Bioconcentration Factor Model Research Articles

A Reduced Model for Bioconcentration and Biotransformation of Neutral Organic Compounds in Midge.

A bioconcentration factor (BCF) database and a toxicokinetic model considering only biota-water partitioning and biotransformation were constructed for neutral organic chemicals in midge. The database contained quality-reviewed BCF and toxicokinetic data with variability constrained to within 0.5 to 1 log unit. Diverse conditions in exposure duration, flow set-up, substrate presence, temperature, and taxonomic classification did not translate into substantial variability in BCF, uptake rate constant (k1 ), or depuration rate constant (kT ), and no systematic bias was observed in BCFs derived in unlabeled versus radiolabeled studies. Substance-specific biotransformation rate constants kM were derived by difference between the calculated biota-water partitioning coefficient (KBW ) and experimental BCF for developing a midge biotransformation model. Experimental midge BCF was modeled as BCF = KBW /(1 + kM/ k2 ) with log kM (kM in h-1 ) = -0.37 log KOW - 0.06T (in K) + 18.87 (root mean square error [RMSE] = 0.60), log k1 (k1 in L kgwet.wt-1 h-1 ) = -0.0747 W (body weight in mgwet.wt ) + 2.35 (RMSE = 0.48). The KBW value was estimated using midge biochemical composition and established polyparameter linear free energy relationships, and the diffusive elimination rate constant (k2 ) was computed as k2 = k1 /KBW. The BCF model predicted >85% of BCFs that associated with neutral organic compounds (log KOW = 1.46 - 7.75) to within 1 log-unit error margin and had comparable accuracy similar to amphipod or fish models. A number of outliers and critical limitations of the kM model were identified and examined, and they largely reflected the inherent limitation of difference-derived kM , the lack of chemical diversity, and inadequate temperature variation in existing data. Future modeling efforts can benefit from more BCF and toxicokinetic observations of BCF on structurally diverse chemicals for model training, validation, and diagnosis. Environ Toxicol Chem 2021;40:57-71. © 2020 SETAC.

QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

ABSTRACT The bioconcentration factor (BCF), a key parameter required by the REACH regulation, estimates the tendency for a xenobiotic to concentrate inside living organisms. In silico methods can be valid alternatives to costly data measurements. However, in the industrial context, these theoretical approaches may fail to predict BCF with reasonable accuracy. We analyzed whether models built on public data only have adequate performances when challenged to predict industrial compounds. A new set of 1129 compounds has been collected by merging publicly available datasets. Generative Topographic Mapping was employed to compare this chemical space with a set of new compounds issued from the industry. Some new chemotypes absent in the training set (such as siloxanes) have been detected. A new BCF model has been built using ISIDA (In SIlico design and Data Analysis) fragment descriptors, support vector regression and random forest machine-learning methods. It has been externally validated on: (i) collected data from the literature and (ii) industrial data. The latter also served as benchmark for the freely available tools VEGA, EPISuite, TEST, OPERA. New model performs (RMSE of 0.58 log BCF units) comparably to existing ones but benefits of an extended applicability, covering the industrial set chemical space (78% data coverage).

Accumulation of Heavy Metals in Water and Some Fish Samples from Onuimo River, Imo State, Nigeria

Accumulation of some heavy metals (Cu, Zn, Cd and Cr) was determined in selected fish; Moon fish (Citharinus citharus), Tilapia fish (Oreochronus niloticus), Mud fish (Clarias anguillaris), Cat fish (Clarias gariepinus) and Carp fish (Labeo coulbie) and water samples from Onuimo River in Imo State in Nigeria. Accumulation order of heavy metals in fish samples comprised of Moon fish > Cling fish > Mud fish > Tilapia fish > Cat fish. Bioconcentration factor model used in the present study showed the following order; Moon fish = Carp fish > Cat fish > Mud fish > Tilapia fish and Moon fish = Carp fish > Tilapia >Mud fish > Cat fish for copper and zinc metals respectively. The concentration of cadmium and chromium in water samples were below detection limits of the Atomic Absorption Spectrophotometer (AAS) Machine. Concentrations of Cu, Cd, Zn and Cr were also below permissible limits of some international regulatory bodies.

Bioconcentration model for non-ionic, polar, and ionizable organic compounds in amphipod.

