Abstract
Quantification of chemical toxicity continues to be generally based on measured external concentrations. Yet, internal chemical concentrations have been suggested to be a more suitable parameter. To better understand the relationship between the external and internal concentrations of chemicals in fish, and to quantify internal concentrations, we compared three toxicokinetic (TK) models with each other and with literature data of measured concentrations of 39 chemicals. Two one-compartment models, together with the physiologically based toxicokinetic (PBTK) model, in which we improved the treatment of lipids, were used to predict concentrations of organic chemicals in two fish species: rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas). All models predicted the measured internal concentrations in fish within 1 order of magnitude for at least 68% of the chemicals. Furthermore, the PBTK model outperformed the one-compartment models with respect to simulating chemical concentrations in the whole body (at least 88% of internal concentrations were predicted within 1 order of magnitude using the PBTK model). All the models can be used to predict concentrations in different fish species without additional experiments. However, further development of TK models is required for polar, ionizable, and easily biotransformed compounds.
Introduction
Toxicokinetic Models in Risk Assessment
Environmental regulations require comprehensive testing and risk assessment before a chemical can be approved for use. In ecological risk assessment of chemicals in water, fish play a very important role, being the only vertebrate representative of freshwater systems.(1) Quantification of chemical toxicity is generally based on measurements of external exposure; however, in order to understand, interpret, and extrapolate toxicological effects, internal concentrations of chemicals are more suitable.(2, 3) For this reason, we need to understand the relationship between the external and internal concentration of chemicals in fish. Further, in silico (model) predictions of concentrations in fish (i.e., bioconcentration) could reduce or replace the need for in vivo (animal) experiments which are costly and involve large numbers of fish.
Toxicokinetics captures information about uptake, distribution, biotransformation, and elimination of a toxicant in an organism, and is important in risk assessment because a chemical needs to enter the organism and reach the site of action in order to elicit an effect.(4-6) Toxicokinetic models, when combined with toxicodynamic models, can predict toxic effects on organisms. In addition, they can be applied to time-variable concentrations, a wide-range of chemicals, and to extrapolation between different species and from in vitro to organism scale.(7-10) Provided that the necessary physiological parameters are known, generally, two groups of TK models can be distinguished: models based on a one-compartment assumption, according to which the chemical concentration is the same throughout the organism, and multicompartment models, which assume that chemical concentrations may differ among various organs and tissues. Thus, the multicompartment approach, apart from chemical uptake, biotransformation, and elimination, also describes the movement of chemicals among various compartments that usually represent different organs.
A comparison of toxicokinetic models is required so that the most suitable model can be chosen for a given question or condition. A comparison of different model structures was presented by Landrum and co-workers(11) who described advantages and disadvantages of equilibrium and kinetic models. Also, Mackay and Fraser(12) presented a review of bioaccumulation models in which they compared the structure of empirical and mechanistic approaches. Both these reviews compared different models based on their underlying assumptions and model structures; they did not compare model predictions with measured data. To our knowledge there is no study using a wide scope of chemicals to test model performance on independent data.
Problem Formulation
One-compartment models can be perceived as simple, because they require only a few physiological parameters and simulate one compartment only. Consequently they can only be used to estimate a chemical concentration in the whole body of an organism. On the other hand, a multicompartment model, e.g., the physiologically based toxicokinetic (PBTK) model developed for fish by Nichols and co-workers,(13) may be viewed as more complex than one-compartment models because it requires more physiological data and simulates multiple compartments. This model is generally used when a chemical’s concentration in a specific organ or tissue plays an important role, e.g., when the tissue or organ is the dominant site of action. This raises the question of whether the PBTK model is a suitable model for predicting chemical concentration in both organism tissues and whole body. If so, another important issue is whether it is worth using the more complex PBTK model with many parameters to predict whole body chemical concentrations, or whether the simpler one-compartment approach with only a few parameters suffices. Thus, we aim to quantify model performance in predicting fish internal concentrations in order to explain model differences and to guide model selection.
Study Overview
In the present study, we compared predictions of one PBTK and two one-compartment models with measured concentrations of organic chemicals in rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas), available from the literature and databases. Differences between models were explained based on a sensitivity analysis of each approach. In addition, we improved the treatment of lipids in the PBTK model.
Materials and Methods
Method
Two one-compartment models (A(14) and B(15)) and the PBTK(13) model were used to simulate internal concentrations of chemicals in fish. Only respiratory uptake routes were considered for both model types and they were described by mass-balance differential equations. None of the models included chemical biotransformation in fish. Chemical concentrations in water were used as model inputs in order to calculate chemical concentrations in the whole fish body. Tissue-specific concentrations were not taken into account, even for the PBTK approach, because a comparison with one-compartment models or whole body residue data is not meaningful.
Origin of Measured Internal Concentrations
Measured internal concentrations of organic chemicals in rainbow trout and fathead minnow were found using the TOXRES Database(16) and by searching the peer-reviewed literature in the Scopus online database (see details about search method in Supporting Information). We used only references with exposure via water and containing all required data, i.e., fish weight, chemical concentration in water, measured internal concentration, exposure time, water temperature, and dissolved oxygen concentration in water (SI Tables S1–S3).
In total, measured internal concentrations for 23 different organic chemicals (39 different chemical concentrations in water) for rainbow trout and for 24 different chemicals (68 different chemical concentrations in water) for the fathead minnow were taken from the TOXRES Database and studies identified in the Scopus reference database (Tables S2–S3). For eight chemicals, internal concentrations were available for both fish species.
Internal concentrations of phenol and 2,4,5-trichlorophenol in the fathead minnow(17) had already been used in the original development of model B for calibration.(15) For this reason, we show these two chemicals in graphs; however, we did not take them into consideration in the statistical model evaluation. To our knowledge, none of the other internal concentrations presented have been used to calibrate any of the models studied here.
Model Design, Formulation, and Description
One-Compartment Approach
We chose two one-compartment approaches to predict internal concentrations of organic chemicals in fish. Common to both models is that they use octanol–water partition coefficients to quantify partitioning, and that they assume that the chemical is homogeneously circulated within the organism.(18) According to the one-compartment concept, a chemical which enters the fish is distributed instantaneously and equally. This concept can be described with the following equation:(11, 15, 19)
(1)where Cint(t) is the internal chemical concentration (amount × mass–1), Cw(t) is the chemical concentration in the water (amount × volume–1), kin is the uptake rate constant (volume × mass–1 × time–1), and kout is the elimination rate constant (time–1).
(1)where Cint(t) is the internal chemical concentration (amount × mass–1), Cw(t) is the chemical concentration in the water (amount × volume–1), kin is the uptake rate constant (volume × mass–1 × time–1), and kout is the elimination rate constant (time–1).The first model, herein referred to as the one-compartment model A, was developed to predict the bioaccumulation processes of organic chemicals in aquatic ecosystems with the aim of providing data on site-specific toxicant concentrations and associated bioconcentration, bioaccumulation, and biota–sediment accumulation factors in organisms of aquatic food webs.(14) Thus, this model uses a small number of chemical, site-specific, and organism parameters. According to Arnot and Gobas,(14) it is possible to describe the exchange of nonionic organic chemicals between the organism and its environment using a single equation for various aquatic animals.
The second toxicokinetic approach, referred to as one-compartment model B, describes the accumulation kinetics of organic chemicals as a function of the octanol–water partition coefficient (Kow), as well as the lipid content, weight, and trophic level of the species.(15) This model is driven by both species and chemical properties and its goal was to explain differences in accumulation between various substances and between species. According to Hendriks et al.,(15) this model may be used in risk assessment, both for predicting the equilibrium accumulation potential and for estimating nonequilibrium kinetics.
Physiologically Based Toxicokinetic Model (PBTK)
The physiologically based multicompartment model for fish(8, 9, 13) was used and further developed by incorporating a relationship between lipid fractions in the whole body and the volume of fat compartment (SI eqs S20–S22). That relationship was suggested by Nichols and colleagues(20) who assumed that the lipid content of lean tissue (consisting of all tissues except adipose fat) is independent of whole body lipid content. Support for this assumption is provided by examining blood lipid content values reported by Bertelsen et al.(21) for several fish species. In their study, the extreme case was represented by channel catfish, which tend to have a low whole-body lipid content. However, despite the “lean” nature of these animals, the blood lipid content in catfish was essentially identical to that of trout. Thus we set the lipid content of the lean tissues to the values derived by Bertelsen et al.(21) while the volume of the adipose fat compartment was adjusted to achieve the required whole body lipid content. Note, that this simplification will not work for the extreme situation when the whole body lipid content is lower than the assumed lipid content of lean tissues. According to SI eq S22, the volume of the fat compartment would then be below zero. However, in the experiments modeled here, this simplified relationship is sufficient.
The PBTK model for rainbow trout takes into account five different compartments (liver, kidney, fat, richly perfused tissues, and poorly perfused tissues). Due to the lack of data characterizing the kidney, a four-compartment (liver, fat, richly perfused tissues, and poorly perfused tissues) PBTK model was created for the fathead minnow. The model assumes that all parts of the whole body belong to one of the compartments, so the sum of the weight or volume of all compartments is equal to the weight or volume of the whole organism. The amount of the chemical in each compartment is calculated based on eq 2,(13) and the total amount of the chemical can be used for calculating the internal concentration in the whole fish body (eq 3).
