SAR and QSAR in Environmental Research, 2015 Vol. 26, No. 3, 165–180, http://dx.doi.org/10.1080/1062936X.2015.1018940

A simple mechanistic model to interpret the effects of narcotics J. Baasa*, D. Spurgeona and M. Broerseb a Centre for Ecology and Hydrology, Wallingford, UK; bBoard for the Authorisation of Plant Protection Products and Biocides, Wageningen, The Netherlands

(Received 7 October 2014; in final form 21 January 2015) In this research we will show the advantages of using a time-independent dose metric in a mechanistic model to evaluate toxic effects for different narcotic compounds on different species. We will show how different already existing QSARs can be combined within a mechanistic framework to 1) make predictions of lethal thresholds; 2) show some limitations in the use of existing QSARs; 3) show how a mechanistic framework solves some conceptual problems in current approaches and 4) show how such a framework can be used to be of aid in an experimental setup in predicting the outcome of a survival experiment. The approach we chose is based on the simplest mechanistic model available, a scaled one-compartment model to describe uptake and elimination and hazard model to link the exposure to effects on survival. Within this theoretical framework a prediction for an internal threshold for effects on survival of 3 mmol/kg bw can be made, which should be similar for different species and independent of the partitioning characteristics of the toxicant. To demonstrate this, a threshold for 51 different species was derived, which indeed appeared to lie in a relatively small range, typically between 1 and 10 mmol/kg bw. Keywords: mechanistic modelling; no effect concentration; critical body residue; narcotics; QSAR; time

1. Introduction 1.1 General Ecotoxicology is about understanding effects of (usually) man-made compounds in our environment. As there are numerous compounds present and the number of compounds is still increasing, a lot of effort has been put into making predictive models to forecast effects of yet untested compounds (on untested species). In particular for narcotic compounds, compounds that have no apparent working mechanism or mode of toxic action, predictive models have been developed to predict toxic effects [1–3]. Because of the lack of a specific toxic action, narcotic compounds are an excellent starting point to compare and predict effects of different compounds on different species using Quantitative Structure–Activity Relations (QSARs). For the magnitude of effect, associated with exposure to strictly narcotic chemicals, the extent of effect has been suggested to relate closely to associated concentrations independent of chemical identity. Under this assumption, the internal concentration acts as a good metric for the effect. Most QSARs use the octanol-water partition coefficient (Kow) as the most important (or only) parameter to describe toxic effects [4]. Indeed,the use of Kow has shown excellent predictive power for *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

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different ecotoxicological endpoints such as uptake, elimination and/or the resulting bioconcentration factor (BCF) [2,5,6], the acute toxicity of compounds expressed as the LC50 (the concentration at which 50% of a population dies at a certain point in time) [2]. For example, a QSAR was developed for chronic toxicity of narcotics to fish [4]. Based on these findings, the use of QSARs is now widespread in ecotoxicology, including regulatory applications using QSARs as a replacement or surrogate for the results of animal testing [7]. 1.2 Time is of the essence QSARs based on tests with a standardized exposure time must be treated with care, especially for compounds with higher log Kow values. If an organism is exposed to a chemical, it takes time until the equilibrium between the internal and the external concentration is reached. If one-compartment kinetics is assumed, the elimination rate determines when this equilibrium is achieved. If the elimination rate is known, the time to reach a fraction saturation can be calculated according to Kooijman et al. [8]. Using the QSAR on elimination rate for the guppy (Poecilia reticulata) [9] it can be calculated that in a 96-h standardized fish test, 90% saturation is only reached for compounds with a log Kow < 4.51 (or 99% saturation for compounds with a log Kow < 4.03). In a daphnid test with a prescribed 48-h duration, 90% saturation is only reached in 2 days (the prescribed time for a daphnid test) for compounds with log Kow < 4.64, based on Hawker and Connell [5]. This implies that the incipient LC50 value for compounds with a log Kow > ±4.5 will not be reached within the timeframe of the test, meaning that the LC50 will decrease if the test is continued and that any QSAR for fish or daphnids based on 48-h LC50 data containing compounds with a log Kow > 4.5 must be treated with care. If we take this one step further we can calculate that for compounds with a log Kow > 5.42 no effects will be visible in a 96-h standardized fish test for the fathead minnow (Pimephalus promelas). This is based on the application of the simplest mechanistic model for survival [10] (see also the section Approach) and using the relations between water solubility and log Kow [11] and the toxicity parameters with log Kow [12]. This prediction is in line with observations made by Koenemann and Veith et al. [2,13]. Veith et al. exposed P. promelas to a number of different compounds for a standardized test duration of 48 h. No 48-h LC50 value could be measured for compounds with a log Kow > 5.16, and deviations from linearity are observed for log Kow > ~4. Koenemann used Poecilia reticulata in 7–14-day tests and had no problems in measuring LC50 for compounds with log Kow up to ~6. Indeed, also Mayer and Reichenberg [14] show that up to a log Kow of 6.5 LC50 can be measured; however, the test needs to be prolonged. The duration of the test should not only depend on the compound, but also on the test species. A larger fish will take more time to get into equilibrium with its environment than a fish larva [14]. In this respect it is good to note that a standard 48-h test with a small organism like a daphnid is much more likely to have reached equilibrium between internal and external concentrations than fish in a 96-h standardized test. A simple way to overcome this problem is to prolong a toxicity test until the incipient LC50 is reached, as Ferguson already advised in 1939 [15], or use models that incorporate the temporal effects in toxicity [16], as prescribed in the OECD guidelines, so that the full progression of toxicity in time can be predicted from the results of short-term studies [17,18]. 1.3 Aim of the research In this research we will show the advantages of using a time-independent dose metric in a mechanistic model to evaluate toxic effects for different compounds on different species.

