Archives of Toxicology

, Volume 91, Issue 11, pp 3477–3505 | Cite as

Adverse outcome pathways: opportunities, limitations and open questions

  • Marcel Leist
  • Ahmed Ghallab
  • Rabea Graepel
  • Rosemarie Marchan
  • Reham Hassan
  • Susanne Hougaard Bennekou
  • Alice Limonciel
  • Mathieu Vinken
  • Stefan Schildknecht
  • Tanja Waldmann
  • Erik Danen
  • Ben van Ravenzwaay
  • Hennicke Kamp
  • Iain Gardner
  • Patricio Godoy
  • Frederic Y. Bois
  • Albert Braeuning
  • Raymond Reif
  • Franz Oesch
  • Dirk Drasdo
  • Stefan Höhme
  • Michael Schwarz
  • Thomas Hartung
  • Thomas Braunbeck
  • Joost Beltman
  • Harry Vrieling
  • Ferran Sanz
  • Anna Forsby
  • Domenico Gadaleta
  • Ciarán Fisher
  • Jens Kelm
  • David Fluri
  • Gerhard Ecker
  • Barbara Zdrazil
  • Andrea Terron
  • Paul Jennings
  • Bart van der Burg
  • Steven Dooley
  • Annemarie H. Meijer
  • Egon Willighagen
  • Marvin Martens
  • Chris Evelo
  • Enrico Mombelli
  • Olivier Taboureau
  • Alberto Mantovani
  • Barry Hardy
  • Bjorn Koch
  • Sylvia Escher
  • Christoph van Thriel
  • Cristina Cadenas
  • D. Kroese
  • Bob van de Water
  • Jan G. Hengstler
Regulatory Toxicology

Abstract

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

Keywords

Regulatory toxicology Systems biology Multi-scale integration Computational toxicology Interspecies extrapolation Metabolism Pathway unidirectionality Liver fibrosis Paracetamol CCl4 Vinyl acetate Tumor promotion Binning of events Multiple hit events Proof of non-toxicity Prioritization of compounds 

Notes

Acknowledgements

This work was supported by the EU-ToxRisk project (An Integrated European “Flagship” Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002). This review is a joint activity of members of the EU-ToxRisk project and additionally incorporates ideas and suggestions of several colleagues who are not members of this consortium. We thank in particular Brigitte Landesmann, Maurice Whelan, Anna Bal-Price, and Christian Desaintes for their valuable discussion. Some work on examples was funded by the German Federal Ministry of Education and Research (BMBF) (LiSyM, SysDT, NeuriTox, LivSys, Lebersimulator projects). We thank M. Turajski and B. Schanze for valuable bibliographic support, and B. Barton for excellent support in handling the manuscript.

References

  1. Ames BN, Whitfield HJ Jr (1966) Frameshift mutagenesis in Salmonella. Cold Spring Harb Symp Quant Biol 31:221–225PubMedCrossRefGoogle Scholar
  2. Andersen ME, Krewski D (2009) Toxicity testing in the 21st century: bringing the vision to life. Toxicol Sci 107:324–330PubMedCrossRefGoogle Scholar
  3. Andersen ME, Krewski D (2010) The vision of toxicity testing in the 21st century: moving from discussion to action. Toxicol Sci 117:17–24PubMedCrossRefGoogle Scholar
  4. Ankley GT, Bennett RS, Erickson RJ et al (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29:730–741PubMedCrossRefGoogle Scholar
  5. Ball N, Cronin MTD, Shen J et al (2016) Toward good read-across practice (GRAP) guidance. ALTEX 33:149–166PubMedPubMedCentralGoogle Scholar
  6. Balmer NV, Leist M (2014) Epigenetics and transcriptomics to detect adverse drug effects in model systems of human development. Basic Clin Pharmacol Toxicol 115:59–68PubMedCrossRefGoogle Scholar
  7. Balmer NV, Weng M, Zimmer B et al (2012) Epigenetic changes and disturbed neural development in a human embryonic stem cell-based model relating to the fetal valproate syndrome. Hum Mol Genet 21:4104–4114PubMedCrossRefGoogle Scholar
  8. Balmer NV, Klima S, Rempel E et al (2014) From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects. Arch Toxicol 88:1451–1468PubMedPubMedCentralCrossRefGoogle Scholar
  9. Bal-Price A, Crofton K, Leist M et al (2015a) International STakeholder NETwork for developmental neurotoxicity (ISTNET): creating a developmental neurotoxicity (DNT) testing roadmap for regulatory purposes. Arch Toxicol 89:269–287PubMedPubMedCentralCrossRefGoogle Scholar
  10. Bal-Price A, Crofton KM, Sachana M et al (2015b) Putative adverse outcome pathways relevant to neurotoxicity. Crit Rev Toxicol 45:83–91PubMedPubMedCentralCrossRefGoogle Scholar
  11. Bal-Price A, Lein PJ, Keil KP et al (2017) Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity. Neurotoxicology 59:240–255PubMedCrossRefGoogle Scholar
  12. Basketter DA, Clewell H, Kimber I et al (2012) A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing—t4 report. ALTEX 29:3–91PubMedCrossRefGoogle Scholar
  13. Bataller R, Brenner DA (2005) Liver fibrosis. J Clin Investig 15:209–218CrossRefGoogle Scholar