(2)where Ai(t) is the amount of chemical in compartment i (amount), Qi is the arterial blood flow to compartment i (volume × time–1), Cart(t) is the chemical concentration in arterial blood (amount × volume–1), Cvi(t) is the chemical concentration in venous blood after compartment i (amount × volume–1), and t is time.
(3)where Cint(t) is the internal chemical concentration (amount × mass–1), ∑Ai(t) is the amount of chemical in all compartments (amount), and BW is the body wet weight (mass).
(2)where Ai(t) is the amount of chemical in compartment i (amount), Qi is the arterial blood flow to compartment i (volume × time–1), Cart(t) is the chemical concentration in arterial blood (amount × volume–1), Cvi(t) is the chemical concentration in venous blood after compartment i (amount × volume–1), and t is time.(3)where Cint(t) is the internal chemical concentration (amount × mass–1), ∑Ai(t) is the amount of chemical in all compartments (amount), and BW is the body wet weight (mass).Detailed model descriptions and parameters for running the models are presented in SI.
Model Calibration
In this study, models were used as calibrated by their original authors. The one-compartment model A was calibrated with measured field bioaccumulation factors, while the one-compartment model B was calibrated with measured values of uptake and elimination rates from laboratory experiments. In general, the PBTK model does not have to be calibrated as it is based on physiological parameters and processes that can be measured directly. However, some parts of this model, e.g., partition coefficients between tissues and blood were calibrated separately by their original authors (all model equations and parameters are available in the SI).
Model Sensitivity Analysis
In the model sensitivity analysis, we took into account the impact of changes of four parameters (log KOW, fish weight, water temperature, and lipid content in fish) on model predictions. The minimum and maximum values of the possible parameter range for each fish species were taken from the literature. The sensitivity analysis was carried out by varying each parameter separately (one at a time sensitivity analysis, 99 runs each).
Model Implementation
All simulations and sensitivity analyses were carried out using ModelMaker software, version 4.0, developed and published by Cherwell Scientific Ltd. (Oxford, UK). In addition, all calculations were also checked in Mathcad 14 (Parametric Technology Corporation, Needham, MA).
Quantification of Model Performance
To evaluate and compare the TK models, we have used the following three methods:
| (i) | Coefficient of determination (r2), here, refers to the square of the correlation coefficient between measured and modeled values, and quantifies the fraction of the variability in the data that is explained by the model (eq 4, based on the FOCUS guidance document(22)). The closer r2 is to 1, the better the model predicts the measured internal concentrations. | ||||
| (ii) | Factor_10 (or Factor_5, see eq 5) quantifies internal chemical concentrations that are predicted with differences between measured and predicted values equal to or smaller than 1 order of magnitude (or five times). This can be seen as a practitioners view of model performance. If Factor_10 or Factor_5 is closer to 100%, the model is in better agreement with the measured internal concentrations. | ||||
| (iii) | General distance (GD) evaluates the model accuracy (i.e., agreement with absolute values of measured data). This approach characterizes over- or under-prediction of measured internal concentrations by the model by quantifying the distance between measured and predicted values (eqs 6 and 7). The closer GD is to 1, the better is the model in agreement with the measured internal concentrations. | ||||
Results and Discussion
Rainbow Trout
For rainbow trout, differences between the TK models were small (Figure 1.1 and Table 1) and r2 values for the one-compartment models A and B and the PBTK model were 0.76, 0.80, and 0.78, respectively. However, overall, the distance between the predicted and measured values of internal concentrations was the smallest for the PBTK model (GD was equal to 3.7, 3.73, and 3.54 for the one-compartment A, the one-compartment B, and the PBTK model, respectively).
Figure 1. Comparison of predicted internal concentrations of chemicals (based on one-compartment A [○], one-compartment B [+], and PBTK [⧫] models) and measured internal concentrations in (1.1) rainbow trout and (1.2) fathead minnow; circles: “outlier” chemicals explained in text; green: hexachlorobenzene (see also black and white graph in SI Figure S2).
Table 1. Statistical Analysis of TK Models for Both Fishes (Rainbow Trout: 23 Chemicals, 39 Data Points; Fathead Minnow: 24 Chemicals, 68 Data Points)a
| rainbow trout | fathead minnow | |||||
|---|---|---|---|---|---|---|
| one-compartment | one-compartment | |||||
| statistical method | A | B | PBTK | A | B | PBTK |
| coefficient of determination (r2), ― | 0.76 | 0.80 | 0.78 | 0.64 | 0.77 | 0.73 |
| factor_10, % | 90 | 95 | 95 | 68 | 76 | 88 |
| factor_5, % | 85 | 82 | 77 | 62 | 61 | 80 |
| general distance (GD), ― | 3.7 | 3.73 | 3.54 | 29 | 25.3 | 16.2 |
a
Italics indicate the best agreement between the model and measured data.
Fathead Minnow
Not all chemicals’ internal concentrations in fathead minnow were predicted well using the TK models (Figure 1.2). For one internal concentration of hexachlorobenzene, the PBTK model underestimated the measured value by a factor of over 20 (predicted: 0.014 μg/g, observed: 0.3014 μg/g). However, in the same experiment, various chemical concentrations were taken into account (green points in Figure 1.2), and only for the lowest concentration, the PBTK model underestimated results by such a large margin.
Other chemicals for which measured internal concentrations were underestimated by the TK models by more than a factor of 10 were phenol, 2,4,5-trichlorophenol (for the PBTK model), and 4-nitrophenol. A possible explanation is that these are polar organic compounds, whose partitioning behavior cannot be well characterized by means of octanol–water partition coefficients.(23) Yet, an internal concentration of the polar compound, 4-nitrophenol, was also predicted in rainbow trout (SI Table S1) but without any underestimation (Figure 1.1). It was noticed by Call and co-workers(17) that phenolic compounds are much more bioconcentrated in fathead minnow than in rainbow trout. In general, the bioconcentration of weak acids, such as 4-nitrophenol, can differ due to water pH.(24) However, in the experiments considered here, the water pH values were not sufficiently different to account for the difference in bioconcentration between species, but we cannot totally exclude its influence as pH might differ also at the actual site of uptake (e.g., gill surface).
In the present study, apart from phenol, 2,4,5-trichlorophenol, and 4-nitrophenol, only C12LAS (sodium dodecylbenzene sulfonate) can be classified as a polar compound. However, unlike the above-mentioned polar toxicants, measured internal concentrations of this chemical were overestimated by all three TK approaches. C12LAS is a surfactant, which is amphiphilic in nature, with a polar head and a nonpolar chain, and if the system is not constantly mixed, this chemical tends to concentrate at interphases (e.g., water/air; water/plastic).(25) For this reason, there are problems in estimating how much C12LAS in water is available for an organism (bioavailability), which may result in very low measurements of internal concentrations in comparison with apparent concentrations in water.
For hexachlorocyclopentadiene, internal concentrations were also overestimated by the TK models. Based on the octanol–water partition coefficient of hexachlorocyclopentadiene (log KOW = 5.04; Table S3), this chemical should bioconcentrate. According to EPI Suite, the calculated bioconcentration factor (BCF) is equal to 3606 while with EUSES, a BCF of 3800 was estimated;(26) however, experiments carried out by Podowski and colleagues(27) have shown that this chemical is hardly bioconcentrated in fish, likely due to biotransformation. According to Spehar and co-workers,(28) the BCF of hexachlorocyclopentadiene for fathead minnow is 11. Thus, predicting internal concentrations of hexachlorocyclopentadiene, without taking its biotransformation into account, causes overestimation by TK models.
According to the GD (Table 1) for fathead minnow, all three approaches overestimated internal concentrations by more than 1 order of magnitude on average. Moreover, the correlations of model predictions and data were lower than for rainbow trout (r2 for the one-compartment A, the one-compartment B, and the PBTK models were equal to 0.64, 0.77, and 0.73, respectively). The difference in agreement between the modeled and measured data, indicated by factor_10 (equal to 68–88%) and GD (16.2–29) methods, can be explained by the “outlier” group of chemicals (described above) which was included in the calculation of both statistical methods. Not many chemicals belong to this “outlier” group (which influences the factor_10). For most of these, however, TK models over- or underestimated measured internal concentrations much more than by a factor of 10 (which influences the GD).
Comparison of Rainbow Trout and Fathead Minnow Results
Differences between results for rainbow trout and fathead minnow can be caused by several factors. First, different data sets were used for each fish species. From the “outlier” group of chemicals (chemicals for which internal concentration was over- or underestimated by more than a factor of 10—discussed above) for the fathead minnow, only hexachlorobenzene and 4-nitrophenol were also used in the TK models for rainbow trout. However, it was decided not to compare predicted internal concentrations of 4-nitrophenol in both fishes, due to the presumed impact of its polar nature and water pH on bioavailability (see Fathead Minnow section). For the other chemicals from this group (i.e., phenol, 2,4,5-trichlorophenol, hexachlorocyclopentadiene, and C12LAS), no data on internal concentrations in rainbow trout were available. The comparison of TK models was made for chemicals that were used in both fishes (Tables S2 and S3 and Figure 2).
