Emilio Benfenati - Academia.edu (original) (raw)

Uploads

Papers by Emilio Benfenati

Research paper thumbnail of Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

Environmental Science & Technology, 2020

Research paper thumbnail of Could deep learning in neural networks improve the QSAR models?

SAR and QSAR in Environmental Research, 2019

Research paper thumbnail of Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

Environment International, 2019

Research paper thumbnail of Tuning Neutral and Fuzzy-Neutral Networks for Toxicity Modeling

Research paper thumbnail of The validation of predictive potential via the system of self-consistent models: the simulation of blood–brain barrier permeation of organic compounds

Journal of Molecular Modeling

Research paper thumbnail of A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

Frontiers in Pharmacology, 2016

Research paper thumbnail of Concentrations of PCDD in different points of a modern refuse incinerator

Research paper thumbnail of In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence

Mutagenesis, Jan 15, 2016

Prior to the downstream development of chemical substances, including pharmaceuticals and cosmeti... more Prior to the downstream development of chemical substances, including pharmaceuticals and cosmetics, their influence on the genetic apparatus has to be tested. Several in vitro and in vivo assays have been developed to test for genotoxicity. In a first tier, a battery of two to three in vitro tests is recommended to cover mutagenicity, clastogenicity and aneugenicity as main endpoints. This regulatory in vitro test battery is known to have a high sensitivity, which is at the expense of the specificity. The high number of false positive in vitro results leads to excessive in vivo follow-up studies. In the case of cosmetics it may even induce the ban of the particular compound since in Europe the use of experimental animals is no longer allowed for cosmetics. In this article, an alternative approach to derisk a misleading positive Ames test is explored. Hereto we first tested the performance of five existing computational tools to predict the potential mutagenicity of a data set of 13...

Research paper thumbnail of Evaluation of carcinogenic potential of perfluorinated compounds using in vitro and in silico alternative approaches

Research paper thumbnail of Supplementary information for The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing

Research paper thumbnail of Comparison of in silico tools for evaluating rat oral acute toxicity

SAR and QSAR in environmental research, 2015

Different in silico models have been developed and implemented for the evaluation of mammalian ac... more Different in silico models have been developed and implemented for the evaluation of mammalian acute toxicity, exploring acute oral toxicity data expressed as median lethal dose (LD(50)). We compared five software programs (TOPKAT, ACD/ToxSuite, TerraQSAR, ADMET Predictor and T.E.S.T.) using a dataset of 7417 chemicals. We tested the models' performance using the quantitative results and, in classification, the toxicity threshold defined within the Classifying, Labelling and Packaging (CLP) regulation. ACD gave the best results with r(2) of 0.79 and 0.66 accuracy. However, its performance dropped when considering the molecules not present in its training set, and the other models behaved similarly. We also considered the information on the applicability domain (AD), which improved the models' performance, but not enough for the molecules external to the models' training set. We also considered the chemical classes and found that all models gave high performance for certa...

Research paper thumbnail of Quantification of 4,4′-diaminodiphenylmethane by gas chromatography negative ion chemical lonization mass spectrometry

Microchemical Journal, 1992

Research paper thumbnail of Pharmaceuticals as Environmental Contaminants: Modelling Distribution and Fate

Pharmaceuticals in the Environment, 2004

Research paper thumbnail of GC‐MS analysis of n‐phosphonomethylglycine (glyphosate) samples through derivatization with a perfluoroanhydride and trifluoroethanol: Identification of by‐products

Toxicological & Environmental Chemistry, 1993

ABSTRACT

Research paper thumbnail of In silico models for predicting ready biodegradability under REACH: A comparative study

Science of The Total Environment, 2013

REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new E... more REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new European law which aims to raise the human protection level and environmental health. Under REACH all chemicals manufactured or imported for more than one ton per year must be evaluated for their ready biodegradability. Ready biodegradability is also used as a screening test for persistent, bioaccumulative and toxic (PBT) substances. REACH encourages the use of non-testing methods such as QSAR (quantitative structure-activity relationship) models in order to save money and time and to reduce the number of animals used for scientific purposes. Some QSAR models are available for predicting ready biodegradability. We used a dataset of 722 compounds to test four models: VEGA, TOPKAT, BIOWIN 5 and 6 and START and compared their performance on the basis of the following parameters: accuracy, sensitivity, specificity and Matthew's correlation coefficient (MCC). Performance was analyzed from different points of view. The first calculation was done on the whole dataset and VEGA and TOPKAT gave the best accuracy (88% and 87% respectively). Then we considered the compounds inside and outside the training set: BIOWIN 6 and 5 gave the best results for accuracy (81%) outside training set. Another analysis examined the applicability domain (AD). VEGA had the highest value for compounds inside the AD for all the parameters taken into account. Finally, compounds outside the training set and in the AD of the models were considered to assess predictive ability. VEGA gave the best accuracy results (99%) for this group of chemicals. Generally, START model gave poor results. Since BIOWIN, TOPKAT and VEGA models performed well, they may be used to predict ready biodegradability.

