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. 2019 Oct:107:104403.
doi: 10.1016/j.yrtph.2019.104403. Epub 2019 Jun 11.

Genetic toxicology in silico protocol

Catrin Hasselgren  1 Ernst Ahlberg  2 Yumi Akahori  3 Alexander Amberg  4 Lennart T Anger  4 Franck Atienzar  5 Scott Auerbach  6 Lisa Beilke  7 Phillip Bellion  8 Romualdo Benigni  9 Joel Bercu  10 Ewan D Booth  11 Dave Bower  12 Alessandro Brigo  13 Zoryana Cammerer  14 Mark T D Cronin  15 Ian Crooks  16 Kevin P Cross  12 Laura Custer  17 Krista Dobo  18 Tatyana Doktorova  19 David Faulkner  20 Kevin A Ford  21 Marie C Fortin  22 Markus Frericks  23 Samantha E Gad-McDonald  24 Nichola Gellatly  25 Helga Gerets  26 Véronique Gervais  27 Susanne Glowienke  28 Jacky Van Gompel  29 James S Harvey  30 Jedd Hillegass  17 Masamitsu Honma  31 Jui-Hua Hsieh  32 Chia-Wen Hsu  33 Tara S Barton-Maclaren  34 Candice Johnson  12 Robert Jolly  35 David Jones  36 Ray Kemper  37 Michelle O Kenyon  18 Naomi L Kruhlak  33 Sunil A Kulkarni  34 Klaus Kümmerer  38 Penny Leavitt  17 Scott Masten  6 Scott Miller  12 Chandrika Moudgal  39 Wolfgang Muster  13 Alexandre Paulino  40 Elena Lo Piparo  41 Mark Powley  42 Donald P Quigley  12 M Vijayaray Reddy  42 Andrea-Nicole Richarz  43 Benoit Schilter  41 Ronald D Snyder  44 Lidiya Stavitskaya  33 Reinhard Stidl  45 David T Szabo  46 Andrew Teasdale  47 Raymond R Tice  48 Alejandra Trejo-Martin  10 Anna Vuorinen  8 Brian A Wall  49 Pete Watts  50 Angela T White  30 Joerg Wichard  51 Kristine L Witt  6 Adam Woolley  52 David Woolley  52 Craig Zwickl  53 Glenn J Myatt  12
Affiliations

Genetic toxicology in silico protocol

Catrin Hasselgren et al. Regul Toxicol Pharmacol. 2019 Oct.

Abstract

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches.

Keywords: (Q)SAR; Computational toxicology protocols; Expert alerts; Expert review; Genetic toxicology; In silico; In silico toxicology.

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Conflict of interest statement

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Basic decision scheme for genetic toxicity.
Figure 2
Figure 2
Common genetic toxicology mechanism/effects and corresponding studies
Figure 3
Figure 3
Effect assessment of the reverse bacterial mutation assay.
Figure 4
Figure 4
Combining information to assess the “gene mutation” endpoint. *The assignment of the “Confidence” is discussed in the following sections.
Figure 5
Figure 5
Current in silico components most relevant to genotoxicity.
Figure 6
Figure 6
Assessment of an acid chloride compound
Figure 7
Figure 7
The conflicting in silico and experimental results of API X feeding into the overall bacterial gene mutation assessment.
Figure 8
Figure 8
Bacterial gene mutation assessment of 3-Methyl-5-isothiazolamine
Figure 9
Figure 9
Gene mutation assessment of an aromatic amide compound
Figure 10
Figure 10
The initial in silico genetic toxicology assessment for the plant protection product active ingredient metabolite. Note the change in assessment outcome for in vitro CA before and after expert review. *NA refers to “Not available” since these results were not possible to generate.
Figure 11
Figure 11
Influence of including the experimental results in genetic toxicology assessment for the plant protection AI metabolite. Differences compared to Figure 10 are indicated in red text. *NA refers to “Not available” since these results were not possible to generate.
Figure 12
Figure 12
In silico assessment of a plant protection product metabolite.

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