Publications by authors named "E Benfenati"

329 Publications

The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella).

Comput Biol Med 2021 Apr 3;133:104370. Epub 2021 Apr 3.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

It is usually held that good-quality models for the biological activity of peptides must take into account their 3D architecture and descriptors of quantum mechanics. However, the present study shows that it is possible to build up models without these complex calculations. The structure of tripeptides represented by sequences of one-symbol abbreviations of the corresponding amino acids serves to build up quantitative structure-activity relationships for the antioxidant activity of tripeptides from frog skin. The statistical quality of the best model for the validation set is n = 27, r = 0.93, RMSE = 0.15.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104370DOI Listing
April 2021

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.

Molecules 2021 Mar 30;26(7). Epub 2021 Mar 30.

Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a "One Health" perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.
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http://dx.doi.org/10.3390/molecules26071928DOI Listing
March 2021

Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants.

Environ Technol 2021 Feb 11:1-6. Epub 2021 Feb 11.

Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.

The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.
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http://dx.doi.org/10.1080/09593330.2021.1882588DOI Listing
February 2021

EFSA's OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments.

Environ Int 2021 Jan 8;146:106293. Epub 2020 Dec 8.

Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.
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http://dx.doi.org/10.1016/j.envint.2020.106293DOI Listing
January 2021

Towards a systematic use of effect biomarkers in population and occupational biomonitoring.

Environ Int 2021 Jan 15;146:106257. Epub 2020 Dec 15.

State Secretariat for Economic Affairs (SECO), Labour Directorate Section Chemicals and Work (ABCH), Switzerland. Electronic address:

Effect biomarkers can be used to elucidate relationships between exposure to environmental chemicals and their mixtures with associated health outcomes, but they are often underused, as underlying biological mechanisms are not understood. We aim to provide an overview of available effect biomarkers for monitoring chemical exposures in the general and occupational populations, and highlight their potential in monitoring humans exposed to chemical mixtures. We also discuss the role of the adverse outcome pathway (AOP) framework and physiologically based kinetic and dynamic (PBK/D) modelling to strengthen the understanding of the biological mechanism of effect biomarkers, and in particular for use in regulatory risk assessments. An interdisciplinary network of experts from the European chapter of the International Society for Exposure Science (ISES Europe) and the Organization for Economic Co-operation and Development (OECD) Occupational Biomonitoring activity of Working Parties of Hazard and Exposure Assessment group worked together to map the conventional framework of biomarkers and provided recommendations for their systematic use. We summarized the key aspects of this work here, and discussed these in three parts. Part I, we inventory available effect biomarkers and promising new biomarkers for the general population based on the H2020 Human Biomonitoring for Europe (HBM4EU) initiative. Part II, we provide an overview AOP and PBK/D modelling use that improved the selection and interpretation of effect biomarkers. Part III, we describe the collected expertise from the OECD Occupational Biomonitoring subtask effect biomarkers in prioritizing relevant mode of actions (MoAs) and suitable effect biomarkers. Furthermore, we propose a tiered risk assessment approach for occupational biomonitoring. Several effect biomarkers, especially for use in occupational settings, are validated. They offer a direct assessment of the overall health risks associated with exposure to chemicals, chemical mixtures and their transformation products. Promising novel effect biomarkers are emerging for biomonitoring of the general population. Efforts are being dedicated to prioritizing molecular and biochemical effect biomarkers that can provide a causal link in exposure-health outcome associations. This mechanistic approach has great potential in improving human health risk assessment. New techniques such as in silico methods (e.g. QSAR, PBK/D modelling) as well as 'omics data will aid this process. Our multidisciplinary review represents a starting point for enhancing the identification of effect biomarkers and their mechanistic pathways following the AOP framework. This may help in prioritizing the effect biomarker implementation as well as defining threshold limits for chemical mixtures in a more structured way. Several ex vivo biomarkers have been proposed to evaluate combined effects including genotoxicity and xeno-estrogenicity. There is a regulatory need to derive effect-based trigger values using the increasing mechanistic knowledge coming from the AOP framework to address adverse health effects due to exposure to chemical mixtures. Such a mechanistic strategy would reduce the fragmentation observed in different regulations. It could also stimulate a harmonized use of effect biomarkers in a more comparable way, in particular for risk assessments to chemical mixtures.
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http://dx.doi.org/10.1016/j.envint.2020.106257DOI Listing
January 2021

QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors.

