A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure–activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

Fragment constant method for prediction of fish bioconcentration factors of non-polar chemicals

A fragment constant method for prediction of fish bioconcentration factor (BCF) was established based on experimental BCF values for 80 non-polar chemicals from nine classes. The model was evaluated using coefficients of determination and mean residuals, which are 0.995 and 0.1836, respectively. Jackknife tests were applied to examine the robustness of the prediction model on a class-by-class basis.     

The influence of gill and liver metabolism on the predicted bioconcentration of three pharmaceuticals in fish

The potential for xenobiotic compounds to bioconcentrate is typically expressed through the bioconcentration factor (BCF), which has gained increased regulatory significance over the past decade. Due to the expense of in vivo bioconcentration studies and the growing regulatory need to assess bioconcentration potential, BCF is often calculated via single-compartment models, using K(OW) as the primary input. Recent efforts to refine BCF models have focused on physiological factors, including the ability of the organism to eliminate the compound through metabolic transformation. This study looks at the ability of in vitro biotransformation assays using S9 fractions to provide an indication of metabolic potential. Given the importance of the fish gill and liver in metabolic transformation, the metabolic loss of ibuprofen, norethindrone and propranolol was measured using rainbow trout (Oncorhynchus mykiss) and channel catfish (Ictalurus punctatus) gill and liver S9 fractions. Metabolic transformation rates (k(M)) were calculated and integrated into a refined BCF model. A significant difference was noted between BCF solely based on K(OW) and BCF including k(M). These studies indicate that the inclusion of k(M) in BCF models can bring predicted bioconcentration estimates closer to in vivo values.     

No enhancement in bioconcentration of organic contaminants by low levels of DOM.

The aim of the present work was to systematically study the effect of low concentrations of dissolved organic matter (DOM) on the bioconcentration of organic contaminants, in order to show whether the phenomenon of enhanced bioconcentration factors (BCFs), that has been reported in the literature, is generally found at low levels of DOM or if BCF enhancements are more likely due to a random scatter in the experimental data. The first part of the study tested the hypothesis that low levels of DOM affect the uptake kinetics of organic contaminants, leading to transient enhancements of BCFs, relative to DOM-free controls, which could have been reported as BCF enhancements in short-term studies. We found that the presence of low concentrations of two different types of DOM consistently decreased the bioconcentration of benzo[a]pyrene (BaP) in the water flea Daphnia magna at all exposure times (1-24 h), and that no transient BCF enhancements occurred. The second part of the study systematically investigated if low concentrations of DOM from a wide range of different aquatic systems can cause enhancements in the bioconcentration of organic contaminants. Water fleas were exposed to combinations of four different organic contaminants (BaP, tetrachlorobiphenyl, pentachlorophenol and naphthalene) with low concentrations of 12 different types of DOM that had been collected from various regions throughout Europe. In several of the DOM treatments, we found mean BCFs being higher than mean BCFs in the controls (especially for naphthalene). This shows that the experimental setup used in this study (and similarly in previous studies) can produce seeming BCF enhancements at low concentrations of DOM. However, statistical analyses showed that treatment means were not significantly different from control means. Thus, this systematic study suggests that the BCF enhancements that have been reported in the literature are more likely the result of random, experimental variations than the result of a systematic enhancement of bioconcentration.     

Influence of dispersants on bioconcentration factors of seven organic compounds with different lipophilicities and structures

Seven compounds with different lipophilicities and structures—1,3,5-trichlorobenzene, pentachlorobenzene, acenaphthylene, 1,4-dimethyl-2-(1-methylphenyl)benzene, 4-ethylbiphenyl, 4,4′-dibromobiphenyl, and 1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane—were subjected to bioconcentration tests in carp at concentrations below the water solubilities of the compounds in the presence or absence of a dispersant (either an organic solvent or a surfactant). The bioconcentration factors (BCFs) of the compounds were on the order of 10²–10⁴. The BCF values remained in the range of 15–49% for all the compounds, whether or not a dispersant was present, i.e., the BCF values in the presence of an organic solvent or a surfactant at a concentration below the critical micelle concentration were not significantly smaller than the BCF values in the absence of the solvent or surfactant. This result indicates that the dispersants had no influence on the evaluation of the bioconcentration potential of these test substances.     

Calculation of bioconcentration factors from kinetic data by non-linear iterative least-squares regression analysis using a programmable minicalculator

A program to fit theoretically modelled uptake and clearance periods of the bioconcentration of chemicals to experimental data using a programmable pocket calculator with thermal printer is reported. The bioconcentration factor (BCF) is calculated from the ratio of uptake and clearance rate constants. Because of the impossibility to linearize the kinetic equations an iterative nonlinear GAUSS-NEWTON least-squares fit has been applied for the pocket calculator programm ACCUTI-59, which calculates from a set of start parameters the best values for uptake and clearance rate constants, BCF, and the standard errors as well as the sum of squared deviations. A listing of the program is given.     

Unique bioconcentration characteristics of new aryl fluoroalkyl ethers in common carp (Cyprinus carpio)

The bioconcentration factors (BCFs) of seven new aryl fluoroalkyl ethers--four bis-4-tetrafluoroethoxyphenyl-type (bis-type) compounds and three mono-4-tetrafluoroethoxyphenyl-type (mono-type) compounds--were obtained by bioconcentration tests using common carp. The BCFs of 4 of the 7 ethers were higher than 5000, indicating their high bioconcentration potential. The bioconcentration characteristics of the bis-type compounds were different from those of the mono-type compounds and non-fluoro diphenylmethanes with a similar skeleton structure to the bis-type compounds, in taking longer to reach a plateau and having a slower elimination rate and in their distribution patterns in the fish body. The BCF of 1 bis-type compound was much higher than the value predicted by an accepted correlation equation between BCF and P(ow). In addition, the logP(ow) of the bis-type compounds calculated by commercially available computer software was remarkably different from that measured.     

Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models.

Models based on molecular connectivity index (MCI) and fragment constant (FC) method were developed for prediction of the bioconcentration factor (BCF) for polychlorobiphenyls (PCBs) in fish. The mean residuals for the MCI and FC models were 0.195 and 0.223 log units, respectively. The two models were then compared in terms of their mean residuals. In addition to the chlorine atom substitution number, other important structural features exhibiting a significant influence on the BCFs of PCB congeners were discussed and incorporated to the models. These features include the degree of the ortho-substitution, the presence of chlorine pairs in the three- and five- positions, and the crowding of chlorine atoms on the phenyl ring.