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We will show how different QSARs can be combined within one consistent framework to make predictions on lethal effects at different points in time, show some limitations in the use of existing QSARs and show how the mechanistic model solves some conceptual problems in current approaches.

2. Approach 2.1 General This research is based on literature data and since the majority of data is available for aquatic tests, this paper will focus on aquatic species; however, the theoretical framework itself is not limited to aquatic species alone [19,20]. Most data originate from the US-EPA ECOTOX database [21]. In addition, the data for Pimephalus promelas (Fathead minnow) originate from the Lake Superior dataset [22–25]. In this database raw survival data are available for a large number of different compounds measured at several points in time. 2.2 Model choice The approach we chose is based on the simplest mechanistic model available and explicitly considers toxicokinetics and toxicodynamics. A one-compartment model is used to link internal and external concentrations. A hazard model is used to link internal concentration and survival. The model assumes that the hazard rate is proportional to the internal concentration once a threshold is exceeded. Below the threshold no effects on mortality will occur as a result of the exposure. The model was originally developed by Bedaux and Kooijman [26] and later acknowledged by the OECD [17,18] to be used in toxicity testing. In this paper, we assume a constant exposure bio-available concentration, one major exposure route and no bio-transformation. This model needs three time-independent parameters to describe the whole time course of the toxic effect. - The No Effect Concentration (NEC), a time-independent toxicological threshold below which no effects occur irrespective of exposure time, basically the incipient LC0, expressed in mmol/L. - The elimination rate (ke), which describes when the equilibrium between internal and external concentration is set, expressed in d−1. - The killing rate (kk), the toxic potency of the compound (once the NEC is exceeded) expressed in (mmol/L)−1 d−1. The higher the killing rate the more toxic a compound is. If these three parameters are known for a specific compound and organism, this allows a dose-response curve to be calculated for any concentration at any point in time. If exposure is below the NEC (i.e. below the LC0) a cohort of organisms will show the same response as the control (note, if there is mortality in the controls, this can be included in the model as a blank mortality rate). When the threshold of the NEC is exceeded, then the killing rate and elimination rate determine the effect. Note that the NEC can be zero and in that case any exposure will lead to an effect. See Jager et al. [10] for an elaborate conceptual comparison of the different survival models that are currently in use, the underlying assumptions and how the different models (including the LC50) are related.

