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Emilio Benfenati - One of the best experts on this subject based on the ideXlab platform.
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comparison between Bioconcentration Factor bcf data provided by industry to the european chemicals agency echa and data derived from qsar models
Environmental Research, 2015Co-Authors: Maria I Petoumenou, Fabiola Pizzo, Josep Cester, Alberto Fernandez, Emilio BenfenatiAbstract:Abstract The Bioconcentration Factor (BCF) is the ratio of the concentration of a chemical in an organism to the concentration in the surrounding environment at steady state. It is a valuable indicator of the bioaccumulation potential of a substance. BCF is an essential environmental property required for regulatory purposes within the Registration, Evaluation, Authorization and restriction of Chemicals (REACH) and Globally Harmonized System (GHS) regulations. In silico models for predicting BCF can facilitate the risk assessment for aquatic toxicology and reduce the cost and number of animals used. The aim of the present study was to examine the correlation of BCF data derived from the dossiers of registered chemicals submitted to the European Chemical Agency (ECHA) with the results of a battery of Quantitative Structure-Activity Relationship (QSAR). After data pruning, statistical analysis was performed using the predictions of the selected models. Results in terms of R2 had low rating around 0.5 for the pruned dataset. The use of the model applicability domain index (ADI) led to an improvement of the performance for compounds falling within it. The variability of the experimental data and the use of different parameters to define the applicability domain can influence the performance of each model. All available information should be adapted to the requirements of the regulation to obtain a safe decision.
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evaluation and comparison of benchmark qsar models to predict a relevant reach endpoint the Bioconcentration Factor bcf
Environmental Research, 2015Co-Authors: Andrea Gissi, Orazio Nicolotti, Domenico Gadaleta, Anna Lombardo, Alessandra Roncaglioni, Giuseppe Felice Mangiatordi, Emilio BenfenatiAbstract:Abstract The Bioconcentration Factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R2) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R2>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R2ext=0.59), followed by the EPISuite Meylan model (R2ext=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R2=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R2=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach.
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An alternative QSAR-based approach for predicting the Bioconcentration Factor for regulatory purposes
Altex, 2014Co-Authors: Andrea Gissi, Stefania Olla, Angelo Carotti, M. Floris, Domenico Gadaleta, Emilio Benfenati, Ettore Novellino, Orazio NicolottiAbstract:The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocide Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of Bioconcentration Factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals. The model's robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisFactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
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an alternative qsar based approach for predicting the Bioconcentration Factor for regulatory purposes
ALTEX-Alternatives to Animal Experimentation, 2014Co-Authors: Andrea Gissi, Stefania Olla, Angelo Carotti, M. Floris, Domenico Gadaleta, Emilio Benfenati, Ettore Novellino, Orazio NicolottiAbstract:Summary The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocidal Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of Bioconcentration Factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals, respectively. The model’s robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisFactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
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coral monte carlo method as a tool for the prediction of the Bioconcentration Factor of industrial pollutants
Molecular Informatics, 2013Co-Authors: Alla P Toropova, Emilio Benfenati, Andrey A Toropov, Giuseppina Gini, Danuta Leszczynska, S E Martyanov, Jerzy LeszczynskiAbstract:The CORAL software (http://www.insilico.eu/ coral/) has been evaluated for application in QSAR model- ing 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 r 2 = 0.880 � 0.017 and standard error of estimation s = 0.559 � 0.131. The best models were obtained with a combined represen- tation of the molecular structure by SMILES together with hydrogen suppressed graph.
Harald J Geyer - One of the best experts on this subject based on the ideXlab platform.
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two compartment thermodynamic model for Bioconcentration of hydrophobic organic chemicals by alga quantitative relationshiop between Bioconcentration Factor and surface area of marine algae or octanol water partition coefficient
Chemosphere, 1997Co-Authors: Xiulin Wang, Weijun Yu, Harald J GeyerAbstract:Abstract A two-compartment thermodynamic model for Bioconcentration of hydrophobic organic chemicals (HOCs) by algae was proposed. In the model, it was assumed that 1) the Bioconcentration is comparable to physicochemical liquid-liquid partitioning, and is predominantly the result of interfacial processes of alga cells as well as HOC; 2) the surface excess quantity of HOC with respect to water phase can be expressed by Gibbs equation, and increases with increasing HOC concentration in alga cells; 3) the hydrophobic nature of alga cells, wherein only dispersion interaction contributes to their surface tension, remain almost unchanged after adsorption of HOC. From the model it was concluded that Bioconcentration Factor (log BCF) has linear relation with specific surface area (log S) of alga cells, n-octanol/water partition coefficient (log Kow) of HOC, and HOC concentration in the water (log Cw) respectively. The model was tested by the Bioconcentration of monochlorobenzene, 1,2-dichlorobenzene, 1,2,3,4-tetrachlorobenzene, and pentachlorobenzene by marine algae including Chlorella marine, Nannochloropsis oculata, Pyramidomonas sp., Platymonas subcordiformis, and Phaeodactylum tricornutum. BCF values were obtained not only with the Bioconcentration model, but also with the combined Bioconcentration and probability model. It was found that the Bioconcentration Factors of a chemical was increaseing with the specific surface area (S) of different marine algae.
Orazio Nicolotti - One of the best experts on this subject based on the ideXlab platform.
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evaluation and comparison of benchmark qsar models to predict a relevant reach endpoint the Bioconcentration Factor bcf
Environmental Research, 2015Co-Authors: Andrea Gissi, Orazio Nicolotti, Domenico Gadaleta, Anna Lombardo, Alessandra Roncaglioni, Giuseppe Felice Mangiatordi, Emilio BenfenatiAbstract:Abstract The Bioconcentration Factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R2) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R2>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R2ext=0.59), followed by the EPISuite Meylan model (R2ext=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R2=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R2=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach.
