Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Mekenyan, Ovanes G.; Dimitrov, S.; Pavlov, Todor; Dimitrova, Gergana; Todorov, Milen; Petkov, P.; Котов, С. В.

doi:10.1080/1062936x.2012.679689

Cited by 38 publications

(25 citation statements)

References 87 publications

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“…TIMES-SS (Dimitrov et al 2005;Mekenyan et al 2012; OASIS-LMC, TIMES model for skin sensitization prediction) is a hybrid model for the semi-quantitative prediction of the skin sensitization potency of substances. The predictor is part of the TIMES platform for toxicity prediction.…”

Section: Hybrid In Silico Modelsmentioning

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: Hybrid In Silico Modelsmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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“…The in vitro mutagenicity models combine a simulator of metabolism and a model assessing the reactivity of chemicals and their metabolites (Mekenyan et al ., ). The tissue metabolism simulator (TIMES) software allows prediction of mutagenicity with and without application of the simulator of S9 metabolism.…”

Section: Methodsmentioning

confidence: 97%

“…The in vivo genotoxicity models are built accounting for three components (Mekenyan et al ., 2012a,b). The reactivity component describes endpoint‐specific interaction of substances using an alerting group approach.…”

Section: Methodsmentioning

confidence: 97%

“…Tissue metabolism simulator genotoxicity and non-genotoxicity quantitative structure-activity relationship models Tissue metabolism simulator in vitro genotoxicity models. The in vitro mutagenicity models combine a simulator of metabolism and a model assessing the reactivity of chemicals and their metabolites (Mekenyan et al, 2007(Mekenyan et al, , 2012a(Mekenyan et al, , 2012b. The tissue metabolism simulator (TIMES) software allows prediction of mutagenicity with and without application of the simulator of S9 metabolism.…”

Section: Non-genotoxic Carcinogenicity Mechanismsmentioning

confidence: 99%

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Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity

Petkov

Schultz

Donner

et al. 2016

J of Applied Toxicology

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We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated database. Genotoxic carcinogenicity was predicted based on positive results from at least two genotoxicity tests: one in vitro and one in vivo (which were associated with mutagenicity categories according to the Globally Harmonized System classification). Substances belonging to double positives mutagenicity categories were assigned to be genotoxic carcinogens. In turn, substances that were positive only in a single mutagenicity test were assigned to be mutagens. Chemicals not classified by the selected genotoxicity endpoints were assigned to be negative genotoxic carcinogens and subsequently evaluated for their capability to elicit non-genotoxic carcinogenicity. However, non-genotoxic carcinogenicity mechanisms were not currently included in the developed IATA. The IATA is docked to the OECD Toolbox and uses measured data for different genotoxicity endpoints when available. Alternatively, the system automatically provides predictions by SAR genotoxicity models using the OASIS Tissue Metabolism Simulator platform. When the developed IATA was tested against the consolidated database, its performance was found to be high, with sensitivity of 74% and specificity of 83%, when measured carcinogenicity data were used along with predictions falling within the models' applicability domains. Performance of the IATA would be slightly changed to a sensitivity of 80% and specificity of 72% when the evaluation by non-genotoxic carcinogenicity mechanisms was taken into account. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Numerous in silico approaches to the prediction of xenobiotic metabolism have been described . Their principal applications include metabolite identification, metabolic soft spot analysis and the assessment of metabolite toxicity through, for example, coupling with in silico models for the assessment of toxicity . Model performance requirements are dependent on application: metabolite identification requires high sensitivity (the ability to identify all possible metabolites) whereas for metabolic soft spot analysis and toxicity assessment, high positive predictivity (the ability to distinguish actual from possible metabolites) is important.…”

Section: Figurementioning

confidence: 99%

A k‐Nearest Neighbours Approach Using Metabolism‐related Fingerprints to Improve In Silico Metabolite Ranking

Marchant

Rosser

Vessey

2016

Molecular Informatics

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The application of biotransformation dictionaries derived by expert evaluation of known metabolic pathways represents one approach to the prediction of both phase I and phase II xenobiotic metabolites. The ranking of metabolites generated by such dictionaries has previously been achieved through the use of qualitative reasoning rules and quantitative probability values. Using the biotransformation dictionary available in the Meteor expert system, we show that metabolite over-prediction by both of these methods can be reduced by the adoption of a k-nearest neighbours methodology in which the likelihood of a predicted biotransformation is determined based on comparison of a query chemical with structurally-similar substrates with known experimental metabolic data which activate the same biotransformation. Optimal performance was achieved when similarity was defined in terms of a combination of two fingerprints, one describing the overall profile of biotransformations a structure can potentially undergo and the other describing the local environment around the predicted site of metabolism for the particular biotransformation under consideration.

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Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Cited by 38 publications

References 87 publications

Computational approaches for skin sensitization prediction

Computational approaches for skin sensitization prediction

Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity

A k‐Nearest Neighbours Approach Using Metabolism‐related Fingerprints to Improve In Silico Metabolite Ranking

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