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Bioconcentration Factor

The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Emilio Benfenati – 1st expert on this subject based on the ideXlab platform

  • comparison between Bioconcentration Factor bcf data provided by industry to the european chemicals agency echa and data derived from qsar models
    Environmental Research, 2015
    Co-Authors: Maria I Petoumenou, Fabiola Pizzo, Josep Cester, Alberto Fernandez, Emilio Benfenati

    Abstract:

    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.

  • evaluation and comparison of benchmark qsar models to predict a relevant reach endpoint the Bioconcentration Factor bcf
    Environmental Research, 2015
    Co-Authors: Andrea Gissi, Orazio Nicolotti, Domenico Gadaleta, Anna Lombardo, Alessandra Roncaglioni, Giuseppe Felice Mangiatordi, Emilio Benfenati

    Abstract:

    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.

  • An alternative QSAR-based approach for predicting the Bioconcentration Factor for regulatory purposes
    Altex, 2014
    Co-Authors: Andrea Gissi, Domenico Gadaleta, M. Floris, Stefania Olla, Angelo Carotti, Ettore Novellino, Emilio Benfenati, Orazio Nicolotti

    Abstract:

    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.

Harald J Geyer – 2nd expert on this subject based on the ideXlab platform

  • 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, 1997
    Co-Authors: Xiulin Wang, Weijun Yu, Harald J Geyer

    Abstract:

    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 – 3rd expert on this subject based on the ideXlab platform

  • evaluation and comparison of benchmark qsar models to predict a relevant reach endpoint the Bioconcentration Factor bcf
    Environmental Research, 2015
    Co-Authors: Andrea Gissi, Orazio Nicolotti, Domenico Gadaleta, Anna Lombardo, Alessandra Roncaglioni, Giuseppe Felice Mangiatordi, Emilio Benfenati

    Abstract:

    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.

  • An alternative QSAR-based approach for predicting the Bioconcentration Factor for regulatory purposes
    Altex, 2014
    Co-Authors: Andrea Gissi, Domenico Gadaleta, M. Floris, Stefania Olla, Angelo Carotti, Ettore Novellino, Emilio Benfenati, Orazio Nicolotti

    Abstract:

    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.

  • an alternative qsar based approach for predicting the Bioconcentration Factor for regulatory purposes
    ALTEX-Alternatives to Animal Experimentation, 2014
    Co-Authors: Andrea Gissi, Domenico Gadaleta, M. Floris, Stefania Olla, Angelo Carotti, Ettore Novellino, Emilio Benfenati, Orazio Nicolotti

    Abstract:

    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.