The Expanding Role of Predictive Toxicology: An Update on the (Q)SAR Models for Mutagens and Carcinogens

Benigni, Romualdo; Netzeva, Tatiana I.; Benfenati, Emilio; Bossa, Cecilia; Franke, R.; Helma, Christoph; Hulzebos, Etje; Marchant, Carol A.; Richard, Ann M.; Woo, Yin Tak; Yang, Chihae

doi:10.1080/10590500701201828

Cited by 106 publications

(79 citation statements)

References 40 publications

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“…Since statistics alone is not sufficient to judge the goodness of a model even though it provides important criteria, we put particular attention in deriving models compatible with the general knowledge in the fields of QSAR, chemistry, and biology [Hansch et al, 2002;Benigni et al, 2007].…”

Section: Discussionmentioning

confidence: 99%

Mechanistic QSAR of aromatic amines: New models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Benigni

Bossa

Netzeva

et al. 2007

Environ and Mol Mutagen

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Because of its environmental and industrial importance, the aromatic amines are the single chemical class most studied for its ability to induce mutations and cancer. The large database of mutagenicity and carcinogenicity results has been studied with Quantitative Structure-Activity Relationship (QSAR) approaches by several authors, leading to models for the following: (a) the mutagenic potency in Salmonella thyphimurium; (b) the carcinogenic potency in rodents; and (c) the discrimination between rodent carcinogens and noncarcinogens. However, satisfactory models for the discrimination between mutagens and nonmutagens are lacking. The present work provides new QSARs for mutagenic/nonmutagenic homocyclic aromatic amines in S. typhimurium strains TA98 and TA100. The two new models are validated by checking their ability to predict the mutagenicity of further aromatic amines not included in the training set, and not used to generate the QSAR models. In addition, we also validated previous QSAR models for the carcinogenicity/noncarcinogenicity of the aromatic amines with external data. The mechanistic implications of the models are discussed in light of the other QSARs for the aromatic amines. The results of the analysis point to two QSAR models (one for mutagenicity and one for rodent carcinogenicity) as reliable tools for the in silico characterization of the risk posed by the aromatic amines.

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Section: Discussionmentioning

confidence: 99%

Mechanistic QSAR of aromatic amines: New models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Benigni

Bossa

Netzeva

et al. 2007

Environ and Mol Mutagen

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“…In mammalian toxicity, the use of SARS and QSARs has so far largely been restricted to prediction of pathways of metabolism, or of toxicity endpoints where there is a single mode of action, such as genotoxicity caused by direct interaction with DNA or chromosomes (Benigni et al, 2007). SARs for prediction of skin irritation and skin corrosion have also been developed (Gallegos Saliner et al, 2006;Rorije et al, 2005).…”

Section: The Threshold Of Toxicological Concern (Ttc) Approachmentioning

confidence: 99%

Existing approaches incorporating replacement, reduction and refinement of animal testing: applicability in food and feed risk assessment

2009

EFS2

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“…This property is of high public concern because it has a close relationship with carcinogenicity and eventually reproductive toxicity [5]: most of the mutagenic substances are suspected carcinogenic substance in case a genotoxic mechanism is considered.…”

Section: The Mutagenic Propertymentioning

confidence: 99%

Support vector machines in the prediction of mutagenicity of chemical compounds

Ferrari

Gini

Benfenati

2009

NAFIPS 2009 - 2009 Annual Meeting of the North American Fuzzy Information Processing Society

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Abstract-In this paper we introduce the problem of predicting the mutagenic toxicity property of chemical compounds and we discuss how this can be partially formulated as a computational intelligence problem. Then we develop a statistical model based on a selected set of descriptors of the molecular structure. The classifier, that we derived from SVM methods, outperforms the available methods in performance and simplicity.

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The Expanding Role of Predictive Toxicology: An Update on the (Q)SAR Models for Mutagens and Carcinogens

Cited by 106 publications

References 40 publications

Mechanistic QSAR of aromatic amines: New models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Mechanistic QSAR of aromatic amines: New models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Existing approaches incorporating replacement, reduction and refinement of animal testing: applicability in food and feed risk assessment

Support vector machines in the prediction of mutagenicity of chemical compounds

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