available from: https://www.lhasalimited.org/products/derek-nexus.htm.
  • MultiCASE. [cited 2021 Jun 21]. Available from: http://www.multicase.com/products.
  • Leadscope [ cited 2021]. [cited 2021 Jun 21]. Available from: https://www.leadscope.com/(Leadscope now is part of Instem https://www.instem.com/).
  • ChemTunes Studio [ cited 2021]. [cited 2021 Jun 21]. Available from: https://www.mn-am.com/products/chemtunestoxgps.
  • Percepta Impurities Suite—Assess Genotoxic and Carcinogenic Risk [ cited 2021 Jun 21]. Available from: https://www.acdlabs.com/products/percepta/impurities.php.
  • LAZAR toxicity predictions 2021. [cited 2021 Jun 21]. Available from: https://lazar.in-silico.ch/predict.
  • Benigni R, Bossa C, Jeliazkova N, et al. The Benigni/Bossa Rulebase for Mutagenicity and Carcinogenicity - A Module of Toxtree. EUR 23241 EN: 2008 JRC43157.
  • Benfenati E, Manganaro A, Gini G, editors. VEGA-QSAR: AI inside a platform for predictive toxicology. CEUR Workshop Proceedings; 2013.
  • Piegorsch W.W and Zeiger E., Measuring intra-assay agreement for the Ames Salmonella assay; in Rienhoff O. and Lindberg D.A.B., Statistical methods in toxicology, 43: 35-41; 1991; Springer-Verlag, Heidelberg.
  • Araya S, Lovsin-Barle E, Glowienke S. Mutagenicity assessment strategy for pharmaceutical intermediates to aid limit setting for occupational exposure. Regul Toxicol Pharmacol. 2015;73(2):515–520.
  • Yoo JW, Kruhlak NL, Landry C, et al. Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay. Regul Toxicol Pharmacol. 2020;113:104620.
  • Hsu C-W, Hewes KP, Stavitskaya L, et al. Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations. Regul Toxicol Pharmacol Epub 2018/ 10/03. PubMed PMID: 30278198. 2018;99:274–288. .
  • Baderna D, Gadaleta D, Lostaglio E, et al. New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. J Hazard Mater. 2020;385:121638. Epub 2019/ 11/24.. PubMed PMID: 31757721. .
  • EC. Directive 2003/15/EC of the European Parliament and of the Council of 27 February 2003 amending Council Directive 76/768/EEC on the approximation of the laws of the Member States relating to cosmetic products. OffJEurUnion. 2003:(L66):26–35.
  • OECD. Guidance on grouping of chemicals. Second Edition. Series on Testing & Assessment, No. 194.: OECD; 2014. [cited 2021 Jun]. Available from: https://www.oecd.org/publications/guidance-on-grouping-of-chemicals-second-edition-9789264274679-en.htm.
  • ECHA. Read-across Assessment Framework (RAAF). Helsinki: ECHA; 2017. [cited 2021 Jun 21]. Available from: https://echa.europa.eu/documents/10162/13628/raaf_en.pdf.
  • Patlewicz G, Helman G, Pradeep P, et al. Navigating through the minefield of read-across tools: a review of in silico tools for grouping. Comput Toxicol. 2017;3:1–18.
  • Patlewicz G, Fitzpatrick JM. Current and future perspectives on the development, evaluation, and application of in silico approaches for predicting toxicity. Chem Res Toxicol. 2016;29(4):438–451. Epub 2015/ 12/22. PubMed PMID: 26686752.
  • Wu S, Blackburn K, Amburgey J, et al. A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments. Regul Toxicol Pharmacol. 2010;56(1):67–81. Epub 2009/ 09/23. PubMed PMID: 19770017.
  • Madden JC. Tools for Grouping Chemicals and Forming Categories. In: Cronin MT, Madden JC, Enoch SJ, et al., editors. Chemical Toxicity Prediction: category Formation and Read-Across Issues in Toxicology. 17. London: Royal Society of Chemistry; 2013. p. 72–97.
  • OASIS TIMES [ cited 2021 Jun]. Available from: http://oasis-lmc.org/products/software/times.aspx.
  • Rathman JF, Yang C, Zhou H. Dempster-Shafer theory for combining in silico evidence and estimating uncertainty in chemical risk assessment. Comput Toxicol. 2018;6:16–31.
  • Yang C, Tarkhov A, Marusczyk J, et al. New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling. J Chem Inf Model. 2015;55(3):510–528.
  • Ridder L, Wagener M. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem. 2008;3(5):821–832. Epub 2008/ 03/04. PubMed PMID: 18311745.
  • OECD. Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways. 2018. doi: 10.1787/5jlv1m9d1g32-en.
  • Ives C, Campia I, Wang R-L, et al. Creating a structured adverse outcome pathway knowledgebase via ontology-based annotations. PubMed PMID: 30057931 Appl In Vitro Toxicol. 2017;34:298–311.
