Current and Future Perspectives on the Development, Evaluation, and Application of in Silico Approaches for Predicting Toxicity

Chemical Research in Toxicology

Volumn 29, Issue 4, 2016, Pages 438-451

  Patlewicz, Grace ^a   Fitzpatrick, Jeremy M ^a  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ANIMAL; ANIMAL TESTING ALTERNATIVE; BIOLOGICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; DRUG DEVELOPMENT; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; EXPERT SYSTEM; HUMAN; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RISK ASSESSMENT; TOXICITY TESTING;

ANIMAL TESTING ALTERNATIVES; ANIMALS; COMPUTER SIMULATION; DRUG DISCOVERY; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; EXPERT SYSTEMS; HUMANS; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RISK ASSESSMENT; TOXICITY TESTS;

EID: 84964691940     PISSN: 0893228X     EISSN: 15205010     Source Type: Journal    
DOI: 10.1021/acs.chemrestox.5b00388     Document Type: Review

References (94)

1. Cook, D., Brown, D., Alexander, R., March, R., Morgan, P., Satterthwaite, G., and Pangalos, M. N. (2014) Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework Nat. Rev. Drug Discovery 13, 419-431 10.1038/nrd4309
2. Otieno, M. A. and Lehman-McKeeman, L. D. (2010) Mechanism-based toxicity studies for drug development, in Predictive Toxicology in Drug Safety (Xu, J. J. and Urban, L., Eds.) pp 230-243, Cambridge University Press, New York.
3. (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, Off. J. Eur. Union L396/1, European Commission.
4. Raunio, H. (2011) In Silico Toxicology-Non-Testing Methods Front. Pharmacol. 2, 33 10.3389/fphar.2011.00033
5. Dearden, J. C., Barratt, M. D., Benigni, R., Bristol, D. W., Combes, R. D., Cronin, M. T. D., Judson, P. N., Payne, M. P., Richard, A. M., Tichy, M., Worth, A. P., and Yourick, J. J. (1997) The Development and Validation of Expert Systems for Predicting Toxicity ATLA 25, 223-252
6. Worth, A. P, Netzeva, T. I., and Patlewicz, G. (2007) Predicting toxicological and ecotoxicological endpoints, in Risk Assessment of Chemicals (Leeuwen, C. J. and Vermeire, T., Eds.) pp 427-465, Springer, New York.
7. Worth, A., Barroso, J., Bremer, S., Burton, J., Casati, S., Coecke, S., Corvi, R., Desprez, B., Dumont, C., Gouliarmou, V., Goumenou, M., Gräpel, R., Griesinger, C., Halder, M., Janusch Roi, A., Kienzler, A., Madia, F., Munn, S., Nepelska, M., Paini, A., Price, A., Prieto, P., Rolaki, A., Schäffer, M., Triebe, J., Whelan, M., Wittwehr, C., and Zuang, V. (2014) Alternative methods for regulatory toxicology-a state-of-the-art review, Report EUR 26797 EN 978-92-79-39651-9.
8. (2013) Chemical Toxicity Prediction: Category Formation and Read-across (Cronin, M., Madden, J., Enoch, S., and Roberts, D., Eds.) Royal Society of Chemistry, Cambridge.
9. Patlewicz, G., Kuseva, C., Mehmed, A., Popova, Y., Dimitrova, G., Ellis, G., Hunziker, R., Kern, P., Low, L., Ringeissen, S., Roberts, D. W., and Mekenyan, O. (2014) TIMES-SS-recent refinements as a result of an Industrial skin sensitisation consortium SAR QSAR Environ. Res. 25, 367-391 10.1080/1062936X.2014.900520
10. Petkov, P., Patlewicz, G., Schultz, T. W., Honma, M., Todorov, M., Kotov, S., Dimitrov, S. D., Donner, E. M., and Mekenyan, O. M. (2015) A feasibility study: Can information collected to classify for mutagenicity be informative in predicting carcinogenicity? Regul. Toxicol. Pharmacol. 72, 17-25 10.1016/j.yrtph.2015.03.003
11. Verhaar, H. J. M., van Leeuwen, C. J., and Hermens, J. L. M. (1992) Classifying environmental pollutants. 1. Structure-activity relationships for prediction of aquatic toxicity Chemosphere 25, 471-491 10.1016/0045-6535(92)90280-5
12. McKim, J. M., Bradbury, S. P., and Niemi, G. J. (1987) Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment Environ. Health Perspect. 71, 171-186 10.1289/ehp.8771171
13. Bradbury, S. P., Henry, T. R., and Carlson, R. W. (1990) Fish acute toxicity syndromes in the development of mechanism specific QSARs, in Practical applications of Quantitative Structure Activity Relationships (QSAR) in Environmental Chemistry and Toxicology (Karcher, W. and Devillers, J., Eds.) pp 295-315, Kluwer, Dordrecht, The Netherlands.