Molecules 2020 Dec 29;26(1). Epub 2020 Dec 29.

Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure-activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.
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http://dx.doi.org/10.3390/molecules26010127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796303PMC
December 2020

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach.

SAR QSAR Environ Res 2020 Dec 12;31(12):1-12. Epub 2020 Nov 12.

Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano, Italy.

Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). The aim of the present study was the building and estimation of models for inhalation toxicity as No Observed Adverse Effect Concentration (NOAEC) based on the OECD guidelines 413. Three random distributions into the training set and validation set were examined. In practice, a structured training set that contains active training set, passive training set and calibration set is used as the training set. The statistical characteristics of the best model for negative logarithm of NOAEC (pNOAEC) are for training set = 108, average  = 0.52 + 0.62 + 0.76/3 = 0.63 and for validation set = 35,  = 0.73.
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http://dx.doi.org/10.1080/1062936X.2020.1841827DOI Listing
December 2020

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach.

Mol Inform 2021 03 2;40(3):e2000072. Epub 2020 Nov 2.

Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France.

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.
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http://dx.doi.org/10.1002/minf.202000072DOI Listing
March 2021

Comparing in vivo data and in silico predictions for acute effects assessment of biocidal active substances and metabolites for aquatic organisms.

Ecotoxicol Environ Saf 2020 Dec 18;205:111291. Epub 2020 Sep 18.

Istituto di Ricerche Farmacologiche "Mario Negri" IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

The purpose of this study was to determine the acute toxicity in aquatic organisms of one biocidal active substance and six metabolites derived from biocidal active substances and to assess the suitability of available QSAR models to predict the obtained values. We have reported the acute toxicity in sewage treatment plant (STP) microorganisms, in the freshwater microalgae Pseudokirchneriella subcapitata and in Daphnia magna following OECD test methods. We have also identified in silico models for acute toxicity of these trophic levels currently available in widely recognized platforms such as VEGA and the OECD QSAR ToolBox. A total of six, four and two models have been selected for Daphnia, algae and microorganisms, respectively. Finally, we have compared the in silico and in vivo data for the seven compounds plus two previously assayed biocidal substances. None of the compounds tested were toxic for Daphnia and STP microorganisms. For microalgae, CGA71019 (1,2,4 triazole) presented an ErC value of 38.3 mg/L. The selected in silico models have provided lower EC values and are therefore more conservative. Models from the OECD QSAR ToolBox predicted values for 7 out of 9 and for 4 out of 9 chemicals for Daphnia and P. subcapitata, respectively. No predictive models were identified in such platform for STP microorganism's acute effects. In terms of models's specificity, biocide-specific models, developed from curated datasets integrated by biocidal active substances and implemented in VEGA, perform better in the case of microalgae but for Daphnia an alternative, non biocide-specific has revealed a better performance. For STP microorganisms only biocide-specific models have been identified.
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http://dx.doi.org/10.1016/j.ecoenv.2020.111291DOI Listing
December 2020

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation.

SAR QSAR Environ Res 2020 Oct 3;31(10):785-801. Epub 2020 Sep 3.

Laboratory of Physical Chemistry and Biology of Materials, Department of Physics and Chemistry, Higher Normal School of Technological Education-Skikda , Skikda, Algeria.

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, , and algae from the VEGA-Hub, as well as and . The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.
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http://dx.doi.org/10.1080/1062936X.2020.1810770DOI Listing
October 2020

Review and priority setting for substances that are listed without a specific migration limit in Table 1 of Annex 1 of Regulation 10/2011 on plastic materials and articles intended to come into contact with food.

EFSA J 2020 Jun 10;18(6):e06124. Epub 2020 Jun 10.