  14. Baumann J, Gassmann K, Masjosthusmann S et al (2016) Comparative human and rat neurospheres reveal species differences in chemical effects on neurodevelopmental key events. Arch Toxicol 90:1415–1427PubMedCrossRefGoogle Scholar
  15. Becker RA, Ankley GT, Edwards SW et al (2015) Increasing scientific confidence in adverse outcome pathways: application of tailored Bradford-Hill considerations for evaluating weight of evidence. Regul Toxicol Pharmacol 72:514–537PubMedCrossRefGoogle Scholar
  16. Beliaeff B, Burgeot T (2002) Integrated biomarkers response: a useful tool for ecological risk assessment. Environ Toxicol Chem 21:1316–1322PubMedCrossRefGoogle Scholar
  17. Bhattacharya S (1948) A test for mutagenicity of methylcholanthrene. Nature 162:573PubMedCrossRefGoogle Scholar
  18. Blaauboer BJ, Boekelheide K, Clewell HJ et al (2012) The use of biomarkers of toxicity for integrating in vitro hazard estimates into risk assessment for humans. Altex 29:411–425PubMedCrossRefGoogle Scholar
  19. Bois FY, Jamei M, Clewell HJ (2010) PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals. Toxicology 278:256–267PubMedCrossRefGoogle Scholar
  20. Bolt HM, Foth H, Hengstler JG, Degen GH (2004) Carcinogenicity categorization of chemicals-new aspects to be considered in a European perspective. Toxicol Lett 151(1):29–41PubMedCrossRefGoogle Scholar
  21. Bouhifd M, Andersen ME, Baghdikian C et al (2015) The human toxome project. ALTEX 32:112–124PubMedPubMedCentralCrossRefGoogle Scholar
  22. Casey WM (2016) Advances in the development and validation of test methods in the United States. Toxicol Res 32:9–14PubMedPubMedCentralCrossRefGoogle Scholar
  23. Carbonell P, Lopez O, Amberg A, Pastor M, Sanz F (2017) Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data. ALTEX 34(2):219-234PubMedGoogle Scholar
  24. Clippinger AJ, Hill E, Curren R et al (2016) Bridging the gap between regulatory acceptance and industry use of non-animal methods. ALTEX 33:453–458PubMedGoogle Scholar
  25. Crawford SE, Hartung T, Hollert H et al (2017) Green toxicology: a strategy for sustainable chemical and material development. Environ Sci Eur 29:16PubMedPubMedCentralCrossRefGoogle Scholar
  26. Daneshian M, Kamp H, Hengstler J et al (2016) Highlight report: launch of a large integrated European in vitro toxicology project: EU-ToxRisk. Arch Toxicol 90:1021–1024PubMedPubMedCentralCrossRefGoogle Scholar
  27. Daston G, Knight DJ, Schwarz M et al (2015) SEURAT: safety evaluation ultimately replacing animal testing—recommendations for future research in the field of predictive toxicology. Arch Toxicol 89:15–23PubMedCrossRefGoogle Scholar
  28. Delrue N, Sachana M, Sakuratani Y et al (2016) The adverse outcome pathway concept: a basis for developing regulatory decision-making tools. Altern Lab Anim 44:417–429PubMedGoogle Scholar
  29. Depledge MH (1994) The rational basis for the use of biomarkers as ecotoxicological tools. In: Fossi MC, Leonzio C (ed.) Nondestructive biomarkers in vertebrates, pp. 271-295Google Scholar
  30. Drasdo D, Hoehme S, Hengstler JG (2014) How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis. J Hepatol 61:951–956PubMedCrossRefGoogle Scholar
  31. Duffield JS, Forbes SJ, Constandinou CM et al (2005) Selective depletion of macrophages reveals distinct, opposing roles during liver injury and repair. J Clin Invest 115:56–65PubMedPubMedCentralCrossRefGoogle Scholar
  32. Edwards SW, Tan YM, Villeneuve DL et al (2016) Adverse outcome pathways-organizing toxicological information to improve decision making. J Pharmacol Exp Ther 356(1):170–181PubMedCrossRefGoogle Scholar
  33. Fasbender F, Widera A, Hengstler JG et al (2016) Natural killer cells and liver fibrosis. Front Immunol 7:19PubMedPubMedCentralCrossRefGoogle Scholar
  34. Fehrenbacher N, Gyrd-Hansen M, Poulsen B et al (2004) Sensitization to the lysosomal cell death pathway upon immortalization and transformation. Cancer Res 64:5301–5310PubMedCrossRefGoogle Scholar
  35. Fischer FC et al (2017) Modeling exposure in the Tox21 in vitro bioassays. Chem Res Toxicol 30(5):1197–1208PubMedCrossRefGoogle Scholar
  36. Foghsgaard L, Wissing D, Mauch D et al (2001) Cathepsin B acts as a dominant execution protease in tumor cell apoptosis induced by tumor necrosis factor. J Cell Biol 153:999–1010PubMedPubMedCentralCrossRefGoogle Scholar
  37. Gantner F, Leist M, Küsters S et al (1996) T cell stimulus-induced crosstalk between lymphocytes and liver macrophages results in augmented cytokine release. Exp Cell Res 229:137–146PubMedCrossRefGoogle Scholar
  38. Gassmann K, Abel J, Bothe H et al (2010) Species-specific differential AhR expression protects human neural progenitor cells against developmental neurotoxicity of PAHs. Environ Health Perspect 118:1571–1577PubMedPubMedCentralCrossRefGoogle Scholar