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2.2.1 How parameter values relate The parameters of biology-based methods have a physiological meaning. This implies that the parameters cannot vary independently; even more, we expect to see relationships between the parameters for chemicals that share a mechanism of toxicity [27]. For non-polar narcosis the amount of effect seems independent of the molecule (in the cell membrane). Therefore the hazard rate must be compound independent. This implies that the NEC and killing rate of all narcotic compounds will be the same when these parameters are expressed as internal molar concentrations. The NECs and killing rates for different narcotic compounds will then be inversely proportional if they are expressed as environmental concentrations. Because hydrophobicity drives the concentration in the cell membrane, the NEC and killing rate should show a strong correlation to Kow (as a proxy for membrane lipids) [12,16,27]. If the assumption of a one-compartment model is valid, ke is expected to be inversely proportional to the square root of Kow [27]. This follows from the argument that uptake and elimination are opposite processes at the same surface, and from the assumption that at equilibrium the ratio of internal and external concentrations of the toxicant is proportional to Kow. This leads to the following relations: -

ke and Kow a log/log plot would give a slope of −0.5. the uptake rate (ku) and Kow a log/log plot would give a slope of 0.5. the BCF and Kow a log/log plot would give a slope of 1. NEC and Kow a log/log plot would give a slope of −1. the kk and Kow a log/log plot would give a slope of 1.

These theoretical dependencies were shown to be very close to observed values [12,16], where the same mechanistic model was used to derive the relations for the NEC, kk and ke with log Kow (see Table 1). In addition, there is a wealth of literature data available on fish, daphnids and mussels on uptake, elimination rates and BCF with values very close to the predicted values [2,5,6,28,29]. The general very good fits (r2 generally between 0.85 and 0.98) show that in a first approximation log Kow is a good sole descriptor to estimate parameter values, at least for fish, daphnids and mussels, and it is easily available for many different compounds, which makes it also a practical starting point. However, there are exceptions, for example the pond snail, where a much steeper slope is found for log Kow against log BCF [30], which is not well understood. 2.2.2 Internal concentrations It is expected that the NEC and killing rate of all narcotic compounds will be the same when these parameters are expressed as internal molar concentrations [16]. The internal NEC is the product of the environmental NEC and the BCF. Using the QSARs that are outlined and described above for the calculation of the NEC (log NEC = −log Kow + 1.80 [12], with the Table 1. QSARs for the parameters of the survival model [12] (see text for further information). Parameter

Slope

Intercept

r2

NEC ke kk

−0.91 0.72 −0.60

1.79 −0.26 1.76

0.904 0.739 0.853

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theoretical slope of −1) and the BCF for fish in general (log BCF = log Kow – 1.32 with r2 = 0.95 [28]) this leads to an internal NEC of 3 mmol/kg bw, irrespective of log Kow. This value is in good agreement with earlier observations, based on critical body residue (CBR) approaches (see Jager et al. for a description of the underlying assumptions [10]), showing that the internal concentrations leading to the death of an exposed organism cover only a relatively narrow range for different species and range between 0.1 and 7 mmol/kg bw [31], 2 and 8 mmol/kg bw [32], 0.3–8 mmol/kg bw [33], 8–14 mmol/kg bw [34], 4 mmol/kg bw [35]. The ranges and values all include the theoretical value of 3 mmol/kg bw close to the centre of the measured values. 2.3 Use of the model The parameterized survival model can be used in different ways: (1) to calculate internal effect concentrations, or (2) to predict the time course of toxic effects. 2.3.1 Calculate internal effect concentrations (section 3) We anticipate an internal threshold for survival that approximates closely to the theoretical value of 3 mmol/kg bw for different compounds (irrespective of log Kow for different species). To check on this the internal NEC must be calculated. For this we need the external NEC and the BCF as input parameters: Internal NEC ¼ external NEC  BCF The ideal way to derive a NEC from survival data would be to analyse the raw survival data, with observations made at intermediate points in time [26]. In a standard fish or daphnid test the survival at intermediate time points must be observed; unfortunately however, this is not a part of the final reporting format for the results derived from standard ecotoxicology tests and these data are usually unavailable. The earlier mentioned data from the Lake Superior toxicity dataset for P. promelas are a positive exception to this general rule. This resource includes survival data for a large number of different compounds measured at several points in time, allowing for a thorough analysis of the available data. Another way to derive the toxicity parameters for this modelling approach is to use the time dependence of LC50 values. If we have observations on at least three LC50 values at different points in time available, the toxicity parameters required for the survival model can be derived [17]. The BCF can be derived from the different available QSARs for different species. An example of this approach and application for different species to check on the theoretically derived internal NEC of 3 mmol/kg bw for different compounds are shown in section 3. 2.3.2 Predicting the time course of toxic effects To predict the time course of toxic effects in an exposure experiment requires information on the external concentrations and the three model parameters (NEC, kk and ke) for each compound for which the calculations are carried out. Since the NEC, kk and ke are all related to log Kow (see section 2.2.1) and relations with log Kow were derived [12], it is possible to make predictions on the values of the toxicity parameter, based on the log Kow according to the relationship previously described. Once the parameter values are known, the whole time course of toxic effects (or dose-response curves) can then be calculated for any exposure concentration at any point in time, of course keeping the solubility limit of the compound in mind.

