Browse > Article
http://dx.doi.org/10.5487/TR.2017.33.3.173

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects  

Cronin, Mark T.D. (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Enoch, Steven J. (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Mellor, Claire L. (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Przybylak, Katarzyna R. (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Richarz, Andrea-Nicole (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Madden, Judith C. (School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University)
Publication Information
Toxicological Research / v.33, no.3, 2017 , pp. 173-182 More about this Journal
Abstract
In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
Keywords
Adverse outcome pathways; Read-across; Structural alert; Liver toxicity; Hepatotoxicity; Quantitative structure-activity relationship (QSAR);
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kleinstreuer, N.C., Sullivan, K., Allen, D., Edwards, S., Mendrick, D.L., Embry, M., Matheson, J., Rowlands, J.C., Munn, S., Maull, E. and Casey, W. (2016) Adverse Outcome Pathways: From research to regulation scientific workshop report. Regul. Toxicol. Pharmacol., 76, 39-50.   DOI
2 Allen, T.H.E., Goodman, J.M., Gutsell, S. and Russell, P.J. (2014) Defining molecular initiating events in the adverse outcome pathway framework for risk assessment. Chem. Res. Toxicol., 27, 2100-2112.   DOI
3 Adler, S., Basketter, D., Creton, S., Pelkonen, O., van Benthem, J., Zuang, V., Andersen, K.E., Angers-Loustau, A., Aptula, A., Bal-Price, A., Benfenati, E., Bernauer, U., Bessems, J., Bois, F.Y., Boobis, A., Brandon, E., Bremer, S., Broschard, T., Casati, S., Coecke, S., Corvi, R., Cronin, M., Daston, G., Dekant, W., Felter, S., Grignard, E., Gundert-Remy, U., Heinonen, T., Kimber, I., Kleinjans, J., Komulainen, H., Kreiling, R., Kreysa, J., Batista Leite, S., Loizou, G., Maxwell, G., Mazzatorta, P., Munn, S., Pfuhler, S., Phrakonkham, P., Piersma, A., Poth, A., Prieto, P., Repetto, G., Rogiers, V., Schoeters, G., Schwarz, M., Serafimova, R., Tahti, H., Testai, E., van Delft, J., van Loveren, H., Vinken, M., Worth, A. and Zaldivar, J.M. (2011) Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010. Arch. Toxicol., 85, 367-485.   DOI
4 Schwarz, M. and Mahony, C. (2011) Introduction to repeated dose (systemic) toxicity in Towards the Replacement of in vivo Repeated Dose Systemic Toxicity Testing (Volume 1). Coach Consortium, Paris, France.
5 National Academies of Sciences, Engineering, and Medicine (2017) Using 21st Century Science to Improve Risk-Related Evaluations. The National Academies Press, Washington, DC.
6 Enoch, S.J., Ellison, C.M., Schultz, T.W. and Cronin, M.T.D. (2011) A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity. Crit. Rev. Toxicol., 41, 783-802.   DOI
7 Zhu, X.W., Xin, Y.J. and Chen, Q.H. (2016) Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests. SAR QSAR Environ. Res., 27, 559-572.   DOI
8 Mulliner, D., Schmidt, F., Stolte, M., Spirkl, H.P., Czich, A. and Amberg, A. (2016) Computational models for human and animal hepatotoxicity with a global application scope. Chem. Res. Toxicol., 29, 757-767.   DOI
9 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 druginduced hepatotoxicity using QSAR and toxicogenomics approaches. Chem. Res. Toxicol., 24, 1251-1262.   DOI
10 Muller, C., Pekthong, D., Alexandre, E., Marcou, G., Horvath, D., Richert, L. and Varnek, A. (2015) Prediction of drug induced liver injury using molecular and biological descriptors. Comb. Chem High Throughput Screen., 18, 315-322.   DOI
11 Schwarz, L. and Watkins, J.B. (2009) The liver in Toxicology and Risk Assessment: A Comprehensive Introduction (Greim, H. and Snyder, R. Ed.). John Wiley, Chichester, pp. 216-227.