14. (2011) Report of the Workshop on Using Mechanistic Information in Forming Chemical Categories, OECD Series on Testing and Assessment Number 138, ENV/JM/MONO(2011) 8, Organisation for Economic Co-operation and Development, Paris, France.
15. Worth, A. and Patlewicz, G. (2015) Integrated approaches to testing and assessment, in Validation of Alternative Methods (Whelan, M. and Chantra, E., Eds.) Springer, New York, submitted for publication.
16. Tollefsen, K. E., Scholz, S., Cronin, M. T., Edwards, S. W., de Knecht, J., Crofton, K., Garcia-Reyero, N., Hartung, T., Worth, A., and Patlewicz, G. (2014) Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA) Regul. Toxicol. Pharmacol. 70, 629-640 10.1016/j.yrtph.2014.09.009
17. (2015) Report of the workshop on a framework for the development and use of integrated approaches to testing and assessment, Series on Testing and Assessment No. 215, Organisation for Economic Co-operation and Development, Paris, France.
18. Mekenyan, O., Dimitrov, S., Pavlov, T., Dimitrova, G., Todorov, M., Petkov, P., and Kotov, S. (2012) Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment SAR QSAR Environ. Res. 23, 553-606 10.1080/1062936X.2012.679689
19. (2001) White Paper Strategy for a future Chemicals Policy, COM(2001) 88 final, European Commission.
20. Worth, A. P. and Cronin, M. T. (2004) Report of the workshop on the validation of QSARs and other computational prediction models ATLA, Altern. Lab. Anim. 32, 703-706
21. Jaworska, J. S., Comber, M., Auer, C., and van Leeuwen, C. J. (2003) Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints Environ. Health Perspect. 111, 1358-1360 10.1289/ehp.5757
22. (2004) OECD Principles for the Validation for Regulatory Purposes of (Quantitative) Structure-Activity Relationship Models, http://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf.
23. (2007) Guidance document on the Validation of (Quantitative) Structure-Activity Relationship Models, OECD Series on Testing and Assessment No. 69, ENV/JM/MONO(2007)2, Organisation for Economic Co-operation and Development, Paris, France
24. (2008) Guidance on information requirements and chemical safety assessment, Chapter R.6: QSARs and grouping of chemicals, European Chemicals Agency, http://echa.europa.eu/documents/10162/13632/information-requirements-r6-en.pdf.
25. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D, Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., van de Sandt, J. J. M., Tong, W., Veith, G., and Yang, C. (2005) Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships. The Report and Recommendations of ECVAM Workshop 52 ATLA, Altern. Lab. Anim. 33, 155-173
26. Jaworska, J., Nikolova-Jeliazkova, N., and Aldenberg, T. (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review ATLA, Altern. Lab. Anim. 33, 445-459
27. Dimitrov, S., Dimitrova, G., Pavlov, T., Dimitrova, N., Patlewicz, G., Niemela, J., and Mekenyan, O. (2005) A Stepwise approach for defining the applicability domain of SAR and QSAR models J. Chem. Inf. Model. 45, 839-849 10.1021/ci0500381
28. Patlewicz, G., Chen, M. W., and Bellin, C. A. (2011) Non-Testing approaches under REACH-help or hindrance? Perspectives from a practitioner within Industry SAR QSAR Environ. Res. 22, 67-88 10.1080/1062936X.2010.528448
29. Norinder, U., Carlsson, L., Boyer, S., and Eklund, M. (2015) Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination Regul. Toxicol. Pharmacol. 71, 279-284 10.1016/j.yrtph.2014.12.021
30. (2014) Guidance on grouping of chemicals, OECD Series on Testing and Assessment No. 194, Organisation for Economic Co-operation and Development, Paris, France.
31. (2012) Technical Report 116: Category approaches, read-across, (Q)SAR, ECETOC, Brussels, Belgium, http://www.ecetoc.org/technical-reports.