Figure 2. Comparison of TK models: One-compartment A (○), one-compartment B (+), and PBTK (⧫), for the same chemicals for rainbow trout and fathead minnow (8 chemicals, 45 data points); blue: results for rainbow trout, green: results for fathead minnow (see also black and white graph in SI Figure S3).
According to Figure 2, results demonstrate a clear relationship between predicted and measured internal concentrations for all models and for both fishes. This indicates that the difference between the statistical results for rainbow trout and fathead minnow was caused mainly by the “outlier” group of chemicals used in predicting internal concentrations in fathead minnow. However, we noticed that both the values of the coefficients of determination and the factor_5 are now much lower for rainbow trout than for fathead minnow (Table 2). Additionally, the GD between predicted and measured internal concentrations increased for rainbow trout in relation to GD for all chemicals in this species (Table 1), while for fathead minnow, the GD is now much lower than it was for all chemicals. The difference between the results for both fish species might be due to two main reasons. The first of them results from the fact that five (out of 12) of the measured internal concentrations used in rainbow trout came from the same study,(29) and all measured internal concentrations taken from this reference were underestimated by all TK models used. The second reason is that different exposure times were used in the experiments. Generally, fathead minnow were not exposed to chemicals for short durations (the shortest exposure time: 28 days; average: 32 days) compared to rainbow trout (the shortest exposure time: < 3 h; average: 70 days). Biotransformation may modify internal concentrations differently under short or long exposure times. In addition, if steady-state conditions occur, the difference between TK models might be smaller than under non-steady-state conditions.
Table 2. Statistical Analysis of TK Models for Selected Chemicals in Both Fishes (8 Chemicals, Rainbow Trout: 12 Data Points; Fathead Minnow: 33 Data Points)a
| rainbow trout | fathead minnow | |||||
|---|---|---|---|---|---|---|
| one-compartment | one-compartment | |||||
| statistical method | A | B | PBTK | A | B | PBTK |
| coefficient of determination (r2), ― | 0.26 | 0.60 | 0.64 | 0.85 | 0.85 | 0.76 |
| factor_10, % | 85 | 85 | 100 | 81 | 86 | 97 |
| factor_5, % | 69 | 77 | 69 | 81 | 78 | 89 |
| general distance (GD), ― | 5.03 | 4.67 | 4.53 | 4.8 | 3.8 | 3.5 |
a
Italics indicate the best agreement between the model and measured data.
Model Sensitivity Analysis
Differences between TK model predictions in relation to exposure times are shown in Figure 3. Model-based predictions differ during short-term exposure (shorter than 10 days, Figure 3.1) more than during long-term exposure (Figure 3.1 and 3.2). This disparity between approaches can be explained by a sensitivity analysis of the model. In Figure 4, the impact of log KOW and lipid fractions on model performance is presented. Sensitivity analysis of other model parameters is shown in SI. Figure 4.1 shows that internal concentration increases with an increase of the lipid fraction in the organism, and that the difference between both one-compartment models is very small. In addition, according to these approaches, chemical internal concentrations in rainbow trout after 4 days are higher for low lipid fractions (<5%) and lower for higher lipid fractions than is the case in the PBTK model. However, after longer exposure (400 days, Figure 4.3), the PBTK model predicts lower internal concentrations than one-compartment models for all lipid fractions simulated. That observation might be caused by reaching steady-state conditions with the PBTK model much faster than with one-compartment models. There is no or a smaller difference between the PBTK model after 4 and 400 days than between one-compartment models after 4 and 400 days. For the parameter values used in the sensitivity analysis, according to the PBTK model, the steady-state conditions were almost reached within 4 days for rainbow trout (with lipid content below 5%). For fathead minnow (Figure 4.2 and 4.4), the PBTK model predicts lower internal concentrations than the one-compartment models for all lipid fractions simulated (at both time points, 4 and 400 days). This observation results from achieving steady-state conditions in fathead minnow earlier than in rainbow trout with all TK models, which is due to different fish parameters and environmental conditions. Generally, fathead minnow are smaller than rainbow trout and they live in warmer water, which also influences the velocity of chemical uptake and elimination processes. In addition, after 400 days, according to both one-compartment models, internal concentrations are almost the same in rainbow trout and in fathead minnow (for the same range of lipid fraction) while they differ during shorter exposure periods (i.e., non-steady-state conditions). The PBTK model predicts slightly different internal concentrations in both fishes at both time points, which is caused by different physiological parameters of both species. Thus, during steady-state conditions, parameters such as body weight or water temperature do not have an impact on predictions while during shorter exposure periods they strongly influence results.
Figure 3. Comparison of toxicokinetic models depending on exposure time; 3.1: rainbow trout, 3.2: fathead minnow.
Figure 4. TK model predictions for rainbow trout and fathead minnow: 4.1, 4.2: internal concentrations for various lipid fractions (after 4 d); 4.3, 4.4: internal concentrations for various lipid fractions (after 400 d); 4.5, 4.6: internal concentrations in both fish species for various log KOW (after 4 d). Simulation parameters: chemical concentration in inspired water: 100 μg/L; log KOW = 4.4; water temperature: 12.8 °C for rainbow trout and 24.8 °C for fathead minnow; body weight: 0.13 kg for rainbow trout and 0.00018 kg for fathead minnow; lipid fraction of body weight: 0.12 for rainbow trout and 0.05 for fathead minnow.
Lower internal concentrations of chemicals predicted by the PBTK model than by one-compartment models can be explained by the impact of log KOW on model performance. For rainbow trout, after 4 days (Figure 4.5), the PBTK model generally predicts higher internal concentrations than the one-compartment approaches, which is in agreement with Figure 4.1 (see values for the same log KOW and lipid fractions in both graphs). However, during steady-state conditions, the relationship between TK models in rainbow trout looked more like that of the fathead minnow after 4 days (where steady-state conditions have almost already been reached, Figure 4.6). Here, chemical internal concentrations are higher according to the one-compartment models for the middle range of log KOW values, while for low and high log KOW, the PBTK model predicts higher internal concentrations than the one-compartment approaches. This is also caused by steady-state conditions which are reached with the PBTK model earlier than with the one-compartment models (lower PBTK values in this case) but which are not achieved with any of the TK models at the time points used here for chemicals with a high log KOW (lower one-compartment values in this case). Thus, for log KOW equals 4.4 (Figure 4.1–4.4), the PBTK model predicts lower internal concentrations during steady-state conditions and higher internal concentrations during non-steady-state conditions than the one-compartment approaches.
Overall, the internal concentration increases faster over time in simulations with the PBTK model, but eventually such concentrations reach lower values at steady-state than is the case in simulations with the one-compartment models. This difference originates from different elimination rate constants in the one-compartment models and from the exchange coefficient between water and fish gills in the PBTK model. In the PBTK model, the exchange coefficient between water and fish gills is much higher than uptake and elimination rate constants in the one-compartment approaches. Hence, the chemical is absorbed very fast in the beginning in the PBTK model; however, its final concentration during steady-state conditions is also impacted by other limiting factors (such as partition coefficient between water and blood or oxygen consumption rate which influences predicted gill uptake clearance—see SI eqs S18 and S19).
Chemical Concentrations in Various Tissues and Organs
According to one-compartment models, the concentration of a chemical is the same in all tissues and organs; however the PBTK model assumes that chemical concentrations differ among various organs and tissues. In our study, based on the PBTK model, the rank order of concentrations in tissues was the following: fat > kidney (liver) > liver (kidney) > muscle > blood (chemical concentrations in liver were higher than in kidney only at very short exposure time). Differences in the pattern of chemical accumulation in each tissue depended on exposure time and log KOW of the chemical (see SI for more details). Organ-specific accumulation might be important to understand toxicity pathways specific to target sites located in only some organs as well as for food-chain bioaccumulation if predators preferentially consume certain organs.
Relevance for Risk Assessment
We compared how well three different TK models predict internal concentrations in different fish species (rainbow trout and fathead minnow) and for different chemicals and concentrations in water (39 different organic chemicals, concentrations varying from 0.000038 to 26185 μg/L). All models tested predict at least 68% of the measured internal concentrations in fish within 1 order of magnitude. In addition, the PBTK model, which predicts chemical concentrations in the whole fish as well as in various tissues, outperformed the one-compartment models with respect to simulating chemical concentrations in the whole body (at least 88% of internal concentrations were predicted within 1 order of magnitude using the PBTK model). Like the one-compartment approaches, this model could also be used to extrapolate to another fish species without additional experiments. However, it is important to take model limitations into account, e.g., in order to use these models for polar narcotics, they should take lipid–water partition coefficient into account since such chemicals tend to partition into polar lipids (of the membrane) more than nonpolar compounds.(30, 31) Modeling of lipid–water partition coefficients was presented by Toropov and Roy(32) and by Pola et al.(33) In addition, simulating internal concentrations of chemicals which are quickly biotransformed (i.e., rate constant of biotransformation at least in the same order of magnitude as elimination rates) in the organism requires adding biotransformation to the models (which usually requires additional experiments). Nichols et al.(10) described procedures for adding in vitro biotransformation data into the PBTK model and tested this incorporation of biotransformation into the PBTK and one-compartment A models.(34) According to their results, at very high rates of biotransformation, the PBTK approach predicts a greater impact of biotransformation on bioaccumulation than the one-compartment model A, which results from the structures of both models.
