The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aardema, 2002, Toxicology and genetic toxicology in the new era of “toxicogenomics”: Impact of “-omics” technologies, Mut. Res., 499, 13, 10.1016/S0027-5107(01)00292-5
2017
Ankley, 2006, The fathead minnow in aquatic toxicology: Past, present and future, Aquat. Toxicol., 78, 91, 10.1016/j.aquatox.2006.01.018
Ankley, 2010, Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment, Environ. Toxicol. Chem., 29, 730, 10.1002/etc.34
Ankley, 2016, Evaluation of the scientific underpinnings for identifying estrogenic chemicals in non-mammalian taxa using mammalian test systems, Environ. Toxicol. Chem., 35, 2806, 10.1002/etc.3456
Antczak, 2015, Systems biology approach reveals a calcium-dependent mechanism for basal toxicity in Daphnia magna, Environ. Sci. Technol, 49, 11132, 10.1021/acs.est.5b02707
Barron, 2015, MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development, Aquat. Toxicol, 161, 102, 10.1016/j.aquatox.2015.02.001
Bourdon-Lacombe, 2015, Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals, Regul. Toxicol. Pharmacol, 72, 292, 10.1016/j.yrtph.2015.04.010
Burden, 2015, Aligning the 3Rs with new paradigms in the safety assessment of chemicals, Toxicology, 330, 62, 10.1016/j.tox.2015.01.014
Celander, 2011, Species extrapolation for the 21st century, Environ. Toxicol. Chem., 30, 52, 10.1002/etc.382
Conolly, 2017, Quantitative adverse outcome pathways and their application to predictive toxicology, Environ. Sci. Technol., 51, 4661, 10.1021/acs.est.6b06230
Cote, 2016, The next generation of risk assessment multiyear study- Highlights of findings and future directions, Environ. Health Perspect., 124, 1671, 10.1289/EHP233
Davidsen, 2016, Multilevel functional genomics data integration as a tool for understanding physiology: A network biology perspective, J. Appl. Physiol, 120, 297, 10.1152/japplphysiol.01110.2014
D’haeseleer, 2000, Genetic network inference: From co-expression clustering to reverse engineering, Bioinformatics, 16, 707, 10.1093/bioinformatics/16.8.707
De Abrew, 2015, A novel transcriptomics based in vitro method to compare and predict hepatotoxicity based on mode of action, Toxicology, 328, 29, 10.1016/j.tox.2014.11.008
De Coen, 2015
Doering, 2014, Identification and expression of aryl hydrocarbon receptors (AhR1 and AhR2) provide insight in an evolutionary context regarding sensitivity of white sturgeon (Acipenser transmontanus) to dioxin-like compounds, Aquat. Toxicol, 150, 27, 10.1016/j.aquatox.2014.02.009
Doering, 2015, Differences in activation of aryl hydrocarbon receptors of white sturgeon relative to lake sturgeon are predicted by identities of key amino acids in the ligand binding domain, Environ. Sci. Technol, 49, 4681, 10.1021/acs.est.5b00085
Dorato, 2005, The no-observed-adverse-effect-level in drug safety evaluations: Use, issues, and definition(s), Regul. Toxicol. Pharmacol., 42, 265, 10.1016/j.yrtph.2005.05.004
Drwal, 2015, Molecular similarity-based predictions of the Tox21 screening outcome, Front. Environ. Sci, 3, 10.3389/fenvs.2015.00054
ECETOC, 2008
ECETOC, 2010
ECETOC, 2016
ECHA (European CHemicals Agency), 2014
ECHA, 2014
ECHA, 2017
ECHA, 2016
Ellison, 2015, Investigation of the Verhaar scheme for predicting acute aquatic toxicity: Improving predictions obtained from Toxtree ver. 2.6, Chemosphere, 139, 146, 10.1016/j.chemosphere.2015.06.009
Embry, 2014, Risk assessment in the 21st century: Roadmap and matrix, Crit. Rev. Toxicol, 44(Suppl 3), 6, 10.3109/10408444.2014.931924