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Making predictions of the time course of toxic effects can be a valuable tool when an experiment is conducted. Concentration ranges and effects at different points can be interpreted by the model. Especially for compounds with higher values of the log Kow where in a standardized test no effects would be visible within the shortened time duration commonly used in regulatory tests, this can be of great help in setting up an experiment that allows for the full development of toxicity in time. Four different examples of this approach are shown in section 4.

3. Results and discussion 3.1 Comparison of different species As stated before, we expect the internal NEC for the different compounds and different species to be more or less constant at the theoretical value of 3 mmol/kg bw. To demonstrate this, a literature search on available data to derive NECs for different species was performed. The US-EPA ECOTOX database [21] was used as a starting point to search for LC50 data at different points in time. The search focussed on chlorinated and methylated benzenes, as these compounds have been indicated to act according to a non-specific (narcotic) mode of action [30]. Further, there are data available for representative from these compound groups for a number of different species, including studies that gave at least three LC50 values for a given species at different points in time. In total over 1300 records, containing 14 different compounds, and well over 100 different species were evaluated. For fish and daphnids the existing QSARs for the BCF were used [5,6,28,36]. If more than one QSAR was available for the BCF, the geometric mean of internal NEC concentrations was used. For amphipods there was no QSAR available to estimate a BCF, therefore we used data published by Landrum et al. [37] to derive a relation between log Kow and the BCF. In this publication, the kinetics appeared to be concentration dependent, so there is no real one-compartment kinetics; however, a plot of the log BCF against log Kow gave an excellent fit with a slope very close to the expected value 1 (log BCF = 1.06 log Kow –0.83, r2 = 0.999). For Asellus aquaticus we could find no data on BCF, therefore these were treated as amphipods. This assumption seems reasonable, as isopoda and amphipoda are closely related phylogenetically and they have similar feeding habits. 3.1.1 Example An example to show the procedure that was followed is given below. We randomly took Jordanella floridae exposed to 1,2,4-trichlorobenzene as an example. From the ECOTOX database 5 LC50 values at different points in time are reported for J. floridae for 1,2,4trichlorobenzene (log Kow = 4.02) were taken, see Table 2. Table 2. LC50 values at different points in time for Jordanella floridae. Time (days) 1 1.5 3 4 4

LC50 (μg/L) 2672 2318 1494 1285 1217

LC50 (mmol/L) 14.7 12.8 8.2 7.1 6.7

× × × × ×

10−3 10−3 10−3 10−3 10−3

Mysida Mysida Mysida Mysida Mysida Mysida Mysida Mysida Mysida Isopoda Cladocera Amphipoda Amphipoda

Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Arthropoda Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata

Amphipoda Amphipoda Decapoda Decapoda Decapoda Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes Cyprinodontiformes