The EFSA Panel on Food Contact Materials, Enzymes and Processing Aids (CEP) was requested by the European Commission to review the substances for which a Specific Migration Limit (SML) is not assigned in Regulation (EU) No 10/2011. These substances had been covered by the Generic SML of 60 mg/kg food, but with Regulation (EU) 2016/1416 it was removed, necessitating their re-examination. EFSA was requested to identify those substances requiring an SML to ensure the authorisation is sufficiently protective to health, grouping them in high, medium and low priority to serve as the basis for future re-evaluations of individual substances. The CEP Panel established a stepwise procedure. This took into account existing hazard assessments for each substance on carcinogenicity/mutagenicity/reprotoxicity (CMR), bioaccumulation and endocrine disruptor (ED) properties along with the use of generated predictions on genotoxicity. Molecular weights and boiling points were considered with regard to their effect on potential consumer exposure. This prioritisation procedure was applied to a total of 451 substances, from which 78 substances were eliminated at the outset, as they had previously been evaluated by EFSA as food contact substances. For 89 substances, the Panel concluded that a migration limit should not be needed. These are in the lists 0 and 1 of the Scientific Committee for Food (SCF), defined as substances for which an Acceptable Daily Intake (ADI) does not need to be established, along with substances that are controlled by existing restrictions and/or generic limits. Of the remaining 284 substances, 179 were placed into the low priority group, 102 were placed into the medium priority group and 3 were placed into the high priority group, i.e. salicylic acid (FCM No 121), styrene (FCM No 193) and lauric acid, vinyl ester (FCM No 436).
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http://dx.doi.org/10.2903/j.efsa.2020.6124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448095PMC
June 2020

First report on chemometric modeling of hydrolysis half-lives of organic chemicals.

Environ Sci Pollut Res Int 2021 Jan 26;28(2):1627-1642. Epub 2020 Aug 26.

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, Kolkata, 700032, India.

Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental chemists as well as authorities involved in the controlling the processing and disposal of such organic chemicals. In this study, we have proposed QSPR models for the prediction of hydrolysis half-life of organic chemicals as a function of different pH and temperature conditions using only two-dimensional molecular descriptors with definite physicochemical significance. For each model, suitable subsets of variables were elected using a genetic algorithm method; next, the elected subsets of variables were subjected to the best subset selection with a key objective to determine the best combination of descriptors for model generation. Finally, QSPR models were constructed using the best combination of variables employing the partial least squares (PLS) regression technique. Next, every final model was subjected for strict validation employing the internationally accepted internal and external validation parameters. The proposed models could be applicable for data gap filling to determine hydrolysis half-lives of organic chemicals at different environmental conditions. Generally, presence of aliphatic ether and ether functional groups, high percentage of oxygen content in the molecule and presence of O-Si pairs of atoms at topological distance one, results in a shorter hydrolysis half-life of organic chemicals. On the other hand, higher unsaturation content and high percentage of nitrogen content in molecules lead to higher hydrolysis half-life. It is also found that branched and compact molecules will have a lower half-life while straight chain analogues will have a higher half-life. To the best of our knowledge, the presented models are the first reported QSPR models for hydrolysis half-lives of organic chemicals at different pH values.
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http://dx.doi.org/10.1007/s11356-020-10500-0DOI Listing
January 2021

QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations".

Aquat Toxicol 2020 Oct 9;227:105589. Epub 2020 Aug 9.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

Pesticides have an impact on the aquatic environment, with ecological effects. The regulation of this impact is of key importance. One of the components of the planning of agricultural and industrial activities is the development of databases and models in order to identify substances that may cause damage. In this study, a quantitative structure-activity relationship (QSAR) approach was established for the prediction of acute toxicity toward rainbow trout of various pesticides. The so-called index of ideality of correlation is the main component of this approach. The validation of this approach has been carried out with three random splits into the training and validation sets. The range of statistical quality of models obtained here for the validation set is R = [0.81-0.86] and RMSE = [0.55-0.65].
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http://dx.doi.org/10.1016/j.aquatox.2020.105589DOI Listing
October 2020

Zebrafish AC modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo.