32. Patlewicz, G., Ball, N., Booth, E. D., Hulzebos, E., Zvinavashe, E., and Hennes, C. (2013) Use of Category approaches, Read-across and (Q)SAR: General considerations Regul. Toxicol. Pharmacol. 67, 1-12 10.1016/j.yrtph.2013.06.002
33. Patlewicz, G., Ball, N., Becker, R. A., Booth, E. D., Cronin, M. T. D., Kroese, D., Setup, D., Van Ravenzwaay, B., and Hartung, T. (2014) Read-across approaches-misconceptions, promises and challenges ahead ALTEX 31, 387-396 10.14573/altex.1410071
34. Tanimoto, T. (1957) An elementary mathematical theory of classification and prediction, Internal IBM Technical Report 1957.
35. Dietrich, R. (2010) Tools for category formation and read-across: overview of the OECD QSAR application toolbox, in In Silico Toxicology Principles and Applications (Cronin, M. T. D. and Madden, J. C., Eds.) pp 385-407, Royal Society of Chemistry, Cambridge.
36. Patlewicz, G., Jeliazkova, N., Gallegos Saliner, A., and Worth, A. P. (2008) Toxmatch-a new software tool to aid in the development and evaluation of chemically similar groups SAR QSAR Environ. Res. 19, 397-412 10.1080/10629360802083848
37. Wu, S., Blackburn, K., Amburgey, J., Jaworska, J., and Federle, T. (2010) A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments Regul. Toxicol. Pharmacol. 56, 67-81 10.1016/j.yrtph.2009.09.006
38. Patlewicz, G., Ball, N., Boogaard, P., Becker, R. A., and Hubesch, B. (2015) Building scientific confidence in the development and evaluation of read-across Regul. Toxicol. Pharmacol. 72, 117-133 10.1016/j.yrtph.2015.03.015
39. Ball, N., Bartels, M. J., Budinsky, R. A., Klapacz, J., Hays, S. M., Kirman, C. R., and Patlewicz, G. Y. (2014) The challenge of using Read-Across within the EU REACH regulatory framework; how much uncertainty is too much? Dipropylene Glycol Methyl Ether Acetate, an exemplary case study Regul. Toxicol. Pharmacol. 68, 212-221 10.1016/j.yrtph.2013.12.007
40. Gallegos Saliner, A. (2006) Mini-review on chemical similarity and prediction of toxicity Curr. Comput.-Aided Drug Des. 2, 105-122 10.2174/157340906777441681
41. Gini, G., Franchi, A. M., Manganaro, A., Golbamaki, A., and Benfenati, E. (2014) ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals SAR QSAR Env. Res. 25, 999-1011 10.1080/1062936X.2014.976267
42. (2015) Read-Across Assessment Framework (RAAF), ECHA-15-R-07-EN, European Chemicals Agency, Helsinki, Finland.
43. Blackburn, K., Bjerke, D., Daston, G., Felter, S., Mahony, C., Naciff, J., Robison, S., and Wu, S. (2011) Case studies to test: A framework for using structural, reactivity, metabolic and physicochemical similarity to evaluate the suitability of analogs for SAR-based toxicological assessments Regul. Toxicol. Pharmacol. 60, 120-135 10.1016/j.yrtph.2011.03.002
44. Blackburn, K. and Stuard, S. B. (2014) A framework to facilitate consistent characterization of read across uncertainty Regul. Toxicol. Pharmacol. 68, 353-362 10.1016/j.yrtph.2014.01.004
45. Patlewicz, G., Roberts, D. W., Aptula, A., Blackburn, K., and Hubesch, B. (2013) Workshop: Use of 'read-across' for chemical safety assessment under REACH Regul. Toxicol. Pharmacol. 65, 226-228 10.1016/j.yrtph.2012.12.004
46. Schultz, T. W., Amcoff, P., Berggren, E., Gautier, F., Klaric, M., Knight, D. J., Mahony, C., Schwarz, M., White, A., and Cronin, M. T. D. (2015) A strategy for structuring and reporting a read-across prediction of toxicity Regul. Toxicol. Pharmacol. 72, 586-601 10.1016/j.yrtph.2015.05.016
47. (2012) The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins. Part 1: Scientific Evidence, Series on Testing and Assessment No. 168 ENV/JM/MONO(2012)10/PART1, Organisation for Economic Co-operation and Development, Paris, France.
48. (2012) The Adverse Outcome Pathway for Skin Sensitisation by Covalent Binding to Proteins. Part 2: Use of the AOP to Develop Chemical Categories and Integrated Testing and Assessment Approaches, Organisation for Economic Co-operation and Development, Paris, France.
