3 Modelling Simple Toxicity Endpoints …
53
8. ICH (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals
to limit potential carcinogenic risk. M7(R1). http://www.ich.org/fileadmin/Public_Web_Site/
ICH_Products/Guidelines/Multidisciplinary/M7/M7_R1_Addendum_Step_4_31Mar2017.
pdf. Accessed 9 Sept 2018
9. EFSA (2016) Guidance on the establishment of the residue definition for dietary risk assessment. EFSA J 14: 180. https://doi.org/10.2903/j.efsa.2016.4549. Accessed 9 Sept 2018
10. OECD (2017) Guidance document on the reporting of defined approaches and individual information sources to be used within Integrated Approaches to Testing and Assessment (IATA)
for skin sensitisation. In: OECD series on testing and assessment, No. 256. OECD Publishing,
Paris. https://doi.org/10.1787/9789264279285-en. Accessed 28 Aug 2018
11. European Union (2009) Regulation (EC) No 1223/2009 of the European Parliament and of the
council of 30 November 2009 on cosmetic products. http://data.europa.eu/eli/reg/2009/1223/
oj. Accessed 28 Aug 2018
12. European Union (2006) 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. http://data.europa.eu/eli/reg/2006/
1907/2018-05-09. Accessed 28 Aug 2018
13. Elder DP et al (2015) Mutagenic impurities: precompetitive/competitive collaborative and data
sharing initiatives. Org Process Res Dev 19:1486–1494
14. Judson PN et al (2013) Assessing confidence in predictions made by knowledge-based systems.
Toxicol Res 2:70–79
15. Williams RV et al (2016) It’s difficult, but important, to make negative predictions. Regul
Toxicol Pharmacol 76:79–86
16. Derek Nexus v6.0 (Lhasa Limited). https://www.lhasalimited.org/products/derek-nexus.htm.
Accessed 28 Aug 2018
17. Chilton ML et al (2018) Making reliable negative predictions of human skin sensitisation using
an in silico fragmentation approach. Regul Toxicol Pharmacol 95:227–235
18. Canipa SJ et al (2016) A quantitative in silico model for predicting skin sensitization using
a nearest neighbours approach within expert-derived structure-activity alert spaces. J Appl
Toxicol 37:985–995
19. OECD (2015) Test No. 442C: In chemico skin sensitisation: Direct Peptide Reactivity Assay
(DPRA). https://doi.org/10.1787/9789264229709-en. Accessed 28 Aug 2018
20. OECD (2018) Key event based test guideline 442D: in vitro skin sensitisation assays addressing the AOP key event on keratinocyte activation. https://doi.org/10.1787/9789264229822-en.
Accessed 28 Aug 2018
21. OECD (2018) Key event based test guideline 442E: In vitro skin sensitisation assays addressing the key event on activation of dendritic cells on the adverse outcome pathway for skin
sensitisation. https://doi.org/10.1787/9789264264359-en. Accessed 28 Aug 2018
22. Kleinstreuer NC et al (2018) Non-animal methods to predict skin sensitization (II): an assessment of defined approaches. Crit Rev Toxicol 48:359–374
23. Macmillan DS et al (2016) Predicting skin sensitisation using a decision tree integrated testing
strategy with an in silico model and in chemico/in vitro assays. Regul Toxicol Pharmacol
76:30–38
24. Barber C et al (2016) Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol 73:367–377
25. Verheyen GR et al (2017) Evaluation of in silico tools to predict the skin sensitization potential
of chemicals. SAR QSAR Environ Res 28:59–73
26. Dobo KL et al (2012) In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey. Regul Toxicol Pharmacol
62:449–455
53
8. ICH (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals
to limit potential carcinogenic risk. M7(R1). http://www.ich.org/fileadmin/Public_Web_Site/
ICH_Products/Guidelines/Multidisciplinary/M7/M7_R1_Addendum_Step_4_31Mar2017.
pdf. Accessed 9 Sept 2018
9. EFSA (2016) Guidance on the establishment of the residue definition for dietary risk assessment. EFSA J 14: 180. https://doi.org/10.2903/j.efsa.2016.4549. Accessed 9 Sept 2018
10. OECD (2017) Guidance document on the reporting of defined approaches and individual information sources to be used within Integrated Approaches to Testing and Assessment (IATA)
for skin sensitisation. In: OECD series on testing and assessment, No. 256. OECD Publishing,
Paris. https://doi.org/10.1787/9789264279285-en. Accessed 28 Aug 2018
11. European Union (2009) Regulation (EC) No 1223/2009 of the European Parliament and of the
council of 30 November 2009 on cosmetic products. http://data.europa.eu/eli/reg/2009/1223/
oj. Accessed 28 Aug 2018
12. European Union (2006) 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. http://data.europa.eu/eli/reg/2006/
1907/2018-05-09. Accessed 28 Aug 2018
13. Elder DP et al (2015) Mutagenic impurities: precompetitive/competitive collaborative and data
sharing initiatives. Org Process Res Dev 19:1486–1494
14. Judson PN et al (2013) Assessing confidence in predictions made by knowledge-based systems.
Toxicol Res 2:70–79
15. Williams RV et al (2016) It’s difficult, but important, to make negative predictions. Regul
Toxicol Pharmacol 76:79–86
16. Derek Nexus v6.0 (Lhasa Limited). https://www.lhasalimited.org/products/derek-nexus.htm.
Accessed 28 Aug 2018
17. Chilton ML et al (2018) Making reliable negative predictions of human skin sensitisation using
an in silico fragmentation approach. Regul Toxicol Pharmacol 95:227–235
18. Canipa SJ et al (2016) A quantitative in silico model for predicting skin sensitization using
a nearest neighbours approach within expert-derived structure-activity alert spaces. J Appl
Toxicol 37:985–995
19. OECD (2015) Test No. 442C: In chemico skin sensitisation: Direct Peptide Reactivity Assay
(DPRA). https://doi.org/10.1787/9789264229709-en. Accessed 28 Aug 2018
20. OECD (2018) Key event based test guideline 442D: in vitro skin sensitisation assays addressing the AOP key event on keratinocyte activation. https://doi.org/10.1787/9789264229822-en.
Accessed 28 Aug 2018
21. OECD (2018) Key event based test guideline 442E: In vitro skin sensitisation assays addressing the key event on activation of dendritic cells on the adverse outcome pathway for skin
sensitisation. https://doi.org/10.1787/9789264264359-en. Accessed 28 Aug 2018
22. Kleinstreuer NC et al (2018) Non-animal methods to predict skin sensitization (II): an assessment of defined approaches. Crit Rev Toxicol 48:359–374
23. Macmillan DS et al (2016) Predicting skin sensitisation using a decision tree integrated testing
strategy with an in silico model and in chemico/in vitro assays. Regul Toxicol Pharmacol
76:30–38
24. Barber C et al (2016) Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol 73:367–377
25. Verheyen GR et al (2017) Evaluation of in silico tools to predict the skin sensitization potential
of chemicals. SAR QSAR Environ Res 28:59–73
26. Dobo KL et al (2012) In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey. Regul Toxicol Pharmacol
62:449–455