  39. Gerhardt E, Kügler S, Leist M et al (2001) Cascade of caspase activation in potassium-deprived cerebellar granule neurons: targets for treatment with peptide and protein inhibitors of apoptosis. Mol Cell Neurosci 17:717–731 (PubMed PMID: 11312607) PubMedCrossRefGoogle Scholar
  40. Ghallab A, Cellière G, Henkel SG et al (2016) Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases. J Hepatol 64:860–871PubMedCrossRefGoogle Scholar
  41. Godoy P, Hewitt NJ, Albrecht U et al (2013) Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol 87:1315–1530PubMedPubMedCentralCrossRefGoogle Scholar
  42. Grinberg M, Stöber RM, Edlund K et al (2014) Toxicogenomics directory of chemically exposed human hepatocytes. Arch Toxicol 88(12):2261–2287PubMedCrossRefGoogle Scholar
  43. Guidance document on developing and assessing adverse outcome pathways (2017) Environment directorate joint meeting of the chemicals committee and the working party on chemicals, pesticides and biotechnology; Series on Testing and Assessment No. 184. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2013)6&doclanguage=en. Accessed 4 Sept 2017
  44. Hamon J, Jennings P, Bois FY (2014) Systems biology modeling of omics data: effect of cyclosporine a on the Nrf2 pathway in human renal cells. BMC Syst Biol 8:76PubMedPubMedCentralCrossRefGoogle Scholar
  45. Hansson O, Castilho RF, Kaminski Schierle GS et al (2000) Additive effects of caspase inhibitor and lazaroid on the survival of transplanted rat and human embryonic dopamine neurons. Exp Neurol 164:102–111PubMedCrossRefGoogle Scholar
  46. Hansson O, Nylandsted J, Castilho RF et al (2003) Overexpression of heat shock protein 70 in R6/2 Huntington’s disease mice has only modest effects on disease progression. Brain Res 970:47–57PubMedCrossRefGoogle Scholar
  47. Hartung T (2009) Food for thought… on evidence-based toxicology. ALTEX 26:75–82PubMedCrossRefGoogle Scholar
  48. Hartung T (2013) Look back in anger—what clinical studies tell us about preclinical work. ALTEX 30:275–291PubMedPubMedCentralCrossRefGoogle Scholar
  49. Hartung T (2016) Making big sense from big data in toxicology by read-across. ALTEX 33:83–93PubMedCrossRefGoogle Scholar
  50. Hartung T (2017) Utility of the adverse outcome pathway concept in drug development. Expert Opin Drug Metab Toxicol 13:1–3PubMedCrossRefGoogle Scholar
  51. Hartung T, McBride M (2011) Food for thought… on mapping the human toxome. ALTEX 28:83–93PubMedCrossRefGoogle Scholar
  52. Hartung T, van Vliet E, Jaworska J et al (2012) Systems toxicology. ALTEX 29:119–128PubMedCrossRefGoogle Scholar
  53. Hartung T, Luechtefeld T, Maertens A et al (2013a) Integrated testing strategies for safety assessments. ALTEX 30:3–18PubMedPubMedCentralCrossRefGoogle Scholar
  54. Hartung T, Stephens M, Hoffmann S (2013b) Mechanistic validation. ALTEX 30:119–130PubMedPubMedCentralCrossRefGoogle Scholar
  55. Hartung T, FitzGerald R, Paul J et al (2017) Systems toxicology—real world applications and opportunities. Chem Res Toxicol 30:870–882PubMedPubMedCentralCrossRefGoogle Scholar
  56. Hengstler JG, Arand M, Herrero ME et al (1998) Polymorphisms of N-acetyltransferases, glutathione S-transferases, microsomal epoxide hydrolase and sulfotransferases: influence on cancer susceptibility. Recent Results Cancer Res 154:47–85PubMedCrossRefGoogle Scholar
  57. Hengstler JG, Van der Burg B, Steinberg P et al (1999) Interspecies differences in cancer susceptibility and toxicity. Drug Metab Rev 31:917–970PubMedCrossRefGoogle Scholar
  58. Hengstler JG, Bogdanffy MS, Bolt HM et al (2003) Challenging dogma: thresholds for genotoxic carcinogens? The case of vinyl acetate. Annu Rev Pharmacol Toxicol 43:485–520PubMedCrossRefGoogle Scholar
  59. Hengstler JG, Marchan R, Leist M (2012) Highlight report: towards the replacement of in vivo repeated dose systemic toxicity testing. Arch Toxicol 86:13–15PubMedCrossRefGoogle Scholar
  60. Hirt U, Gantner F, Leist M (2000) Phagocytosis of non-apoptotic cells dying by caspase-independent mechanisms. J Immunol 164:6520–6529PubMedCrossRefGoogle Scholar
  61. Hoehme S, Hengstler JG, Brulport M et al (2007) Mathematical modelling of liver regeneration after intoxication with CCl(4). Chem Biol Interact 168:74–93CrossRefGoogle Scholar
  62. Hoehme S, Brulport M, Bauer A et al (2010) Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci USA 107:10371–10376PubMedPubMedCentralCrossRefGoogle Scholar
  63. Hoffmann S, Hartung T (2006) Towards an evidence-based toxicology. Hum Exp Toxicol 25:497–513PubMedCrossRefGoogle Scholar
