Toxicological Research

Korean Society of Toxicology & Korea Environmental Mutagen Society (한국독성학회)
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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)
  • Received : 2017.02.23
  • Accepted : 2017.04.06
  • Published : 2017.07.15
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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

References

  1. Cronin, M.T.D. and Madden J.C. (2010) In Silico Toxicology: Principles and Applications. The Royal Society of Chemistry, Cambridge, p. 669.
  2. 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.
  3. 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. https://doi.org/10.1080/10629360902949153
  4. 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. https://doi.org/10.1080/1062936X.2011.623321
  5. 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. https://doi.org/10.3109/10408444.2010.494175
  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. https://doi.org/10.3109/10408444.2011.598141
  7. 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. https://doi.org/10.1016/j.yrtph.2016.01.007
  8. 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. https://doi.org/10.1021/tx500345j
  9. 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. https://doi.org/10.1007/s00204-011-0693-2
  10. 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.
  11. National Academies of Sciences, Engineering, and Medicine (2017) Using 21st Century Science to Improve Risk-Related Evaluations. The National Academies Press, Washington, DC.
  12. 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. https://doi.org/10.1016/j.tiv.2016.12.004
  13. 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. https://doi.org/10.1093/toxsci/kfs159
  14. 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. https://doi.org/10.1021/tx100428e
  15. 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.
  16. 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. https://doi.org/10.1016/j.yrtph.2006.05.013
  17. 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. https://doi.org/10.3390/ijms13021805
  18. 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. https://doi.org/10.1080/1062936X.2013.773375
  19. 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. https://doi.org/10.3390/ijms151121136
  20. 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.
  21. 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].
  22. 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. https://doi.org/10.2131/jts.40.163
  23. 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. https://doi.org/10.1080/10629360802550689
  24. 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. https://doi.org/10.1016/j.comtox.2016.11.001
  25. 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. https://doi.org/10.1016/j.yrtph.2017.02.015
  26. 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. https://doi.org/10.1007/s00204-011-0769-z
  27. 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. https://doi.org/10.1016/j.toxlet.2011.05.1030
  28. Russmann, S., Kullak-Ublick, G.A. and Grattagliano, I. (2009) Current concepts of mechanisms in drug-induced hepatotoxicity. Curr. Med. Chem., 16, 3041-3053. https://doi.org/10.2174/092986709788803097
  29. 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. https://doi.org/10.1517/17425255.2012.648613
  30. Ozer, J., Ratner, M., Shaw, M., Bailey, W. and Schomaker, S. (2008) The current state of serum biomarkers of hepatotoxicity. Toxicology, 245, 194-205. https://doi.org/10.1016/j.tox.2007.11.021
  31. 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. https://doi.org/10.1016/j.tox.2014.03.009
  32. Rincon-Villamizar, E. and Restrepo, G. (2014) Rules relating hepatotoxicity with structural attributes of drugs. Toxicol. Environ. Chem., 96, 594-613. https://doi.org/10.1080/02772248.2014.961458
  33. 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. https://doi.org/10.1080/10629360290002316
  34. 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. https://doi.org/10.1093/toxsci/kft189
  35. 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. https://doi.org/10.1021/acs.chemrestox.5b00465
  36. 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. https://doi.org/10.1021/tx200148a
  37. 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. https://doi.org/10.2174/1386207318666150305144650
  38. 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. https://doi.org/10.1080/1062936X.2016.1201142
  39. 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.
  40. 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. https://doi.org/10.1093/toxsci/65.2.166
  41. Yuan, L. and Kaplowitz, N. (2013) Mechanisms of druginduced liver injury. Clin. Liver Dis., 17, 507-518. https://doi.org/10.1016/j.cld.2013.07.002
  42. 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.
  43. 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.
  44. 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.
  45. 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. https://doi.org/10.1002/etc.34
  46. 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. https://doi.org/10.1016/j.yrtph.2014.09.009
  47. Vinken, M. (2015) Adverse outcome pathways and druginduced liver injury testing. Chem. Res. Toxicol., 28, 1391-1397. https://doi.org/10.1021/acs.chemrestox.5b00208
  48. 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. https://doi.org/10.1002/jat.2949
  49. 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. https://doi.org/10.1093/toxsci/kft177
  50. 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.
  51. 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. https://doi.org/10.1016/j.yrtph.2015.06.015
  52. 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. https://doi.org/10.1517/17425255.2011.629186
  53. 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.
  54. 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. https://doi.org/10.1021/ci500233k
  55. 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. https://doi.org/10.1002/minf.201000164
  56. 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. https://doi.org/10.1021/acs.chemrestox.5b00275
  57. 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. https://doi.org/10.1007/s00204-014-1277-8
  58. 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. https://doi.org/10.3109/10408444.2013.811215
  59. 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. https://doi.org/10.3109/10408444.2015.1089471
  60. 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. https://doi.org/10.1021/acs.chemrestox.5b00480
  61. 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. https://doi.org/10.3390/ijms15057651
  62. 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.
  63. 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. https://doi.org/10.1021/acs.chemrestox.6b00135
  64. 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. https://doi.org/10.1002/minf.201400188
  65. 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. https://doi.org/10.1002/qsar.200960011
  66. 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. https://doi.org/10.1021/ci400050v
  67. 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. https://doi.org/10.1016/S0378-4274(02)00367-3

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