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Alexander-White C, Bury D, Cronin M, Dent M, Hack E, Hewitt NJ, Kenna G, Naciff J, Ouedraogo G, Schepky A, Mahony C, Europe C. A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment. Regul Toxicol Pharmacol 2022; 129:105094. [PMID: 34990780 DOI: 10.1016/j.yrtph.2021.105094] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 07/12/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023]
Abstract
This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.
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Affiliation(s)
- Camilla Alexander-White
- MKTox & Co Ltd, 36 Fairford Crescent, Downhead Park, Milton Keynes, Buckinghamshire, MK15 9AQ, UK.
| | - Dagmar Bury
- L'Oreal Research & Innovation, 9 Rue Pierre Dreyfus, 92110, Clichy, France
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 AF, UK
| | - Matthew Dent
- Unilever, Safety & Environmental Assurance Centre, Colworth House, Sharnbrook, Bedfordshire, MK44 1ET, UK
| | - Eric Hack
- ScitoVation, Research Triangle Park, Durham, NC, USA
| | - Nicola J Hewitt
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Gerry Kenna
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium
| | - Jorge Naciff
- The Procter & Gamble Company, Cincinnati, OH, 45040, USA
| | - Gladys Ouedraogo
- L'Oreal Research & Innovation, 1 Avenue Eugène Schueller, Aulnay sous bois, France
| | | | | | - Cosmetics Europe
- Cosmetics Europe, 40 Avenue Hermann-Debroux, 1160, Brussels, Belgium.
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Yordanova DG, Schultz TW, Kuseva CD, Mekenyan OG. Assessing metabolic similarity for read-across predictions. ACTA ACUST UNITED AC 2021. [DOI: 10.1016/j.comtox.2021.100160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Pestana CB, Firman JW, Cronin MT. Incorporating lines of evidence from New Approach Methodologies (NAMs) to reduce uncertainties in a category based read-across: A case study for repeated dose toxicity. Regul Toxicol Pharmacol 2021; 120:104855. [DOI: 10.1016/j.yrtph.2020.104855] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/30/2020] [Accepted: 12/18/2020] [Indexed: 12/20/2022]
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Firman JW, Punt A, Cronin MTD, Boobis AR, Wilks MF, Hepburn PA, Thiel A, Fussell KC. Exploring the Potential of ToxCast Data in Supporting Read-Across for Evaluation of Food Chemical Safety. Chem Res Toxicol 2020; 34:300-312. [PMID: 33253545 DOI: 10.1021/acs.chemrestox.0c00240] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The intention of this study was to determine the utility of high-throughput screening (HTS) data, as exemplified by ToxCast and Tox21, for application in toxicological read-across in food-relevant chemicals. Key questions were addressed on the extent to which the HTS data could provide information enabling (1) the elucidation of underlying bioactivities associated with apical toxicological outcomes, (2) the closing of existing toxicological data gaps, and (3) the definition of the boundaries of chemical space across which bioactivity could reliably be extrapolated. Results revealed that many biological targets apparently activated within the chemical groupings lack, at this time, validated toxicity pathway associations. Therefore, as means of providing proof-of-principle, a comparatively well-characterized end point-estrogenicity-was selected for evaluation. This was facilitated through the preparation of two exploratory case studies, focusing upon groupings of paraben-gallates and pyranone-type compounds (notably flavonoids). Within both, the HTS data were seen to reflect estrogenic potencies in a manner which broadly corresponded to established structure-activity group relationships, with parabens and flavonoids displaying greater estrogen receptor affinity than benzoate esters and alternative pyranone-containing molecules, respectively. As such, utility in the identification of out-of-domain compounds was demonstrated, indicating potential for application in addressing point (3) as detailed above.
