The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project
Restricted accessResearch articleFirst published online November, 2009
The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project
This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.
SchaafsmaG., KroeseE.D., TielemansE.L., van de SandtJ.J. & van LeeuwenC.J. (2009). REACH, non-testing approaches and the urgent need for a change in mind set. Regulatory Toxicology & Pharmacology53, 70–80.
2.
van LeeuwenK., SchultzT.W., HenryT., DederichB. & VeithG.D. (2009). Using chemical categories to fill data gaps in hazard assessment. SAR & QSAR in Environmental Research20, 207–220.
3.
ECETOC (2007). Technical Report No. 102. Intelligent Testing Strategies in Ecotoxicology: Mode of Action Approach for Specifically Acting Chemicals. ISSN-0773-8072-102, 151 pp. Available at: http://www.ecetoc.org/technical-reports (Accessed 12.10. 09). Brussels, Belgium: European Centre for Ecotoxicology and Toxicology of Chemicals.
4.
CroninM.T.D. (2006). The role of hydrophobicity in toxicity prediction. Current Computer-aided Drug Design2, 405–413.
5.
BorgertC.J., QuillT.F., McCartyL.S. & MasonA.M. (2004). Can mode of action predict mixture toxicity for risk assessment? Toxicology & Applied Pharmacology201, 85–96.
6.
ButterworthB.E., ConollyR.B. & MorganK.T. (1995). A strategy for establishing mode of action of chemical carcinogens as a guide for approaches to risk assessments. Cancer Letters93, 129–146.
7.
DellarcoV.L. & WiltseJ.A. (1998). US Environmental Protection Agency's revised guidelines for carcinogen risk assessment: incorporating mode of action data. Mutation Research — Fundamental & Molecular Mechanisms of Mutagenesis405, 273–277.
8.
SchlosserP.M. & BogdanffyM.S. (1999). Determining modes of action for biologically based risk assessments. Regulatory Toxicology & Pharmacology30, 75–79.
9.
US EPA (2000). Draft Dioxin Reassessment, Part III: Integrated Summary and Risk Characterization for 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) and Related Compounds, 196 pp. Washington, DC, USA: US Environmental Protection Agency.
10.
US EPA (2001). Office of Research and Development: Draft Final Guidelines for Carcinogen Risk Assessment, 123 pp. Washington, DC, USA: US Environmental Protection Agency.
11.
CsermelyP., AgostonV. & PongorS. (2005). The efficiency of multi-target drugs: the network approach might help drug design. Trends in Pharmacological Sciences26, 178–182.
12.
KrishnanA., GiulianiA. & TomitaM. (2007). Indeterminacy of reverse engineering of gene regulatory networks: the curse of gene elasticity. PlosOnee562.
13.
RandG.M., WellsP.G. & McCartyL.S. (1995). Introduction to aquatic toxicology. In Fundamentals of Aquatic Toxicology: Effects, Environmental Fate and Risk Assessment, pp. 3–67. Washington, DC, USA: Taylor & Francis.
14.
SpycherS., NendzaM. & GasteigerJ. (2004). Comparison of different classification methods applied to a mode of toxic action data set. QSAR & Combinatorial Science23, 779–791.
15.
Sonich-MullinC., FielderR., WiltseJ., BaetckeK., DempseyJ., Fenner-CrispP., GrantD., HartleyM., KnaapA., KroeseD., MangelsdorfI., MeekE., RiceJ.M. & YounesM. (2001). IPCS conceptual framework for evaluating a mode of action for chemical carcinogenesis. Regulatory Toxicology & Pharmacology34, 146–152.
16.
BoobisA.R., CohenS.M., DellarcoV., McGregorD., MeekM.E., VickersC., WillcocksD. & FarlandW. (2006). IPCS framework for analysing the relevance of a cancer mode of action for humans. Critical Reviews in Toxicology36, 781–792.
17.
BoobisA.R., DoeJ.E., Heinrich-HirscheB., MeekM.E., MunnS., RuchirawatM., SchlatterJ., SeedJ. & VickersC. (2008). IPCS Framework for analysing the relevance of a non-cancer mode of action for humans. Critical Reviews in Toxicology38, 87–96.