Order

Phylum Americamysis bahia (no data) Americamysis bahia (no data) Americamysis bahia (no data) Americamysis bahia (no data) Americamysis bahia (no data) Americamysis bahia (2–8 d juv) Americamysis bahia (no data) Americamysis bahia (no data) Americamysis bahia (no data) Asellus aquaticus Ceriodaphnia dubia (neonate, 12 h) Gammarus fossarum Gammarus pulex (adults) Gammarus pulex (adults, 3 cm) Hyalella azteca (7–10 d) Elasmopus pectenicrus (adults) Palaemonetes pugio (no data) Palaemonetes pugio (no data) Palaemonetes pugio (no data) Cyprinodon variegatus (14–28 d juv, 8–15 Cyprinodon variegatus (14–28 d juv, 8–15 Cyprinodon variegatus (14–28 d juv, 8–15 Cyprinodon variegatus (14–28 d juv, 8–15 Cyprinodon variegatus (14–28 d juv, 8–15 Cyprinodon variegatus (14–28 d juv, 8–15 Gambusia affinis Gambusia affinis Gambusia affinis Jordanella floridae (1–3 mon juv) Jordanella floridae (1–3 mon juv) Jordanella floridae (2–4 mon juv) Poecillia reticulata (3.8–6.4 cm; 1–2 gr)

Species (life stage)

mm) mm) mm) mm) mm) mm)

1,2-dichlorobenzene 1,3-dichlorobenzene 1,2,4-trichlorobenzene 1,4-dichlorobenzene 1,2,4,5-tetrachlorobenzene 1,2,3,5-tetrachlorobenzene 1,2,3,5-tetrachlorobenzene Pentachlorobenzene Acenaphthene Benzene Chlorobenzene Benzene 1,2,3-trichlorobenzene 1,2,3-trichlorobenzene Pentachlorobenzene 1,2,4-trimethylbenzene Benzene 1,2-dichlorobenzene Toluene 1,2-dichlorobenzene 1,4-dichlorobenzene 1,3-dichlorobenzene 1,2,4,5-tetrachlorobenzene 1,2,3,5-tetrachlorobenzene Pentachlorobenzene Pentachlorobenzene** 1,4-dibromobenzene** 1,2,3-dibromobenzene** 1,4-dichlorobenzene 1,2,4-trichlorobenzene 1,2,4,5-tetrachlorobenzene Benzene

Compound

(Continued)

11 15 1.1 8.2 65 4.2 15 19 3.1 68 1.0 17 0.9 0.2 19 29 6.0 46 4.0 9.3 6.9 8.4 3.6 16 16 2.7 1.5 2.5 1.6 2.8 3.5 4.9

NEC_int mmol/kg wwt

Table 3. Summary of the calculated NEC for different compounds and different species (data extracted from the US-EPA ECOTOX database, see text).

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Order

Cyprinodontiformes Cyprinodontiformes

Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Cypriniformes Perciformes

Perciformes Perciformes Salmoniformes Salmoniformes Salmoniformes Siluriformes Veneroida Sorbeoconcha Hygrophyla

Phylum

Chordata Chordata

Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata Chordata

Chordata Chordata Chordata Chordata Chordata Chordata Mollusca Mollusca Mollusca

Lepomis macrochirus (juv, 3.65 cm, 0.90 g) Lepomis macrochirus (3.8–6.4 cm; 1–2 g) Oncorhynchus mykiss (fingerling 5.3 cm, 2.1 g) Oncorhynchus mykiss (fingerling 5.6 cm, 2.7 g) Oncorhynchus mykiss (54 mm; 2.187 g) Clarias lazera (19–23 cm; 150–180 g) Katelysia opima (4.3 g) Amphimelania holandrii Lymnaea stagnalis

Carassius auratus (3.8–6.4 cm; 1–2 g) Carassius auratus (20–80 g; 13–20 cm) Pimephalus promelas (3.8–6.4 cm; 1–2 g) Pimephalus promelas (juv, 2 cm) Pimephalus promelas (3.8–6.4 cm; 1–2 g) Pimephalus promelas (juv, 2 cm) Pimephales promelas (3.8–6.4 cm; 1–2 g) Pimephalus promelas (juv, 2 cm) Pimephalus promelas (juv, 2 cm) Pimephalus promelas (juv, 2 cm) Terapon jarbua

Poecilia reticulate (6 m; 1.9–2.5 cm; 0.1–0.2 g) Poecilia reticulata (6 m, 1.9–2.5 cm; 0.1–0.2 g)

Species (life stage)

*Data based on the Lake Superior dataset. **Data recalculated from time-to-mortality data, based on (Chaisuksant et al. [45]).

(Continued).

Table 3.