49. Patlewicz, G., Simon, T., Rowlands, J. C., Budinsky, R., and Becker, R. A. (2015) Proposing a scientific confidence framework to support application of Adverse Outcome Pathways for regulatory purposes Regul. Toxicol. Pharmacol. 71, 463-477 10.1016/j.yrtph.2015.02.011
50. (2010) High information content technologies in support of read-across in chemical risk assessment, TR109, ECETOC, Brussels, Belgium.
51. Low, Y., Sedykh, A., Fourches, D., Golbraikh, A., Whelan, M., Rusyn, I., and Tropsha, A. (2013) Integrative chemical-biological read-across approach for chemical hazard classification Chem. Res. Toxicol. 26, 1199-1208 10.1021/tx400110f
52. Shah, I., Lie, J., Judson, R. J., Thomas, R., and Patlewicz, G. (2015) Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. Regul. Toxicol. Pharmacol. Under Review.
53. Patlewicz, G., Houck, K., Richard, A., Judson, R., Shah, I., and Williams, A. (2015) Exploiting enhanced non-testing approaches to meet the needs for sustainable chemistry, ACS Annual Meeting, August 16-20, Boston, MA.
54. Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., Mount, D. R., Nichols, J. W., Russom, C. L., Schmieder, P. K., Serrrano, J. A., Tietge, J. E., and Villeneuve, D. L. (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment Environ. Toxicol. Chem. 29, 730-741 10.1002/etc.34
55. Patlewicz, G., Kuseva, C., Kesova, A., Popova, I., Zhechev, T., Pavlov, T., Roberts, D. W., and Mekenyan, O. M. (2014) Towards AOP application-implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization Regul. Toxicol. Pharmacol. 69, 529-545 10.1016/j.yrtph.2014.06.001
56. Mekenyan, O., Patlewicz, G., Kuseva, C., Popova, I., Mehmed, A., Kotov, S., Zhechev, T., Pavlov, T., Temelkov, S., and Roberts, D. W. (2014) A mechanistic approach to modelling respiratory sensitization Chem. Res. Toxicol. 27, 219-239 10.1021/tx400345b
57. Prieto, P., Kinsner-Ovaskainen, A., Stanzel, S., Albella, B., Artursson, P., Campillo, N., Cecchelli, R., Cerrato, L., Diaz, L., Di Consiglio, E., Guerra, A., Gombau, L., Herrera, G., Honegger, P., Landry, C., O'Connor, J. E., Páez, J. A., Quintas, G., Svensson, R., Turco, L., Zürich, M. G., Zurbano, M. J., and Kopp-Schneider, A. (2013) The value of selected in vitro and in silico methods to predict acute oral toxicity in a regulatory context: results from the European Project ACuteTox Toxicol. In Vitro 27, 1357-1376 10.1016/j.tiv.2012.07.013
58. Sedykh, A., Zhu, H., Tang, H., Zhang, L., Richard, A., Rusyn, I., and Tropsha, A. (2010) Use of in vitro HTS derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity Environ. Health Perspect. 119, 364-370 10.1289/ehp.1002476
59. Yang, C., Tarkhov, A., Marusczyk, J., Bienfait, B., Gasteiger, J., Kleinoeder, T., Magdziarz, T., Sacher, O., Schwab, C. H., Schwoebel, J., Terfloth, L., Arvidson, K., Richard, A., Worth, A., and Rathman, J. (2015) New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling J. Chem. Inf. Model. 55, 510-528 10.1021/ci500667v