18.
CohenS.M., MeekM.E., KlaunigJ.E., PattonD.E. & Fenner-CrispP.A. (2003). The human relevance of information on carcinogenic modes of action: overview. Critical Reviews in Toxicology33, 581–589.
19.
MeekM.E. (2008). Recent developments in frameworks to consider human relevance of hypothesized modes of action for tumours in animals. Environmental & Molecular Mutagenesis49, 110–116.
20.
MeekM.E., BucherJ.R., CohenS.M., DellarcoV., HillR.N., Lehman-McKeemanL.D., LongfellowD.G., PastoorT., SeedJ. & PattonD.E. (2003). A framework for human relevance analysis of information on carcinogenic modes of action. Critical Reviews in Toxicology, 33, 591–653.
21.
SeedJ., CarneyE., CorleyR., CroftonK., DeSessoJ., FosterP., KavlockR., KimmelG., KlaunigJ., MeekM.E., PrestonR.J., SlikkerW.Jr, TabacovaS., WilliamsG.M., WiltseJ., ZoellerR.T., Fenner-CrispP. & PattonD.E. (2005). Overview: Using mode of action and life stage information to evaluate the human relevance of animal toxicity data. Critical Reviews in Toxicology35, 664–672.
22.
EscherB.I. & HermensJ.L.M. (2002). Modes of action in ecotoxicology: their role in body burdens, species sensitivity, QSARs, and mixture effects. Environmental Science & Technology36, 4201–4217.
23.
AptulaA.O. & RobertsD.W. (2006). Mechanistic applicability domains for non-animal based toxicological endpoints. General principles and application to reactive toxicity. Chemical Research in Toxicology19, 1097–1105.
24.
EnochS.J., CroninM.T.D., SchultzT.W. & MaddenJ.C. (2008). Quantitative and mechanistic read across for predicting the skin sensitisation potential of alkenes acting via Michael addition. Chemical Research in Toxicology21, 513–520.
25.
EnochS.J., MaddenJ.C. & CroninM.T.D. (2008). Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach. SAR & QSAR in Environmental Research19, 555–578.
26.
RobertsD.W., AptulaA.O. & PatlewiczG. (2006). Mechanistic applicability domains for non-animal based prediction of toxicological endpoints. QSAR analysis of the Schiff base applicability domain for skin sensitization. Chemical Research in Toxicology19, 1228–1233.
27.
RobertsD.W., PatlewiczG., KernP.S., GerberickF., KimberI., DearmanR.J., RyanC.A., BasketterD.A. & AptulaA.O. (2007). Mechanistic applicability domain classification of a local lymph node assay dataset for skin sensitization. Chemical Research & Toxicology20, 1019–1030.
28.
SakurataniY., SatoS., NishikawaS., YamadaJ., MaekawaA. & HayashiM. (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 in Environmental Research19, 681–696.
29.
BenigniR. & ZitoR. (2004). The second national toxicology program comparative exercise on the prediction of rodent carcinogenicity: definitive results. Mutation Research566, 49–63.
30.
BenigniR. & BossaC. (2008). Structure alerts for carcinogenicity, and the Salmonella assay system: A novel insight through the chemical relational databases technology. Mutation Research659, 248–261.
31.
KaziusJ., McGuireR. & BursiR. (2005). Derivation and validation of toxicophores for mutagenicity prediction. Journal of Medicinal Chemistry48, 312–320.
32.
MekenyanO., DimitrovS., SerafimovaR., ThompsonE., KotovS., DimitrovaN. & WalkerJ.D. (2004). Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals. I: TA100 Model. Chemical Research in Toxicology17, 753–766.
33.
MekenyanO., TodorovM., SerafimovaR., StoevaS., AptulaA., FinkingR. & JacobE. (2007). Identifying the structural requirements for chromosomal aberration by incorporating molecular flexibility and metabolic activation of chemicals. Chemical Research in Toxicology20, 1927–1941.
34.