  • Wittwehr C, Aladjov H, Ankley G, et al. How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. Toxicol Sci. 2017;155(2):326–336. Epub 2016/ 12/21. PubMed PMID: 27994170; PubMed Central PMCID: PMCPMC5340205.
  • Hartwig A, Arand M, Epe B, et al. Mode of action-based risk assessment of genotoxic carcinogens. Arch Toxicol. 2020;94(6):1787–1877.
  • Willett C. (2019) The use of Adverse Outcome Pathways to support chemical safety decisions within the context of integrated approaches to testing and assessment (IATA). In Kojima H., Seidle T., and Spielmann H. (Eds), Alternatives to animal testing, pp: 83-90; Springer, Singapore.
  • Yauk C, Lambert I, Marchetti F, et al. Adverse Outcome Pathway on Alkylation of DNA in Male Pre-Meiotic Germ Cells Leading to Heritable Mutations. 2016. doi: 10.1787/5jlsvvxn1zjc-en.
  • Marchetti F, Massarotti A, Yauk CL, et al. The adverse outcome pathway (AOP) for chemical binding to tubulin in oocytes leading to aneuploid offspring. Environ Mol Mutagen. 2016;57(2):87–113. Epub 2015/ 11/20. PubMed PMID: 26581746.
  • Yauk CL, Lambert IB, Meek ME, et al. Development of the adverse outcome pathway “alkylation of DNA in male premeiotic germ cells leading to heritable mutations” using the OECD’s users‘ handbook supplement. Environ Mol Mutagen. 2015;56(9):724–750. Epub 2015/ 05/27. PubMed PMID: 26010389.
  • OECD. Guidance Document for the Use of Adverse Outcome Pathways in Developing Integrated Approaches to Testing and Assessment (IATA). ENV/JM/MONO(2016)67. 2016.
  • Prachi Pradeep RJ, Patlewicz G. Evaluation of the Predictive Accuracy of QSAR Models and Alerts for Genotoxicity Using a Newly Compiled Experimental Dataset. 9th Annual Meeting of the ASCCT. 2020.
  • OECD. Case Study on Grouping and Read-Across for Nanomaterials — genotoxicity of Nano-TiO2. Series on Testing and Assessment No 292. [cited 2021 Jun]. Available from: https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=ENV/JM/MONO(2018)28&docLanguage=En.
  • Case OECD Study on the Use of Integrated Approaches for Testing and Assessment for In Vitro Mutagenicity of 3,3ʹ Dimethoxybenzidine (DMOB) Based Direct Dyes. ENV/JM/MONO(2016)49. [cited 2021 Jun]. Available from: https://one.oecd.org/document/ENV/JM/MONO%282016%2949/en/pdf.
  • OECD. Report of a Workshop on Integrated Approaches to Testing and Assessment (IATA). ENV/JM/MONO(2008) 10: OECD Publishing; 2008.
  • Tollefsen KE, Scholz S, Cronin MT, et al. Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA). Regul Toxicol Pharmacol. 2014;70(3):629–640. PubMed PMID: 25261300.
  • OECD. Draft OECD Guideline Defined Approaches for Skin 1Sensitisation. [cited 2021 Jun]. Available from: https://wwwoecdorg/env/ehs/testing/GL%20DASS_22Sep2019v2pdf. 2019.
  • Petkov PI, Ivanova H, Schultz TW, et al. Criteria for assessing the reliability of toxicity predictions: i. TIMES Ames mutagenicity model. Comput Toxicol. 2021;17:100143.
  • Blackburn K, Stuard SB. A framework to facilitate consistent characterization of read across uncertainty. Regul Toxicol Pharmacol. 2014;68(3):353–362. Epub 2014/ 01/25. PubMed PMID: 24457134.
  • Schultz TW, Cronin MTD. Lessons learned from read-across case studies for repeated-dose toxicity. Regul Toxicol Pharmacol. 2017;88(1):85–91. Epub 2017/ 06/29. PubMed PMID: 28655656.
  • OECD. The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins 2014.
  • Benigni R, Bossa C, Tcheremenskaia O. A data-based exploration of the adverse outcome pathway for skin sensitization points to the necessary requirements for its prediction with alternative methods. Regul Toxicol Pharmacol Epub 2016/ 04/20. PubMed PMID: 27090483. 2016;78:45–52.
  • Benigni R, Bossa C, Tcheremenskaia O. Nongenotoxic carcinogenicity of chemicals: mechanisms of action and early recognition through a new set of structural alerts. Chem Rev. 2013;113(5):2940–2957. Epub 2013/ 03/09. PubMed PMID: 23469814.
  • Benigni R, Serafimova R, Parra Morte JM, et al. Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: an EFSA funded project. Regul Toxicol Pharmacol. Epub 2020/ 04/26. PubMed PMID: 32334037. 2020;114:104658. .
  • Myatt GJ, Ahlberg E, Akahori Y, et al. In silico toxicology protocols. Regul Toxicol Pharmacol. 2018;96:1–17. Epub 2018/ 04/22.. PubMed PMID: 29678766; PubMed Central PMCID: PMCPMC6026539. .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.