In conclusion, this study shows that the difference between TK models is small and all approaches can successfully predict the internal concentrations of many organic chemicals. However, as the PBTK model slightly outperformed one-compartment approaches, and can also be used to predict chemical concentrations in tissues, we encourage efforts to parameterize PBTK models for additional species. In addition, further development of TK models (e.g., by adding biotransformation data(35, 36) or lipid–water partition coefficient for polar compounds) would improve all these models.
Supporting Information
Detailed experimental conditions, information on chemicals, model equations, and implementation. This information is available free of charge via the Internet at http://pubs.acs.org/.
The authors declare no competing financial interest.
Acknowledgment
This research is financially supported by the European Union under the seventh Framework Programme (project acronym CREAM, contract PITN-GA-2009-238148). We thank Walter Schmitt for advice and discussions.
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- 2.Escher
, B. I.; Hermens, J. L. M. Modes of action in ecotoxicology: Their role in body burdens, species sensitivity, QSARs, and mixture effects Environ. Sci. Technol. 2002, 36 (20) 4201– 4217[ACS Full Text
], [CAS]2.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XmvVGnsbs%253D&md5=fcaeb407753c796cffcc1e84852e7f24Escher, Beate I.; Hermens, Joop L. M.Environmental Science and Technology (2002),A review. In contrast to the general research attitude in the basic sciences, environmental sciences are often goal-driven and should provide the scientific basis for risk assessment procedures, cleanup, and precautionary measures and finally provide a decision support for policy and management. Hence, the prominent role of mechanistic studies in ecotoxicol. is not only to understand the impact of pollutants on living organisms but also to deduce general principles for the categorization and assessment of effects. The goal of this review is, therefore, not to provide an exhaustive coverage of modes of toxic action and their underlying biochem. mechanisms but rather to discuss critically the application of this knowledge in ecotoxicol. risk assessment. Knowing the mechanism or, at least, the mode of toxic action is indispensable for developing descriptive and predictive models in ecotoxicol. This review seeks to show the crucial role of target sites, interactions with the target site(s), and mechanisms for an adequate and efficient ecotoxicol. risk assessment. Emphasis in the discussion is on target effect concns. (or target occupancy), species selectivity and species sensitivity, time perspective of effect studies, Quant. Structure-Activity Relationships (QSAR), and mixt. toxicity. A particular focus of this review is on multiple mechanisms. Although the illustrative examples were mainly taken from studies in aquatic ecotoxicol., the proposed conceptual approach is also in principle applicable and even particularly useful for soil and sediment systems. Recommendations for further research and developments include the use of internal effect concns. and target site concns. in site-specific risk assessment and as a mixt. toxicity parameter as well as general considerations for the derivation of mechanistically meaningful QSAR and other predictive models. - 3.Escher
, B. I.; Hermens, J. L. M. Internal exposure: Linking bioavailability to effects Environ. Sci. Technol. 2004, 38 (23) 455A– 462A[ACS Full Text], [CAS]3.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXhtVehsbbO&md5=e38c338ef415c768bf549337cd26ba36Escher, Beate I.; Hermens, Joop L. M.Environmental Science and Technology (2004),A review. In the early days of ecotoxicol., researchers tested the effects of nominal concns. in static systems. Basically, a pollutant was added to a test system, such as an aquarium with fish, until a clearly distinguishable effect was obsd., such as death or immobilization. As the discipline has developed, the scope of projects has become more sophisticated. Now, researchers have moved from establishing baseline data sets to distinguishing between nominal, free, internal, and target concns. In the following discussion, we will review these developments and show how internal exposure data could improve risk assessment and bridge the gap between human health and environmental risk assessment. - 4.McCarty
, L. S.; Mackay, D. Enhancing ecotoxicological modeling and assessment Environ. Sci. Technol. 1993, 27 (9) 1719– 1728[ACS Full Text]There is no corresponding record for this reference. - 5.Ashauer
, R.; Escher, B. I. Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment J. Environ. Monit. 2010, 12 (11) 2056– 2061[Crossref], [PubMed], [CAS]5.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXhtlKku7nI&md5=f0c57ca1972d81212c2827b5e6957375Ashauer, Roman; Escher, Beate I.Journal of Environmental Monitoring (2010),A review. Toxicokinetic-toxicodynamic (TK-TD) models simulate the processes that lead to toxicity at the level of organisms over time. These dynamic simulation models quantify toxicity, but more importantly they also provide a conceptual framework to better understand the causes for variability in different species' sensitivity to the same compd. as well as causes for different toxicity of different compds. to the same species. Thus TK-TD models bring advantages for very diverse ecotoxicol. questions as they can address two major challenges: the large no. of species that are potentially affected and the large no. of chems. of concern. The first important benefit of TK-TD models is the role that they can play to formalize established knowledge about toxicity of compds., sensitivity of organisms, organism recovery times and carry-over toxicity. The second important aspect of TK-TD models is their ability to simulate temporal aspects of toxicity which makes them excellent extrapolation tools for risk assessment of fluctuating or pulsed exposures to pollutants. We provide a general introduction to the concept of TK-TD modeling for environmental scientists and discuss opportunities as well as current limitations. - 6.Heinrich-Hirsch
, B.; Madle, S.; Oberemm, A.; Gundert-Remy, U. The Use of Toxicodynamics in Risk Assessment Toxicol. Lett. 2001, 120, 131– 141[Crossref], [PubMed], [CAS]6.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXivFejsLg%253D&md5=998ce5a034c369807d4097597115037dHeinrich-Hirsch, B.; Madle, S.; Oberemm, A.; Gundert-Remy, U.Toxicology Letters (2001),A review with 50 refs. Risk assessment of xenobiotics is a qual. and quant. assessment of toxic properties conventionally based on data resulting from tests in animals exposed to the substance. The assessment of dose-effect relationship includes evaluation of exposure at the site of action. More recently, emphasis is put on understanding the relationship between exposure at the site of action and the resulting effect, i.e. toxicodynamic. In this respect, results from genotoxicity studies may be a measure for exposure and at the same time of an effect. Results of toxicodynamic endpoints such as binding to receptors or release of hormones were used when replacing default values for interspecies extrapolation. It may also be envisaged to use toxicodynamic endpoints to get an est. of intraspecies variability. It was demonstrated that this approach may be helpful only if the relationship between the toxicodynamic endpoint and the definite endpoint is known by the example of bisphenol A. Whereas there are clear effects of bisphenol A in in vitro and ex vivo studies, the classical 2 generation study was not able to detect an effect on reprodn. and/or fertility. Looking in the future development of toxicodynamic endpoints, gene profiling and the anal. of proteins (proteomics) may be helpful tools employed in screening and being related to the mode of action are explored for their suitability in terms of toxicodynamic endpoints. - 7.Nichols
, J. W.; Fitzsimmons, F. A Physiologically Based Toxicokinetic Model for Dietary Uptake of Hydrophobic Organic Compounds by Fish. II. Simulation of Chronic Exposure Scenarios Toxicol. Sci. 2004, 77, 219– 229[Crossref], [PubMed], [CAS]7.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXht1KltLc%253D&md5=0566e431f1eb56478a15bec766567e01Nichols, John W.; Fitzsimmons, Patrick N.; Whiteman, Frank W.Toxicological Sciences (2004),A physiol. based toxicokinetic (PBTK) model for dietary uptake of hydrophobic org. compds. by fish was used to simulate dosing scenarios commonly encountered in exptl. and field studies. Simulations were initially generated for the model compd. [UL-14C] 2,2',5,5'-tetrachlorobiphenyl ([14C] PCB 52). Steady-state exposures were simulated by calcg. chem. concns. in tissues of the predator corresponding to an equil. distribution between the fish and water (termed the bioconcn. or BCF residue data set). This residue data set was then varied in a proportional manner until whole-body chem. concns. exhibited no net change for each set of exposure conditions. For [14C] PCB 52, the proportional increase in BCF residues (termed the biomagnification factor or BMF) required to achieve steady state in a food-only exposure was 2.24, while in a combined food and water exposure the BMF was 3.11. Addnl. simulations for the food and water exposure scenario were obtained for a set of hypothetical org. compds. with increasing log KOW values. Using gut permeability coeffs. detd. for [14C] PCB 52, predicted BMFs increased with chem. log KOW, achieving levels much higher than those reported in field sampling efforts. BMFs comparable to measured values were obtained by reducing permeability coeffs. within each gut segment in a log KOW-dependent manner. This predicted decrease in chem. permeability is consistent with earlier work, suggesting that dietary absorption of hydrophobic compds. by fish is controlled in part by factors that vary with chem. log KOW. Relatively low rates of metab. or growth were shown to have a substantial impact on steady-state biomagnification of chem. residues. - 8.Nichols
