Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project

Read-across is one of the most frequently used alternative tools for hazard assessment, in particular for complex endpoints such as repeated dose or developmental and reproductive toxicity. Read-across extrapolates the outcome of a specific toxicological in vivo endpoint from tested (source) compounds to “similar” (target) compound(s). If appropriately applied, a read-across approach can be used instead of de novo animal testing. The read-across approach starts with structural/physicochemical similarity between target and source compounds, assuming that similar structural characteristics lead to similar human hazards. In addition, similarity also has to be shown for the toxicokinetic and toxicodynamic properties of the grouped compounds. To date, many read-across cases fail to demonstrate toxicokinetic and toxicodynamic similarities. New concepts, in vitro and in silico tools are needed to better characterise these properties, collectively called new approach methodologies (NAMs). This white paper outlines a general read-across assessment concept using NAMs to support hazard characterization of the grouped compounds by generating data on their dynamic and kinetic properties. Based on the overarching read-across hypothesis, the read-across workflow suggests targeted or untargeted NAM testing also outlining how mechanistic knowledge such as adverse outcome pathways (AOPs) can be utilized. Toxicokinetic models (biokinetic and PBPK), enriched by in vitro parameters such as plasma protein binding and hepatocellular clearance, are proposed to show (dis)similarity of target and source compound toxicokinetics. Furthermore, in vitro to in vivo extrapolation is proposed to predict a human equivalent dose, as potential point of departure for risk assessment. Finally, the generated NAM data are anchored to the existing in vivo data of source compounds to predict the hazard of the target compound in a qualitative and/or quantitative manner. To build this EU-ToxRisk read-across concept, case studies have been conducted and discussed with the regulatory community. These case studies are briefly outlined.

[1]  Alexander Golbraikh,et al.  Integrative chemical-biological read-across approach for chemical hazard classification. , 2013, Chemical research in toxicology.

[2]  Jonathan I Levy,et al.  Science and Decisions: Advancing Risk Assessment , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[3]  Inge Mangelsdorf,et al.  Indoor air guide values for glycol ethers and glycol esters-A category approach. , 2016, International journal of hygiene and environmental health.

[4]  H. Kamp,et al.  Metabolomics as read-across tool: A case study with phenoxy herbicides. , 2016, Regulatory toxicology and pharmacology : RTP.

[5]  Jin Dong,et al.  Discussions on the hepatic well‐stirred model: Re‐derivation from the dispersion model and re‐analysis of the lidocaine data , 2018, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[6]  M Jamei,et al.  VIVD: Virtual in vitro distribution model for the mechanistic prediction of intracellular concentrations of chemicals in in vitro toxicity assays. , 2019, Toxicology in vitro : an international journal published in association with BIBRA.

[7]  Andreas Bender,et al.  Computational approaches in cheminformatics and bioinformatics , 2012 .

[8]  Rajarshi Guha,et al.  Structure—Activity Landscape Index: Identifying and Quantifying Activity Cliffs. , 2008 .

[9]  T W Schultz,et al.  A strategy for structuring and reporting a read-across prediction of toxicity. , 2015, Regulatory toxicology and pharmacology : RTP.

[10]  Richard S Judson,et al.  Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study , 2017, Regulatory toxicology and pharmacology : RTP.

[11]  Sharon Munn,et al.  Adverse outcome pathway development from protein alkylation to liver fibrosis , 2016, Archives of Toxicology.

[12]  G. Tucker,et al.  Prediction of in vivo drug clearance from in vitro data. I: Impact of inter-individual variability , 2006, Xenobiotica; the fate of foreign compounds in biological systems.

[13]  Michael Siegrist,et al.  Guidance on Communication of Uncertainty in Scientific Assessments , 2019, EFSA journal. European Food Safety Authority.

[14]  Imran Shah,et al.  Navigating through the minefield of read-across frameworks: A commentary perspective , 2018 .

[15]  Imran Shah,et al.  Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. , 2016, Regulatory toxicology and pharmacology : RTP.

[16]  Dongmei Wu,et al.  Stat-6 signaling pathway and not Interleukin-1 mediates multi-walled carbon nanotube-induced lung fibrosis in mice: insights from an adverse outcome pathway framework , 2017, Particle and Fibre Toxicology.

[17]  G Patlewicz,et al.  Building scientific confidence in the development and evaluation of read-across. , 2015, Regulatory toxicology and pharmacology : RTP.

[18]  M. Rowland,et al.  Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. , 2005, Journal of pharmaceutical sciences.

[19]  Hao Zhu,et al.  Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants , 2014, Chemical research in toxicology.