12 Spielmann, H., Sauer, U.G. and Mekenyan, O. (2011) A critical evaluation of the 2011 ECHA reports on compliance with the REACH and CLP regulations and on the use of alternatives to testing on animals for compliance with the REACH regulation. Altern. Lab. Anim., 39, 481-493.
13 Jaeschke, H., Gores, G.J., Cederbaum, A.I., Hinson, J.A., Pessayre, D. and Lemasters, J.J. (2002) Mechanisms of hepatotoxicity. Toxicol. Sci., 65, 166-176.   DOI
14 Yuan, L. and Kaplowitz, N. (2013) Mechanisms of druginduced liver injury. Clin. Liver Dis., 17, 507-518.   DOI
15 Williams, D.P., Shipley, R., Ellis, M.J., Webb, S., Ward, J., Gardner, I. and Creton, S. (2013) Novel in vitro and mathematical models for the prediction of chemical toxicity. Toxicol. Res. (Camb.), 2, 40-59.
16 Enoch, S.J., Roberts, D.W., Madden, J.C. and Cronin, M.T.D. (2014) Development of an in silico profiler for respiratory sensitisation. Altern. Lab. Anim., 42, 367-375.
17 Hewitt, M., Enoch, S.J., Madden, J.C., Przybylak, K.R. and Cronin, M.T.D. (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.   DOI
18 Mellor, C.L., Steinmetz, F.P. and Cronin, M.T.D. (2016) The identification of nuclear receptors associated with hepatic steatosis to develop and extend adverse outcome pathways. Crit. Rev. Toxicol., 46, 138-152.   DOI
19 Mellor, C.L., Steinmetz, F.P. and Cronin, M.T.D. (2016) Using molecular initiating events to develop a structural alert based screening workflow for nuclear receptor ligands associated with hepatic steatosis. Chem. Res. Toxicol., 29, 203-212.   DOI
20 Tsakovska, I., Al Sharif, M., Alov, P., Diukendjieva, A., Fioravanzo, E., Cronin, M.T.D. and Pajeva, I. (2014) Molecular modelling study of the $PPAR{\gamma}$ receptor in relation to the mode of action/Adverse Outcome Pathway framework for liver steatosis. Int. J. Mol. Sci., 15, 7651-7666.   DOI
21 Richard, A.M., Judson, R.S., Houck, K.A., Grulke, C.M., Volarath, P., Thillainadarajah, I., Yang, C., Rathman, J., Martin, M.T., Wambaugh, J.F., Knudsen, T.B., Kancherla, J., Mansouri, K., Patlewicz, G., Williams, A.J., Little, S.B., Crofton, K.M. and Thomas, R.S. (2016) ToxCast chemical landscape: paving the road to 21st Century toxicology. Chem. Res. Toxicol., 29, 1225-1251.   DOI
22 Steinmetz, F.P., Mellor, C.L., Meinl, T. and Cronin, M.T.D. (2015) Screening chemicals for receptor-mediated toxicological and pharmacological endpoints: using public data to build screening tools within a KNIME workflow. Mol. Inform., 34, 171-178.   DOI
23 Cronin, M.T.D. (2013) Evaluation of categories and readacross for toxicity prediction allowing for regulatory acceptance in Chemical Toxicity Prediction: Category Formation and Read-Across (Cronin, M.T.D., Madden, J.C., Enoch, S.J. and Roberts, D.W. Ed.). The Royal Society of Chemistry, Cambridge, pp. 155-167.