Ecotoxicol Environ Saf 2020 Oct 11;202:110936. Epub 2020 Jul 11.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Environmental Health Sciences, Laboratory of Environmental Toxicology, Via Mario Negri 2, 20156, Milan, Italy.

Developmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCast Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R 0.70 in external validation and leave-one-out cross-validated Q 0.73 in internal validation.
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http://dx.doi.org/10.1016/j.ecoenv.2020.110936DOI Listing
October 2020

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

Environ Sci Technol 2020 09 13;54(18):11424-11433. Epub 2020 Aug 13.

State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China.

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.
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http://dx.doi.org/10.1021/acs.est.0c02639DOI Listing
September 2020

Ecosystem ecology: Models for acute toxicity of pesticides towards Daphnia magna.

Environ Toxicol Pharmacol 2020 Nov 25;80:103459. Epub 2020 Jul 25.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

Quantitative structure - activity relationships (QSARs) which are obtained with a representation of the molecular architecture via simplified molecular input-line entry system (SMILES) are applied to build up predictive models of acute toxicity of pesticides towards Daphnia magna. The acute toxicity towards Daphnia magna is an adequate measure of the ecological impact of various substances. The Monte Carlo technique is the basis to build up the above QSAR models. The statistical quality of suggested models is good: the best model is characterized by n = 103, R = 0.76, RMSE = 0.91 (training set); n = 53, R = 0.82, RMSE = 0.87 (validation set). The approach provides the mechanistic interpretation (e.g. aromaticity and branching of carbon skeleton are promoters of increase for toxicity towards Daphnia magna in the case of the examined set of pesticides). The approach is attractive to build up predictive models since instead of a large number of different molecular descriptors the corresponding model is based on solely one optimal descriptor calculated with SMILES and all necessary calculations can be done using the CORAL software available on the Internet (http://ww.insilico.eu/coral).
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http://dx.doi.org/10.1016/j.etap.2020.103459DOI Listing
November 2020

'Ideal correlations' for the predictive toxicity to .

Toxicol Mech Methods 2020 Oct 14;30(8):605-610. Epub 2020 Aug 14.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.

Objectives: Predictive models for toxicity to are an important component of natural sciences. The present study aims to build up a predictive model for the endpoint using the so-called index of ideality of correlation (). Besides, the comparison of the predictive potential of these models with the predictive potential of models suggested in the literature is the task of the present study.

Methods: The Monte Carlo technique is a tool to build up the predictive model applied in this study. The molecular structure is represented via a simplified molecular input-line entry system (SMILES). The is a statistical characteristic sensitive to both the correlation coefficient and mean absolute error. Applying of the to build up quantitative structure-activity relationships (QSARs) for the toxicity to improves the predictive potential of those models for random splits into the training set and the validation set. The calculation was carried out with CORAL software (http://www.insilico.eu/coral).

Results: The statistical quality of the suggested models is incredibly good for the external validation set, but the statistical quality of the models for the training set is modest. This is the paradox of ideal correlation, which is obtained with applying the

Conclusions: The Monte Carlo technique is a convenient and reliable way to build up a predictive model for toxicity to . The is a useful statistical criterion for building up predictive models as well as for the assessment of their statistical quality.
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http://dx.doi.org/10.1080/15376516.2020.1801928DOI Listing
October 2020

Integrated Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity.

Chem Res Toxicol 2021 Feb 4;34(2):247-257. Epub 2020 Aug 4.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy.

Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61-0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using models.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00176DOI Listing
February 2021

Homology Modeling of the Human P-glycoprotein (ABCB1) and Insights into Ligand Binding through Molecular Docking Studies.

Int J Mol Sci 2020 Jun 5;21(11). Epub 2020 Jun 5.

Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, 1000 Ljubljana, Slovenia.