The present study presents a bioconcentration model for non-ionic, polar, and ionizable organic compounds in amphipod based on first-order kinetics. Uptake rate constant k1 is modeled as logk1=10.81logKOW+0.15 (root mean square error [RMSE] = 0.52). Biotransformation rate constant kM is estimated using an existing polyparameter linear free energy relationship model. Respiratory elimination k2 is calculated as modeled k1 over theoretical biota-water partition coefficient Kbiow considering the contributions of lipid, protein, carbohydrate, and water. With negligible contributions of growth and egestion over a typical amphipod bioconcentration experiment, the bioconcentration factor (BCF) is modeled as k1 /(kM + k2 ) (RMSE = 0.68). The proposed model performs well for non-ionic organic compounds (log KOW range = 3.3-7.62) within 1 log-unit error margin. Approximately 12% of the BCFs are underpredicted for polar and ionizable compounds. However, >50% of the estimated k2 values are found to exceed the total depuration rate constants. Analyses suggest that these excessive k2 values and underpredicted BCFs reflect underestimation in Kbiow , which may be improved by incorporating exoskeleton as a relevant partitioning component and refining the membrane-water partitioning model. The immediate needs to build up high-quality experimental kM values, explore the sorptive role of exoskeleton, and investigate the prevalence of k2 overestimation in other bioconcentration models are also identified. The resulting BCF model can support, within its limitations, the ecotoxicological and risk assessment of emerging polar and ionizable organic contaminants in aquatic environments and advance the science of invertebrate bioaccumulation. Environ Toxicol Chem 2018;37:1378-1386. © 2018 SETAC.

Expert QSAR system for predicting the bioconcentration factor under the REACH regulation

Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework.

The discrimination of excess toxicity from baseline effect: Effect of bioconcentration

Toxic ratio TR is a valuable tool in the discrimination of excess toxicity from baseline effect. Although some authors realized that internal effect concentration or critical body residual (CBR) calculated from bioconcentration factor (BCF) should be used in the TR, the effect of BCF on the discrimination of excess toxicity from baseline effect has not been investigated. In this paper, 951 acute toxicity data to fish (LC50) and 1088 BCFs were used to investigate the relationship between TR and BCF. The results showed that some compounds identified as reactive compounds exhibit excess toxicity, but some do not. BCF is closely related to TR and can significantly affect the TR value. The real excess toxicity which is used to identify reactive chemicals from baseline should be based on the toxic ratio of internal effect concentrations, rather than on the ratio of external effect concentrations, TR. The use of LC50 alone to determine TR can result in errors in TR because toxicokinetics (as estimated by the BCF) are ignored. The foundation in the discrimination of excess toxicity from baseline effect is based on the linear relationship between log BCF and hydrophobicity expressed as log KOW. However, log BCF is not linearly related with log KOW for all the compounds. The BCFs with log KOW >7 or <0 are either overestimated or underestimated by the linear baseline BCF model. Parallel lines are observed from calculated log CBR values for baseline and less inert compounds. The log BCF values overestimated or underestimated by log KOW from the baseline BCF model can result in mis-prediction and mis-classification among baseline, less inert and reactive compounds.

Integration of QSAR models for bioconcentration suitable for REACH

QSAR (Quantitative Structure Activity Relationship) models can be a valuable alternative method to replace or reduce animal test required by REACH. In particular, some endpoints such as bioconcentration factor (BCF) are easier to predict and many useful models have been already developed. In this paper we describe how to integrate two popular BCF models to obtain more reliable predictions. In particular, the herein presented integrated model relies on the predictions of two among the most used BCF models (CAESAR and Meylan), together with the Applicability Domain Index (ADI) provided by the software VEGA. Using a set of simple rules, the integrated model selects the most reliable and conservative predictions and discards possible outliers. In this way, for the prediction of the 851 compounds included in the ANTARES BCF dataset, the integrated model discloses a R2 (coefficient of determination) of 0.80, a RMSE (Root Mean Square Error) of 0.61log units and a sensitivity of 76%, with a considerable improvement in respect to the CAESAR (R2=0.63; RMSE=0.84log units; sensitivity 55%) and Meylan (R2=0.66; RMSE=0.77log units; sensitivity 65%) without discarding too many predictions (118 out of 851). Importantly, considering solely the compounds within the new integrated ADI, the R2 increased to 0.92, and the sensitivity to 85%, with a RMSE of 0.44log units. Finally, the use of properly set safety thresholds applied for monitoring the so called “suspicious” compounds, which are those chemicals predicted in proximity of the border normally accepted to discern non-bioaccumulative from bioaccumulative substances, permitted to obtain an integrated model with sensitivity equal to 100%.