Embry, 2010, The fish embryo toxicity test as an animal alternative method in hazard and risk assessment and scientific research, Aquat. Toxicol, 97, 79, 10.1016/j.aquatox.2009.12.008
Enoch, 2008, Classification of chemicals according to mechanism of aquatic toxicity: An evaluation of the implementation of the Verhaar scheme in Toxtree, Chemosphere, 73, 243, 10.1016/j.chemosphere.2008.06.052
Eren, 2012, A comparative analysis of biclustering algorithms for gene expression data, Brief. Bioinformatics, 14, 279, 10.1093/bib/bbs032
Escher, 2011, Crucial role of mechanisms and modes of toxic action for understanding tissue residue toxicity and internal effect concentrations of organic chemicals, Integr. Environ. Assess. Manag, 7, 28, 10.1002/ieam.100
EURL ECVAM, 2016
Fabian, 2016, Metabolite profiles of rats in repeated dose toxicological studies after oral and inhalative exposure, Toxicol. Lett, 255, 11, 10.1016/j.toxlet.2016.05.003
Fay, 2017, Practical approaches to adverse outcome pathway (AOP) development and weight-of-evidence evaluation as illustrated by ecotoxicological case studies, Environ. Toxicol. Chem, 10.1002/etc.3770
Forbes, 2017, A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals, Environ. Toxicol. Chem, 36, 845, 10.1002/etc.3720
Garcia-Reyero, 2011, Systems biology: Leading the revolution in ecotoxicology, Environ. Toxicol. Chem., 30, 265, 10.1002/etc.401
Garcia-Reyero, 2016, Targeted Gene Expression in Zebrafish Exposed to Chlorpyrifos-Oxon Confirms Phenotype-Specific Mechanisms Leading to Adverse Outcomes, Bull. Environ. Contam. Toxicol, 96, 707, 10.1007/s00128-016-1798-3
Gatzidou, 2007, Toxicogenomics: A pivotal piece in the puzzle of toxicological research, Journal of Applied Toxicology, 27, 302, 10.1002/jat.1248
Groh, 2015, Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology, Chemosphere, 120, 764, 10.1016/j.chemosphere.2014.09.068
Gunnarsson, 2008, Evolutionary conservation of human drug targets in organisms used for environmental risk assessments, Environ. Sci. Technol, 42, 5807, 10.1021/es8005173
Hecker, 2016, A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk Assessment
Hodges, A Systems Biology Approach to Advancing Adverse Outcome Pathways for Risk Assessment
Hubal, 2009, Biologically relevant exposure science for 21st century toxicity testing, Toxicol. Sci., 111, 226, 10.1093/toxsci/kfp159
Hutchinson, 2014, Comparative metabolism as a key driver of wildlife species sensitivity to human and veterinary pharmaceuticals, Philos. Trans. R Soc. Lond. B Biol. Sci., 369, 10.1098/rstb.2013.0583
Karchner, 2006, The molecular basis for differential dioxin sensitivity in birds: Role of the aryl hydrocarbon receptor, Proc. Natl. Acad. Sci. U.S.A, 103, 6252, 10.1073/pnas.0509950103
Klanjscek, 2013, Dynamic energy budget approach to modeling mechanisms of CdSe quantum dot toxicity, Ecotoxicology, 22, 319, 10.1007/s10646-012-1028-7
Kramer, 2011, Adverse outcome pathways and ecological risk assessment: Bridging to population-level effects, Environ. Toxicol. Chem., 30, 64, 10.1002/etc.375
Krewski, 2010, Toxicity testing in the 21st century: A vision and a strategy, Journal of Toxicol. Environ. Health B Crit. Rev, 13, 51, 10.1080/10937404.2010.483176
Lalone, 2013, Molecular target sequence similarity as a basis for species extrapolation to assess the ecological risk of chemicals with known modes of action, Aquat. Toxicol., 144–145