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Affiliation(s)
- James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Ans Punt
- Wageningen Food Safety Research, P.O. Box 230, 6700 AE Wageningen, The Netherlands
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, United Kingdom
| | - Alan R Boobis
- National Heart and Lung Institute, Imperial College London, London W12 0NN, United Kingdom
| | - Martin F Wilks
- Swiss Centre for Applied Human Toxicology and Department of Pharmaceutical Sciences, University of Basel, CH-4055 Basel, Switzerland
| | - Paul A Hepburn
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook MK44 1LQ, United Kingdom
| | - Anette Thiel
- DSM Nutritional Products, Wurmisweg 576, 4303 Kaiseraugst, Switzerland
| | - Karma C Fussell
- Nestlé Research, Case Postale 44, CH-1000 Lausanne 26, Switzerland
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Hisaki T, Kaneko MAN, Hirota M, Matsuoka M, Kouzuki H. Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid. J Toxicol Sci 2020; 45:95-108. [PMID: 32062621 DOI: 10.2131/jts.45.95] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.
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Affiliation(s)
- Tomoka Hisaki
- Shiseido Global Innovation Center.,Department of Hygiene and Public Health, Tokyo Women's Medical University
| | | | | | - Masato Matsuoka
- Department of Hygiene and Public Health, Tokyo Women's Medical University
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Date MS, O'Brien D, Botelho DJ, Schultz TW, Liebler DC, Penning TM, Salvito DT. Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps. Chem Res Toxicol 2020; 33:1709-1718. [PMID: 32338872 PMCID: PMC7374741 DOI: 10.1021/acs.chemrestox.9b00518] [Citation(s) in RCA: 341] [Impact Index Per Article: 85.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
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A valuable
approach to chemical safety assessment is the use of
read-across chemicals to provide safety data to support the assessment
of structurally similar chemicals. An inventory of over 6000 discrete
organic chemicals used as fragrance materials in consumer products
has been clustered into chemical class-based groups for efficient
search of read-across sources. We developed a robust, tiered system
for chemical classification based on (1) organic functional group,
(2) structural similarity and reactivity features of the hydrocarbon
skeletons, (3) predicted or experimentally verified Phase I and Phase
II metabolism, and (4) expert pruning to consider these variables
in the context of specific toxicity end points. The systematic combination
of these data yielded clusters, which may be visualized as a top-down
hierarchical clustering tree. In this tree, chemical classes are formed
at the highest level according to organic functional groups. Each
subsequent subcluster stemming from classes in this hierarchy of the
cluster is a chemical cluster defined by common organic functional
groups and close similarity in the hydrocarbon skeleton. By examining
the available experimental data for a toxicological endpoint within
each cluster, users can better identify potential read-across chemicals
to support safety assessments.
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Affiliation(s)
- Mihir S Date
- Research Institute of Fragrance Materials, Incorporated, 50 Tice Boulevard, Woodcliff Lake, New Jersey 07677, United States
| | | | | | - Terry W Schultz
- The University of Tennessee, College of Veterinary Medicine, 2407 River Drive, Knoxville, Tennessee 37996-4500, United States
| | - Daniel C Liebler
- Protypia, LLC., 111 10th Avenue South, Suite 102, Nashville, Tennessee 37203, United States
| | - Trevor M Penning
- Center of Excellence in Environmental Toxicology, The University of Pennsylvania, Perelman School of Medicine, 1315 Biomedical Research Building (BRB) II/III, 421 Curie Boulevard, Philadelphia, Pennsylvania 19104-3083, United States
| | - Daniel T Salvito
- Research Institute of Fragrance Materials, Incorporated, 50 Tice Boulevard, Woodcliff Lake, New Jersey 07677, United States
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Kuseva C, Schultz TW, Yordanova D, Tankova K, Kutsarova S, Pavlov T, Chapkanov A, Georgiev M, Gissi A, Sobanski T, Mekenyan OG. The implementation of RAAF in the OECD QSAR Toolbox. Regul Toxicol Pharmacol 2019; 105:51-61. [DOI: 10.1016/j.yrtph.2019.03.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 03/13/2019] [Accepted: 03/31/2019] [Indexed: 12/18/2022]
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