60. Richard, A. (2015) Chemotypes to explore ToxCast, OpenTox USA 2015, February 10-12, Baltimore, MD.
61. Kroes, R., Renwick, A. G., Cheeseman, M., Kleiner, J., Mangelsdorf, I., Piersma, A., Schilter, B., Schlatter, J., van Schothorst, F., Vos, J. G., and Würtzen, G. (2004) Structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet Food Chem. Toxicol. 42, 65-83 10.1016/j.fct.2003.08.006
62. Mueller, L., Mauthe, R. J., Riley, C. M., Andino, M. M., De Antonis, D., Beels, C., DeGeorge, J., De Knaep, A. K. M., Ellison, D., Fagerland, J. A., Frank, R., Fritschel, B., Galloway, S., Harpur, E., Humfrey, C. D. N., Jacks, A. S., Jagota, N., MacKinnon, J., Mohan, G., Ness, D. K., O'Donovan, M. R., Smith, M. D., Vudathala, G., and Yotti, L. (2006) A Rationale for Testing, and Controlling Specified Impurities in Pharmaceuticals that Posses the Potential for Genotoxicity Regul. Toxicol. Pharmacol. 44, 198-211 10.1016/j.yrtph.2005.12.001
63. Bouder, F. (2008) Regulating impurities in pharmaceutical products: A tolerability of risk approach? Expert Rev. Clin. Pharmacol. 1, 241-250 10.1586/17512433.1.2.241
64. EFSA (2012) Scientific Opinion on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC) EFSA J. 10, 2750 10.2903/j.efsa.2012.2750
65. Safford, R. J. (2008) The Dermal Sensitisation Threshold- a TTC approach for allergic contact dermatitis Regul. Toxicol. Pharmacol. 51, 195-200 10.1016/j.yrtph.2008.02.010
66. Safford, R. J., Aptula, A. O., and Gilmour, N. (2011) Refinement of the Dermal Sensitisation Threshold (DST) approach using a larger dataset and incorporating mechanistic chemistry domains Regul. Toxicol. Pharmacol. 60, 218-224 10.1016/j.yrtph.2011.03.009
67. Roberts, D. W., Api, A. M., Safford, R. J., and Lalko, J. F. (2015) Principles for identification of High Potency Category Chemicals for which the Dermal Sensitisation Threshold (DST) approach should not be applied Regul. Toxicol. Pharmacol. 72, 683-693 10.1016/j.yrtph.2015.03.001
68. Carthew, P., Clapp, C., and Gutsell, S. (2009) Exposure based waiving: the application of the toxicological threshold of concern (TTC) to inhalation exposure for aerosol ingredients in consumer products Food Chem. Toxicol. 47, 1287-1295 10.1016/j.fct.2009.02.024
69. Escher, S. E., Tluczkiewicz, I., Batke, M., Bitsch, A., Melber, C., Kroese, E. D., Buist, H. E., and Mangelsdorf, I. (2010) Evaluation of inhalation TTC values with the database RepDose Regul. Toxicol. Pharmacol. 58, 259-274 10.1016/j.yrtph.2010.06.009
70. Hughes, J. D., Blagg, J., Price, D. A., Bailey, S., DeCrescenzo, G. A., Devraj, R. V., Ellsworth, E., Fobian, Y., Gibbs, M. E., Gilles, R. W., Greene, N., Huang, E., Krieger-Burke, T., Loesel, J., Wager, T., Whiteley, L., and Zhang, Y. (2008) Physiochemical drug properties associated with in vivo toxicological outcomes Bioorg. Med. Chem. Lett. 18, 4872-4875 10.1016/j.bmcl.2008.07.071
71. Bowes, J., Brown, A. J., Hamon, J., Jarolimek, W., Sridhar, A., Waldron, G., and Whitebread, S. (2012) Reducing safety-related drug attrition: the use of in vitro pharmacological profiling Nat. Rev. Drug Discovery 11, 909-922 10.1038/nrd3845
72. Price, D. A., Blagg, J., Jones, L., Greene, N., and Wager, T. (2009) Physicochemical drug properties associated with in vivo toxicological outcomes: a review Expert Opin. Drug Metab. Toxicol. 5, 921-931 10.1517/17425250903042318
73. Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Sochaski, M. A., Rotroff, D. M., Freeman, K., Clewell, H. J., 3rd, Dix, D. J., Andersen, M. E., Houck, K. A., Allen, B., Judson, R. S., Singh, R., Kavlock, R. J., Richard, A. M., and Thomas, R. S. (2012) Integration of Dosimetry, Exposure, and High-Throughput Screening Data in Chemical Toxicity Assessment Toxicol. Sci. 125, 157-174 10.1093/toxsci/kfr254
74. Becker, R. A., Friedman, K. P., Simon, T. W., Marty, S., Patlewicz, G., and Rowlands, J. C. (2015) An Exposure:Activity Profiling Method for Interpreting High-Throughput Screening Data for Estrogenic Activity - Proof of Concept Regul. Toxicol. Pharmacol. 71, 398-408 10.1016/j.yrtph.2015.01.008
75. Shah, F. and Greene, N. (2014) Analysis of Pfizer compounds in EPA's ToxCast chemicals-assay space Chem. Res. Toxicol. 27, 86-98 10.1021/tx400343t
76. (2015) Application of Modern Toxicology Approaches for Predicting Acute Toxicity for Chemical Defense, The National Academies of Sciences, Engineering, and Medicine, Washington, D.C., DOI: 10.17226/21775.