LaLone, 2016, Sequence alignment to predict across species susceptibility (SeqAPASS): A web-based tool for addressing the challenges of species extrapolation of chemical toxicity, Tox. Sci, 153, 228, 10.1093/toxsci/kfw119
Macarron, 2011, Impact of high-throughput screening in biomedical research, Nat. Rev. Drug Discov., 10, 188, 10.1038/nrd3368
Madden, 2014, Application of in silico and in vitro methods in the development of adverse outcome pathway constructs in wildlife, Philos. Trans. R Soc. Lond. B Biol. Sci., 369, 10.1098/rstb.2013.0584
Margiotta-Casaluci, 2016, Internal exposure dynamics drive the Adverse Outcome Pathways of synthetic glucocorticoids in fish, Sci. Rep., 6, 21978., 10.1038/srep21978
Massart, 2015, Impact of the omic technologies for understanding the modes of action of biological control agents against plant pathogens, BioControl, 60, 725, 10.1007/s10526-015-9686-z
McBride, 2017, Future platforms for toxicity testing, International Journal of Risk Assessment and Management, 20, 59, 10.1504/IJRAM.2017.082556
Mitra, 2013, Integrative approaches for finding modular structure in biological networks, Nat. Rev. Genet., 14, 719, 10.1038/nrg3552
Moreira, 2010, Toxicogenomic profiling in maternal and fetal rodent brains following gestational exposure to chlorpyrifos, Toxicol. Appl. Pharmacol., 245, 310, 10.1016/j.taap.2010.03.015
National Research Council of the National Academies, 2007, Toxicity Testing in the 21st Century: A Vision and a Strategy
Nookaew, 2012, A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: A case study in Saccharomyces cerevisiae, Nucleic Acids Res., 40, 10084, 10.1093/nar/gks804
Norinder, 2016, Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays, Chem. Res. Toxicol., 29, 1003, 10.1021/acs.chemrestox.6b00037
OECD, 2016
Pastoor, 2014, A 21st century roadmap for human health risk assessment, Crit. Rev. Toxicol, 44(Suppl 3), 1, 10.3109/10408444.2014.931923
Patlewicz, 2015, Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes, Regul. Toxicol. Pharmacol, 71, 463, 10.1016/j.yrtph.2015.02.011
Perkins, 2011, Reverse engineering adverse outcome pathways, Environ Toxicol Chem, 30, 22, 10.1002/etc.374
Perkins, 2015, Computational Systems Toxicology, 1
Quercioli, 2017, The use of omics-based approaches in regulatory toxicology: An alternative approach to assess the no observed transcriptional effect level, Microchem. J
Rand-Weaver, 2013, The read-across hypothesis and environmental risk assessment of pharmaceuticals, Environ. Sci. Technol, 47, 11384, 10.1021/es402065a
Rowlands, 2013, A genomics-based analysis of relative potencies of dioxin-like compounds in primary rat hepatocytes, Toxicol. Sci, 136, 595, 10.1093/toxsci/kft203
Russom, 1997, Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas), Environ. Toxicol. Chem, 16, 948, 10.1002/etc.5620160514
Russom, 2014, Development of an adverse outcome pathway for acetylcholinesterase inhibition leading to acute mortality, Environ. Toxicol. Chem, 33, 2157, 10.1002/etc.2662
SCENIHR (Scientific Committee on Emerging and Newly Identified Health Risks), 2012
Schmeits, 2015, Detection of the mechanism of immunotoxicity of cyclosporine A in murine in vitro and in vivo models, Arch. Toxicol., 89, 2325, 10.1007/s00204-014-1365-9
Schroeder, 2016, Environmental surveillance and monitoring–The next frontiers for high-throughput toxicology, Environ. Toxicol. Chem., 35, 513, 10.1002/etc.3309
Soetaert, 2007, Daphnia magna and ecotoxicogenomics: Gene expression profiles of the anti-ecdysteroidal fungicide fenarimol using energy-, molting- and life stage-related cDNA libraries, Chemosphere, 67, 60, 10.1016/j.chemosphere.2006.09.076
Soetaert, 2007, Molecular responses during cadmium-induced stress in Daphnia magna: Integration of differential gene expression with higher-level effects, Aquat. Toxicol, 83, 212, 10.1016/j.aquatox.2007.04.010
Sohm, 2015, Insight into the primary mode of action of TiO2 nanoparticles on Escherichia coli in the dark, Proteomics, 15, 98, 10.1002/pmic.201400101
Song, 2016, Whole-organism transcriptomic analysis provides mechanistic insight into the acute toxicity of emamectin benzoate in Daphnia magna, Environ. Sci. Technol, 50, 1194, 10.1021/acs.est.6b03456
Sturla, 2014, Systems toxicology: From basic research to risk assessment, Chem. Res. Toxicol, 27, 314, 10.1021/tx400410s
Teeguarden, 2016, Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework, Environ. Sci. Technol, 50, 4579, 10.1021/acs.est.5b05311
The modENCODE Consortium, 2010, Identification of functional elements and regulatory circuits by Drosophila modENCODE, Science, 330, 1787, 10.1126/science.1198374
Thomas, 2013, Temporal concordance between apical and transcriptional points of departure for chemical risk assessment, Toxicol. Sci, 134, 180, 10.1093/toxsci/kft094
Thomas, 2013, Cross-species transcriptomic analysis of mouse and rat lung exposed to chloroprene, Toxicol. Sci, 131, 629, 10.1093/toxsci/kfs314
Thomas, 2017, Risk science in the 21st century: A data-driven framework for incorporating new technologies into chemical safety assessment, Int. J. Risk Assess. Manage, 20, 88, 10.1504/IJRAM.2017.082560
Tilton, 2011, Transcriptional impact of organophosphate and metal mixtures on olfaction: Copper dominates the chlorpyrifos-induced response in adult zebrafish, Aquat. Toxicol., 102, 205, 10.1016/j.aquatox.2011.01.012
Tollefsen, 2014, Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA), Regul. Toxicol. Pharmacol, 70, 629, 10.1016/j.yrtph.2014.09.009
Tralau, 2015, Moving from rats to cellular omics in regulatory toxicology: Great challenge toward sustainability or “up-shit-creek without a paddle”?, Arch. Toxicol, 89, 819, 10.1007/s00204-015-1511-z
USEPA, 1998
USEPA, 2004
US EPA, 2014
USEPA, 2016
USEPA, 2016
Van Aggelen, 2010, Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: Hurdles, achievements, and future outlook, Environ. Health Perspect, 118, 1, 10.1289/ehp.0900985
van Ravenzwaay, 2016, Metabolomics as read-across tool: A case study with phenoxy herbicides, Regul. Toxicol. Pharmacol., 81, 288, 10.1016/j.yrtph.2016.09.013
Verhaar, 2000, Classifying environmental pollutants: Part 3. External validation of the classification system, Chemosphere, 40, 875, 10.1016/S0045-6535(99)00317-3
Verhaar, 1992, Classifying environmental pollutants, Chemosphere, 25, 471, 10.1016/0045-6535(92)90280-5
Vinuela, 2010, Genome-wide gene expression analysis in response to organophosphorus pesticide chlorpyrifos and diazinon in C. elegan, Plos One, 5, e12145, 10.1371/journal.pone.0012145
Villeneuve, 2014, Adverse outcome pathway (AOP) development I: Strategies and principles, Toxicol. Sci, 142, 312, 10.1093/toxsci/kfu199
Villeneuve, 2014, Adverse outcome pathway development II: Best practices, Toxicol. Sci, 142, 321, 10.1093/toxsci/kfu200
Weston, 2004, Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine, J. Proteome Res, 3, 179, 10.1021/pr0499693
Wittwehr, 2017, How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology, Toxicol. Sci, 155, 326, 10.1093/toxsci/kfw207