  64. Hoffmann S, de Vries RBM, Stephens ML et al (2017) A primer on systematic reviews in toxicology. Arch Toxicol 91(7):2551–2575PubMedPubMedCentralCrossRefGoogle Scholar
  65. Horvat T, Landesmann B, Lostia A et al (2017) Adverse outcome pathway development from protein alkylation to liver fibrosis. Arch Toxicol 91:1523–1543PubMedCrossRefGoogle Scholar
  66. Huggett RJ, Kimerle RA, Mehrle PM, Bergman HL (1992) Biomarkers. Biochemical, physiological, and histological markers of anthropogenic stress. Lewis Publishers, Boca Raton, p 347Google Scholar
  67. Jacobs MN, Colacci A, Louekari K et al (2016) International regulatory needs for development of an IATA for non-genotoxic carcinogenic chemical substances. ALTEX 33:359–392PubMedGoogle Scholar
  68. Jalan R, Moreau R, Kamath PS, Arroyo V (2016) Acute-on-chronic liver failure: a distinct clinical condition. Semin Liver Dis 36(2):107–108PubMedCrossRefGoogle Scholar
  69. Jansen PL, Ghallab A, Vartak N et al (2017) The ascending pathophysiology of cholestatic liver disease. Hepatology 65(2):722–738PubMedCrossRefGoogle Scholar
  70. Jennings P (2013) Stress response pathways, toxicity pathways and adverse outcome pathways. Arch Toxicol 87:13–14PubMedCrossRefGoogle Scholar
  71. Jennings P, Limonciel A, Felice L et al (2013) An overview of transcriptional regulation in response to toxicological insult. Arch Toxicol 87:49–72PubMedCrossRefGoogle Scholar
  72. Kim KH, Chen CC, Monzon RI et al (2013) Matricellular protein CCN1 promotes regression of liver fibrosis through induction of cellular senescence in hepatic myofibroblasts. Mol Cell Biol 33:2078–2090PubMedPubMedCentralCrossRefGoogle Scholar
  73. Kisseleva T, Cong M, Paik Y et al (2012) Myofibroblasts revert to an inactive phenotype during regression of liver fibrosis. Proc Natl Acad Sci USA 109:9448–9453PubMedPubMedCentralCrossRefGoogle Scholar
  74. Kleensang A, Maertens A, Rosenberg M et al (2014) Pathways of toxicity. ALTEX 31:53–61PubMedCrossRefGoogle Scholar
  75. Kleinstreuer NC, Ceger PC, Allen DG et al (2016) A curated database of rodent uterotrophic bioactivity. Environ Health Perspect 124:556–562PubMedGoogle Scholar
  76. Kleinstreuer NC, Ceger P, Watt ED et al (2017) Development and validation of a computational model for androgen receptor activity. Chem Res Toxicol 30:946–964PubMedCrossRefGoogle Scholar
  77. Knapen D, Vergauwen L, Villeneuve DL et al (2015) The potential of AOP networks for reproductive and developmental toxicity assay development. Reprod Toxicol 56:52–55PubMedCrossRefGoogle Scholar
  78. Krizhanovsky V, Yon M, Dickins RA et al (2008) Senescence of activated stellate cells limits liver fibrosis. Cell 134:657–667PubMedPubMedCentralCrossRefGoogle Scholar
  79. Krug AK, Balmer NV, Matt F et al (2013a) Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants. Arch Toxicol 87:2215–2231PubMedCrossRefGoogle Scholar
  80. Krug AK, Kolde R, Gaspar JA et al (2013b) Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol 87:123–143PubMedCrossRefGoogle Scholar
  81. Krug AK, Gutbier S, Zhao L et al (2014) Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+). Cell Death Dis. 5:e1222PubMedPubMedCentralCrossRefGoogle Scholar
  82. Kuegler PB, Zimmer B, Waldmann T et al (2010) Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for developmental neurotoxicitiy testing—a review by the Transatlantic Think Tank for toxicology (t4). ALTEX 27:17–42PubMedGoogle Scholar
  83. Latta M, Künstle G, Leist M et al (2000) Metabolic depletion of ATP by fructose inversely controls CD95- and tumor necrosis factor receptor 1-mediated hepatic apoptosis. J Exp Med 191:1975–1985PubMedPubMedCentralCrossRefGoogle Scholar
  84. Leist M, Hartung T (2013) Inflammatory findings on species extrapolations: humans are definitely no 70-kg mice. Arch Toxicol 87:563–567PubMedPubMedCentralCrossRefGoogle Scholar
  85. Leist M, Jäättelä M (2001) Four deaths and a funeral: from caspases to alternative mechanisms. Nat Rev Mol Cell Biol 2:589–598PubMedCrossRefGoogle Scholar
  86. Leist M, Nicotera P (1997) Calcium and neuronal death. Rev Physiol Biochem Pharmacol 132:79–125CrossRefGoogle Scholar
  87. Leist M, Gantner F, Künstle G et al (1996) The 55-kD tumor necrosis factor receptor and CD95 independently signal murine hepatocyte apoptosis and subsequent liver failure. Mol Med 2:109–124PubMedPubMedCentralGoogle Scholar
  88. Leist M, Fava E, Montecucco C, Nicotera P (1997a) Peroxynitrite and nitric oxide donors induce neuronal apoptosis by eliciting autocrine excitotoxicity. Eur J Neurosci 9:1488–1498PubMedCrossRefGoogle Scholar
  89. Leist M, Gantner F, Naumann H et al (1997b) Tumor necrosis factor-induced apoptosis during the poisoning of mice with hepatotoxins. Gastroenterology 112:923–934PubMedCrossRefGoogle Scholar