Research paper thumbnail of CORAL: Monte Carlo Method as a Tool for the Prediction of the Bioconcentration Factor of Industrial Pollutants

Molecular Informatics, 2012

The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR mod... more The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR modeling of the bioconcentration factor in fish (logBCF). The data used include 237 organic substances (industrial pollutants). Six random splits of the data into sub‐training (30–50 %), calibration (20–30 %), test (13–30 %), and validation sets (7–25 %) have been carried out. The following numbers display the average statistical characteristics of the models for the external validation set: correlation coefficient r2=0.880±0.017 and standard error of estimation s=0.559±0.131. The best models were obtained with a combined representation of the molecular structure by SMILES together with hydrogen suppressed graph.

Research paper thumbnail of Comparison of In Silico Models for Prediction of Mutagenicity

Journal of Environmental Science and Health, Part C, 2013

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure ac... more Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

Research paper thumbnail of Regulatory Assessment of Chemicals within OECD Member Countries, EU and in Russia

Journal of Environmental Science and Health, Part C, 2008

The chemical risk assessment is essesntial part of new chemical legislation registration, evaluat... more The chemical risk assessment is essesntial part of new chemical legislation registration, evaluation, and authorization of chemicals (REACH). The article presents a review of chemical legislation policies in the European Union (EU) and in Russia, and changes in chemicals regulations to meet the requirement of REACH. The risk assessment paradigm, toxicological parameters, safe limits and classification criteria used by different agencies and authorities in different countries are reported. Our investigation also focuses on comparison of chemical risk assessment criteria used in OECD member countries and in Russia. Tendencies in harmonization in accordance with the globally harmonized system of classification and labeling of chemicals (GHS) are discussed.

Research paper thumbnail of Directions in QSAR Modeling for Regulatory Uses in OECD Member Countries, EU and in Russia

Journal of Environmental Science and Health, Part C, 2008

The aim of this article is to show the main aspects of quantitative structure activity relationsh... more The aim of this article is to show the main aspects of quantitative structure activity relationship (QSAR) modeling for regulatory purposes. We try to answer the question; what makes QSAR models suitable for regulatory uses. The article focuses on directions in QSAR modeling in European Union (EU) and Russia. Difficulties in validation models have been discussed.

Research paper thumbnail of Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

Journal of Computational Chemistry, 2012

Research paper thumbnail of Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

Environmental Science & Technology, 2020

Research paper thumbnail of Could deep learning in neural networks improve the QSAR models?

SAR and QSAR in Environmental Research, 2019

Research paper thumbnail of Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

Environment International, 2019

Research paper thumbnail of Tuning Neutral and Fuzzy-Neutral Networks for Toxicity Modeling

Research paper thumbnail of The validation of predictive potential via the system of self-consistent models: the simulation of blood–brain barrier permeation of organic compounds

Journal of Molecular Modeling

Research paper thumbnail of A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

Frontiers in Pharmacology, 2016

Research paper thumbnail of Concentrations of PCDD in different points of a modern refuse incinerator

Research paper thumbnail of In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence

Mutagenesis, Jan 15, 2016

Prior to the downstream development of chemical substances, including pharmaceuticals and cosmeti... more Prior to the downstream development of chemical substances, including pharmaceuticals and cosmetics, their influence on the genetic apparatus has to be tested. Several in vitro and in vivo assays have been developed to test for genotoxicity. In a first tier, a battery of two to three in vitro tests is recommended to cover mutagenicity, clastogenicity and aneugenicity as main endpoints. This regulatory in vitro test battery is known to have a high sensitivity, which is at the expense of the specificity. The high number of false positive in vitro results leads to excessive in vivo follow-up studies. In the case of cosmetics it may even induce the ban of the particular compound since in Europe the use of experimental animals is no longer allowed for cosmetics. In this article, an alternative approach to derisk a misleading positive Ames test is explored. Hereto we first tested the performance of five existing computational tools to predict the potential mutagenicity of a data set of 13...

Research paper thumbnail of Evaluation of carcinogenic potential of perfluorinated compounds using in vitro and in silico alternative approaches

Research paper thumbnail of Supplementary information for The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing

Research paper thumbnail of Comparison of in silico tools for evaluating rat oral acute toxicity

SAR and QSAR in environmental research, 2015

Different in silico models have been developed and implemented for the evaluation of mammalian ac... more Different in silico models have been developed and implemented for the evaluation of mammalian acute toxicity, exploring acute oral toxicity data expressed as median lethal dose (LD(50)). We compared five software programs (TOPKAT, ACD/ToxSuite, TerraQSAR, ADMET Predictor and T.E.S.T.) using a dataset of 7417 chemicals. We tested the models' performance using the quantitative results and, in classification, the toxicity threshold defined within the Classifying, Labelling and Packaging (CLP) regulation. ACD gave the best results with r(2) of 0.79 and 0.66 accuracy. However, its performance dropped when considering the molecules not present in its training set, and the other models behaved similarly. We also considered the information on the applicability domain (AD), which improved the models' performance, but not enough for the molecules external to the models' training set. We also considered the chemical classes and found that all models gave high performance for certa...