SerafimovaR., TodorovM., PavoloT., KotovS., JacobE., AptulaA. & MekenyanO. (2007). Identification of the structural requirements for mutagenicity, by incorporating molecular flexibility and metabolic activation of chemicals. II: General Ames mutagenicity model. Chemical Research in Toxicology20, 662–676.
35.
AptulaA.O., PatlewiczG. & RobertsD.W. (2005). Skin sensitisation: reaction mechanistic applicability domains for structure activity relationships. Chemical Research in Toxicology18, 1420–1426.
36.
JowseyI.R., BasketterD.A., WestmorelandC. & KimberI. (2006). A future approach to measuring relative skin sensitising potency: a proposal. Journal of Applied Toxicology26, 341–350.
37.
VerhaarH.J.M., van LeeuwenC.J. & HermensJ.L.M. (1992). Classifying environmental pollutants 1: Structure–activity relationships for prediction of aquatic toxicity. Chemosphere25, 471–491.
38.
BradburyS.P. (1994). Predicting modes of toxic action from chemical structure: an overview. SAR & QSAR in Environmental Research2, 89–104.
39.
NendzaM. & WenzelA. (2006). Discriminating toxicant classes by mode of action: 1. (Eco)toxicity profiles. Environmental Science & Pollution Research.21, 192–203.
40.
RussomC.L., BradburyS.P., BroderiusS.J., HammermeisterD.E. & DrummondR.A. (1997). Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas). Environmental Toxicology & Chemistry16, 948–967.
41.
LipnickR.L., WatsonK.R. & StrauszA.K. (1987). A QSAR study of the acute toxicity of some industrial organic chemicals to goldfish. Narcosis, electrophile and proelectrophile mechanisms. Xenobiotica17, 1011–1025.
42.
FreidigA.P., VerhaarH.J.M. & HermensJ.L.M. (1999). Comparing the potency of chemicals with multiple modes of action in aquatic toxicology: acute toxicity due to narcosis versus reactive toxicity of acrylic compounds. Environmental Science & Technology33, 3038–3043.
43.
JäckelH. & NendzaM. (1994). Reactive substructures in the prediction of aquatic toxicity data. Aquatic Toxicology29, 305–314.
44.
von der OheP.C., KuhneR., EbertR.U., AltenburgerR., LiessM. & SchüürmannG. (2005). Structural alerts — a new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay. Chemical Research in Toxicology18, 536–555.
45.
AhlersJ., RiedhammerC., VoglianoM., EbertR-U., KühneR. & SchüürmannG. (2006). Acute to chronic ratios in aquatic toxicity — Variation across trophic levels and relationship with chemical structure. Environmental Toxicology & Chemistry25, 2937–2945.
PatlewiczG., JeliazkovaN., Gallegos SalinerA. & WorthA.P. (2008). Toxmatch — a new software tool to aid in the development and evaluation of chemically similar groups. SAR and QSAR in Environmental Research19, 397–412. [Program is available for free download from: http://ecb.jrc.ec.europa.eu/qsar/qsar-tools/index.php?c=TOXMATCH (Accessed 12.10.09).]
48.
NendzaM. & MüllerM. (2000). Discriminating toxicant classes by mode of action: 2. Physico–chemical descriptors. Quantitative Structure–Activity Relationships19, 581–598.
49.
AptulaA.O., NetzevaT.I., ValkovaI.V., CroninM.T.D., SchultzT.W., KühneR. & SchüürmannG. (2002). Multivariate discrimination between modes of toxic action of phenols. Quantitative Structure–Activity Relationships21, 12–22.
50.
SchüürmannG., AptulaA.O., KhuneR. & EbertR. (2003). Stepwise discrimination between four models of toxic action of phenols in the Tetrahymena pyriformis assay. Chemical Research in Toxicology16, 974–987.
51.
TongW., FangH., HongH., XieQ., PerkinsR. & SheehanD.M. (2004). Receptor-mediated toxicity: QSARs for estrogen receptor binding and priority setting of potential estrogenic endocrine disruptors. In Predicting Chemical Toxicity and Fate (ed. CroninM.T.D. & LivingstoneD.J.), pp. 285–314. London, UK: Taylor and Francis.