  90. Leist M, Volbracht C, Kühnle S et al (1997c) Caspase-mediated apoptosis in neuronal excitotoxicity triggered by nitric oxide. Mol Med 3:750–764PubMedPubMedCentralGoogle Scholar
  91. Leist M, Gantner F, Künstle G et al (1998a) Cytokine-mediated hepatic apoptosis. Rev Physiol Biochem Pharmacol 133:109–155PubMedGoogle Scholar
  92. Leist M, Volbracht C, Fava E et al (1998b) 1-Methyl-4-phenylpyridinium induces autocrine excitotoxicity, protease activation, and neuronal apoptosis. Mol Pharmacol 54:789–801PubMedGoogle Scholar
  93. Leist M, Hartung T, Nicotera P (2008) The dawning of a new age of toxicology. ALTEX 25:103–114PubMedCrossRefGoogle Scholar
  94. Leist M, Efremova L, Karreman C (2010) Food for thought on considerations and guidelines for basic test method descriptions in toxicology. ALTEX 27:309–317PubMedCrossRefGoogle Scholar
  95. Leist M, Hasiwa N, Daneshian M et al (2012) Validation and quality control of replacement alternatives—current status and future challenges. Toxicol Res 1:8–22CrossRefGoogle Scholar
  96. Leist M, Hasiwa N, Rovida C et al (2014) Consensus report on the future of animal-free systemic toxicity testing. ALTEX 31:341–356PubMedGoogle Scholar
  97. Lewin L (1885) Lehrbuch der Toxikologie: für Aerzte, Studierende und Apotheker (translation: textbook of Toxicology for physicians, students and pharmacists). Urban & Schwarzenberg, WienGoogle Scholar
  98. Lewin L (1924) Phantastica. Die betäubenden und erregenden Genussmittel. Für Ärzte und Nichtärzte (translation: Phantastica. Anaesthetic and stimulating drugs for physicians and laymen). Verlag von Georg Stilke, BerlinGoogle Scholar
  99. Limonciel A, Aschauer L, Wilmes A et al (2011) Lactate is an ideal non-invasive marker for evaluating temporal alterations in cell stress and toxicity in repeat dose testing regimes. Toxicol In Vitro 25:1855–1862PubMedCrossRefGoogle Scholar
  100. Maertens A, Anastas N, Spencer PJ et al (2014) Green Toxicology. ALTEX 31:243–249PubMedCrossRefGoogle Scholar
  101. Matozzo V, Gagné F, Marin MG, Ricciardi F, Blaise C (2008) Vitellogenin as a biomarker of exposure to estrogenic compounds in aquatic invertebrates: a review. Environ Int 34:531–545PubMedCrossRefGoogle Scholar
  102. McCarty JF, Shugart LR (1990) Biomarkers of environmental contamination. Lewis Publishers, CRC Press, Boca Raton, p 457Google Scholar
  103. Nicotera P, Leist M, Manzo L (1999) Neuronal cell death: a demise with different shapes. Trends Pharmacol Sci 20(2):46–51PubMedCrossRefGoogle Scholar
  104. NRC (2007) Toxicity testing in the 21st century: a vision and a strategy. The National Academies Press, WashingtonGoogle Scholar
  105. Nyffeler J, Karreman C, Leisner H et al (2017) Design of a high-throughput human neural crest cell migration assay to indicate potential developmental toxicants. ALTEX 34:7555–7594Google Scholar
  106. Nylandsted J, Wick W, Hirt UA et al (2002) Eradication of glioblastoma, and breast and colon carcinoma xenografts by Hsp70 depletion. Cancer Res 62:7139–7142PubMedGoogle Scholar
  107. Obiol-Pardo C, Gomis-Tena J, Sanz F, Saiz J, Pastor M (2011) A multiscale simulation system for the prediction of drug-induced cardiotoxicity. J Chem Inf Model 51:483–492PubMedCrossRefGoogle Scholar
  108. Ockleford C et al (2017) EFSA panel on plant protection products and their residues (PPR) investigation into experimental toxicological properties of plant protection products having a potential link to Parkinson’s disease and childhood leukaemia. EFSA J 15:4691Google Scholar
  109. Oesch F, Herrero ME, Hengstler JG, Lohmann M, Arand M (2000) Metabolic detoxification: implications for thresholds. Toxicol Pathol 28(3):382–387PubMedCrossRefGoogle Scholar
  110. Oesch F, Herrero ME, Lohmann M, Hengstler JG, Arand M (2001) Sequestration of biological reactive intermediates by trapping as covalent enzyme-intermediate complex. Adv Exp Med Biol 500:577–586PubMedCrossRefGoogle Scholar
  111. Oki NO, Edwards SW (2016) An integrative data mining approach to identifying adverse outcome pathway signatures. Toxicology 28(350–352):49–61CrossRefGoogle Scholar
  112. Paparella M, Colacci A, Jacobs MN (2017) Uncertainties of testing methods: what do we (want to) know about carcinogenicity? ALTEX 34(2):235–252PubMedGoogle Scholar
  113. Patlewicz G, Ball N, Becker RA et al (2014) Read-across approaches—misconceptions, promises and challenges ahead. ALTEX 31:387–396PubMedCrossRefGoogle Scholar
  114. Pelkonen O, Terron A, Hernandez AF et al (2017) Chemical exposure and infant leukaemia: development of an adverse outcome pathway (AOP) for aetiology and risk assessment research. Arch Toxicol 91(8):2763–2780PubMedCrossRefGoogle Scholar