Chlorobenzene Toluene 1,4-trichlorobenzene 1,2,4-trichlorobenzene toluene Toluene Benzene Benzene Benzene

Chlorobenzene Toluene Benzene Benzene* toluene toluene* Chlorobenzene Chlorobenzene* 1,2,4-trimethylbenzene* acenaphthene* Benzene

Chlorobenzene Toluene

Compound

1.2 7.1 1.1 3.4 2.0 4.1 0.6 22 23

13 5.1 4.3 2.1 12 2.5 7.9 1.4 10 1.3 11

12 17

NEC_int mmol/kg wwt

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From these data an environmental NEC of 6.0 E-03 mmol/L can be derived [17]. Three different BCF values were calculated, based on the three available QSARs for fish: 501 (Log BCF = log Kow –1.32 [28]); 208 and 930 [36]. This leads to values for the internal NEC of: 3.0; 1.2 and 5.6 mmol/kg bw, respectively, with a geometric mean of 2.8 mmol/kg bw. This is consistent with the theoretical value of 3 mmol/kg bw that is predicted from the QSARs and survival model. The same procedure was followed for all other compounds and species, and the result is summarized in Table 3. 3.2 Variability in the data 3.2.1 LC50 values It is difficult to estimate the variability in the data. For most compounds we only have one data source that gives LC50 values at various points in time. A comparison of LC50 values at a fixed (96 h) exposure time can usually be made and shows that the typical range in the values is between a factor of 2 and 8; however, much higher variations can be found in the ECOTOX database. In some cases we can compare NECs from the Lake Superior dataset with NECs derived from LC50 values in time from the ECOTOX database. Differences between the Lake Superior dataset and other datasets vary between a factor of 2 and a factor of 6. For data based on independent sources, measured in different laboratories, this is within the normal expectation for variation, in line with Raimundo et al. [38]. 3.2.2 QSAR estimate of the BCF For fish we had three different QSARs available for the BCF. Typically the different values show a variation of approximately a factor of thee. This can also be considered to be good. 3.3 Comparison of different phyla In Figure 1 the cumulative distributions for the Arthropoda, Chordata and all available species are presented, based on a log normal distribution of the data from Table 3, which gave the best fits. The parameter values of the fits are presented in Table 4. For the Mollusca we only had three datapoints, so no distribution could be fitted. The average value for all species equals 1.56 mmol/kg bw and is close to the theoretical value of 3 mmol/kg bw that was derived earlier. The NECs for all species, for which we had data available, have internal NECs in a small range, typical between 1 and 10 mmol/kg bodyweight. There are some exceptions, the data for benzene for G. Fossarum, L. Stagnalis, A. holandri and A. aquaticus all show high > 20 mg/kg bw. The snail L. stagnalis is known to have some unexplained deviations in its kinetics [30]. However, they are not exceptionally high. A direct comparison with other measurements is not possible; however, the 2-day LC50 values are also relatively high for A. aquaticus and G. fossarum. But this is no obvious reason to reject these data, so they were taken up in further analysis. It is notable that these data all come from the same source in the ECOTOX database [39]. Another high value for the internal NEC is for A. bahia exposed to 1,2,4,5-tetrachlorobenzene with an internal NEC of 68 mmol/kg bw. This is out of the range of all other data for this species (see Table 3). Statistical testing indeed gives this value as an outlier; therefore this value was not taken up in the distribution fits.

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Figure 1. Cumulative log normal distribution fits of the internal No Effect Concentration for the Arthropoda (panel 1 16 datapoints), Chordata (panel 2 32 datapoints) and all species (panel 3 51 datapoints).

Table 4. Cumulative distribution parameters for the different phyla, values in parenthesis give the standard deviation. Phylum Arthropoda Chordata Mollusca All species

Median (95% conf. interval)

Standard deviation (95% conf. interval)

1.63 (0.35) 1.49 (0.15)

1.41 (0.26) 0.85 (0.11)

1.56 (0.16)

1.11 (0.11)