52.
CasadoS., AlonsoM., HerradonB., TarazonaJ.V. & NavasJ.M. (2006). Activation of the aryl hydrocarbon receptor by carbaryl: computational evidence of the ability of carbaryl to assume a planar conformation. Environmental Toxicology & Chemistry25, 3141–3147.
53.
McKimJ.M., BradburyS.P. & NiemiG.J. (1987). Fish acute toxicity syndromes and their use in the QSAR approach to hazard assessment. Environmental Health Perspectives71, 171–186.
54.
SchultzT.W., CarlsonR.E., CroninM.T.D., HermensJ.L.M., JohnsonR., O'BrienP.J., RobertsD.W., SirakiA., WallaceK.B. & VeithG.D. (2006). A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity. SAR & QSAR in Environmental Research17, 413–428.
55.
SchultzT.W., YarbroughJ.W., HunterR.S. & AptulaA.O. (2007). Verification of the structural alerts for Michael acceptors. Chemical Research in Toxicology20, 1359–1363.
56.
CedilloA., ContrerasR., GalvanM., AizmanA., AndresJ. & SafontV.S. (2007). Nucleophilicity index from perturbed electrostatic potentials. Journal of Physical Chemistry A111, 2442–2447.
57.
NatschA., EmterR. & EllisG. (2008). Filling the concept with data: integrating data from different in vitro and in silico assays on skin sensitizers to explore the battery approach for animal-free skin sensitization testing. Toxicological Sciences107, 106–121.
58.
DardenneF., van DongenS., NoblesI., SmoldersR., de CoenW. & BlustR. (2007). Mode of action clustering of chemicals and environmental samples on the bases of bacterial stress gene inductions. Toxicological Sciences101, 206–214.
59.
CraneM., WattsC. & BoucardT. (2006). Chronic aquatic environmental risks from exposure to human pharmaceuticals. Science of the Total Environment367, 23–41.
60.
MackayD., ArnotJ.A., PetkovaE.P., WallaceK.B., CallD.J., BrookeL.T. & VeithG.D. (2009). The physicochemical basis of QSARs for baseline toxicity. SAR & QSAR in Environmental Research20, 393–414.
61.
SchüürmannG. (2004). Quantum chemical descriptors in structure–activity relationships — calculation, interpretation and comparison of methods. In Predicting Chemical Toxicity and Fate (ed. CroninM.T.D. & LivingstoneD.J.), pp. 85–149. London, UK: Taylor and Francis.
62.
EnochS.J., CroninM.T.D., MaddenJ.C. & HewittM. (2009). Formation of structural categories to allow for read-across for teratogenicity. QSAR & Combinatorial Science28, 696–708.
63.
AhlersJ., StockF. & WerschkunB. (2008). Integrated testing and intelligent assessment — new challenges under REACH. Environmental Science & Pollution Research15, 565–572.
64.
de WolfW., Siebel-SauerA., LeclouxA., KochV., HoltM., FeijtelT., ComberM. & BoeijeG. (2005). Mode of action and aquatic exposure thresholds of no concern. Environmental Toxicology & Chemistry24, 479–485.
65.
RoexE.W.M., van GestelC.A.M., van WezelA.P. & van StraalenN.M. (2000). Ratios between acute aquatic toxicity and effects on population growth rates in relation to toxicant mode of action. Environmental Toxicology & Chemistry19, 685–693.
66.
JagerT., PosthumaL., de ZwartD. & van de MeentD. (2007). Novel view on predicting acute toxicity: decomposing toxicity data in species vulnerability and chemical potency. Ecotoxicology & Environmental Safety67, 311–322.
67.
EllisonC.M., CroninM.T.D., MaddenJ.C. & SchultzT.W. (2008). Definition of the structural domain of the baseline non-polar narcosis model for Tetrahymena pyriformis. SAR & QSAR in Environmental Research19, 751–783.
68.