  115. Polasek TM, Patel F, Jensen BP, Sorich MJ, Wiese MD, Doogue MP (2013) Predicted metabolic drug clearance with increasing adult age. Br J Clin Pharmacol 75(4):1019–1028PubMedCrossRefGoogle Scholar
  116. Poulin P, Theil FP (2002) Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci 91:129–156PubMedCrossRefGoogle Scholar
  117. Radaeva S, Sun R, Jaruga B et al (2006) Natural killer cells ameliorate liver fibrosis by killing activated stellate cells in NKG2D-dependent and tumor necrosis factor-related apoptosis-inducing ligand-dependent manners. Gastroenterology 130:435–452PubMedCrossRefGoogle Scholar
  118. Rahnenführer J, Leist M (2015) From smoking guns to footprints: mining for critical events of toxicity pathways in transcriptome data. Arch Toxicol 89:813–817PubMedPubMedCentralCrossRefGoogle Scholar
  119. Ramachandran P, Iredale JP (2012) Macrophages: central regulators of hepatic fibrogenesis and fibrosis resolution. J Hepatol 56:1417–1419PubMedCrossRefGoogle Scholar
  120. Ramachandran P, Pellicoro A, Vernon MA et al (2012) Differential Ly-6C expression identifies the recruited macrophage phenotype, which orchestrates the regression of murine liver fibrosis. Proc Natl Acad Sci USA 109:E3186–E3195PubMedPubMedCentralCrossRefGoogle Scholar
  121. Rempel E, Hoelting L, Waldmann T et al (2015) A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors. Arch Toxicol 89:1599–1618PubMedPubMedCentralCrossRefGoogle Scholar
  122. Rostami-Hodjegan A (2012) Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther 92(1):50–61PubMedCrossRefGoogle Scholar
  123. Rovida C, Alépée N, Api AM et al (2015) Integrated testing strategies (ITS) for safety assessment. ALTEX 32:171–181PubMedCrossRefGoogle Scholar
  124. Samuel GO, Hoffmann S, Wright R et al (2016) Guidance on assessing the methodological and reporting quality of toxicologically relevant studies: a scoping review. Environ Int 92–93:630–646PubMedCrossRefGoogle Scholar
  125. Sauer JM, Hartung T, Leist M et al (2015) Systems toxicology: the future of risk assessment. Int J Toxicol 34:346–348PubMedPubMedCentralCrossRefGoogle Scholar
  126. Schierle GS, Hansson O, Leist M et al (1999) Caspase inhibition reduces apoptosis and increases survival of nigral transplants. Nat Med 5:97–100PubMedCrossRefGoogle Scholar
  127. Schildknecht S, Pöltl D, Nagel DM et al (2009) Requirement of a dopaminergic neuronal phenotype for toxicity of low concentrations of 1-methyl-4-phenylpyridinium to human cells. Toxicol Appl Pharmacol 241:23–35PubMedCrossRefGoogle Scholar
  128. Schildknecht S, Pape R, Müller N et al (2011) Neuroprotection by minocycline caused by direct and specific scavenging of peroxynitrite. J Biol Chem 286:4991–5002PubMedCrossRefGoogle Scholar
  129. Schildknecht S, Karreman C, Pöltl D et al (2013) Generation of genetically-modified human differentiated cells for toxicological tests and the study of neurodegenerative diseases. ALTEX 30:427–444PubMedCrossRefGoogle Scholar
  130. Schildknecht S, Pape R, Meiser J et al (2015) Preferential extracellular generation of the active parkinsonian toxicant MPP+ by transporter-independent export of the intermediate MPDP+. Antioxid Redox Signal 23:1001–1016PubMedPubMedCentralCrossRefGoogle Scholar
  131. Schildknecht S, Di Monte DA, Pape R et al (2017) Tipping points and endogenous determinants of nigrostriatal degeneration by MPTP. Trends Pharmacol Sci 38:541–555PubMedCrossRefGoogle Scholar
  132. Schliess F, Hoehme S, Henkel SG et al (2014) Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology 60:2040–2051PubMedCrossRefGoogle Scholar
  133. Shinde V, Hoelting L, Perumal SS et al (2016) Definition of transcriptome-based indices for quantitative characterization of chemically disturbed stem cell development—introduction of the STOP-Toxukn and STOP-Toxukk t. Arch Toxicol 91:839–864PubMedPubMedCentralCrossRefGoogle Scholar
  134. Smirnova L, Hogberg HT, Leist M et al (2014) Developmental neurotoxicity—challenges in the 21st century and in vitro opportunities. ALTEX 31:129–156PubMedPubMedCentralGoogle Scholar
  135. Stephens ML, Betts K, Beck NB et al (2016) The emergence of systematic review in toxicology. Toxicol Sci 152:10–16PubMedPubMedCentralCrossRefGoogle Scholar
  136. Stiegler N, Krug A, Matt F et al (2011) Assessment of chemical-induced impairment of human neurite outgrowth by multiparametric live cell imaging in high-density cultures. Toxicol Sci 121:73–87PubMedCrossRefGoogle Scholar
  137. Tacke F, Zimmermann HW (2014) Macrophage heterogeneity in liver injury and fibrosis. J Hepatol 60:1090–1096PubMedCrossRefGoogle Scholar
