Application of IATA – A case study in evaluating the global and local performance of a Bayesian network model for skin sensitization

Fitzpatrick, Jeremy; Patlewicz, Grace

doi:10.1080/1062936x.2017.1311941

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…All wrong predictions were caused by misclassifications into a directly neighboring class. A recent study showed for version 2 of this ITS that the TIMES-SS (a commercial product) can be substituted by the structural alerts set implemented in the protein binding for skin sensitization profiler of the OECD QSAR toolbox (which is free software and the structural alerts are related to those covered by TIMES-SS) without a substantial decrease in prediction accuracy (Fitzpatrick and Patlewicz 2017). Luechtefeld et al (2015) trained dose-informed random forest/hidden Markov classification models on categorical LLNA data compiled for 145 substances (mainly derived from the Jaworska data set; see section "Data sets").…”

Section: Computational Methods Used In Combination With Nonanimal Tesmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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Drugs, cosmetics, preservatives, fragrances, pesticides, metals, and other chemicals can cause skin sensitization. The ability to predict the skin sensitization potential and potency of substances is therefore of enormous importance to a host of different industries, to customers' and workers' safety. Animal experiments have been the preferred testing method for most risk assessment and regulatory purposes but considerable efforts to replace them with non-animal models and in silico models are ongoing. This review provides a comprehensive overview of the computational approaches and models that have been developed for skin sensitization prediction over the last 10 years. The scope and limitations of rule-based approaches, read-across, linear and nonlinear (quantitative) structure-activity relationship ((Q)SAR) modeling, hybrid or combined approaches, and models integrating computational methods with experimental results are discussed followed by examples of relevant models. Emphasis is placed on models that are accessible to the scientific community, and on model validation. A dedicated section reports on comparative performance assessments of various approaches and models. The review also provides a concise overview of relevant data sources on skin sensitization.

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Section: Computational Methods Used In Combination With Nonanimal Tesmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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“…29 Other computational approaches, beyond the ab initio risk assessment consideration, that utilise and extend the AOP framework are the development of Integrated Assessment and Testing Approaches (IATAs). 13,18,[30][31][32][33][34][35][36][37][38] The Links Between in Silico Models and the Different Steps of an AOP Figure 1 illustrates that models, or in silico approaches, may potentially be utilised at all stages of the AOP to provide knowledge and information (it should be noted that the structure of the generic AOP shown in Figure 1 is illustrative only and many AOPs do not include exposure or proceed to the ecosystem level). There are many purposes to the use of models within the AOP framework; these include:…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Cronin

RicharzAndrea-Nicole

2017

Applied In Vitro Toxicology

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The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs).

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“…For some human adverse outcomes (e.g., skin sensitization), various mechanistically informed DAs have already been developed based on AOPs (AOP-informed IATA). AOP-informed IATA for skin sensitization incorporates methods anchored against KE identified in the published AOP in conjunction with non-testing approaches ((Q)SAR, read-across) (Patlewicz et al, 2014;Fitzpatrick and Patlewicz, 2017;OECD, 2016b).…”

Section: Towards An Ontology-based Concept Of Future Dnt Testingmentioning

confidence: 99%

Recommendation on test readiness criteria for new approach methods in toxicology: Exemplified for developmental neurotoxicity

Bal‐Price

Högberg

Crofton

et al. 2018

ALTEX

140

117

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Multiple non-animal-based test methods have never been formally validated. In order to use such new approach methods (NAMs) in a regulatory context, criteria to define their readiness are necessary. The field of developmental neurotoxicity (DNT) testing is used to exemplify the application of readiness criteria. The costs and number of untested chemicals are overwhelming for in vivo DNT testing. Thus, there is a need for inexpensive, high-throughput NAMs to obtain initial information on potential hazards, and to allow prioritization for further testing. A background on the regulatory and scientific status of DNT testing is provided showing different types of test readiness levels, depending on the intended use of data from NAMs. Readiness criteria, compiled during a stakeholder workshop that united scientists from academia, industry and regulatory authorities, are presented. An important step beyond the listing of criteria was the suggestion of a preliminary scoring scheme. On this basis a (semi)-quantitative analysis process was assembled on test readiness of 17 NAMs with respect to various uses (e.g., prioritization/screening, risk assessment). The scoring results suggest that several assays are currently at high readiness levels. Therefore, suggestions are made on how DNT NAMs may be assembled into an integrated approach to testing and assessment (IATA). In parallel, the testing state in these assays was compiled for more than 1000 compounds. Finally, a vision is presented on how further NAM development may be guided by knowledge of signaling pathways necessary for brain development, DNT pathophysiology, and relevant adverse outcome pathways (AOP).

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Application of IATA – A case study in evaluating the global and local performance of a Bayesian network model for skin sensitization

Cited by 7 publications

References 28 publications

Computational approaches for skin sensitization prediction

Computational approaches for skin sensitization prediction

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Recommendation on test readiness criteria for new approach methods in toxicology: Exemplified for developmental neurotoxicity

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