Quantitative consensus of bioaccumulation models for integrated testing strategies

A quantitative consensus model based on bioconcentration factor (BCF) predictions obtained from five quantitative structure–activity relationship models was developed for bioaccumulation assessment as an integrated testing approach for waiving. Three categories were considered: non-bioaccumulative, bioaccumulative and very bioaccumulative. Five in silico BCF models were selected and included into a quantitative consensus model by means of the continuous formulation of Bayes' theorem. The discrete likelihoods commonly used in the qualitative Bayesian model were substituted by probability density functions to reduce the loss of information that occurred when continuous BCF values were distributed across the three bioaccumulation categories. Results showed that the continuous Bayesian model yielded the best classification predictions compared not only to the discrete Bayesian model, but also to the individual BCF models. The proposed quantitative consensus model proved to be a suitable approach for integrated testing strategies for continuous endpoints of environmental interest.

Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model

The new development of the bioconcentration factor (BCF) base-line model of Dimitrov et al. [SAR QSAR Environ. Res. 6 (2005), pp. 531–554] is presented. The model applicability domain was expanded by enlarging the training set of the model up to 705 chemicals. The list of chemical-dependent mitigating factors was expanded by including water solubility of chemicals. The original empirical term for estimating ionization of chemicals was mechanistically analysed using two different approaches. In the first one, the ionization potential of chemicals was estimated based on the acid dissociation constant (pKa ). This term was found to be less adequate for inclusion in the ultimate BCF model, due to overestimating ionization of chemicals. The second approach, estimating the ionization as a ratio between distribution and partition coefficients (log P and log D), was found to be more successful. The new ionization term allows modelling of chemicals with both acidic and basic functionalities and chemicals undergoing different degrees of ionization. The significance of the different mitigating factors which can reduce the maximum bioconcentration potential of the chemicals was re-formulated and model parameters re-evaluated.

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.

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

BackgroundBioconcentration 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.ResultsTo 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).ConclusionsThe 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.

Fuzzy filtering for robust bioconcentration factor modelling

This study introduces a fuzzy filtering based technique for rendering robustness to the modelling methods. We consider a case study dealing with the development of a model for predicting the bioconcentration factor (BCF) of chemicals. The conventional neural/fuzzy BCF models, due to the involved uncertainties, may have a poor generalization performance (i.e. poor prediction performance for new chemicals). Our approach to improve the generalization performance of neural/fuzzy BCF models consists of (1) exploiting a fuzzy filter to filter out the uncertainties from the modelling problem, (2) utilizing the information about uncertainties, being provided by the fuzzy filter, for the identification of robust BCF models with an increased generalization performance. The approach has been illustrated with a data set of 511 chemicals (Dimitrov, S., Dimitrova, N., Parkerton, T., Comber, M., Bonnell, M., Mekenyan, O., 2005. Base-line model for identifying the bioaccumulation potential of chemicals. SAR and QSAR in Environmental Research 16 (6), 531–554) taking different types of neural/fuzzy modelling techniques.

Approach for extrapolating in vitro metabolism data to refine bioconcentration factor estimates

National and international chemical management programs are assessing thousands of chemicals for their persistence, bioaccumulative and environmental toxic properties; however, data for evaluating the bioaccumulation potential for fish are limited. Computer based models that account for the uptake and elimination processes that contribute to bioaccumulation may help to meet the need for reliable estimates. One critical elimination process of chemicals is metabolic transformation. It has been suggested that in vitro metabolic transformation tests using fish liver hepatocytes or S9 fractions can provide rapid and cost-effective measurements of fish metabolic potential, which could be used to refine bioconcentration factor (BCF) computer model estimates. Therefore, recent activity has focused on developing in vitro methods to measure metabolic transformation in cellular and subcellular fish liver fractions. A method to extrapolate in vitro test data to the whole body metabolic transformation rates is presented that could be used to refine BCF computer model estimates. This extrapolation approach is based on concepts used to determine the fate and distribution of drugs within the human body which have successfully supported the development of new pharmaceuticals for years. In addition, this approach has already been applied in physiologically-based toxicokinetic models for fish. The validity of the in vitro to in vivo extrapolation is illustrated using the rate of loss of parent chemical measured in two independent in vitro test systems: (1) subcellular enzymatic test using the trout liver S9 fraction, and (2) primary hepatocytes isolated from the common carp. The test chemicals evaluated have high quality in vivo BCF values and a range of log K ow from 3.5 to 6.7. The results show very good agreement between the measured BCF and estimated BCF values when the extrapolated whole body metabolism rates are included, thus suggesting that in vitro biotransformation data could effectively be used to reduce in vivo BCF testing and refine BCF model estimates. However, additional fish physiological data for parameterization and validation for a wider range of chemicals are needed.