  138. Tian Z, Chen Y, Gao B (2013) Natural killer cells in liver disease. Hepatology 57:1654–1662. doi: 10.1002/hep.26115 PubMedPubMedCentralCrossRefGoogle Scholar
  139. Tollefsen KE, Scholz S, Cronin MT et al (2014) Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Reg Toxicol Pharmacol 70:629–640CrossRefGoogle Scholar
  140. Trevan JW (1927) The error of determination of toxicity. Proc R Soc Lond B Biol Sci 101(712):483–514Google Scholar
  141. Tsaioun K, Blaauboer BJ, Hartung T (2016) Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. ALTEX 33:343–358PubMedCrossRefGoogle Scholar
  142. Users’ handbook supplement to the guidance document for developing and assessing AOPs (2017) http://www.oecd-ilibrary.org/environment/users-handbook-supplement-to-the-guidance-document-for-developing-and-assessing-adverse-outcome-pathways_5jlv1m9d1g32-en. Accessed 4 Sept 2017
  143. van der Burg B, Pieterse B, Buist H et al (2015a) A high throughput screening system for predicting chemically-induced reproductive organ deformities. Reprod Toxicol 55:95–103PubMedCrossRefGoogle Scholar
  144. van der Burg B, Wedebye EB, Dietrich DR et al (2015b) The ChemScreen project to design a pragmatic alternative approach to predict reproductive toxicity of chemicals. Reprod Toxicol 55:114–123PubMedCrossRefGoogle Scholar
  145. van Thriel C, Westerink R, Beste C et al (2011) Translating neurobehavioural endpoints of developmental neurotoxicity tests into in vitro assays and readouts. NeuroToxicol 33:911–924CrossRefGoogle Scholar
  146. Vartak N, Damle-Vartak A, Richter B et al (2016) Cholestasis-induced adaptive remodeling of interlobular bile ducts. Hepatology 63:951–964PubMedPubMedCentralCrossRefGoogle Scholar
  147. Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH, LaLone CA, Landesmann B, Lettieri T, Munn S, Nepelska M, Ottinger MA, Vergauwen L, Whelan M (2014a) Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci 142:312–320PubMedPubMedCentralCrossRefGoogle Scholar
  148. Villeneuve DL, Crump D, Garcia-Reyero N, Hecker M, Hutchinson TH, LaLone CA, Landesmann B, Lettieri T, Munn S, Nepelska M, Ottinger MA, Vergauwen L, Whelan M (2014b) Adverse outcome pathway development. II: best practices. Toxicol Sci 142:321–330PubMedPubMedCentralCrossRefGoogle Scholar
  149. Vinken M (2015) Adverse outcome pathways and drug-induced liver injury testing. Chem Res Toxicol 28:1391–1397PubMedPubMedCentralCrossRefGoogle Scholar
  150. Volbracht C, Leist M, Nicotera P (1999) ATP controls neuronal apoptosis triggered by microtubule breakdown or potassium deprivation. Mol Med. 5:477–489PubMedPubMedCentralGoogle Scholar
  151. Volbracht C, Fava E, Leist M et al (2001a) Calpain inhibitors prevent nitric oxide-triggered excitotoxic apoptosis. NeuroReport 12:3645–3648PubMedCrossRefGoogle Scholar
  152. Volbracht C, Leist M, Kolb SA et al (2001b) Apoptosis in caspase-inhibited neurons. Mol Med 7:36–48PubMedPubMedCentralGoogle Scholar
  153. Wilmes A, Limonciel A, Aschauer L et al (2013) Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress. J Proteomics 79:180–194PubMedCrossRefGoogle Scholar
  154. Wilmes A, Bielow C, Ranninger C et al (2015) Mechanism of cisplatin proximal tubule toxicity revealed by integrating transcriptomics, proteomics, metabolomics and biokinetics. Toxicol In Vitro 30:117–127PubMedCrossRefGoogle Scholar
  155. Wittwehr C, Aladjov H, Ankley G et al (2017) How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology. Toxicol Sci 155:326–336PubMedCrossRefGoogle Scholar
  156. Wynn TA (2008) Cellular and molecular mechanisms of fibrosis. J Pathol 214:199–210PubMedPubMedCentralCrossRefGoogle Scholar
  157. Zhu H, Bouhifd M, Kleinstreuer N et al (2016) Supporting read-across using biological data. ALTEX 33:167–182PubMedPubMedCentralGoogle Scholar
  158. Zimmer B, Lee G, Balmer NV et al (2012) Evaluation of developmental toxicants and signaling pathways in a functional test based on the migration of human neural crest cells. Environ Health Perspect 120:1116–1122PubMedPubMedCentralCrossRefGoogle Scholar
  159. Zimmer B, Pallocca G, Dreser N et al (2014) Profiling of drugs and environmental chemicals for functional impairment of neural crest migration in a novel stem cell-based test battery. Arch Toxicol 88:1109–1126PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Marcel Leist
    • 1
  • Ahmed Ghallab
    • 2
    • 3
  • Rabea Graepel
    • 4
  • Rosemarie Marchan
    • 2
  • Reham Hassan
    • 2
    • 3
  • Susanne Hougaard Bennekou
    • 5
  • Alice Limonciel
    • 6
  • Mathieu Vinken
    • 7
  • Stefan Schildknecht
    • 1
  • Tanja Waldmann
    • 1
  • Erik Danen