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An alternative QSAR-based approach for predicting the Bioconcentration Factor for regulatory purposes
Altex, 2014Co-Authors: Andrea Gissi, Stefania Olla, Angelo Carotti, M. Floris, Domenico Gadaleta, Emilio Benfenati, Ettore Novellino, Orazio NicolottiAbstract:The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocide Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of Bioconcentration Factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals. The model's robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisFactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
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an alternative qsar based approach for predicting the Bioconcentration Factor for regulatory purposes
ALTEX-Alternatives to Animal Experimentation, 2014Co-Authors: Andrea Gissi, Stefania Olla, Angelo Carotti, M. Floris, Domenico Gadaleta, Emilio Benfenati, Ettore Novellino, Orazio NicolottiAbstract:Summary The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocidal Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of Bioconcentration Factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals, respectively. The model’s robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisFactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.
Paul Connett - One of the best experts on this subject based on the ideXlab platform.
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Estimating Bioconcentration Factors and half-lives in humans using physiologically based pharmacokinetic modelling : 2,3,7,8-TCDD
Chemosphere, 1991Co-Authors: Thomas Webster, Paul ConnettAbstract:Abstract A Bioconcentration Factor and a half-life of 2,3,7,8-TCDD in 70 kg reference humans are estimated from a physiologically based pharmacokinetic model. The system of differential equations is solved using eigenvalue methods. The results are approximated using a standard first-order Bioconcentration equation.
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The use of Bioconcentration Factors in estimating the 2,3,7,8-TCDD content of cow's milk
Chemosphere, 1990Co-Authors: Thomas Webster, Paul ConnettAbstract:Abstract Five models for estimating the 2,3,7,8-TCDD contamination of cow's milk from air are compared with field data from a known source of dioxin in Austria. Bioconcentration from soil is a very minor pathway for contamination of hay in this setting, leading to underpredictions of greater than an order of magnitude. Direct deposition of particulate produces better results, but vapor transfer of PCDD/PCDF to plants should be considered. A Bioconcentration Factor from dry hay to milk fat of 1.7 was observed, less than that assumed by the five models. 2,3,7,8-TCDD equivalents transferred from air to milk fat less efficiently than 2,3,7,8-TCDD. Important model parameters should be based on experiments which accurately represent the modelled situation.
Stefan Trapp - One of the best experts on this subject based on the ideXlab platform.
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methods for estimating the Bioconcentration Factor of ionizable organic chemicals
Environmental Toxicology and Chemistry, 2009Co-Authors: Wenjing Fu, Antonio Franco, Stefan TrappAbstract:The bioaccumulation potential is an important criterion in risk assessment of chemicals. Several regressions between Bioconcentration Factor (BCF) in fish and octanol-water partition coefficient (KOW) have been developed for neutral organic compounds, but very few approaches address the BCF of ionizable compounds. A database with BCFs of 73 acids and 65 bases was collected from the literature. The BCF estimation method recommended by the Technical Guidance Document (TGD) for chemical risk assessment in the European Union was tested for ionizing substances using log KOW (corrected for the neutral species, log[fn·KOW]) and log D (sum of log KOW of neutral and ionic molecule, apparent log KOW) as predictors. In addition, the method of Meylan et al. (Environ Toxicol Chem 1999; 18:664–672) for ionizable compounds and a dynamic cell model based on the Fick-Nernst-Planck equation were tested. Moreover, our own regressions for the BCF were established from log KOW and pKa. The bioaccumulation of lipophilic compounds depends mainly on their lipophilicity, and the best predictor is log D. Dissociation, the pH-dependent ion trap, and electrical attraction of cations impact the BCF. Several methods showed acceptable results. The TGD regressions gave good predictions when log(fn·KOW) or log D were used as a predictor instead of log KOW. The new regressions to log KOW and pKa performed similarly, with mean errors of approximately 0.4. The method of Meylan et al. did not perform as well. The cell model showed weak results for acids but was among the best methods for bases.
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environmental chemistry methods for estimating the Bioconcentration Factor of ionizable organic chemicals
2009Co-Authors: Wenjing Fu, Antonio Franco, Stefan TrappAbstract:The bioaccumulation potential is an important criterion in risk assessment of chemicals. Several regressions between Bioconcentration Factor (BCF) in fish and octanol-water partition coefficient (KOW) have been developed for neutral organic com- pounds, but very few approaches address the BCF of ionizable compounds. A database with BCFs of 73 acids and 65 bases was collected from the literature. The BCF estimation method recommended by the Technical Guidance Document (TGD) for chemical risk assessment in the European Union was tested for ionizing substances using log KOW (corrected for the neutral species, log( fn ·KOW)) and log D (sum of logKOW of neutral and ionic molecule, apparent log KOW) as predictors. In addition, the method of Meylan et al. (Environ Toxicol Chem 1999; 18:664-672) for ionizable compounds and a dynamic cell model based on the Fick- Nernst-Planck equation were tested. Moreover, our own regressions for the BCF were established from log KOW and pKa. The bioaccumulation of lipophilic compounds depends mainly on their lipophilicity, and the best predictor is log D. Dissociation, the pH-dependent ion trap, and electrical attraction of cations impact the BCF. Several methods showed acceptable results. The TGD regressions gave good predictions when log( fn ·KOW )o r logD were used as a predictor instead of log KOW. The new regressions to log KOW and pKa performed similarly, with mean errors of approximately 0.4. The method of Meylan et al. did not perform as well. The cell model showed weak results for acids but was among the best methods for bases.