24 Vinken, M. and Blaauboer, B.J. (2017) In vitro testing of basal cytotoxicity: Establishment of an adverse outcome pathway from chemical insult to cell death. Toxicol. In Vitro, 39, 104-110.   DOI
25 Thomas, R.S., Black, M.B., Li, L.L., Healy, E., Chu, T.M., Bao, W.J., Andersen, M.E. and Wolfinger, R.D. (2012) A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening. Toxicol. Sci., 128, 398-417.   DOI
26 Judson, R.S., Kavlock, R.J., Setzer, R.W., Hubal, E.A.C., Martin, M.T., Knudsen, T.B., Houck, K.A., Thomas, R.S., Wetmore, B.A. and Dix, D.J. (2011) Estimating toxicityrelated biological pathway altering doses for high-throughput chemical risk assessment. Chem. Res. Toxicol., 24, 451-462.   DOI
27 Ball, N., Cronin, M.T.D., Shen, J., Blackburn, K., Booth, E.D., Bouhifd, M., Donley, E., Egnash, L., Hastings, C., Juberg, D.R., Kleensang, A., Kleinstreuer, N., Kroese, E.D., Lee, A.C., Luechtefeld, T., Maertens, A., Marty, S., Naciff, J.M., Palmer, J., Pamies, D., Penman, M., Richarz, A.-N., Russo, D.P., Stuard, S.B., Patlewicz, G., van Ravenzwaay, B., Wu S., Zhu H. and Hartung, T. (2016) Toward Good Read- Across Practice (GRAP) guidance. ALTEX, 33, 149-166.
28 Bitsch, A., Jacobi, S., Melber, C., Wahnschaffe, U., Simetska, N. and Mangelsdorf, I. (2006) REPDOSE: A database on repeated dose toxicity studies of commercial chemicals - A multifunctional tool. Regul. Toxicol. Pharmacol., 46, 202-210.   DOI
29 Judson, R.S., Martin, M.T., Egeghy, P., Gangwal, S., Reif, D.M., Kothiya, P., Wolf, M., Cathey, T., Transue, T., Smith, D., Vail, J., Frame, A., Mosher, S., Hubal, E.A.C. and Richard, A.M. (2012) Aggregating data for computational toxicology applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) system. Int. J. Mol. Sci., 13, 1805-1831.   DOI
30 Sakuratani, Y., Zhang, H.Q., Nishikawa, S., Yamazaki, K., Yamada, T., Yamada, J., Gerova, K., Chankov, G., Mekenyan, O. and Hayashi, M. (2013) Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories. SAR QSAR Environ. Res., 24, 351-363.   DOI
31 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.   DOI
32 Meek, M.E., Boobis, A., Cote, I., Dellarco, V., Fotakis, G., Munn, S., Seed, J. and Vickers, C. (2014) New developments in the evolution and application of the WHO/IPCS framework on mode of action/species concordance analysis. J. Appl. Toxicol., 34, 1-18.   DOI
33 Enoch, S.J., Cronin, M.T.D., Madden, J.C. and Hewitt, M. (2009) Formation of structural categories to allow for readacross for teratogenicity. QSAR Comb. Sci., 28, 696-708.   DOI
34 Piechota, P., Cronin, M.T.D., Hewitt, M. and Madden, J.C. (2013) Pragmatic approaches to using computational methods to predict xenobiotic metabolism. J. Chem. Inf. Model., 53, 1282-1293.   DOI
35 Blaauboer, B.J. (2003) The integration of data on physicochemical properties, in vitro-derived toxicity data and physiologically based kinetic and dynamic modelling as a tool in hazard and risk assessment. A commentary. Toxicol. Lett., 138, 161-171.   DOI
36 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.   DOI
37 Vinken, M. (2015) Adverse outcome pathways and druginduced liver injury testing. Chem. Res. Toxicol., 28, 1391-1397.   DOI
38 Vinken, M., Landesmann, B., Goumenou, M., Vinken, S., Shah, I., Jaeschke, H., Willett, C., Whelan, M. and Rogiers, V. (2013) development of an adverse outcome pathway from drug-mediated bile salt export pump inhibition to cholestatic liver injury. Toxicol. Sci., 136, 97-106.   DOI
39 Yang, C., Hristozov, D., Tarkhov, A., Kleinoeder, T., Boyer, I., Cronin, M., Fioravanzo, E., Kim, H., Heldreth, B., Mostrag- Szylchtying, A., Rathman, J., Richarz, A., Schwab, C., Vitcheva, V. and Worth, A. (2015) COSMOS DB as an international share point for exchanging regulatory and toxicity data of cosmetics ingredients and related substances. Toxicol. Lett., 238, S382.