[20]  Mardas Daneshian,et al.  Recommendation on test readiness criteria for new approach methods in toxicology: Exemplified for developmental neurotoxicity. , 2018, ALTEX.

[21]  Andrea-Nicole Richarz,et al.  Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction. , 2019, Regulatory toxicology and pharmacology : RTP.

[22]  S. Ait-Oudhia,et al.  Mechanisms, monitoring, and management of tyrosine kinase inhibitors–associated cardiovascular toxicities , 2018, OncoTargets and therapy.

[23]  Matthias Greiner,et al.  Guidance on the use of the weight of evidence approach in scientific assessments , 2017, EFSA journal. European Food Safety Authority.

[24]  Imran Shah,et al.  Development of an adverse outcome pathway from drug-mediated bile salt export pump inhibition to cholestatic liver injury. , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[25]  Read-Across Assessment Framework (RAAF) , 2017 .

[26]  Karen Blackburn,et al.  A framework to facilitate consistent characterization of read across uncertainty. , 2014, Regulatory toxicology and pharmacology : RTP.

[27]  S. H. Bennekou,et al.  Adverse outcome pathways: opportunities, limitations and open questions , 2017, Archives of Toxicology.

[28]  Mardas Daneshian,et al.  Advanced Good Cell Culture Practice for human primary, stem cell-derived and organoid models as well as microphysiological systems. , 2018, ALTEX.

[29]  Sebastian Hoffmann,et al.  Toward Good In Vitro Reporting Standards. , 2019, ALTEX.

[30]  Andrea-Nicole Richarz,et al.  Read-across of 90-day rat oral repeated-dose toxicity: A case study for selected 2-alkyl-1-alkanols , 2017 .

[31]  Ans Punt,et al.  Toxicokinetics in Risk Evaluations , 2018, Chemical research in toxicology.

[32]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[33]  Amin Rostami‐Hodjegan,et al.  Reverse Translation in PBPK and QSP: Going Backwards in Order to Go Forward With Confidence , 2017, Clinical pharmacology and therapeutics.

[34]  MANUAL FOR THE ASSESSMENT OF CHEMICALS , 2011 .

[35]  Hao Zhu,et al.  t4 report*: Toward Good Read-Across Practice (GRAP) Guidance , 2016, ALTEX.

[36]  Alexander Staab,et al.  Pharmacometric Models for Characterizing the Pharmacokinetics of Orally Inhaled Drugs , 2015, The AAPS Journal.

[37]  Imran Shah,et al.  Generalized Read-Across (GenRA): A workflow implemented into the EPA CompTox Chemicals Dashboard. , 2019, ALTEX.

[38]  M. Rowland,et al.  Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. , 2006, Journal of pharmaceutical sciences.

[39]  Alicia Paini,et al.  Adverse Outcome Pathway on Inhibition of the mitochondrial complex I of nigro-striatal neurons leading to parkinsonian motor deficits , 2018, OECD Series on Adverse Outcome Pathways.

[40]  R M McClain,et al.  Thyroid gland neoplasia: non-genotoxic mechanisms. , 1992, Toxicology letters.

[41]  Min Cong,et al.  Cell Signals Influencing Hepatic Fibrosis , 2012, International journal of hepatology.

[42]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[43]  S. H. Bennekou,et al.  An adverse outcome pathway for parkinsonian motor deficits associated with mitochondrial complex I inhibition , 2017, Archives of Toxicology.

[44]  D B Turner,et al.  Application of the MechPeff model to predict passive effective intestinal permeability in the different regions of the rodent small intestine and colon , 2017, Biopharmaceutics & drug disposition.

[45]  Mark T. D. Cronin,et al.  Chemical toxicity prediction : category formation and read-across , 2013 .

[46]  R. T. Williams,et al.  Studies in detoxication. XLVI. The metabolism of aliphatic alcohols; the glucuronic acid conjugation of acyclic aliphatic alcohols. , 1953, The Biochemical journal.

[47]  Alexandra Maertens,et al.  t4 report: Toward Good Read-Across Practice (GRAP) Guidance , 2016, ALTEX.

[48]  Chihae Yang,et al.  Dempster-Shafer theory for combining in silico evidence and estimating uncertainty in chemical risk assessment , 2018 .

[49]  Daniel L Villeneuve,et al.  Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment , 2010, Environmental toxicology and chemistry.

[50]  D. Doerge,et al.  Inhibition of thyroid peroxidase by dietary flavonoids. , 1996, Chemical research in toxicology.

[51]  Sharon Munn,et al.  Adverse outcome pathway development II: best practices. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.