2-(2H-benzotriazol-2-yl)-4-(1,1,3,3-tetramethylbutyl)phenol

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Reference 1

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

experimental study

Adequacy of study:

key study

Study period:

2016 - 2017

Reliability:

1 (reliable without restriction)

Rationale for reliability incl. deficiencies:

guideline study

Qualifier:

according to guideline

Guideline:

OECD Guideline 305 (Bioaccumulation in Fish: Aqueous and Dietary Exposure) -I: Aqueous Exposure Bioconcentration Fish Test

Deviations:

no

Qualifier:

according to guideline

Guideline:

EPA OPPTS 850.1730 (Fish Bioconcentration Test)

Deviations:

no

GLP compliance:

yes (incl. QA statement)

Radiolabelling:

yes

Remarks:

Radiochemical purity: 99.7%; the radiochemical purity was determined before its use in the study and at the end of the study

Details on sampling:

Water and fish were sampled for radioactivity measurement during the uptake and depuration periods according to the schedule below. Both water samples and fish samples were collected concurrently and always prior to first feeding on the sampling day. Water samples (10 mL per sample) were collected directly from the middle of the test vessel and analyzed on the same day of sampling or stored in a refrigerator if necessary. Additional water samples are taken between the scheduled sampling days in order to control the proper function of the dilution system. These measurements are of lower accuracy (1 minute program) and are not used to calculate the mean measured concentration of the test substance in water. At each sampling time, 5 fish were removed from the test vessel using a net and were sacrificed by immersion in a buffered anesthetic solution (MS222) followed by a brief rinse under running tap water. This procedure also removed residual test solution from the surfaces of the organisms. The fish were blotted dry then weighed and total length measured. Since fish were too large to be combusted whole, they were dissected and combusted in up to 5 parts comprising the whole fish: edible (fillet in 2 parts; muscle tissue only to the extent possible); and non-edible (remaining fish including head, viscera and bones, and skin and fins). The parts from each fish were weighed (the total weight of the fish was calculated from the individual parts) and dried separately on filter paper overnight then combusted in an oxidizer and analyzed by liquid scintillation as soon as possible. Since the same number of fish are removed from the control group at the same time, the biological loading (fish/water) in the control and in the test group was kept equivalent. At each sampling time 5 control fish were sacrificed for radioactivity background correction and to determine lipid content. For background correction, two fish were dried and combusted identically as the fish from the concentration group. The lipid content was determined in the remaining 3 control fish which were stored in a freezer until analyzed. The control fish sampled at additional time points were discarded. The sampling schedule was determined using the estimated Pow (log Pow = 6.5) to calculate estimates of k2 and time for effective steady state (tess) according to equations in OECD 305. The calculated tess is 864 days which is impractically long for the execution of a study. According to the OECD TG 305, the estimated values generated with the provided equations should be used with caution, especially if the test substance is outside the applicability domain. The equation is based on a limited data set predominated by chlorobenzenes and in the present case the test substance is clearly not within the applicability domain of the equation. Thus, the calculated tess of 864 days is not regarded as a reliable indicator of the time needed to reach effective steady state conditions. The optimal uptake duration to generate a statistically acceptable kinetic BCF should be between the time required to reach at least 50% of steady-state (i.e. 0.69 / k2 = 12 days) but not more than 95% of steady-state (i.e. 50 days). The duration of depuration should be at least half of the uptake phase. Due to the uncertainty of the calculated values based on the equation provided in the guideline, a preliminary minimized test (experimental conduct in accordance with GLP but without a GLP status) was conducted to evaluate the feasibility of the test design. Fish were exposed to test substance at 0.2 μg/L for uptake period of 14 days followed by a clean water depuration period of 7 days. Water and fish were sampled on 4 occasions during uptake and 3 occasions during depuration. Measured concentrations of test substance in filtered water samples as total radioactive residues (TRR) remained within ± 20% of the mean measured concentration, 0.18 μg/L. Thus truly dissolved concentrations of the test substance were consistently maintained over this time. There was approx. 28% difference between the day 7 Cf and day 14 Cf indicating that steady state conditions in fish tissues were nearly reached over the uptake period. The data were suitable to calculate kinetic rate constants (k1 and k2) as well as a kinetic BCF with a first order one-compartment model. Using the k2 derived from the preliminary experiment, the time needed to reach 95% steady state is 11 days (t95 = 3.0 / 0.27). Fish lipid content and growth rate were not determined as part of the preliminary test and the fish originated from a different batch than used in the definitive test. Thus the preliminary calculated BCFk value should be treated judiciously.