40 Cases, M., Briggs, K., Steger-Hartmann, T., Pognan, F., Marc, P., Kleinoder, T., Schwab, C.H., Pastor, M., Wichard, J. and Sanz, F. (2014) The eTOX data-sharing project to advance in silico drug-induced toxicity prediction. Int. J. Mol. Sci., 15, 21136-21154.   DOI
41 Yang, C., Barlow, S.M., Muldoon Jacobs, K.L., Vitcheva, V., Boobis, A.R., Felter, S.F., Arvidson, K.B., Keller, D., Cronin, M., Enoch, S., Worth, A. and Hollnagel, H.M. (2017) Thresholds of toxicological concern for cosmetics-related substances. Fd Chem. Toxicol. [in press].
42 Hisaki, T., Kaneko, M.A.N., Yamaguchi, M., Sasa, H. and Kouzuki, H. (2015) Development of QSAR models using artificial neural network analysis for risk assessment of repeateddose, reproductive, and developmental toxicities of cosmetic ingredients. J. Toxicol. Sci., 40, 163-180.   DOI
43 Sakuratani, Y., Sato, S., Nishikawa, S., Yamada, J., Maekawa, A. and Hayashi, M. (2008) Category analysis of the substituted anilines studied in a 28-day repeat-dose toxicity test conducted on rats: Correlation between toxicity and chemical structure. SAR QSAR Environ. Res., 19, 681-696.   DOI
44 Przybylak, K.R., Schultz, T.W., Richarz, A.-N., Mellor, C.L., Escher, S.E. and Cronin M.T.D. (2017) Read-across of 90-day rat oral repeated-dose toxicity: A case study for selected ${\beta}$-olefinic alcohols. Comput. Toxicol., 1, 22-32.   DOI
45 Judson, R.S., Martin, M.T., Patlewicz, G. and Wood, C.E. (2017) Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study. Regul. Toxicol. Pharmacol., 86, 74-92.   DOI
46 European Chemicals Agency (ECHA) (2016) New approach methodologies in Regulatory Science, Proceedings of a Scientific Workshop, 19-20 April 2016, Helsinki. ECHA, Helsinki, ECHA-16-R-21-EN.
47 Willett, C., Rae, J.C., Goyak, K.O., Minsavage, G., Westmoreland, C., Andersen, M., Avigan, M., Duche, D., Harris, G., Hartung, T., Jaeschke, H., Kleensang, A., Landesmann, B., Martos, S., Matevia, M., Toole, C., Rowan, A., Schultz, T., Seed, J., Senior, J., Shah, I., Subramanian, K., Vinken, M. and Watkins, P. (2014) Building shared experience to advance practical application of pathway-based toxicology: liver toxicity mode-of-action. ALTEX, 31, 500-519.