A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms

Bioaccumulation assessment is important in the scientific evaluation of risks that chemicals may pose to humans and the environment and is a current focus of regulatory effort. The status of bioaccumulation evaluations for organic chemicals in aquatic systems is reviewed to reduce uncertainty in bioaccumulation measurement, to provide quality data for assessment, and to assist in model development. A review of 392 scientific literature and database sources includes 5317 bioconcentration factor (BCF) and 1656 bioaccumulation factor (BAF) values measured for 842 organic chemicals in 219 aquatic species. A data quality assessment finds that 45% of BCF values are subject to at least one major source of uncertainty and that measurement errors generally result in an underestimation of actual BCF values. A case study of organic chemicals on the Canadian Domestic Substances List indicates that empirical data are available for less than 4% of the chemicals that require evaluation and of these chemicals, 76% have less than three acceptable quality BCF or BAF values. Field BAFs tend to be greater than laboratory BCFs emphasizing the importance of environmental measurement for reliable assessment; however, only 0.2% of current use organic chemicals have BAF measurements. Key parameters influencing uncertainty and variability in BCF and BAF data are discussed using reviewed data and models. A critical evaluation of representative BCF and BAF models in relation to existing measurements and regulatory criteria in Canada indicate the probability of Type II errors, i.e., false negatives or ``misses'', using BCF models for bioaccumulation assessment may be as high as 70.6% depending on the model. Recommendations for the selection of measured and modelled values used in bioaccumulation assessment are provided, and improvements for the science and regulatory criteria are proposed.Key words: bioconcentration, bioconcentration factor, bioaccumulation, bioaccumulation factor, octanol–water partition coefficient, fish.

Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR)

During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment that produce widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSARs for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets.

QSPR Study on the Bioconcentration Factors of Nonionic Organic Compounds in Fish by Characteristic Root Index and Semiempirical Molecular Descriptors

The characteristic root index (CRI) was modeled together with four semiempirical molecular descriptors, namely-energies of the highest occupied and the lowest unoccupied molecular orbital (E(HOMO) and E(LUMO)), heat of formation (DeltaH(f)), and dipole moment (micro)-to predict the fish bioconcentration factor (BCF) of 122 nonionic organic compounds. The best fit equation found by "forward multiple linear regression" showed that the topology based CRI was the most important parameter. The addition of quantum chemical descriptors made only a slight improvement in the predictive capability of the Quantitative Structure-Property Relationship (QSPR) model. The CRI was followed by E(HOMO). A two-parameter equation with a correlation coefficient of r = 0.921 was obtained for a diverse set of nonionic organic chemicals. Statistical robustness of the developed model was validated by modified jackknife tests where random deletion of a class of compounds and specific deletion of a set of compounds were both performed. The predictive accuracy of the proposed model was compared with the commonly used K(ow) model and recently published studies in which BCF models were developed. Particular emphasis has been made to clearly define the boundaries for the application of the alternative developed model as well as the quality of estimates.

Inverse relationship between bioconcentration factor and exposure concentration for metals: Implications for hazard assessment of metals in the aquatic environment

The bioconcentration factor (BCF) and bioaccumulation factor (BAF) are used as the criteria for bioaccumulation in the context of identifying and classifying substances that are hazardous to the aquatic environment. The BCF/BAF criteria, while developed as surrogates for chronic toxicity and/or biomagnification of anthropogenic organic substances, are applied to all substances including metals. This work examines the theoretical and experimental basis for the use of BCF/BAF in the hazard assessment of Zn, Cd, Cu, Pb, Ni, and Ag. As well, BCF/BAFs for Hg (methyl and inorganic forms) and hexachlorobenzene (HCB) were evaluated. The BCF/BAF data for Zn, Cd, Cu, Pb, Ni, and Ag were characterized by extreme variability in mean BCF/BAF values and a clear inverse relationship between BCF/BAF and aqueous exposure. The high variability persisted when even when data were limited to an exposure range where chronic toxicity would be expected. Mean BCF/BAF values for Hg were also variable, but the inverse relationship was equivocal, in contrast with HCB, which conformed to the BCF model. This study illustrates that the BCF/BAF criteria, as currently applied, are inappropriate for the hazard identification and classification of metals. Furthermore, using BCF and BAF data leads to conclusions that are inconsistent with the toxicological data, as values are highest (indicating hazard) at low exposure concentrations and are lowest (indicating no hazard) at high exposure concentrations, where impacts are likely. Bioconcentration and bioaccumulation factors do not distinguish between essential mineral nutrient, normal background metal bioaccumulation, the adaptive capabilities of animals to vary uptake and elimination within the spectrum of exposure regimes, nor the specific ability to sequester, detoxify, and store internalized metal from metal uptake that results in adverse effect. An alternative to BCF, the accumulation factor (ACF), for metals was assessed and, while providing an improvement, it did not provide a complete solution. A bioaccumulation criterion for the hazard identification of metals is required, and work directed at linking chronic toxicity and bioaccumulation may provide some solutions.