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

Benigni, Romualdo; Bossa, Cecilia; Netzeva, Tatiana I.; Rodomonte, Andrea; Tsakovska, Ivanka

doi:10.1002/em.20355

Cited by 76 publications

(39 citation statements)

References 40 publications

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“…Evidence from QSAR analyses of aromatic amines, and of mutagenic/carcinogenic chemicals in general, suggests that the structural factors and chemical properties that differentiate between active and inactive compounds are different from those that modulate the gradation of potency of the active compounds. In addition to several studies previously performed in our laboratory [Benigni et al, 2007a], a recent analysis showed that the probability for aromatic amines to be carcinogenic, as calculated by QSAR models, is not correlated with their potency (our unpublished results). The above results, taken together, indicate that the assessment of the carcinogenic risk of the amines through theoretical models should be performed in two steps: first, discrimination between positives and negatives; and second, estimation of the potency of the actives alone.…”

Section: Discussionmentioning

confidence: 54%

“…These chemicals have considerable environmental and industrial importance, and toxicological data have been reported for a large number of them [Lai et al, 1996;Sugimura, 1997;Vineis and Pirastu, 1997;Skog et al, 1998;Woo and Lai, 2001]. In previous work we have performed QSAR investigations on the activity and the potency of the aromatic amines, for both mutagenic (Salmonella) and carcinogenic (rat and mouse) endpoints [Benigni et al, 2000[Benigni et al, , 2007aFranke et al, 2001]. In the present investigation the effect of structural motifs on the carcinogenic potency of aromatic amines in rats and mice are investigated by using the software program EVAL.…”

Section: Introductionmentioning

confidence: 98%

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Structural motifs modulating the carcinogenic risk of aromatic amines

Benigni

Worth

Netzeva

et al. 2009

Environ and Mol Mutagen

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The structure alerts (SA) for carcinogenicity/mutagenicity are a repository of the science on chemical biological interactions; in addition, they have a crucial role in practical applications for risk assessment. In predictive toxicology, it is crucial that knowledge of SAs is accompanied by knowledge of the structural motifs that modulate their effects. Recently, we have compiled an updated list of SAs implemented in the expert system Tox-tree 1.50 (open source, freely available). These SAs are aimed at discriminating between active and inactive chemicals, and include only modulating factors with a high probability of eliminating completely the effect of the SA. Here we have examined the factors that modulate carcinogenic potency: this is an additional piece of information that can have a role in fine-tuning a risk assessment. The case study selected is the carcinogenic potential of the aromatic amines in rats and mice. As the carcinogenic potency of these compounds is different in mice and rats (correlation coefficient = 0.546), there are both agreements and differences in the pattern of these motifs. Differences are observed mainly for the motifs that decrease the carcinogenic potency of aromatic amines. In mice, substitutions ortho and meta to the amino group tend to decrease the potency, as well as -NO(2) in any position. In rats, these motifs affect the potency to a more limited extent. On the other hand, increasing effects are quite similar in the two animals and are exerted mainly by additional rings, tricyclic systems, five-numbered rings, and N-heteroaromatic systems.

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Section: Discussionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 98%

Structural motifs modulating the carcinogenic risk of aromatic amines

Benigni

Worth

Netzeva

et al. 2009

Environ and Mol Mutagen

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“…This rule could evaluate compound toxicity based on certain functional groups or chemical fragments that may induce mutagenicity or carcinogenicity (Benigni et al, 2008(Benigni et al, , 2007. The results of ligands toxicity prediction by using Toxtree are seen in Table 4.…”

Section: In Silico Preclinical Trialmentioning

confidence: 99%

In silico identification of 2-oxo-1,3-thiazolidine derivatives as novel inhibitor candidate of class II histone deacetylase (HDAC) in cervical cancer treatment

Tambunan

Parikesit

Ghifari

et al. 2019

Arabian Journal of Chemistry

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Cervical cancer ranks third among the most prevalent deadly cancer in women worldwide and ranks first in developing countries. It is caused by human papilloma virus (HPV) infection. Thus HDACs have become prominent inhibition target for cervical cancer treatment. In order to discover the new alternative HDAC inhibitors (HDACIs), we conducted a computer-aided drug discovery and development (CADDD) based on de novo approach. The compound library is based on 4-[(2-oxo-1,3-thiazolidin-3-yl)carbonyl] aniline. Screening of the best drug leads was evaluated from several parameters, such as docking and interaction analysis, pharmacology, in silico preclinical trial and molecular dynamics analysis. The inhibitory activity of these new designed ligands against Homo sapiens class II HDAC was determined by molecular docking simulation. Docking analysis yielded eight best ligands which have better binding affinity than the standards. Therefore, interaction analysis indicated that all best ligands performed coordination with Zn 2+ cofactor in HDAC charge-relay system which are essential for HDAC inhibitory activities of these inhibitors. Pharmacology analysis and preclinical trials of these compounds including pharmacology properties, bioactivity, mutagenicity-carcinogenicity, absorption, distribution, metabolism, excretion and toxicity (ADMET) properties were done through in silico methods. Through this analysis, the best ligands meet Lipinski's rule of five, have a better drug score than standards, and show good bioactivities, oral bioavailability and ADMET properties. All best ligands also have good synthetic accessibility and were proved to be new compounds that have never been synthesized

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“…The development of the model and its mechanistic meaning were described [Benigni et al, 2007a]. The work reports the set of chemicals (and data) used to develop the model (training set).…”

Section: Qsar Modelmentioning

confidence: 99%

“…We show how a QSAR that predicts a dichotomous biological activity (such as mutagen/nonmutagen) can be used in a non-naïve way, and how the user can maximize alternatively the reliability of the prediction of negative compounds (i.e., safe chemicals) or that of positive chemicals (i.e., chemicals that pose high hazard). As case study, we use a previously established QSAR model that discriminates between mutagenic and nonmutagenic aromatic amines in the TA100 strain of Salmonella typhimurium [Benigni et al, 2007a].…”

Section: Introductionmentioning

confidence: 99%

Flexible use of qsar models in predictive toxicology: A case study on aromatic amines

Benigni

Bossa

2011

Environ and Mol Mutagen

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Over the last years, predictive toxicology approaches based on Structure-Activity Relationships have emerged as fundamental tools in the regulatory assessments of chemicals, especially in those programs where regulatory constraints and assessment schemes limit the amount of data available from experimental test methods. Both the qualitative (e.g., Structural Alerts) and the quantitative (Quantitative Structure-Activity Relationships, QSAR) approach can play important roles. However, the two approaches are not familiar to the same extent to the regulators that most often use only the qualitative approach, so that the potentiality of the more sophisticated QSAR approach is neglected. In fact, QSAR is a very flexible tool that allows the user to modulate its response according to different goals and requirements. Here, we present a non-naïve approach to the use of a QSAR relative to a dichotomous biological activity (such as mutagen/nonmutagen), and we show how the user can maximize alternatively the reliability of the prediction of negative compounds (i.e., safe chemicals) or that of positive chemicals (i.e., chemicals that pose high hazard). Because of the environmental and industrial importance of the class of aromatic amines, we apply the approach to a previously published QSAR on the Salmonella typhimurium mutagenicity of these chemicals.

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

Cited by 76 publications

References 40 publications

Structural motifs modulating the carcinogenic risk of aromatic amines

Structural motifs modulating the carcinogenic risk of aromatic amines

In silico identification of 2-oxo-1,3-thiazolidine derivatives as novel inhibitor candidate of class II histone deacetylase (HDAC) in cervical cancer treatment

Flexible use of qsar models in predictive toxicology: A case study on aromatic amines

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