48 Becker, R.A., Patlewicz, G., Simon, T.W., Rowlands, J.C. and Budinsky, R.A. (2015) The adverse outcome pathway for rodent liver tumor promotion by sustained activation of the aryl hydrocarbon receptor. Regul. Toxicol. Pharmacol., 73, 172-190.   DOI
49 Ellison, C.M., Enoch, S.J. and Cronin M.T.D. (2011) A review of the use of in silico methods to predict the chemistry of molecular initiating events related to drug toxicity. Expert Opin. Drug Metab. Toxicol., 7, 1481-1495.   DOI
50 Przybylak, K.R., Alzahrani, A.R. and Cronin, M.T.D. (2014) How does the quality of phospholipidosis data influence the predictivity of structural alerts? J. Chem. Inf. Model., 54, 2224-2232.   DOI
51 Przybylak, K.R. and Cronin, M.T.D. (2011) In silico studies of the relationship between chemical structure and drug induced phospholipidosis. Mol. Inform., 30, 415-429.   DOI
52 Nelms, M.D., Mellor, C.L., Cronin, M.T.D., Madden, J.C. and Enoch, S.J. (2015) Development of an in silico profiler for mitochondrial toxicity. Chem. Res. Toxicol., 28, 1891-1902.   DOI
53 Enoch, S.J. and Cronin, M.T.D. (2010) A review of the electrophilic reaction chemistry involved in covalent DNA binding. Crit. Rev. Toxicol., 40, 728-748.   DOI
54 Nelms, M.D., Ates, G., Madden, J.C., Vinken, M., Cronin, M.T.D., Rogiers, V. and Enoch, S.J. (2015) Proposal of an in silico profiler for categorisation of repeat dose toxicity data of hair dyes. Arch. Toxicol., 89, 733-741.   DOI
55 Cronin, M.T.D. and Madden J.C. (2010) In Silico Toxicology: Principles and Applications. The Royal Society of Chemistry, Cambridge, p. 669.
56 Mackay, D., Arnot, J.A., Petkova, E.P., Wallace, K.B., Call, D.J., Brooke, L.T. and Veith, G.D. (2009) The physicochemical basis of QSARs for baseline toxicity. SAR QSAR Environ. Res., 20, 393-414.   DOI
57 Dimitrov, S., Dimitrova, N., Georgieva, D., Vasilev, K., Hatfield, T., Straka J. and Mekenyan O. (2012) Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model. SAR QSAR Environ. Res., 23, 17-36.   DOI
58 Cronin, M.T.D., Madden, J.C., Enoch, S.J. and Roberts, D.W. (2013) Chemical Toxicity Prediction: Category Formation and Read-Across, The Royal Society of Chemistry, Cambridge, p. 191.
59 Batke, M., Escher, S., Hoffmann-Doerr, S., Melber, C., Messinger, H. and Mangelsdorf, I. (2011) Evaluation of time extrapolation factors based on the database RepDose. Toxicol. Lett., 205, 122-129.   DOI
60 Vinken, M., Pauwels, M., Ates, G., Vivier, M., Vanhaecke, T. and Rogiers, V. (2012) Screening of repeated dose toxicity data present in SCC(NF)P/SCCS safety evaluations of cosmetic ingredients. Arch. Toxicol., 86, 405-412.   DOI
61 Russmann, S., Kullak-Ublick, G.A. and Grattagliano, I. (2009) Current concepts of mechanisms in drug-induced hepatotoxicity. Curr. Med. Chem., 16, 3041-3053.   DOI
62 Przybylak, K.R. and Cronin, M.T.D. (2012) In silico models for drug-induced liver injury - current status. Expert Opin. Drug Metab. Toxicol., 8, 201-217.   DOI
63 Ozer, J., Ratner, M., Shaw, M., Bailey, W. and Schomaker, S. (2008) The current state of serum biomarkers of hepatotoxicity. Toxicology, 245, 194-205.   DOI
64 Zhu, X. and Kruhlak, N.L. (2014) Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data. Toxicology, 321, 62-72.   DOI
65 Rincon-Villamizar, E. and Restrepo, G. (2014) Rules relating hepatotoxicity with structural attributes of drugs. Toxicol. Environ. Chem., 96, 594-613.   DOI
66 Chen, M.J., Hong, H.X., Fang, H., Kelly, R., Zhou, G.X., Borlak, J. and Tong, W.D. (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.   DOI
67 Cronin, M.T.D., Dearden, J.C., Duffy, J.C., Edwards, R., Manga, N., Worth, A.P. and Worgan, A.D.P. (2002) The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints. SAR QSAR Environ. Res., 13, 167-176.   DOI