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4. Implications 4.1 Mixture effects Neglecting the time course of toxic effects for single compounds hampers the interpretation of the traditional QSARs but is likely to be problematic in predicting effects of mixtures. The use of mechanistic models in mixture ecotoxicology is not widespread. Instead, typically effects of mixtures of compounds are estimated with the concentration addition (CA) or independent action (IA) models [40,41]. Both these models predict a mixture effect, based on the known effects of the individual compounds based on time dependent LCx (or ECx) values. As such, both these models neglect the time dependence of LC50 values. In Table 5, LC50 data for the standard 48-h exposure for daphnids combined with data for 16 days of exposure are shown [42]. Typical environmental exposures depend on the compound and the environmental conditions, but a constant exposure for 48 h is highly unlikely. Instead, long-term exposure to relatively low concentrations is a much more realistic scenario in most cases. If the combined effect of the five compounds mentioned in Table 5 is taken and CA is assumed for an equitoxic mixture, the predicted longer-term toxicity of the mixture to daphnids will be underestimated by a factor of 5 if the standard 48-h LC50 values are used, which is still common practice [43,44], instead of the incipient LC50 values. Hence ignoring the time dependence of toxicity results can lead to a large underestimation of effects, especially if the narcotic mixtures include one or more compounds with a high Kow. For example, the underestimation above is based on data for mixtures with log Kow values ranging from 2.84 to 5.17. Effects of mixtures of PCBs or PAHs, which can have higher Kow values, can be shown to underestimate the environmental toxicity of a mixture by an order of magnitude, just by ignoring the time dependence of an LC50 value. Hence, CA and IA models can only make valid predictions of mixture effects for the point in time on which the toxic effect values of the individual compounds were based. 4.2 Critical body residues The CBR approach is based on the individual tolerance model where it is assumed that as soon as a certain internal threshold, the CBR, is reached an organism dies [33,35]. This implies that the CBR is regarded as an internal threshold for survival at the individual level. However, mostly the CBR is calculated from the incipient LC50, using the BCF: CBR ¼ incipient  LC50  BCF: Using the incipient LC50 assures that problems with the time dependence of the LC50 value are avoided. However, the underlying assumption of an LC50 is that individuals differ in their sensitivity to a toxicant and that at the LC50 level, the 50% most sensitive individuals Table 5. Example of the time-dependence of LC50 values for Daphnia magna (Hermens et al., 1984 [42]). Compound Chlorobenzene 1,2,4-Trichlorobenzene 1,2,3,4-Tetrachlorobenzene Pentachlorobenzene 4-Chlorotoluene

log Kow 2.84 4.02 4.60 5.17 3.33

48 hr LC50 mmol/L 2.3 1.5 2.5 4.9 2.8

× × × × ×

−1

10 10−2 10−3 10−4 10−2

16 d LC50 mmol/L 3.5 3.1 1.5 4.4 1.3

× × × × ×

10−2 10−3 10−3 10−4 10−2

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will die and the 50% least sensitive individuals will survive [10]. So formally the CBR is the internal concentration at which 50% of the population will be affected by the toxicant and the other 50% can have the same internal concentration but, because they are less sensitive, live on. Obviously the LC50 refers to 50% of the population, but the CBR is regarded to affect every single individual, or 100% of the population. In a number of cases the CBR is referred to as an internal LC50 concentration (ILC50) [31,45], but also here the ILC50 is treated as a toxicological threshold for each individual, which does not comply with the underlying assumption of an LC50. A related problem is that under the LC50 assumption it is not possible to explain the observation that a dose-response curve for lethal effects becomes steeper in time [46]. The steepness of the dose-response curve is supposed to reflect the sensitivity distribution of the exposed individuals, which should be independent of time. Also, the observation that the CBR depends on time [34,45] is not consistent with the underlying CBR assumptions, but can be easily explained by this toxicokinetic-toxicodynamic (TKTD) approach. If the NEC is exceeded death is not immediate, but depends on the values of the elimination rate and the killing rate, appearing in an experiment as a decrease in the CBR. The TKTD approach that we used here does not suffer from the inconsistency in the interpretation of the CBR, but gives numerically comparable results and is able to describe the whole time course of toxic effects within one single consistent framework. 4.3 Use of the modelling approach 4.3.1 Predicting effects based on log Kow values Using a TKTD model to describe survival allows calculation of the whole time course of toxic effects and can be of help in setting up an experiment. As a first step the log Kow of a compound can be used as the sole input parameter for the calculations using the QSARs presented previously to determine DEBtox parameters that can be used for subsequent modelling. In this section the results of such an approach are shown. We took four published examples showing the time course of toxic effects. Chaisuksant et al. [45] published the time course of toxic effects for G. affinis, exposed to a single concentration of 0.2 mg/L pentachlorobenzene (log Kow 5.22) and 2 mg/L 1,2-dibromobenzene (log Kow 3.77). In addition we used the exposure of P. promelas exposed to benzene (log Kow 1.99) and toluene (log Kow 2.54) from the Lake Superior dataset. Based on the log Kow value of the compounds, the toxicity parameters can be estimated using available QSARs for fish for the NEC, ke and kk [12] (see Table 1, but with the theoretical slopes of 1 for the NEC; −0.5 for ke and +1 for kk): log NEC ¼ log Kow þ 1:80 log ke ¼ 0:5log Kow þ 1:76 log kk ¼ log Kow  0:26 In addition, we assume the earlier mentioned theoretical value of the internal NEC to be 3 mmol/kg bw is valid for these cases, and use this as the input value for this parameter within the DEBtox models. Using these values, this is sufficient to calculate the whole time course of toxic effects and make a comparison with the measured data. For the Lake Superior dataset only concentrations with partial effect are shown, and irregularities in the raw data (like increases in numbers surviving at certain points in time) were omitted. The result is shown in Figure 2.

