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27. Kruhlak NL et al (2012) (Q)SAR modeling and safety assessment in regulatory review. Clin
Pharmacol Ther 91:529–534
28. Naven RT et al (2012) Latest advances in computational genotoxicity prediction. Expert Opin
Drug Metab Toxicol 8:1579–1587
29. Sutter A et al (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol 67:39–52
30. Powley MW (2015) (Q)SAR assessments of potentially mutagenic impurities: a regulatory
perspective on the utility of expert knowledge and data submission. Regul Toxicol Pharmacol
71:295–300
31. Greene N et al (2015) A practical application of two in silico systems for identification of
potentially mutagenic impurities. Regul Toxicol Pharmacol 72:335–349
32. Amberg A et al (2016) Principles and procedures for implementation of ICH M7 recommended
(Q)SAR analyses. Regul Toxicol Pharmacol 77:13–24
33. Myatt GJ et al (2018) In silico toxicology protocols. Regul Toxicol Pharmacol 96:1–17
34. Roberts DW et al (2016) Chemical applicability domain of the Local Lymph Node Assay
(LLNA) for skin sensitisation potency. Part 3. Apparent discrepancies between LLNA and
GPMT sensitisation potential: false positives or differences in sensitivity? Regul Toxicol Pharmacol 80:260–267
35. Honda H et al (2016) Modified Ames test using a strain expressing human sulfotransferase 1C2
to assess the mutagenicity of methyleugenol. Genes Environ. https://doi.org/10.1186/s41021016-0028-x
36. Amberg A et al (2015) Do carboxylic/sulfonic acid halides really present a mutagenic and
carcinogenic risk as impurities in final drug products? Org Process Res Dev 19:1495–1506
37. Sarah Nexus v3.0 (Lhasa Limited). https://www.lhasalimited.org/products/sarah-nexus.htm.
Accessed 28 Aug 2018
38. Myden A et al (2017) Utility of published DNA reactivity alerts. Regul Toxicol Pharmacol
88:77–86
39. Faulkner D et al (2017) Tools for green molecular design to reduce toxicological risk. In:
Johnson DE, Richardson RJ (eds) Computational systems pharmacology and toxicology, Royal
Society of Chemistry, London, Chapter 3, p 36–59
40. Canipa S et al (2015) Using in vitro structural alerts for chromosome damage to predict in vivo
activity and direct future testing. Mutagenesis 31:17–25
41. Egan WJ et al (2004) In silico prediction of drug safety: despite progress there is abundant
room for improvement. Drug Discov Today Technol 1:381–387
42. Greene N et al (2010) Developing structure-activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol 23:1215–1222
43. Hewitt M et al (2013) Hepatotoxicity: a scheme for generating chemical categories for readacross, structural alerts and insights into mechanism(s) of action. Crit Rev Toxicol 43:537–558
44. Pizzo F et al (2016) A new structure-activity relationship (SAR) model for predicting druginduced liver injury, based on statistical and expert-based structural alerts. Front Pharmacol.
https://doi.org/10.3389/fphar.2016.00442
45. Liu R et al (2015) Data-driven identification of structural alerts for mitigating the risk of
drug-induced human liver injuries. J Cheminf. https://doi.org/10.1186/s13321-015-0053-y
46. Myshkin E et al (2012) Prediction of organ toxicity endpoints by QSAR modeling based on
precise chemical-histopathology annotations. Chem Biol Drug Des 80:406–416
47. OECD (2012) AOP knowledgebase. https://aopkb.oecd.org/. Accessed 28 Aug 2018
48. Thompson RA et al (2016) Reactive metabolites: current and emerging risk and hazard assessments. Chem Res Toxicol 29:505–533
49. Warner DJ et al (2012) Mitigating the inhibition of human bile salt export pump by drugs: opportunities provided by physicochemical property modulation, in silico modeling, and structural
modification. Drug Metab Dispos 40:2332–2341. https://doi.org/10.1124/dmd.112.047068
50. Qiu T et al (2018) Finding the molecular scaffold of nuclear receptor inhibitors through highthroughput screening based on proteochemometric modelling. J Cheminf. https://doi.org/10.