, J. W.; McKim, J. M.; Lien, G. J.; Hoffman, A. D.; Bertelsen, S. L. Physiologically Based Toxicokinetic Modeling of Three Waterborne Chloroethanes in Rainbow Trout (Oncorhynchus) Toxicol. Appl. Pharmacol. 1991, 110, 374– 389[Crossref], [PubMed], [CAS]8.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXmt1GksL0%253D&md5=e0de48df80a5e9fb4574293651955b9eNichols, John W.; McKim, James M.; Lien, Gregory J.; Hoffman, Alexc D.; Bertelsen, Sharon L.Toxicology and Applied Pharmacology (1991),Physiol. based toxicokinetic model for fish was used to simulate the uptake and disposition of three waterborne chloroethanes in rainbow trout (O. mykiss). Trout were exposed to 1,1,2,2-tetrachloroethane, pentachloroethane, and hexachloroethane in fish respirometer-metab. chambers to assess the kinetics of chem. accumulation in arterial blood and chem. extn. efficiency from inspired water. Chem. residues in tissues were measured at the end of each expt. Trout exposed to tetrachloroethane were close to steady state in 48 h. Fish exposed to pentachloroethane were near steady state in 264 h. Extn. efficiency data showed that systemic (extrabranchial) elimination of both chems. was small. Hexachloroethane continued to accumulate in fish exposed for 600 h. Parameterized with chem. partitioning data obtained in vitro, the model accurately simulated the uptake of all three chloroethanes in blood and tissues and their extn. from inspired water. These results provide support for the basic model structure and the accuracy of physiol. input parameters. - 9.Nichols
, J. W.; McKim, J. M.; Lien, G. J.; Hoffman, A. D.; Bertelsen, S. L.; Gallinat, C. A. Physiologically-based toxicokinetic modeling of three waterborne chloroethanes in channel catfish Ictalurus punctatus Aquat. Toxicol. 1993, 27, 83– 112[Crossref], [CAS]9.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXisVGg&md5=06466ae4288fbe3b1e98f65f60723a11Nichols, J. W.; McKim, J. M.; Lien, G. J.; Hoffman, A. D.; Bertelsen, S. L.; Gallinat, C. A.Aquatic Toxicology (1993),A physiol.-based toxicokinetic model for fish was used to describe the uptake and disposition of three chlorinated ethanes in channel catfish (I. punctatus). Catfish were simultaneously exposed to 1,1,2,2-tetrachloroethane (TCE), pentachlorethane (PCE), and hexachloroethane (HCE) in fish respirometer-metab. chambers to assess the kinetics of chem. accumulation in arterial blood and chem. extn. efficiency from inspired water. Chem. residues in tissues were measured at the end of each expt. These data were used to evaluate the accuracy of model simulations and to form a basis for comparison with information collected previously from rainbow trout. TCE was at or near steady-state in catfish after 48 h. For PCE and HCE the time to steady-state appeared to be considerably longer than 48 h. Parameterized with in vitro chem. partitioning information, the model accurately simulated the accumulation of TCE in arterial blood and its uptake from inspired water, but consistently underestimated the uptake and accumulation of both PCE and HCE. The cause of these discrepancies was not conclusively detd.; however, several possible sources of error were evaluated, including physiol. and chem. partitioning inputs, and underlying modeling assumptions. A comparison of data sets and modeling efforts for rainbow trout and channel catfish suggests that gross similarities between the two species can be attributed to the comparability of relevant physiol. and chem. partitioning parameters. - 10.Nichols
, J. W.; Schultz, I. R.; N., F. P. In vitro-in vivo extrapolation of quantitative hepatic biotransformation data for fish. I. A review of methods, and strategies for incorporating intrinsic clearance estimates into chemical kinetic models Aquat. Toxicol. 2006, 78, 74– 90[Crossref], [PubMed], [CAS]10.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD28Xks1eksr4%253D&md5=adcd9fef5f1296a0e9b10460a50a2505Nichols, John W.; Schultz, Irvin R.; Fitzsimmons, Patrick N.Aquatic Toxicology (2006),A review. Scientists studying mammals have developed a stepwise approach to predict in vivo hepatic clearance from measurements of in vitro hepatic biotransformation. The resulting clearance ests. were used to screen drug candidates, investigate idiosyncratic drug responses, and support chem. risk assessments. In this report, we review these methods, discuss their potential application to studies with fish, and describe how extrapolated values could be incorporated into well-known compartmental kinetic models. Empirical equations that relate extrapolation factors to chem. log K ow are given to facilitate the incorporation of metab. data into bioconcn. and bioaccumulation models. Because they explicitly incorporate the concept of clearance, compartmental clearance-vol. models are particularly well suited for incorporating hepatic clearance ests. The manner in which these clearance values are incorporated into a given model depends, however, on the measurement frame of ref. Procedures for the incorporation of in vitro biotransformation data into physiol. based toxicokinetic (PBTK) models are also described. Unlike most compartmental models, PBTK models are developed to describe the effects of metab. in the tissue where it occurs. In addn., PBTK models are well suited to modeling metab. in more than one tissue. - 11.Landrum
, P. F.; Lee, H.; Lydy, M. J. Toxicokinetics in aquatic systems - model comparisons and use in hazard assessment Environ. Toxicol. Chem. 1992, 11 (12) 1709– 1725[Crossref], [CAS]11.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK38XmsFOlsrk%253D&md5=b89ea1e8bf90344eca9724a7ca480102Landrum, Peter F.; Lee, Henry, II; Lydy, Michael J.Environmental Toxicology and Chemistry (1992),Toxicokinetic models, i.e. compartment-based models, physiol.-based models, and energetics-based models, are reviewed with 126 refs. and the different math. formalisms compared. Addnl., the residue-based toxicity approach is reviewed. Coupling toxicokinetic models with tissue concns. at which toxicity occurs offers a direct link between exposure and hazard. Basing hazard on tissue rather than environmental concns. avoids the errors assocd. with accommodating multiple sources, pulsed exposures, and non-steady-state accumulation. - 12.Mackay
, D.; Fraser, A. Bioaccumulation of persistent organic chemicals: Mechanisms and models Environ. Pollut. 2000, 110 (3) 375– 391[Crossref], [PubMed], [CAS]12.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXmvVykurs%253D&md5=b14c8298297b9b0af183847db31925dfMackay, D.; Fraser, A.Environmental Pollution (Oxford, United Kingdom) (2000),A review and discussion with many refs. on the bioaccumulation of org. substances in organisms, esp. fish, including the incentives for developing a tiered predictive approach for addressing the large no. of chems. of commerce. From a review of the existing estn. methods it is suggested that the simplest Tier 1 approach is an empirical correlation for bioconcn. factor as a function of the octanol-water partition coeff. For more detailed Tier 2 evaluation, the bioaccumulation factor is best predicted using a mechanistic mass balance model applied to the organism at steady state in which relevant uptake and loss processes are quantified. The equivalence of rate const. and fugacity models is demonstrated and methods of obtaining parameter values are discussed. Such a model reveals the relative significance of gill ventilation, food uptake, egestion, and metab. The most detailed Tier 3 evaluation should involve the prediction of the potential for biomagnification in a food chain involving both fish and air-breathing animals. Research needs are discussed with a view to understanding the mechanisms more fully and developing more accurate quant. descriptions or models of bioaccumulation phenomena. - 13.Nichols
, J. W.; McKim, J. M.; Andersen, M. E.; Gargas, M. L.; Ckewell, H. J.; Erickson, R. J. A Physiologically Based Toxicokinetic Model for the Uptake and Disposition of Waterborne Organic Chemicals in Fish Toxicol. Appl. Pharmacol. 1990, 106, 433– 447[Crossref], [PubMed], [CAS]13.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXhtlOlsro%253D&md5=ed612470de33cbc0636fee9b11d2022eNichols, John W.; McKim, James M.; Andersen, Melvin E.; Gargas, Michael L.; Clewell, Harvey J., III; Erickson, Russell J.Toxicology and Applied Pharmacology (1990),A physiol. based toxicokinetic model was developed to predict the uptake and disposition of waterborne org. chems. in fish. The model consists of a set of mass-balance differential equations which describe the time course of chem. concn. within each of five tissue compartments: liver, kidney, fat, and richly perfused and poorly perfused tissue. Model compartmentalization and blood perfusion relationships were designed to reflect the physiol. of fishes. Chem. uptake and elimination at the gills were modeled as countercurrent exchange processes, limited by the chem. capacity of blood and water flows. The model was evaluated by exposing rainbow trout (Oncorhynchus mykiss) to pentachloroethane (PCE) in water in fish respirometer-metab. chambers. Exposure to 1500, 150, or 15 μg PCE/L for 48 h resulted in corresponding changes in the magnitude of blood concns. without any change in uptake kinetics. The extn. efficiency for the chem. from water decreased throughout each exposure, declining from 65 to 20% in 48 h. Extn. efficiency was close to 0% in fish exposed to PCE to near steady state (264 h), suggesting that very little PCE was eliminated by metab. or other extrabranchial routes. Parameterized for trout with physiol. information from the literature and chem. partitioning ests. obtained in vitro, the model accurately predicted the accumulation of PCE in blood and tissues, and its extn. from inspired water. These results demonstrate the potential utility of this model for use in aquatic toxicol. and environmental risk assessment. - 14.Arnot
, J. A.; Gobas, F. A food web bioaccumulation model for organic chemicals in aquatic ecosystems Environ. Toxicol. Chem. 2004, 23 (10) 2343– 2355[Crossref], [PubMed], [CAS]14.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXnvVGrs7w%253D&md5=41495635eb8ce27ab6385637f838b32dArnot, Jon A.; Gobas, Frank A. P. C.Environmental Toxicology and Chemistry (2004),This study examines a new bioaccumulation model for hydrophobic org. chems. in aquatic food webs. The purpose of the model is to provide site-specific ests. of chem. concns. and assocd. bioconcn. factors, bioaccumulation factors, and biota-sediment accumulation factors in organisms of aquatic food webs using a limited no. of chem., organism, and site-specific data inputs. The model is a modification of a previous model and incorporates new insights regarding the mechanism of bioaccumulation derived from lab. expts. and field studies as well as improvements in model parameterization. The new elements of the model include: a model for the partitioning of chems. into organisms; kinetic models for predicting chem. concns. in algae, phytoplankton, and zooplankton; new allometric relationships for predicting gill ventilation rates in a wide range of aquatic species; and a mechanistic model for predicting gastrointestinal magnification of org. chems. in a range of species. Model performance is evaluated using empirical data from three different freshwater ecosystems involving 1019 observations for 35 species and 64 chems. The effects of each modification on the model's performance are illustrated. The new model is able to provide better ests. of bioaccumulation factors in comparison to the previous food web bioaccumulation model while the model input requirements remain largely unchanged. - 15.Hendriks