Research paper thumbnail of Quantification of 4,4′-diaminodiphenylmethane by gas chromatography negative ion chemical lonization mass spectrometry

Microchemical Journal, 1992

Research paper thumbnail of Pharmaceuticals as Environmental Contaminants: Modelling Distribution and Fate

Pharmaceuticals in the Environment, 2004

Research paper thumbnail of GC‐MS analysis of n‐phosphonomethylglycine (glyphosate) samples through derivatization with a perfluoroanhydride and trifluoroethanol: Identification of by‐products

Toxicological & Environmental Chemistry, 1993

ABSTRACT

Research paper thumbnail of In silico models for predicting ready biodegradability under REACH: A comparative study

Science of The Total Environment, 2013

REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new E... more REACH (Registration Evaluation Authorization and restriction of Chemicals) legislation is a new European law which aims to raise the human protection level and environmental health. Under REACH all chemicals manufactured or imported for more than one ton per year must be evaluated for their ready biodegradability. Ready biodegradability is also used as a screening test for persistent, bioaccumulative and toxic (PBT) substances. REACH encourages the use of non-testing methods such as QSAR (quantitative structure-activity relationship) models in order to save money and time and to reduce the number of animals used for scientific purposes. Some QSAR models are available for predicting ready biodegradability. We used a dataset of 722 compounds to test four models: VEGA, TOPKAT, BIOWIN 5 and 6 and START and compared their performance on the basis of the following parameters: accuracy, sensitivity, specificity and Matthew's correlation coefficient (MCC). Performance was analyzed from different points of view. The first calculation was done on the whole dataset and VEGA and TOPKAT gave the best accuracy (88% and 87% respectively). Then we considered the compounds inside and outside the training set: BIOWIN 6 and 5 gave the best results for accuracy (81%) outside training set. Another analysis examined the applicability domain (AD). VEGA had the highest value for compounds inside the AD for all the parameters taken into account. Finally, compounds outside the training set and in the AD of the models were considered to assess predictive ability. VEGA gave the best accuracy results (99%) for this group of chemicals. Generally, START model gave poor results. Since BIOWIN, TOPKAT and VEGA models performed well, they may be used to predict ready biodegradability.

Research paper thumbnail of CORAL: Monte Carlo Method as a Tool for the Prediction of the Bioconcentration Factor of Industrial Pollutants

Molecular Informatics, 2012

The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR mod... more The CORAL software (http://www.insilico.eu/coral/) has been evaluated for application in QSAR modeling of the bioconcentration factor in fish (logBCF). The data used include 237 organic substances (industrial pollutants). Six random splits of the data into sub‐training (30–50 %), calibration (20–30 %), test (13–30 %), and validation sets (7–25 %) have been carried out. The following numbers display the average statistical characteristics of the models for the external validation set: correlation coefficient r2=0.880±0.017 and standard error of estimation s=0.559±0.131. The best models were obtained with a combined representation of the molecular structure by SMILES together with hydrogen suppressed graph.

Research paper thumbnail of Comparison of In Silico Models for Prediction of Mutagenicity

Journal of Environmental Science and Health, Part C, 2013

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure ac... more Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

Research paper thumbnail of Regulatory Assessment of Chemicals within OECD Member Countries, EU and in Russia

Journal of Environmental Science and Health, Part C, 2008

The chemical risk assessment is essesntial part of new chemical legislation registration, evaluat... more The chemical risk assessment is essesntial part of new chemical legislation registration, evaluation, and authorization of chemicals (REACH). The article presents a review of chemical legislation policies in the European Union (EU) and in Russia, and changes in chemicals regulations to meet the requirement of REACH. The risk assessment paradigm, toxicological parameters, safe limits and classification criteria used by different agencies and authorities in different countries are reported. Our investigation also focuses on comparison of chemical risk assessment criteria used in OECD member countries and in Russia. Tendencies in harmonization in accordance with the globally harmonized system of classification and labeling of chemicals (GHS) are discussed.

Research paper thumbnail of Directions in QSAR Modeling for Regulatory Uses in OECD Member Countries, EU and in Russia

Journal of Environmental Science and Health, Part C, 2008

The aim of this article is to show the main aspects of quantitative structure activity relationsh... more The aim of this article is to show the main aspects of quantitative structure activity relationship (QSAR) modeling for regulatory purposes. We try to answer the question; what makes QSAR models suitable for regulatory uses. The article focuses on directions in QSAR modeling in European Union (EU) and Russia. Difficulties in validation models have been discussed.

Research paper thumbnail of Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

Journal of Computational Chemistry, 2012