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Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Expert opinion on drug metabolism & toxicology, 2021-08, Vol.17 (8), p.987-1005

2021

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Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

Chemical reviews, 2005-05, Vol.105 (5), p.1767-1800

Benigni, Romualdo

2005

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In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided

Regulatory toxicology and pharmacology, 2021-11, Vol.126, p.105042, Article 105042

Benigni, Romualdo

2021

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Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology

Chemical reviews, 2011-04, Vol.111 (4), p.2507-2536

2011

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Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making

Environmental health perspectives, 2015-12, Vol.123 (12), p.1232-1240

2015

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Risk assessment of N‐nitrosamines in food

EFSA journal, 2023-03, Vol.21 (3), p.e07884-n/a

2023

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Towards quantitative read across: Prediction of Ames mutagenicity in a large database

Regulatory toxicology and pharmacology, 2019-11, Vol.108, p.104434, Article 104434

Benigni, Romualdo

2019

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Carcinogenicity of the aromatic amines: from structure–activity relationships to mechanisms of action and risk assessment

Mutation Research-Reviews in Mutation Research, 2002-07, Vol.511 (3), p.191-206

2002

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In silico approaches to genetic toxicology: progress and future

Mutagenesis, 2019-03, Vol.34 (1), p.1-2

Benigni, Romualdo

2019

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Data-based review of QSARs for predicting genotoxicity: the state of the art

Mutagenesis, 2019-03, Vol.34 (1), p.17-23

2019

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Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships

Alternatives to laboratory animals, 2005, Vol.33 (2), p.155-173

2005

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Predictive toxicology today: the transition from biological knowledge to practicable models

Expert opinion on drug metabolism & toxicology, 2016-09, Vol.12 (9), p.989-992

Benigni, Romualdo

2016

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QSARs of aromatic amines: Identification of potent carcinogens

Mutation research, 2010-09, Vol.691 (1), p.27-40

2010

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Predicting the carcinogenicity of chemicals with alternative approaches: recent advances

Expert opinion on drug metabolism & toxicology, 2014-09, Vol.10 (9), p.1199-1208

Benigni, Romualdo

2014

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Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

Computational toxicology, 2022-02, Vol.21, p.100204, Article 100204

2022

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Flavouring Group Evaluation 413 (FGE.413): Naringenin

EFSA journal, 2024-05, Vol.22 (5), p.e8747-n/a

2024

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Quantitative Structure−Activity Relationships of Mutagenic and Carcinogenic Aromatic Amines

Chemical reviews, 2000-10, Vol.100 (10), p.3697-3714

2000

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Alternatives to the carcinogenicity bioassay for toxicity prediction: are we there yet?

Expert opinion on drug metabolism & toxicology, 2012-04, Vol.8 (4), p.407-417

Benigni, Romualdo

2012

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Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Structure−Activity Relationship Studies of Chemical Mutagens and Carcinogens: Mechanistic Investigations and Prediction Approaches

In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided

Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project

Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology

Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making

Risk assessment of N‐nitrosamines in food

Towards quantitative read across: Prediction of Ames mutagenicity in a large database

Carcinogenicity of the aromatic amines: from structure–activity relationships to mechanisms of action and risk assessment

In silico approaches to genetic toxicology: progress and future

Data-based review of QSARs for predicting genotoxicity: the state of the art

Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships

Predictive toxicology today: the transition from biological knowledge to practicable models

QSARs of aromatic amines: Identification of potent carcinogens

Predicting the carcinogenicity of chemicals with alternative approaches: recent advances

Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

Flavouring Group Evaluation 80, Revision 2 (FGE.80Rev2): Consideration of alicyclic, alicyclic‐fused and aromatic‐fused ring lactones evaluated by the JECFA (61st and 82nd meetings) structurally related to an aromatic lactone evaluated in FGE.27

Scientific opinion on the renewal of the authorisation of proFagus Smoke R709 (SF‐008) as a smoke flavouring Primary Product

Scientific opinion on the renewal of the authorisation of proFagus Smoke R714 (SF‐001) as a smoke flavouring Primary Product

Scientific opinion on the renewal of the authorisation of Smoke Concentrate 809045 (SF‐003) as a smoke flavouring Primary Product

Flavouring Group Evaluation 413 (FGE.413): Naringenin

Flavouring group evaluation 419 (FGE.419): 2‐methyl‐1‐(2‐(5‐(p‐tolyl)‐1H‐imidazol‐2‐yl)piperidin‐1‐yl)butan‐1‐one

Quantitative Structure−Activity Relationships of Mutagenic and Carcinogenic Aromatic Amines

Alternatives to the carcinogenicity bioassay for toxicity prediction: are we there yet?

Scientific Guidance for the preparation of applications on smoke flavouring primary products

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