, A. J.; van der Linde, A.; Cornelissen, G.; Sijm, D. The power of size. 1. Rate constants and equilibrium ratios for accumulation of organic substances related to octanol-water partition ratio and species weight Environ. Toxicol. Chem. 2001, 20 (7) 1399– 1420[Crossref], [PubMed], [CAS]15.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXks1GltrY%253D&md5=f24d4f973c26d3f76f40a277d67e1621Hendriks, A. Jan; Van der Linde, Alex; Cornelissen, Gerard; Sijm, Dick T. H. M.Environmental Toxicology and Chemistry (2001),Most of the thousands of substances and species that risk assessment has to deal with are not investigated empirically because of financial, practical, and ethical constraints. To facilitate extrapolation, we have developed a model for accumulation kinetics of org. substances as a function of the octanol-water partition ratio (Kow) of the chem. and the wt., lipid content, and trophic level of the species. The ecol. parameters were obtained from a previous review on allometric regressions. The chem. parameters, i.e., resistances that substances encounter in water and lipid layers of organisms, were calibrated on 1,939 rate consts. for absorption from water for assimilation from food and for elimination. Their ratio was validated on 37 lab. bioconcn. and biomagnification regressions and on 2,700 field bioaccumulation data. The rate const. for absorption increased with the hydrophobicity of the substances with a Kow up to about 1,000 and then leveled off, decreasing with the wt. of the species. About 39% of the variation was explained by the model, while deviations of more than a factor of 5 were noted for labile, large, and less hydrophobic mols. as well as for algae, mollusks, and arthropods. The efficiency for assimilation of contaminants from food was detd. mainly by the food digestibility and thus by the trophic level of the species. A distinction was made between substances that are stable, i.e., with a min. elimination only, and those that are labile, i.e., with an excess elimination probably largely due to biotransformation. The rate const. for min. elimination decreased with the hydrophobicity of the substance and the wt. of the species. About 70% of the variation was explained by the model, while deviations of more than a factor of 5 were noted for algae, terrestrial plants, and benthic animals. Labile substances were eliminated faster than isolipophilic stable compds., but differences in lab. elimination and accumulation were small compared with those in field accumulation. Excess elimination by vertebrates was faster than by invertebrates. Differences between terrestrial and aquatic species were attributed to water turnover rates, whereas differences between trophic levels were due to the food digestibility. Food web accumulation, expressed as organism-org. solids and organism-food concns. ratios could be largely explained by ecol. variables only. The model is believed to facilitate various types of scientific interpretation as well as environmental risk assessment. - 16.USEPA.Tissue/ResidueDatabase; U.S. Environmental Protection Agency, 2000, avalaible at http://www.epa.gov/med/Prods_Pubs/tox_residue.htm.There is no corresponding record for this reference.
- 17.Call
, D. J.; Brooke, D. N.; Lu, P.-Y. Uptake, Elimination, and Metabolism of Three Phenols by Fathead Minnows Arch. Environ. Contam. Toxicol. 1980, 9, 699– 714[Crossref], [PubMed], [CAS]17.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3MXhtFGgt7k%253D&md5=084ca0ec0247923c315d257dfefecb51Call, Daniel J.; Brooke, L. T.; Lu, Po-YungArchives of Environmental Contamination and Toxicology (1980),Uptake rates of total 14C in fathead minnows (Pimephales promelas) exposed to sublethal concns. of radiolabeled test compds. followed the order: phenol [108-95-2] > 2,4,5-trichlorophenol (I) [95-95-4] > p-nitrophenol [100-02-7]. Mean whole body 14C concn. factors were 15,800,-1850, and 180 for phenol, I, and p-nitrophenol exposures, resp. Only minor amts. of tissue 14C was parent compd. after 28 days of exposure in fish exposed to phenol and p-nitrophenol, while 78.6% of the 14C was parent compd. in I-exposed fish. Tissue 14C in fish exposed to I was eliminated at a faster rate than in fish exposed to phenol or p-nitrophenol. Obsd. mean 14C depuration half-lives for lower and higher exposures combined were 387, 150, and 12 h for phenol, p-nitrophenol, and I, resp. Parent compd. comprised 1.5, 2.7, and 0.7% of total 14C for phenol, I, and p-nitrophenol, resp., after 28 days of depuration. The percentage of acetone-unextractable 14C increased from the end of uptake to the end of depuration for phenol and I, and decreased slightly for p-nitrophenol. 14C contribution from polar metabolites increased relative to total 14C during the depuration phase for I and p-nitrophenol. - 18.Russell
, R. W.; Gobas, F. A. P. C; Haffner, G. D. Maternal Transfer and in Ovo Exposure of Organochlorines in Oviparous Organisms: A Model and Field Verification Environ. Sci. Technol. 1999, 33, 416– 420[ACS Full Text], [CAS]18.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXnvVKmu74%253D&md5=e1d7fcae908383b17d1f422a3e2460a2Russell, Ronald W.; Gobas, Frank A. P. C.; Haffner, G. DouglasEnvironmental Science and Technology (1999),Because early life stages of many species exhibit a greater toxicol. sensitivity to contaminants than the adult life stages, knowledge of the exposure of contaminants during embryonic development is a crucial prerequisite for toxicol. risk assessment. This study presents a chem. equil. model for estg. the maternal transfer and resulting exposure of developing embryos in eggs of several classes of oviparous organisms to hydrophobic org. chems. The model is tested against (i) the results of a field study, including the anal. of 44 chems. in eggs and maternal tissues of 6 fish species and snapping turtles, and (ii) literature data for 8 addnl. fish and 3 bird species. Lipid normalization of egg and maternal concns. reduces the variability in obsd. egg-to-maternal tissue concn. ratios between species by approx. 20-fold. The majority of obsd. lipid normalized egg/maternal tissue concn. ratios for individual chems. and fish were not significantly different from 1.0, and the combined data set shows a logarithmic distribution with a mean of 1.22, and 95% probability intervals of 0.56 and 2.51. This indicates that during fish development, the embryos can be expected to be exposed to the same effective internal concn. as the maternal organisms from which the eggs originated. - 19.Barber
, M. C. A review and comparison of models for predicting dynamic chemical bioconcentration in fish Environ. Toxicol. Chem. 2003, 22 (9) 1963– 1992[Crossref], [PubMed], [CAS]19.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXotF2gsLo%253D&md5=ef456b471c99da446c0fb2ccf97f1e86Barber, M. CraigEnvironmental Toxicology and Chemistry (2003),A review. Over the past 20 yr, a variety of models were developed to simulate the bioconcn. of hydrophobic org. chems. by fish. These models differ not only in the processes they address but also in the way a given process is described. Processes described by these models include chem. diffusion through the gill's interlamellar water, epithelium, and lamellar blood plasma; advective chem. transport to and from the gill by ventilation and perfusion, resp.; and internal chem. deposition by thermodn. partitioning to lipid and other org. phases. This article reviews the construction and assocd. assumptions of 10 of the most widely cited fish bioconcn. models. These models are then compared with respect to their ability to predict obsd. uptake and elimination rates using a common database for those model parameters that they have in common. Statistical analyses of obsd. and predicted exchange rates reveal that rates predicted by these models can be calibrated almost equally well to obsd. data. This fact is independent of how well any given model is able to predict obsd. exchange rates without calibration. The importance of gill exchange models and how they might by improved are also discussed. - 20.Nichols
, J. W.; Jensen, K. M.; Tietge, J. E.; Johnson, R. D. Physiologically based toxicokinetic model for maternal transfer of 2,3,7,8-tetrachlorodibenzo-p-dioxin in brook trout (Salvelinus fontinalis) Environ. Toxicol. Chem. 1998, 17, 2422– 2434[Crossref], [CAS]20.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1cXnsFersLs%253D&md5=f587d20f3b1dc9fa238e5404b6edaf45Nichols, John W.; Jensen, Kathleen M.; Tietge, Joseph E.; Johnson, Rodney D.Environmental Toxicology and Chemistry (1998),A physiol. based toxicokinetic (PB-TK) model was developed to describe the uptake, distribution, and elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in female brook trout during a 6-mo feeding and depuration study. Dietary uptake was modeled under 2 assumptions: uptake proceeds to equil. between blood exiting the intestinal tract and the contents of the intestinal tract, and uptake is limited by desorption of TCDD from gut contents and(or) diffusion from the lumen into tissues. Model outputs were evaluated by comparison with measured TCDD residues. The best model fit to the data was obtained by imposing a moderate diffusion limitation on gut uptake. Of the parameters that comprise the gut submodel, whole-body residue predictions were most sensitive to changes in the diffusion rate const. and fecal egestion rate. Chem. residues in fat were indicative of an internal disequil. with other tissues during the loading phase of the study. Accurate simulations of this behavior were obtained using a diffusion-limited tissue description. Chem. residues in liver, muscle, and ovaries were adequately described by assuming that uptake and elimination were blood flow-limited. Spawning probably resulted in a small increase in whole-body TCDD concn. and did not appear to represent an important route of chem. elimination. These results suggest that field-derived whole-body TCDD residues could be used to est. residues in developing ovaries of brook trout to within a factor of two, provided that whole-body and ovary lipid content were known. - 21.Bertelsen
, S. L.; Hoffman, A. D.; Gallinat, C. A.; Elonen, C. M.; Nichols, J. W.Evaluation of Log ow and Tissue Lipid Content as Predictors of Chemical Partitioning to Fish Tissues; 1998.There is no corresponding record for this reference. - 22.FOCUS. Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0; 2006, 434.There is no corresponding record for this reference.
