It's difficult, but important, to make negative predictions

Williams, Richard V.; Amberg, Alexander; Brigo, Alessandro; Coquin, Laurence; Giddings, Amanda; Glowienke, Susanne; Greene, Nigel; Jolly, Robert A.; Kemper, Ray; O’Leary-Steele, Catherine; Parenty, Alexis; Spirkl, Hans-Peter; Stalford, S. A.; Weiner, Sandy; Wichard, Joerg

doi:10.1016/j.yrtph.2016.01.008

Cited by 49 publications

(33 citation statements)

References 23 publications

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“…The reality is that it is widely acknowledged that predictions of DNA-reactive mutagenicity from structural alerts are likely to be more acceptable (from a precautionary perspective) than negative predictions. Nevertheless, in their recent review of the TTC approach, EFSA and WHO concluded that negative predictions using appropriate software were acceptable in the application of the TTC approach (EFSA 2016) and the reliability of negative predictions is supported by a recent analysis by Williams et al (2016).…”

Section: Advances In Software For Identifying Structural Alertsmentioning

confidence: 99%

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Boobis

Brown

Cronin

et al. 2017

Critical Reviews in Toxicology

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(2017) Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation, Critical Reviews in Toxicology, 47:8, 710-732, DOI: 10.1080/10408444.2017 The threshold of toxicological concern (TTC) approach is a resource-effective de minimis method for the safety assessment of chemicals, based on distributional analysis of the results of a large number of toxicological studies. It is being increasingly used to screen and prioritize substances with low exposure for which there is little or no toxicological information. The first step in the approach is the identification of substances that may be DNA-reactive mutagens, to which the lowest TTC value is applied. This TTC value was based on the analysis of the cancer potency database and involved a number of assumptions that no longer reflect the state-of-the-science and some of which were not as transparent as they could have been. Hence, review and updating of the database is proposed, using inclusion and exclusion criteria reflecting current knowledge. A strategy for the selection of appropriate substances for TTC determination, based on consideration of weight of evidence for genotoxicity and carcinogenicity is outlined. Identification of substances that are carcinogenic by a DNA-reactive mutagenic mode of action and those that clearly act by a non-genotoxic mode of action will enable the protectiveness to be determined of both the TTC for DNA-reactive mutagenicity and that applied by default to substances that may be carcinogenic but are unlikely to be DNA-reactive mutagens (i.e. for Cramer class I-III compounds). Critical to the application of the TTC approach to substances that are likely to be DNA-reactive mutagens is the reliability of the software tools used to identify such compounds. Current methods for this task are reviewed and recommendations made for their application.

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Section: Advances In Software For Identifying Structural Alertsmentioning

confidence: 99%

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Boobis

Brown

Cronin

et al. 2017

Critical Reviews in Toxicology

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“…When assessing a compound with structural alerts it is important to understand the absence of an alert does not denote a molecule to be nontoxic, rather it states no known alert could be found. However, with sufficient available data this can be made possible . It was also shown that the potency of an alert might be corresponding to the daily dose, therefore alerts might not be relevant for low doses of drugs .…”

Section: Expert Methodsmentioning

confidence: 99%

“…For instance, the absence of a structural alert does not necessarily denote that a compound is inactive. In combination with the applicability or novelty detection, for example, by looking at the closest negative compounds, this negative prediction can, however, be justified as a true negative …”

Section: Machine Learning Based Predictionsmentioning

confidence: 99%

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich

Ecker

2020

WIREs Comput Mol Sci

121

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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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“…For instance, data from the Istituto Superiore de Sanità (ISSSTY), FDA center for food safety and applied nutrition (CFSAN) and Hansen data sets are used. 24 It is further characterised by a balanced distribution of mutagenic (5177) and non-mutagenic (5066) compounds. 24 In Sarah Nexus, the prediction for mutagenicity is accompanied by a so-called confidence level, 25 reflecting the structural similarity or correlation of the molecular descriptors of the query compound with other molecular structures used to make a prediction about a query compound.…”

Section: Acceptance Criteria For Model Predictionsmentioning

confidence: 99%

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

Herrmann

Holzwarth

Rime

et al. 2020

Pest Management Science

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The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure‐triggered testing to in silico‐based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure‐Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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It's difficult, but important, to make negative predictions

Cited by 49 publications

References 23 publications

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

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