VerhaarH.J.M., RamosE.U. & HermensJ.L.M. (1996). Classifying environmental pollutants. 2: Separation of class 1 (baseline toxicity) and class 2 (‘polar narcosis’) type compounds based on chemical descriptors. Journal of Chemometrics10, 149–162.
69.
VeithG.D. (1983). Structure–toxicity relationships for the fathead minnow, Pimephales promelas: narcotic industrial chemicals. Canadian Journal of Fisheries & Aquatic Science40, 743–748.
70.
de ZwartD. & PosthumaL. (2005). Complex mixture toxicity for single and multiple species: proposed methodologies. Environmental Toxicology & Chemistry24, 2665–2676.
71.
RaJ.S., LeeB.C., ChangN.I. & KimS.D. (2006). Estimating the combined toxicity by two-step prediction model on the complicated chemical mixtures from wastewater treatment plant effluents. Environmental Toxicology & Chemistry25, 2107–2113.
72.
AltenburgerR., NendzaM. & SchüürmannG. (2003). Mixture toxicity and its modelling by quantitative structure–activity relationships. Environmental Toxicology & Chemistry22, 1900–1915.
73.
KortenkampA. & AltenburgerR. (1998). Synergisms with mixtures of xenoestrogens: A reevaluation using the method of isoboles. The Science of the Total Environment221, 59–73.
74.
van den BergM., BirnbaumL.S., DenisonM., de VitoM., FarlandW., FeeleyM., FiedlerH., HakanssonH., HanbergA., HawsL., RoseM., SafeS., SchrenkD., TohyamaC., TritscherA., TuomistoJ., TysklindM., WalkerN. & PetersonR.E. (2006). The 2005 World Health Organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicological Sciences93, 223–241.
75.
WanB., YarbroughJ.W. & SchultzT.W. (2008). Structure-related clustering of gene expression fingerprints of THP-1 cells exposed to smaller polycyclic aromatic hydrocarbons. SAR & QSAR in Environmental Research19, 351–373.
PatlewiczG., RobertsD.W. & WalkerJ.D. (2003). QSARs for the skin sensitisation potential of aldehdyes and related compounds. QSAR & Combinatorial Science22, 196–203.
78.
JaworskaJ., HarolA., KernP. & GerberickG.F. (2009). Bayesian evaluation of non-animal information to support decision making — skin sensitization test case. Toxicology Letters189, S239.
79.
RobertsD.W., AptulaA.O., PatlewiczG. & PeaseC. (2008). Chemical reactivity indices and mechanism-based read-across for non-animal based assessment of skin sensitisation potential. Journal of Applied Toxicology28, 443–454.
80.
BöhmeA., ThaensD., PaschkeA. & SchüürmannG. (2009). Kinetic glutathione chemoassay to quantify thiol reactivity of organic electrophiles application to α,β-unsaturated ketones, acrylates, and propiolates. Chemical Research in Toxicology22, 742–750.
81.
CroninM.T.D, BajotF., EnochS.J., MaddenJ.C., RobertsD.W., SchwöbelJ. (2009). The in chemico–in silico interface: challenges for integrating experimental and computational chemistry to identify toxicity. ATLA37, 513–521.
82.
GerberickF.G., VassalloJ.D., BaileyR.E., ChaneyJ.G., MorrallS.W. & LepoittevinJ. (2004). Development of a peptide reactivity assay for screening contact allergens. Toxicological Sciences.81, 332–343.
83.
GerberickF., AleksicM., BasketterD., CasatiS., KarlbergA.T., KernP., KimberI., LepoittevinJ.P., NatschA., OvigneJ.M., RovidaC., SakaguchiH. & SchultzT. (2008). Chemical reactivity measurement and the predictive identification of skin sensitisers. ATLA36, 215–242.
84.
NatschA., GfellerH., RothauptM. & EllisG. (2007). Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro. Toxicology in Vitro21, 1220–1226.
85.
RobertsD.W., AptulaA.O., CroninM.T.D., HulzebosE. & PatlewiczG. (2007). Global (Q)SARs for skin sensitisation — assessment against OECD principles. SAR & QSAR in Environmental Research18, 343–365.