- 23.Ramos
, E.; Vaes, W.; Verhaar, H.; Hermens, J. Polar narcosis: Designing a suitable training set for QSAR studies Environ. Sci. Pollut. Res. 1997, 4 (2) 83– 90[Crossref], [PubMed], [CAS]23.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjjs1WhtA%253D%253D&md5=2e789e0955ab1d34e24acab8b1e4ad9aRamos E U; Vaes W H; Verhaar H J; Hermens J LEnvironmental science and pollution research international (1997),Substituted phenols, anilines, pyridines and mononitrobenzenes can be classified as polar narcotics. These chemicals differ from non-polar narcotic compounds not only in their toxic potency (normalized by log K(ow)), but also in their Fish Acute Toxicity Syndrome profiles, together suggesting a different mode of action. For 97 polar narcotics, which are not ionized under physiological conditions, 11 physico-chemical and quantum-chemical descriptors were calculated. Using principal component analysis, 91% of the total variance in this descriptor space could be explained by three principal components which were subsequently used as factors in a statistical design. Eleven compounds were selected based on a two-level full factorial design including three compounds near the center of the chemical domain (a 2(3)+3 design). QSARs were developed for both the design set and the whole set of 63 polar narcotics for which guppy and/or fathead minnow data were available in the literature. Both QSARs, based on partial least squares regression (3 latent variables), resulted in good models (R(2)=0.96 and Q(2)=0.82; R(2)=0.86 and Q(2)=0.83 respectively) and provided similar pseudo-regression coefficients. In addition, the model based on the design chemicals was able to predict the toxicity of the 63 compounds (R(2) =0.85). Models show that acute fish toxicity is determined by hydrophobicity, HOMO-LUMO energy gap and hydrogen-bond acceptor capacity. - 24.Rendal
, C.; Kusk, K. O.; Trapp, S. Optimal choice of pH for toxicity and bioaccumulation studies of ionizing organic chemicals. Environ. Toxicol. Chem. 2011, not supplied.There is no corresponding record for this reference. - 25.Tanneberger
, K.; Rico-Rico, A.; Kramer, N. I.; Busser, F. J. M.; Hermens, J. L. M.; Schirmer, K. Effects of Solvents and Dosing Procedure on Chemical Toxicity in Cell-Based in Vitro Assays Environ. Sci. Technol. 2010, 44 (12) 4775– 4781[ACS Full Text], [CAS]25.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXmsFSnsLw%253D&md5=61a05679b6bb66c782965425d2922ca5Tanneberger, Katrin; Rico-Rico, Angeles; Kramer, Nynke I.; Busser, Frans J. M.; Hermens, Joop L. M.; Schirmer, KristinEnvironmental Science & Technology (2010),Due to the implementation of new legislation, such as REACh, a dramatic increase of animal use for toxicity testing is expected and the search for alternatives is timely. Cell-based in vitro assays are promising alternatives. However, the behavior of chems. in these assays is still poorly understood. The authors set out to quantify the exposure and assocd. toxicity of chems. with different physicochem. properties toward a fish gill cell line when different solvents and procedural steps are used to introduce test chems. to cells. Three chems. with a range of hydrophobicity and volatility were selected and delivered in 3 different solvents using 2 common dosing procedures. Toxicity tests were coupled with chem. anal. to quantify the chem. concns. within culture wells. The impact of solvents and dosing procedure was greatest for the most volatile and hydrophobic test chem. Certain combinations of the test chem., solvent, and procedural steps can lead to inhomogeneous distribution of the test chem. and thus differing degrees of bioavailability, resulting in quant. differences in apparent toxicity. - 26.OSPAR. OSPAR Background Document on Hexachlorocyclopentadiene (HCCP); OSPAR Commission, 2004There is no corresponding record for this reference.
- 27.Podowski
, A. A.; Sclove, S. L.; Pilipowicz, A.; Khan, M. A. Q. Biotransformation and disposition of hexachlorocyclopentadiene in fish Arch. Environ. Contam. Toxicol. 1991, 20 (4) 488– 496[Crossref], [PubMed], [CAS]27.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK3MXisVagsbw%253D&md5=63bc1afac24b4503ee17ef6a1d5bafe4Podowski, A. A.; Sclove, S. L.; Pilipowicz, A.; Khan, M. A. Q.Archives of Environmental Contamination and Toxicology (1991),Fate of hexachlorocyclopentadiene (Hex) was studied in freshwater fish using in vivo and in vitro systems. Hex injected i.p. into goldfish is readily distributed, stored, and metabolized (>11 organosol. and hydrophilic metabolites). The body radioactivity in tissues declines, but levels in bile remain high, indicating biliary excretion as a major route of elimination for Hex and its metabolites. Total radioactivity eliminated in water indicated three phases with a calcd. half-life (t1/2) of 7 days and predicted 90 and 95% clearance of 162 and 211 days, resp. A 3-segment straight line model gave the best fit of the elimination and one reabsorption phase. For a static system, two phases of elimination were detected with a calcd. t1/2 of 9 days and predicted 90 and 95% clearance of 77 and 107 days, resp. A compartmental model indicated that one elimination and one reabsorption phase were involved. - 28.Spehar
, R. L.; Veith, G. D.; DeFoe, D. L.; Bergstedt, B. V. Toxicity and Bioaccumulation of Hexachlorocyclopentadiene, Hexachloronorbornadiene and Heptachloronorbornene in Larval and Early Juvenile Fathead Minnows, Pimephales promelas Bull. Environ. Contam. Toxicol. 1979, 21, 576– 583[Crossref], [PubMed], [CAS]28.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE1MXltFyhsbY%253D&md5=4d81596556e4a8ef4fb3a835df3f5d45Spehar, R. L.; Veith, G. D.; DeFoe, D. L.; Bergstedt, B. V.Bulletin of Environmental Contamination and Toxicology (1979),In larval and early juvenile fathead minnows, the 96-h LC50 and 30-day LC50 values for hexachlorocycopentadiene (I) [77-47-4], hexachloronorbornadiene [3389-71-7], and heptachloronorbornene [5202-36-8] were 7.0 and 6.7 μg/L, 188 and 123 μg/L, and 85.6 and 60.1 μg/L. Residues of I in 30-day-old fathead minnows were <0.1 μg/L. The concn. of hexachloronorbornadiene and heptachloronorbornene accumulated by fathead minnows was directly proportional to mean exposure concn. ≤38.4 and 40 μg/L, resp. - 29.Oliver
, B. G.; Niimi, A. J. Bioconcentration of Chlorobenzenes from Water by Rainbow Trout: Correlations with Partition Coefficients and Environmental Residues Environ. Sci. Technol. 1983, 17, 287– 291[ACS Full Text], [CAS]29.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL3sXit1Wnt7g%253D&md5=cd0a416dcd650c18190e4ce7a8986c96Oliver, Barry G.; Niimi, Arthur J.Environmental Science and Technology (1983),The bioconcn. by rainbow trout of 10 chloro benzenes (CB's), hexachlorobutadiene (I) [87-68-3] and hexachloroethane (II) [67-72-1] at water concns. near environmental (ng/L range) levels was studied. The bioconcn. factor (BCF) for the CB's increased dramatically as the degree of Cl substitution on the arom. ring increased. There was a high correlation between the BCF and the octanol [111-87-5] -water partition coeff. for I, II, and all the chloro benzenes except hexachlorobenzene [118-74-1]. Ten adult rainbow trout from Lake Ontario were analyzed for these contaminants. Then, with use of field values for contaminant concns. in water and lab. values for BCF's, predictions of field concns. in fish (on the basis of accumulation from water only) were made. These values were then compared to the fish field data. - 30.Schwarzenbach
, R. P.; Gschwend, P. M.; Imboden, D. M. Environmental Organic Chemistry; Wiley, 2003.There is no corresponding record for this reference. - 31.Hendriks