1186/s13321-018-0275-x
R. Williams et al.
27. Kruhlak NL et al (2012) (Q)SAR modeling and safety assessment in regulatory review. Clin
Pharmacol Ther 91:529–534
28. Naven RT et al (2012) Latest advances in computational genotoxicity prediction. Expert Opin
Drug Metab Toxicol 8:1579–1587
29. Sutter A et al (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol 67:39–52
30. Powley MW (2015) (Q)SAR assessments of potentially mutagenic impurities: a regulatory
perspective on the utility of expert knowledge and data submission. Regul Toxicol Pharmacol
71:295–300
31. Greene N et al (2015) A practical application of two in silico systems for identification of
potentially mutagenic impurities. Regul Toxicol Pharmacol 72:335–349
32. Amberg A et al (2016) Principles and procedures for implementation of ICH M7 recommended
(Q)SAR analyses. Regul Toxicol Pharmacol 77:13–24
33. Myatt GJ et al (2018) In silico toxicology protocols. Regul Toxicol Pharmacol 96:1–17
34. Roberts DW et al (2016) Chemical applicability domain of the Local Lymph Node Assay
(LLNA) for skin sensitisation potency. Part 3. Apparent discrepancies between LLNA and
GPMT sensitisation potential: false positives or differences in sensitivity? Regul Toxicol Pharmacol 80:260–267
35. Honda H et al (2016) Modified Ames test using a strain expressing human sulfotransferase 1C2
to assess the mutagenicity of methyleugenol. Genes Environ. https://doi.org/10.1186/s41021016-0028-x
36. Amberg A et al (2015) Do carboxylic/sulfonic acid halides really present a mutagenic and
carcinogenic risk as impurities in final drug products? Org Process Res Dev 19:1495–1506
37. Sarah Nexus v3.0 (Lhasa Limited). https://www.lhasalimited.org/products/sarah-nexus.htm.
Accessed 28 Aug 2018
38. Myden A et al (2017) Utility of published DNA reactivity alerts. Regul Toxicol Pharmacol
88:77–86
39. Faulkner D et al (2017) Tools for green molecular design to reduce toxicological risk. In:
Johnson DE, Richardson RJ (eds) Computational systems pharmacology and toxicology, Royal
Society of Chemistry, London, Chapter 3, p 36–59
40. Canipa S et al (2015) Using in vitro structural alerts for chromosome damage to predict in vivo
activity and direct future testing. Mutagenesis 31:17–25
41. Egan WJ et al (2004) In silico prediction of drug safety: despite progress there is abundant
room for improvement. Drug Discov Today Technol 1:381–387
42. Greene N et al (2010) Developing structure-activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol 23:1215–1222
43. Hewitt M et al (2013) Hepatotoxicity: a scheme for generating chemical categories for readacross, structural alerts and insights into mechanism(s) of action. Crit Rev Toxicol 43:537–558
44. Pizzo F et al (2016) A new structure-activity relationship (SAR) model for predicting druginduced liver injury, based on statistical and expert-based structural alerts. Front Pharmacol.
https://doi.org/10.3389/fphar.2016.00442
45. Liu R et al (2015) Data-driven identification of structural alerts for mitigating the risk of
drug-induced human liver injuries. J Cheminf. https://doi.org/10.1186/s13321-015-0053-y
46. Myshkin E et al (2012) Prediction of organ toxicity endpoints by QSAR modeling based on
precise chemical-histopathology annotations. Chem Biol Drug Des 80:406–416
47. OECD (2012) AOP knowledgebase. https://aopkb.oecd.org/. Accessed 28 Aug 2018
48. Thompson RA et al (2016) Reactive metabolites: current and emerging risk and hazard assessments. Chem Res Toxicol 29:505–533
49. Warner DJ et al (2012) Mitigating the inhibition of human bile salt export pump by drugs: opportunities provided by physicochemical property modulation, in silico modeling, and structural
modification. Drug Metab Dispos 40:2332–2341. https://doi.org/10.1124/dmd.112.047068
50. Qiu T et al (2018) Finding the molecular scaffold of nuclear receptor inhibitors through highthroughput screening based on proteochemometric modelling. J Cheminf. https://doi.org/10.
1186/s13321-018-0275-x