The ABCB1 transporter also known as P-glycoprotein (P-gp) is a transmembrane protein belonging to the ATP binding cassette super-family of transporters; it is a xenobiotic efflux pump that limits intracellular drug accumulation by pumping the compounds out of cells. P-gp contributes to a decrease of toxicity and possesses broad substrate specificity. It is involved in the failure of numerous anticancer and antiviral chemotherapies due to the multidrug resistance (MDR) phenomenon, where it removes the chemotherapeutics out of the targeted cells. Understanding the details of the ligand-P-gp interaction is therefore crucial for the development of drugs that might overcome the MRD phenomenon and for obtaining a more effective prediction of the toxicity of certain compounds. In this work, an in silico modeling was performed using homology modeling and molecular docking methods with the aim of better understanding the ligand-P-gp interactions. Based on different mouse P-gp structural templates from the PDB repository, a 3D model of the human P-gp (P-gp) was constructed by means of protein homology modeling. The homology model was then used to perform molecular docking calculations on a set of thirteen compounds, including some well-known compounds that interact with P-gp as substrates, inhibitors, or both. The sum of ranking differences (SRD) was employed for the comparison of the different scoring functions used in the docking calculations. A consensus-ranking scheme was employed for the selection of the top-ranked pose for each docked ligand. The docking results showed that a high number of π interactions, mainly π-sigma, π-alkyl, and π-π type of interactions, together with the simultaneous presence of hydrogen bond interactions contribute to the stability of the ligand-protein complex in the binding site. It was also observed that some interacting residues in P-gp are the same when compared to those observed in a co-crystallized ligand (PBDE-100) with mouse P-gp (PDB ID: 4XWK). Our in silico approach is consistent with available experimental results regarding P-gp efflux transport assay; therefore it could be useful in the prediction of the role of new compounds in systemic toxicity.
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http://dx.doi.org/10.3390/ijms21114058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7312539PMC
June 2020

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation.

Sci Total Environ 2020 Sep 17;735:139243. Epub 2020 May 17.

European Food Safety Authority (EFSA), Scientific Committee and Emerging Risks Unit, Via Carlo Magno 1A, 43126 Parma, Italy.

Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.
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http://dx.doi.org/10.1016/j.scitotenv.2020.139243DOI Listing
September 2020

Automated integration of structural, biological and metabolic similarities to improve read-across.

ALTEX 2020 9;37(3):469-481. Epub 2020 May 9.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri - IRCCS, Milan, Italy.

Read-across (RAX) is a popular data-gap filling technique that uses category and analogue approaches to predict toxicological endpoints for a target. Despite its increasing relevance, RAX relies on human expert judgement and lacks a reproducible and automated protocol. It also only relies on structural similarity for identifying the analogues, while other aspects are often neglected. In this paper, we propose an automated procedure for the selection of analogues for data gap-filling. Analogues were identified with a decision algorithm that integrates three similarity metrics, each considering different toxicologically relevant aspects (i.e., structural, biological and metabolic similarity). Structural filters based on the presence of maximum common substructures (MCS) and common functional groups were applied to narrow the chemical space for the analogues search. The procedure has been implemented as a workflow in KNIME and is freely available. The workflow provides informative tabular and graphical outputs to support toxicologists and risk assessors in drawing conclusion based on the RAX approach. The procedure has been validated for its predictive power on two datasets related to high-tier in vivo toxicological endpoints, i.e. human hepatotoxicity and drug-induced liver injury (DILI). The validation results gave good accuracy values (i.e., up to 0.79 for the binary hepatotoxicity classification and up to 0.67 for the three-class DILI classification) that were higher than those returned by RAX based on the sole use of structural similarity. Results confirmed the suitability of the procedure as a source of data to support regulatory decision-making.
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http://dx.doi.org/10.14573/altex.2002281DOI Listing
May 2020

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.

ALTEX 2020 30;37(4):579-606. Epub 2020 Apr 30.

Center for Alternatives to Animal Testing, CAAT-Europe, University of Konstanz, Konstanz, Germany.

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission’s H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.
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http://dx.doi.org/10.14573/altex.1912181DOI Listing
April 2020

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test.

Toxicol Lett 2020 Sep 29;329:80-84. Epub 2020 Apr 29.

Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.
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http://dx.doi.org/10.1016/j.toxlet.2020.04.016DOI Listing
September 2020

Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.

Mol Divers 2020 Apr 23. Epub 2020 Apr 23.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.