    • 4
  • Ben van Ravenzwaay
    • 8
  • Hennicke Kamp
    • 8
  • Iain Gardner
    • 9
  • Patricio Godoy
    • 2
  • Frederic Y. Bois
    • 10
  • Albert Braeuning
    • 11
  • Raymond Reif
    • 2
  • Franz Oesch
    • 12
  • Dirk Drasdo
    • 13
    • 14
  • Stefan Höhme
    • 15
  • Michael Schwarz
    • 16
  • Thomas Hartung
    • 17
  • Thomas Braunbeck
    • 18
  • Joost Beltman
    • 4
  • Harry Vrieling
    • 19
  • Ferran Sanz
    • 20
  • Anna Forsby
    • 21
    • 38
  • Domenico Gadaleta
    • 22
  • Ciarán Fisher
    • 9
  • Jens Kelm
    • 23
  • David Fluri
    • 23
  • Gerhard Ecker
    • 24
  • Barbara Zdrazil
    • 24
  • Andrea Terron
    • 25
  • Paul Jennings
    • 26
  • Bart van der Burg
    • 27
  • Steven Dooley
    • 28
  • Annemarie H. Meijer
    • 29
  • Egon Willighagen
    • 30
    • 31
  • Marvin Martens
    • 30
  • Chris Evelo
    • 30
    • 31
  • Enrico Mombelli
    • 10
  • Olivier Taboureau
    • 32
    • 33
  • Alberto Mantovani
    • 34
  • Barry Hardy
    • 35
  • Bjorn Koch
    • 29
  • Sylvia Escher
    • 36
  • Christoph van Thriel
    • 2
  • Cristina Cadenas
    • 2
  • D. Kroese
    • 37
  • Bob van de Water
    • 4
  • Jan G. Hengstler
    • 2
  1. 1.In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden FoundationUniversity of KonstanzKonstanzGermany
  2. 2.Leibniz Research Centre for Working Environment and Human FactorsTechnical University DortmundDortmundGermany
  3. 3.Department of Forensic Medicine and Toxicology, Faculty of Veterinary MedicineSouth Valley UniversityQenaEgypt
  4. 4.Research Division of Drug Discovery and SafetyLeiden UniversityLeidenThe Netherlands
  5. 5.The Danish EPACopenhagenDenmark
  6. 6.Division of Physiology, Department of Physiology and Medical PhysicsMedical University of InnsbruckInnsbruckAustria
  7. 7.Department of In Vitro Toxicology and Dermato-CosmetologyVrije Universiteit BrusselBrusselsBelgium
  8. 8.BASF SE, Experimental Toxicology and EcologyLudwigshafen am RheinGermany
  9. 9.Simcyp (A Certara Company)SheffieldUK
  10. 10.INERIS, DRC/VIVA/METO, Parc ALATA BP2Verneuil en HalatteFrance
  11. 11.Department of Food SafetyGerman Federal Institute for Risk AssessmentBerlinGermany
  12. 12.Institute of ToxicologyUniversity of MainzMainzGermany
  13. 13.INRIA, Unit RocquencourtLe Chesnay CedexFrance
  14. 14.Laboratoire Jacques-Louis LionsFrance Université of Paris 06, CNRS, UMR 7598ParisFrance
  15. 15.Institute for Computer ScienceUniversity of LeipzigLeipzigGermany
  16. 16.Department of Toxicology, Institute of Experimental and Clinical Pharmacology and ToxicologyEberhard Karls UniversityTübingenGermany
  17. 17.Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  18. 18.Aquatic Ecology and Toxicology Section, Centre for Organismal StudiesUniversity of HeidelbergHeidelbergGermany
  19. 19.Department of Human GeneticsLeiden University Medical CenterLeidenThe Netherlands
  20. 20.Research Programme on Biomedical Informatics (GRIB), Department of Health and Life Sciences, Hospital del Mar Medical Research Institute (IMIM)Universitat Pompeu FabraBarcelonaSpain
  21. 21.Unit of Toxicology SciencesSwetox, Karolinska InstitutetSödertäljeSweden
  22. 22.Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health SciencesIRCCS-Istituto di Ricerche Farmacologiche Mario NegriMilanoItaly
  23. 23.InSphero AGSchlierenSwitzerland
  24. 24.Pharmacoinformatics Research Group, Department of Pharmaceutical ChemistryUniversity of ViennaViennaAustria
  25. 25.European Food Safety Authority (EFSA)ParmaItaly
  26. 26.Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
  27. 27.BioDetection Systems b.v. (BDS)AmsterdamThe Netherlands
  28. 28.Section Molekular Hepatology, II. Medical Clinic, Medical Faculty MannheimUniversity of HeidelbergMannheimGermany
  29. 29.Institute of BiologyLeiden UniversityLeidenThe Netherlands
  30. 30.Department of Bioinformatics, BiGCaT, NUTRIMMaastricht UniversityMaastrichtThe Netherlands
  31. 31.Open PHACTS FoundationCambridgeUK
  32. 32.Inserm UMR-S973, Molécules Thérapeutiques in silico, Paris Diderot UniversityParisFrance
  33. 33.Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
  34. 34.Istituto Superiore di SanitàRomaItaly
  35. 35.Douglas Connect GmbHAargauSwitzerland
  36. 36.Fraunhofer Institute of Toxicology and Experimental Medicine (ITEM) Chemical Risk Assessment Group Manager Structure Activity Relationships/databases, and expert systems Nikolai-Fuchs-Strasse 1HannoverGermany
  37. 37.TNO (Department of Risk Analysis of Products in Development)ZeistThe Netherlands
  38. 38.Department of NeurochemistryStockholm UniversityStockholmSweden

Personalised recommendations