, A. J.; Traas, T. P.; Huijbregts, M. A. J. Critical Body Residues Linked to Octanol–Water Partitioning, Organism Composition, and LC50 QSARs: Meta-analysis and Model Environ. Sci. Technol. 2005, 39 (9) 3226– 3236[ACS Full Text], [CAS]31.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXis1Ohsbc%253D&md5=b3faa629756659d59b2509b8cce97aabHendriks, A. Jan; Traas, Theo P.; Huijbregts, Mark A. J.Environmental Science and Technology (2005),To protect thousands of species from thousands of chems. released in the environment, various risk assessment tools have been developed. Here, the authors link quant. structure-activity relationships (QSARs) for response concns. in water (LC50) to crit. concns. in organisms (C50) by a model for accumulation in lipid or non-lipid phases vs. water Kpw. The model indicates that affinity for neutral body components such as storage fat yields steep Kpw-Kow relationships, whereas slopes for accumulation in polar phases such as proteins are gentle. This pattern is confirmed by LC50 QSARs for different modes of action, such as neutral vs. polar narcotics and organochlorine vs. organophosphor insecticides. LC50 QSARs were all between 0.00002 and 0.2Kow-1. After calibrating the model with the intercepts and, for the first time also, with the slopes of the LC50 QSARs, crit. concns. in organisms C50 are calcd. and compared to an independent validation data set. About 60% of the variability in lethal body burdens C50 is explained by the model. Explanations for differences between estd. and measured levels for 11 modes of action are discussed. In particular, relationships between the crit. concns. in organisms C50 and chem. (Kow) or species (lipid content) characteristics are specified and tested. The anal. combines different models proposed before and provides a substantial extension of the data set in comparison to previous work. Moreover, the concept is applied to species (e.g., plants, lean animals) and substances (e.g., specific modes of action) that were scarcely studied quant. so far. - 32.Tropov
, A. A.; Roy, K. QSPR Modeling of Lipid-Water Partition Coefficient by Optimization of Correlation Weights of Local Graph Invariants J. Chem. Inform. Computer. Sci. 2004, 44, 179– 186[ACS Full Text]There is no corresponding record for this reference. - 33.Poła
, A.; Michalak, K.; Burliga, A.; Motohashi, N.; Kawase, M. Determination of lipid bilayer/water partition coefficient of new phenothiazines using the second derivative of absorption spectra method Eur. J. Pharm. Sci. 2004, 21 (4) 421– 427[Crossref], [PubMed], [CAS]33.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXhslWntb4%253D&md5=a98fca1c1ef9a862bb9ac477367a285dPola, Andrzej; Michalak, Krystyna; Burliga, Anna; Motohashi, Noboru; Kawase, MasamiEuropean Journal of Pharmaceutical Sciences (2004),The partition coeffs. (Kp) between lipid bilayer of phosphatidylcholine (PC) vesicles and buffer for five new phenothiazines were detd. using the second derivs. of UV absorption spectra. The λmax of absorption band for each of the investigated phenothiazine derivs. (PDs) was shifted to the longer wavelengths in the presence of PC vesicles with increasing of lipid concn. As a result of light scattering in liposome suspension no isosbestic point could be obsd. in absorption spectra. However, the background signal could be eliminated using the method of second deriv. of absorption spectra. In the second deriv. of absorption spectra two isosbestic points were obsd. Changes of intensity (ΔD) of second deriv. of absorption spectra at the λmax (wavelength of absorption max. for drug in buffer) caused by the increase in lipid concn. were measured for set of phenothiazine derivs. Kp for these drugs were calcd. from the relationship between ΔD and lipid concn. The Kp values for all studied phenothiazine derivs. are in the order of magnitude of 105 and they increase about 1.7-fold when length of the alkyl phenothiazine chain was enhanced by addn. of the each next one (-CH2) group. Substitution of -H atom by -CF3 group at position 2 of phenothiazine ring results in 3.5-fold increase in Kp values. - 34.Nichols
, J. W.; Fitzsimmons, P. N.; Burkhard, L. P. In vitro–in vivo Extrapolation of Quantitative Hepatic Biotransformation Data for Fish. II. Modeled Effects on Chemical Bioaccumulation Environ. Toxicol. Chem. 2007, 26 (6) 1304– 1319[Crossref], [PubMed], [CAS]34.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXls1yjt78%253D&md5=abf4641e3443c598ff01e3e1b8762747Nichols, John W.; Fitzsimmons, Patrick N.; Burkhard, Lawrence P.Environmental Toxicology and Chemistry (2007),Hypothetical in vitro biotransformation rate and affinity values for fish were extrapolated to a set of in vivo wholebody metab. rate consts. A one-compartment model was then used to investigate potential effects of metab. on chem. bioaccumulation as a function of octanol/water partitioning (KOW). In a second model-based effort, in vitro data were incorporated into a physiol. based toxicokinetic (PBTK) model for fish. The two models predict similar effects on bioaccumulation when calcd. in vivo intrinsic clearance values (CLIN VIVO,INT) are less than 50% of estd. liver blood flow (QLIVER). When CLIN VIVO,INT approaches QLIVER, the PBTK model predicts a greater effect on bioaccumulation than the one-compartment model. This result is attributed to the structure of the PBTK model, which provides for first-pass clearance of chems. taken up from food. Uncertainties inherent to in vitro-in vivo extrapolations of hepatic metab. data include the effects of protein binding, inaccurate estn. of in vivo metab. by in vitro assays, and failure to account for metab. in other tissues. Model-based predictions of bioaccumulation within a natural setting also must account for possible metab. at multiple trophic levels. The models described in this study can be used to perform in vitro-in vivo metab. comparisons with fish, est. in vitro biotransformation parameters on the basis of measured chem. residues in field-collected animals, and calc. the level of in vitro metabolic activity required to limit bioaccumulation of all compds. to a specified value. - 35.Arnot
, J. A.; Mackay, D.; Bonnell, M. Estimating Metabolic Biotransformation Rates in Fish from Laboratory Data Environ. Toxicol. Chem. 2008, 27 (2) 341– 351[Crossref], [PubMed], [CAS]35.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXht12jtbw%253D&md5=bf060e5398687ac7494b18a7df9a3711Arnot, Jon A.; Mackay, Don; Bonnell, MarkEnvironmental Toxicology and Chemistry (2008),A method is proposed for estg. metabolic biotransformation rate consts. for nonionic org. chems. from measured lab. bioconcn. and dietary bioaccumulation data in fish. Data have been selected based on a quality review to reduce uncertainty in the measured values. A kinetic mass balance model is used to est. rates of chem. uptake and elimination. Biotransformation rate consts. are essentially calcd. as the difference between two quantities, a measured bioconcn. factor or elimination rate const., and a model-derived bioconcn. factor or elimination rate const. estd. assuming no biotransformation. Model parameterization exploits key empirical data when they are available and assumes default values when study specific data are unavailable. Uncertainty analyses provide screening level assessments for confidence in the biotransformation rate const. ests. The uncertainty analyses include the range for 95% of the predicted values and 95% confidence intervals for the calcd. biotransformation values. Case studies are provided to illustrate the calcn. and uncertainty methods. Biotransformation rate consts. calcd. by the proposed method are compared with other published ests. for 31 chems. that range in octanol-water partition coeffs. from approx. 101 to 108 and represent over four orders of magnitude in biotransformation potential. The comparison of previously published values with those calcd. by the proposed method shows general agreement with 82% of the estd. values falling within a factor of three. - 36.van der Linde
, A.; Jan Hendriks, A.; Sijm, D. T. H. M. Estimating biotransformation rate constants of organic chemicals from modeled and measured elimination rates Chemosphere 2001, 44 (3) 423– 435[Crossref], [PubMed], [CAS]36.https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3MXksVygt70%253D&md5=0fbd49ed9ae340b3209ce3cfe0381174van der Linde, A.; Hendriks, A. Jan; Sijm, D. T. H. M.Chemosphere (2001),In this study, biotransformation rate consts. are estd. for a large set of org. compds. Biotransformation (km) is considered part of the total elimination, further consisting of physico-chem. elimination to water (kw), depuration by feces (kf) and growth diln. (γ). Existing models are used to est. kw and kf, and γ. The difference between measured elimination rate consts. and the sum of predicted elimination rate consts. for water, feces and growth indicates the ration of biotransformation in the total elimination. In all examd. animal classes, polycyclic arom. hydrocarbons seem to be metabolized at an intermediate rate. Because of the relative low hydrophobicity of some of the studied compds., their physico-chem. elimination rate const. is relatively high, and the relative contribution of metab. to total elimination of these compds. is therefore relatively low. Fish seem to be capable of metabolizing chlorodibenzo-p-dioxins and -furans, DDT, chloroanilines and phenol.
