Abstract
Grouping approaches like read-across (RAx) are one of the most widely used methods to fill data gaps in human risk assessment. In a RAx approach, in vivo animal data from one to several source substances are extrapolated to a target substance that has not been tested. Here, we describe the currently accepted read-across workflow, which begins with the problem formulation that defines the level of the acceptable uncertainty. The evaluation progresses iteratively from an initial list of structurally similar substances to source compounds with similar toxicodynamic and toxicokinetic properties. Finally, the data gap is closed with a worst-case or a regression analysis, and the uncertainty of the prediction is identified. New approach methodologies, such as in vitro assays and in silico models, have great potential to strengthen read-across assessments by providing mechanistic data and estimates of bioavailability of the grouped compounds.
Similar content being viewed by others
References
Abe T, Kobayashi K, Nishikawa S et al (2012) Development of Hazard evaluation support system database (HESS DB). Pharm Stage 12:39–47
Arora S, Pansari A, Kilford P, Jamei M, Gardner I, Turner DB (2020) Biopharmaceutic in vitro in vivo extrapolation (IVIV_E) informed physiologically-based pharmacokinetic model of ritonavir norvir tablet absorption in humans under fasted and fed state conditions. Mol Pharm 17:2329–2344. https://doi.org/10.1021/acs.molpharmaceut.0c00043
Bajusz D, Rácz A, Héberger K (2015) Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminformat 7:20. https://doi.org/10.1186/s13321-015-0069-3
Ball N, Cronin MT, Shen J, Adenuga MD, Blackburn K, Booth ED, Bouhifd M, Donley E, Egnash L, Freeman JJ, Hastings C, Juberg DR, Kleensang A, Kleinstreuer N, Kroese ED, Luechtefeld T, Maertens A, Marty S, Naciff JM, Palmer J, Pamies D, Penman M, Richarz AN, Russo DP, Stuard SB, Patlewicz G, van Ravenzwaay B, Wu S, Zhu H, Hartung T (2016) Toward good read-across practice (GRAP) guidance. ALTEX 33:149–166
Benfenati E, Chaudhry Q, Gini G, Dorne JL (2019) Integrating in silico models and read-across methods for predicting toxicity of chemicals: a step-wise strategy. Environ Int 131:105060
Bitsch A, Jacobi S, Melber C, Wahnschaffe U, Simetska N, Mangelsdorf I (2006) Repdose: a database on repeated dose toxicity studies of commercial chemicals – a multifunctional tool. Regul Toxicol Pharmacol 46:202–210
Blackburn K, Stuard SB (2014) A framework to facilitate consistent characterization of read across uncertainty. Regul Toxicol Pharmacol 68:353–362
ECETOC (2003) Derivation of Assessment Factors for Human Health Risk Assessment; Technical report no. 86, ISSN-0773-6347-86
ECHA (2014) The use of alternatives to testing on animals for the REACH regulation. Second report under Article 117(3) of the REACH Regulation. 131p. European Chemicals Agency, Helsinki
ECHA (2017) Read-across assessment framework (RAAF). ECHA-17-R-01-EN, ISBN 978-92-9495-758-0. https://doi.org/10.2823/619212
Ecobichon DJ, Davies JE, Doull J, Ehrich M, Joy R, McMillan D, MacPhail R, Reiter LW, Slikker W Jr, Tilson H (1990) Neurotoxic effects of pesticides. In: Baker SR, Wilkinson CF (eds) The effects of pesticides on human health, vol 18. Princeton Scientific, Princeton, pp 131–199
EFSA (2017) Guidance on the use of the weight of evidence approach in scientific assessments. EFSA J 15(8):69
EFSA (2018) Guidance on uncertainty analysis in scientific assessments. EFSA J 16(1):39
Escher SE, Kamp H, Bennekou SH et al (2019) Towards grouping concepts based on new approach methodologies in chemical hazard assessment – the read-across concept of the EU-ToxRisk project. Arch Toxicol 93(12):3643–3667. https://doi.org/10.1007/s00204-019-02591-7. Epub 2019 Nov 28
Escher SE, Aguayo-Orozco A, Benfenati E et al (2021) Reducing the uncertainty of read-across by using mechanistic evidence from new approach methodologies (NAMs) – experiences form a case study on systemic toxicity with a group of branched carboxylic acids. ALTEX submitted
EU Regulation (1223/2009). https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:02009R1223-20150416&from=EN
Gold LS, Manley NB, Slone TH, Rohrbach L (1999) Supplement to the Carcinogenic Potency Database (CPDB): results of animal bioassays published in the general literature in 1993 to 1994 and by the National Toxicology Program in 1995 to 1996. Environ Health Perspect 107(Supplement 4). https://doi.org/10.1289/ehp.99107s4527
Helman G, Shah I, Antony J, Williams AJ, Edwards J, Dunne J, Patlewic G (2019) Generalised read-across (GenRA): a workflow implemented into the EPA CompTox chemicals dashboard. ALTEX 36(3):462–465