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Figure 2. Panel 1, measured (+) time course of mortality of G. affinis exposed to a single concentration of pentachlorobenzene [45] and the predicted time course of mortality (line), based on the log Kow value of pentachlorobenzene. Panel 2, measured (+) time course of mortality of P. promelas exposed to benzene and the predicted time course of mortality (line), based on the log Kow value of benzene. Panel 3, measured (+) time course of mortality of P. promelas exposed to toluene and the predicted time course of mortality (line), based on the log Kow value of benzene.

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Figure 2 shows that the prediction of the whole time course of the toxic effects for these examples is in excellent agreement with the measurements, for all three compounds covering a wide range of log Kow values. For 1,4-dibromobenzene no effects would be predicted for the exposure of 2 mg/L. This is in sharp contrast to the observations, where 100% mortality is reached after 275 h of exposure. The internal NEC that was derived for this single concentration exposure equals 1.5 mmol/kg bw, which is below the theoretical value of 3 mmol/kg bw and therefore no effect is predicted. As in the use of any QSAR, observed values can be different from predictions. In this case, this leads to a prediction of no effects whereas 100% mortality is observed. However, the advice before this experiment would be conducted would be to increase the exposure concentration. Simulations with a concentration slightly above the NEC indicate that for the first ~65 h no effects will be visible, which is indeed the case. In general terms the time frame of toxic effects is captured very well by the calculations, based solely on the log Kow values of the toxicants, and an indication of what to expect in a toxicity test can be based on calculations like this. In addition, the observation made by Chaisuksant et al. [45] that the CBR appears to be time dependent does not conflict with this approach, and no further assumptions are needed to understand the observed timeframe of toxic effects. Also note that the measurements on pentachlorobenzene were continued for more than 600 h, and that during the first 200 h no effects are observed (which is captured very well by the model prediction). This again emphasizes the need to continue measurements beyond the timeframe of a standardized 4-day fish test.

5. Conclusions In this research we show that it is possible to use a simple mechanistic model, combined with existing QSARs on kinetics, to make estimates of time-independent internal thresholds for survival. Based on theoretical considerations it is predicted that the internal threshold is similar for different species over a wide range of log Kow values. This prediction was verified by deriving the internal threshold for survival for 51 different species, which were generally within a very narrow spectrum, between 1 and 10 mmol/kg bw, values which are close to a theoretical prediction of 3 for an idealized species. These results are comparable with experimental data based on CBR modelling, but the mechanistic fundament does not suffer from the inconsistencies in typical CBR approaches. The model can be used to predict the time course of toxic effects for different concentrations, based on the log Kow value of the compound involved, and may be a valuable asset in setting up experiments. Acknowledgement The study was supported by the EU Marie Curie Actions - Research Fellowship Programme 2012 (FP7-PEOPLE-2012-IEF), project acronym BIOME, contract nr 328931.

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