77. Gallegos Salinger, A., Tsakovska, I., Pavan, M., Patlewicz, G., and Worth, A. P. (2007) Evaluation of SARs for the prediction of skin irritation/corrosion potential: structural inclusion rules in the BfR decision support system SAR QSAR Environ. Res. 18, 331-342 10.1080/10629360701304014
78. Tsakovska, I., Gallegos Salinger, A., Netzeva, T., Pavan, M., and Worth, A. P. (2007) Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system SAR QSAR Environ. Res. 18, 221-235 10.1080/10629360701304063
79. Patlewicz, G. and Worth, A. (2008) Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation, EUR 23225 EN.
80. Myshkin, E., Brennan, R., Khasanova, T., Sitnik, T., Serebriyskaya, T., Litvinova, E., Guryanov, A., Nikolsky, Y., Nikolskaya, T., and Bureeva, S. (2012) Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical-Histopathology Annotations Chem. Biol. Drug Des. 80, 406-416 10.1111/j.1747-0285.2012.01411.x
81. Wu, S., Fisher, J., Naciff, J., Laufersweiler, M., Lester, C., Daston, G., and Blackburn, K. (2013) Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants Chem. Res. Toxicol. 26, 1840-1861 10.1021/tx400226u
82. Benigni, R. and Bossa, C. (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology Mutat. Res., Rev. Mutat. Res. 659, 248-261 10.1016/j.mrrev.2008.05.003
83. Greene, N., Fisk, L., Naven, R. T., Note, R. R., Patel, M. L., and Pelletier, D. J. (2010) Developing structure-activity relationships for the prediction of hepatotoxicity Chem. Res. Toxicol. 23, 1215-1222 10.1021/tx1000865
84. Hewitt, M., Enoch, S. J., Madden, J. C., Przybylak, K. R., and Cronin, M. T. (2013) Hepatotoxicity: a scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action Crit. Rev. Toxicol. 43, 537-558 10.3109/10408444.2013.811215
85. Rodgers, A. D., Zhu, H., Fourches, D., Rusyn, I., and Tropsha, A. (2010) Modeling liver-related adverse effects of drugs using k-nearest neighbor quantitative structure-activity relationship method Chem. Res. Toxicol. 23, 724-732 10.1021/tx900451r
86. Low, Y., Uehara, T., Minowa, Y., Yamada, H., Ohno, Y., Urushidani, T., Sedykh, A., Muratov, E., Kuz'min, V., Fourches, D., Zhu, H., Rusyn, I., and Tropsha, A. (2011) Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches Chem. Res. Toxicol. 24, 1251-1262 10.1021/tx200148a
87. Chen, M., Hong, H., Fang, H., Kelly, R., Zhou, G., Borlak, J., and Tong, W. (2013) Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs Toxicol. Sci. 136, 242-249 10.1093/toxsci/kft189
88. Przybylak, K. R. and Cronin, M. T. (2012) In silico models for drug-induced liver injury - current status Expert Opin. Drug Metab. Toxicol. 8, 201-217 10.1517/17425255.2012.648613
89. Chen, M., Bisgin, H., Tong, L., Hong, H., Fang, H., Borlak, J., and Tong, W. (2014) Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomarkers Med. 8, 201-213 10.2217/bmm.13.146
90. Wiśniowska, B., Mendyk, A., Szlęk, J., Kołaczkowski, M., and Polak, S. (2015) Enhanced QSAR models for drug-triggered inhibition of the main cardiac ion currents J. Appl. Toxicol. 35, 1030-1039 10.1002/jat.3095
91. + channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models J. Mol. Model. 18, 1023-1036 10.1007/s00894-011-1136-y
92. Braga, R. C., Alves, V. M., Silva, M. F., Muratov, E., Fourches, D., Tropsha, A., and Andrade, C. H. (2014) Tuning HERG out: antitarget QSAR models for drug development Curr. Top. Med. Chem. 14, 1399-1415 10.2174/1568026614666140506124442
93. Wang, S., Li, Y., Xu, L., Li, D., and Hou, T. (2013) Recent developments in computational prediction of HERG blockage Curr. Top. Med. Chem. 13, 1317-1326 10.2174/15680266113139990036
94. Jing, Y., Easter, A., Peters, D., Kim, N., and Enyedy, I. J. (2015) In silico prediction of hERG inhibition Future Med. Chem. 7, 571-586 10.4155/fmc.15.18