Krebs A, Waldmann T, Wilks MF et al (2019) Template for the description of cell-based toxicological test methods to allow evaluation and regulatory use of the data. ALTEX 36:682–699. https://doi.org/10.14573/altex.1909271
Krebs A, van Vugt-Lussenburg BMA, Waldmann T et al (2020) The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods. Arch Toxicol 94(7):2435–2461. https://doi.org/10.1007/s00204-020-02802-6
Leist M, Ghallab A, Graepel R et al (2017) Adverse outcome pathways: opportunities, limitations and open questions. Arch Toxicol 91(11):3477–3505
Martin MT, Mendez E, Corum DG, Judson RS, Kavlock RJ, Rotroff DM, Dix DJ (2009) Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicol Sci 110(1):181–190. https://doi.org/10.1093/toxsci/kfp080
Murgia M, Villasenor JL (2003) Estimating the effect of the similarity coefficient and the cluster algorithm on biogeographic classifications. Ann Bot Fenn 40:415–421
OECD (2004) OECD principles for the validation, for regulatory purposes, of (quantitative) structure activity relationship models. https://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf
OECD (2020a) Case study on the use of integrated approaches to testing and assessment for prediction of a 90 day repeated dose toxicity study (Oecd 408) for 2-ethylbutyric acid using a read-across approach from other branched carboxylic acids, ENV/JM/MONO(2020)20
OECD (2020b) Report on considerations from case studies on integrated approaches for testing and assessment (IATA), Fith review cycle (2019). Series on testing and assessment no. 328. ENV/JM/MONO(2020)24
Patlewicz G, Lizarraga LE, Rua D, Allen DG, Daniel AB, Fitzpatrick SC, Garcia-Reyero N, Gordon J, Hakkinen P, Howard AS, Karmaus A, Matheson J, Mumtaz M, Richarz AN, Ruiz P, Scarano L, Yamada T, Kleinstreuer N (2019) Exploring current read-across applications and needs among selected U.S. Federal Agencies. Regul Toxicol Pharmacol 106:197–209
Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD (2019) In silico toxicology data resources to support read-across and (Q)SAR. Front Pharmacol 10:561
Pearce RG, Woodrow Setzer R, Davis JL, Wambaugh JF (2017a) Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 44(6):549–565. https://doi.org/10.1007/s10928-017-9548-7
Pearce RG, Woodrow Setzer R, Davis JL, Wambaugh JF (2017b) Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 44(6):549–565
Rovida C, Barton-Maclaren T, Benfenati E, Caloni F, Chandrasekera PC, Chesné C, Cronin MTD, De Knecht J, Dietrich DR, Escher SE, Fitzpatrick S, Flannery B, Herzler M, Hougaard Bennekou S, Hubesch B, Kamp H, Kisitu J, Kleinstreuer N, Kovarich S, Leist M, Maertens A, Nugent K, Pallocca G, Pastor M, Patlewicz G, Pavan M, Presgrave O, Smirnova L, Schwarz M, Yamada T, Hartung T (2020) Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology. ALTEX 00. https://doi.org/10.14573/altex.1912181
Sanz F, Pognan F, Steger-Hartmann T et al (2017) Legacy data sharing to improve drug safety assessment: the eTOX project. Nat Rev Drug Discov 16:811. https://doi.org/10.1038/nrd.2017.177
Schultz TW, Cronin MTD (2017) Lessons learned from read-across case studies for repeated-dose toxicity. Regul Toxicol Pharmacol 88:185–191. https://doi.org/10.1016/j.yrtph.2017.06.011
Schultz TW, Amcoff P, Berggren E, Gautier F, Klaric M, Knight DJ, Mahony C, Schwarz M, White A, Cronin MT (2015) A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol 72:586–601
US EPA (2010) Tsca New Chemicals Program (Ncp) chemical categories. https://www.epa.gov/reviewing-new-chemicals-under-toxic-substances-control-act-tsca/new-chemicals-program-under-tsca
Wetmore, A., Wambaugh, J.F., Allen, B. et al. (2015) Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing. Toxicol Sci 148 1: 121-136.
Wiecek W, Quignot N, Amzal B, Dorne J-L (2019) TKPlate: R package prototype for TK models graphical interface. Zenodo. https://zenodo.org/record/2548850#.YL-U7Kgza70
Zhu H, Bouhifd M, Donley E et al (2016) Supporting read-across using biological data. ALTEX 33(2):167–182
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer-Verlag GmbH Germany, part of Springer Nature
About this entry
Cite this entry
Escher, S.E., Bitsch, A. (2021). Read-Across Methodology in Toxicological Risk Assessment. In: Reichl, FX., Schwenk, M. (eds) Regulatory Toxicology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36206-4_132-1
Download citation
DOI: https://doi.org/10.1007/978-3-642-36206-4_132-1
Received:
Accepted:
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36206-4
Online ISBN: 978-3-642-36206-4
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences