Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish

Background

Bioconcentration factor (BCF) describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish.

Results

To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development). The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R² and Q² were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors) dropped considerably (less than 0.6% of chemicals).

Conclusions

The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF?) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r ²?=?0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(log?BSF? ^Obs???log?BCF? ^Calc)?≤?0.75)) for 59 chemicals included within the model applicability domain was 80%.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and K OW for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

Estimation and Prediction of Bioconcentration Factors of Nonionic Organic Chemicals in Fish by Electrotopological State Indices and Structural Parameter

Based on the characteristics of atom types, Hall＇s electrotopological state indices （En） are calculated for 165 nonionic organic compounds. On the basis of the characteristics of substituent and conjugated matrix, a novel molecular structure parameter （G） is defined and calculated for 165 molecules in this paper. En and G show good structural selectivity for organic molecules. G, a satisfactory relationship between bioconcentration factor （BCF） and En, is expressed as： 1gBCF = -0.283 ＋ 1.246G ＋ 0.079E42 ＋ 0.351E9- 0.063E17 （n＇ = 122, R = 0.967, F = 425.636, s = 0.394）, which could provide estimation and prediction for the lgBCF of nonionic organic chemicals. Furthermore, the model is examined to validate overall robustness with Jackknife tests, and the independent variables in model do not exist cross correlation with VIF. All these regression results show that the new parameter G and electrotopological state index have good rationality and efficiency. It is concluded that the En and G will be used widely in quantitative structure-property/activity relationship （QSPR/QSAR） research.     

Practical analysis of high-purity chemicals-I Preparation and characterization of high-purity EDTA

Advanced laboratories have requirements for high-purity chemicals with less than 500 ppm total impurities (ultrapure chemicals) and with broad analytical definition of each lot. Some economically feasible approaches to the practical analysis of such chemicals, both inorganic and organic, are delineated. Compounds used in the study of lunar samples and in other advanced programmes are noted. EDTA, as the free add, has been prepared by dissolution in water with base and precipitation by addition of add. The product has been broadly characterized. Precision assay is achieved by weight titrimetry, potentiometrically as a triprotic add and photometrically as a chelatmg agent. Other tests applied include elemental analysis, ash, loss on drying, particulatc matter, and tests for nitrilotriacetate, arsenic, and chloride. Boron, silicon, and trace metals are determined by emission spectrography. Many of the procedures are applicable to other high-purity organic chemicals.     

Estimation of soil organic partition coefficients: from retention factors measured by soil column chromatography with water as eluent

The retention factors (k) of 104 hydrophobic organic chemicals (HOCs) were measured in soil column chromatography (SCC) over columns filled with three naturally occurring reference soils and eluted with Milli-Q water. A novel method for the estimation of soil organic partition coefficient (Koc) was developed based on correlations with k in soil/water systems. Strong log Koc versus log k correlations (r>0.96) were found. The estimated Koc values were in accordance with the literature values with a maximum deviation of less than 0.4 log units. All estimated Koc values from three soils were consistent with each other. The SCC approach is promising for fast screening of a large number of chemicals in their environmental applications.     

脂肪醇气相色谱保留指数与结构的相关性研究

在分子图的邻接矩阵基础上,构建了一个化合物均价连接性指数mL,mL=∑(Ai·Aj·Ak…)-0.5,其中一阶指数1L及定位基参数β与25种脂肪醇在6种固定相(SE-30,OV-3,OV-7,OV-11,OV-17和OV-25)上的气相色谱保留指数I显著相关,相关系数均大于0.98。所建定量结 构-保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了脂肪醇在不同固定相上气相色谱保留指数的变化规律。     

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

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.     

Analytical challenges and the development of biomarkers to measure and to monitor the effects of ocean acidification

Changing ocean-carbonate chemistry caused by oceanic uptake of anthropogenic atmospheric carbon dioxide leads to the formation of carbonic acid, thus lowering the pH of the sea with predictions of a decrease from current levels at 8.15 to 7.82 by the end of the century. The exact measurement of subtle pH changes in seawater over time presents significant analytical challenges, as the equilibrium constants are governed by water temperature and pressure, salinity effects, and the existence of other ionic species in seawater.Here, we review these challenges and how pH also affects dissolved inorganic and organic chemicals that affect biological systems. This includes toxic compounds (xenobiotics) as well as chemicals that are beneficial for marine organisms, such as the chemical signals (i.e. pheromones) that are utilized to coordinate animal behavior. We review how combining analytical, molecular and biochemical tools can lead to the development of biosensors to detect pH effects to enable predictive modeling of the ecological consequences of ocean acidification.