The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.
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http://dx.doi.org/10.1007/s11030-020-10085-3DOI Listing
April 2020

The way forward for assessing the human health safety of cosmetics in the EU - Workshop proceedings.

Toxicology 2020 04 28;436:152421. Epub 2020 Feb 28.

European Commission, Joint Research Centre (JRC), Ispra, Italy.

Although the need for non-animal alternatives has been well recognised for the human health hazard assessment of chemicals in general, it has become especially pressing for cosmetic ingredients due to the full implementation of testing and marketing bans on animal testing under the European Cosmetics Regulation. This means that for the safety assessment of cosmetics, the necessary safety data for both the ingredients and the finished product can be drawn from validated (or scientifically-valid), so-called "Replacement methods". In view of the challenges for safety assessment without recourse to animal test data, the Methodology Working Group of the Scientific Committee on Consumer Safety organised a workshop in February 2019 to discuss the key issues in regard to the use of animal-free alternative methods for the safety evaluation of cosmetic ingredients. This perspective article summarises the outcomes of this workshop and reflects on the state-of-the-art and possible way forward for the safety assessment of cosmetic ingredients for which no experimental animal data exist. The use and optimisation of "New Approach Methodology" that could be useful tools in the context of the "Next Generation Risk Assessment" and the strategic framework for safety assessment of cosmetics were discussed in depth.
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http://dx.doi.org/10.1016/j.tox.2020.152421DOI Listing
April 2020

Value and limitation of structure-based profilers to characterize developmental and reproductive toxicity potential.

Arch Toxicol 2020 03 25;94(3):939-954. Epub 2020 Feb 25.

Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland.

The uncertainty regarding the safety of chemicals leaching from food packaging triggers attention. In silico models provide solutions for screening of these chemicals, since many are toxicologically uncharacterized. For hazard assessment, information on developmental and reproductive toxicity (DART) is needed. The possibility to apply in silico toxicology to identify and quantify DART alerts was investigated. Open-source models and profilers were applied to 195 packaging chemicals and analogues. An approach based on DART and estrogen receptor (ER) binding profilers and molecular docking was able to identify all except for one chemical with documented DART properties. Twenty percent of the chemicals in the database known to be negative in experimental studies were classified as positive. The scheme was then applied to 121 untested chemicals. Alerts were identified for sixteen of them, five being packaging substances, the others structural analogues. Read-across was then developed to translate alerts into quantitative toxicological values. They can be used to calculate margins of exposure (MoE), the size of which reflects safety concern. The application of this approach appears valuable for hazard characterization of toxicologically untested packaging migrants. It is an alternative to the use of default uncertainty factor (UF) applied to animal chronic toxicity value to handle absence of DART data in hazard characterization.
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http://dx.doi.org/10.1007/s00204-020-02671-zDOI Listing
March 2020

Ecotoxicological effects of atmospheric particulate produced by braking systems on aquatic and edaphic organisms.

Environ Int 2020 04 18;137:105564. Epub 2020 Feb 18.

Laboratory of Environmental Chemistry and Toxicology, IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milan, Italy; IAS CNR, Via De Marini 6, 16149 Genova, Italy. Electronic address:

Vehicles generate particulate matter (PM) in significant amounts as their brake systems wear. These particles can influence air quality and their transport/deposition may affect the edaphic and aquatic ecosystems. As part of the LOWBRASYS H2020 project, new more eco-friendly brake disc and pad formulations were developed. PMs generated from traditional (FM1-BD1) and innovative (FM4-BD2, FMB-BD7) brake systems in bench tests were studied. The PMs' physical/chemical characteristics were preliminarily investigated. To study the possible environmental impact of the nano-micro particulate, we used a battery of ecotoxicological tests. We employed the microalga Pseudokirchneriella subcapitata, the crustacean Daphnia magna and the bacteria Vibrio fischeri as aquatic bioindicators, while for the edaphic ecosystem we used the seeds of Lepidium sativum and Sorghum saccharatum, the nematode Caenorhabditis elegans, the earthworm Eisenia andrei and the ameba Dictyostelium discoideum. The results showed a higher sensitivity of the freshwater organisms exposed to the soluble PM fraction, with respect to the edaphic ones. FM4-BD2 brake formulation was slightly more toxic for algae (200 mg/L) than FM1-BD1 (500 mg/L). The new system FMB-BD7 particulate was not harmful for crustacean survival, and resulted weakly toxic for algal reproduction only at 500 mg/L. The particulate material per se was found to affect the algal reproduction. No toxic effects were found on nematodes, earthworms and seeds up to 1000 mg/L. However, in D. discoideum the reproduction rate was significantly reduced starting from 100 mg/L; and the lysosomal membrane stability showed a relevant alteration also at minimal concentration (0.1 mg/L). The results demonstrated a minimal risk for biodiversity of the particulates from the different brake systems and highlighted a more eco-friendly performance the new brake-pad FMB-BD7. However, the occurrence of sublethal effects should be considered as a possible contribution of the particle toxicity to the biological effects of the environmental pollution.
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http://dx.doi.org/10.1016/j.envint.2020.105564DOI Listing
April 2020

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

Environ Health Perspect 2020 02 7;128(2):27002. Epub 2020 Feb 7.

National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) approaches and computational modeling.

Objectives: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

Methods: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS assays.

Results: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

Discussion: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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http://dx.doi.org/10.1289/EHP5580DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064318PMC
February 2020

Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis.

Molecules 2020 Feb 8;25(3). Epub 2020 Feb 8.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di RicercheFarmacologiche "Mario Negri"-IRCCS, Via Mario Negri, 2, 20156 Milano, Italy.

Aromatase is an enzyme member of the cytochrome P450 superfamily coded by the CYP19A1 gene. Its main action is the conversion of androgens into estrogens, transforming androstenedione into estrone and testosterone into estradiol. This enzyme is present in several tissues and it has a key role in the maintenance of the balance of androgens and estrogens, and therefore in the regulation of the endocrine system. With regard to chemical safety and human health, azoles, which are used as agrochemicals and pharmaceuticals, are potential endocrine disruptors due to their agonist or antagonist interactions with the human aromatase enzyme. This theoretical study investigated the active agonist and antagonist properties of "chemical classes of azoles" to determine the relationships of azole interaction with CYP19A1, using stereochemical and electronic properties of the molecules through classification and multilinear regression (MLR) modeling. The antagonist activities for the same substituent on diazoles and triazoles vary with its chemical composition and its position and both heterocyclic systems require aromatic substituents. The triazoles require the spherical shape and diazoles have to be in proper proportion of the branching index and the number of ring systems for the inhibition. Considering the electronic aspects, triazole antagonist activity depends on the electrophilicity index that originates from interelectronic exchange interaction () and the LUMO energy ( E LUMO PM 7 ), and the diazole antagonist activity originates from the penultimate orbital ( E HOMONL PM 7 ) of diazoles. The regression models for agonist activity show that it is opposed by the static charges but favored by the delocalized charges on the diazoles and thiazoles. This study proposes that the electron penetration of azoles toward heme group decides the binding behavior and stereochemistry requirement for antagonist activity against CYP19A1 enzyme.
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http://dx.doi.org/10.3390/molecules25030739DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037385PMC
February 2020

The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides.

Environ Sci Pollut Res Int 2020 Apr 4;27(12):13339-13347. Epub 2020 Feb 4.

Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, 43126, Parma, Italy.

Models for water solubility of pesticides suggested in this manuscript are important data from point of view of ecologic engineering. The Index of Ideality of Correlation (IIC) of groups of quantitative structure-property relationships (QSPRs) for water solubility of pesticides related to the calibration sets was used to identify good in silico models. This comparison confirmed the high IIC set provides better statistical quality of the model for the validation set. Though there are large databases on solubility, the reliable prediction of the endpoint for new substances which are potential pesticides is an important ecologic task. Unfortunately, predictive models for various endpoints suffer overtraining, and the IIC serves to avoid or at least reduce this. Thus, the approach suggested has both theoretical and economic effects for ecology.
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http://dx.doi.org/10.1007/s11356-020-07820-6DOI Listing
April 2020