Vehicle:

no

Details on preparation of test solutions, spiked fish food or sediment:

The test substance is sparingly water soluble. An organic solvent-free saturated stock solution was prepared using a solid-liquid (slow-stir) saturator technique. Approx. 1000 μg of the [14C] test substance was weighed into a glass vial. The weighed radiolabeled test substance was stored in a freezer (approx. -18°C or colder) until use if necessary. To facilitate substance handling, test substance was dissolved in approx. 100 mL acetone. The acetone solution was checked for complete dissolution of the test substance, then the solution was distributed onto approx. 20 g of glass wool placed on bottom of the stainless steel stock solution tank. All the acetone was then completely evaporated to leave a thin layer of test substance coating the glass wool. The purpose of the glass wool was to facilitate dissolution by increasing the surface area where the substance was attached. Afterwards, the glass wool was covered by a stainless steel grate and 200 L of dilution water were added (taking care to minimize the disturbance of the glass wool). The stock solution was stirred with a paddle stirrer at the test temperature up to 7 days. Since all acetone was evaporated prior to adding water, no solvent was present in the final test solution and no solvent control is required. Potentially undissolved substance or particles of glass wool was separated by filtration via a paper filtration unit. The concentration in the stock solution tank was verified in each newly prepared stock solution without a GLP status. The results were used as the basis to adjust the flow rates of the stock solution pumps and were not reported in detail. Throughout the exposure period the stock solution was continuously diluted with aerated dilution water at a constant rate (flow controlled by a rotameter) per test group. With exception on day 21 to 22 where the test system was static due to a lack of stock solution for about 19 hours. The flow rates were calibrated (maximum deviation less than 10%) before the exposure was started and checked weekly during exposure. The required test temperature of 13 ± 1 °C was ensured by adjusting the dilution water temperature prior to entering the mixing tanks. The flow through system was allowed to saturate (for 2 days) with samples of test water collected and analyzed (without a GLP status) to monitor test concentrations during this time. Exposure was started once stable analytical results were obtained. The true dissolution of the test substance was demonstrated by analyzing centrifuged solutions prior to the start of exposure. Centrifuges tubes are conditioned with test solution at least 3 x 30min prior to sample collection. Theoretical radioactivity in the solutions: 790 dpm / 10 mL in the test solution 0.2 μg/L

Test organisms (species):

Oncorhynchus mykiss (previous name: Salmo gairdneri)

Details on test organisms:

Species: Rainbow trout (Oncorhynchus mykiss)
Origin: All fish are taken from the same batch of fish of the same origin with the same hatch date.
Supplier: Forellenzucht Trostadt GbR, Dorfstrasse 7, 98646 Trostadt, Germany
Sex: Juvenile male and female fish. Sex was not determined in this study.
Body size / age at start of exposure: Body weight: overall mean of 2.20 g/fish (±0.25, SD) with a range of 1.83 – 2.80 g/fish. The minimum fish body weight was 65% of the maximum body weight.
Body length (overall mean): 6.2 cm ± 0.2
Hatch date: approx. 11 Jun 2016
Arrival in the test facility: 22 Aug 2016
The fish were approximately 4 months old at the start of exposure.
Reason for species selection: Rainbow trout are used routinely for toxicity tests in the test facility. According to the test guidelines rainbow trout are a
suitable species for this test.

Route of exposure:

aqueous

Justification for method:

aqueous exposure method used for following reason:

Remarks:

According to ECHA's Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment, draft version 3.0, this route of expsure is preferred as the resulting BCF can directly be compared with the B and vB criteria.

Test type:

flow-through

Water / sediment media type:

natural water: freshwater

Total exposure / uptake duration:

28 d

Total depuration duration:

28 d

Hardness:

102 mg/L CaCO3

Test temperature:

13 °C

pH:

7.8 - 8.0

Dissolved oxygen:

8.3 - 9.2 mg/L

TOC:

0.8 - 1.8

Salinity:

n/a

Conductivity:

n/a

Details on test conditions:

Acclimatization: The fish were kept in the test facility under the same conditions as during the test for at least 14 days.
Housing: Prior to testing, the batch of fish were housed in a fiberglass tank (approx. 300 L) receiving a continuous flow through supply of fresh test water.
Temperature: 13 °C
Photoperiod: 16 hours light, 8 hours darkness
Feeding: Commercial trout diet: Inicio 917 1.1 mm, BioMar, 7330 Brande, Denmark, daily. Additionally, frozen brine shrimp (Artemia) are provided generally on workdays. The composition is suitable for the test species.
Amount of feed: During adaptation approx. 1 - 2% of the mean body weight per day
Preliminary medical treatment: The test fish were not treated. The animals were visually inspected for their state of health upon arrival at the test facility and before the start of exposure. Only healthy fish were used. The mortality during the adaptation time was < 1% per week.
Water temperature: 13°C
Aeration: The test vessels were not aerated
Photoperiod: 16 hours light, 8 hours darkness, illumination by fluorescent tubes at the ceiling of the room, control by timer
Apparatus: Flow through system
Test vessels: During the test the fish were maintained in silicon-sealed glass aquaria with an overflow at approx. 36 cm, dimensions: 80 x 35 x 55 cm; water volume approx. 100 L. One test vessel was used per test concentration.
Flow rate: Approx. 21 L/hour per test aquarium, approx. 5-fold volume exchange per day in the test vessels.
Loading: At start of the uptake period 70 fish with a mean body weight of 2.20 g (wet weight) were inserted into each test vessel. Therefore based on the flow rate of 504 L/d the loading was 0.31 g/L test water per day at start of the uptake period. During the rest of the uptake and depuration period the loading rate was decreased by removing fish from the test vessels for samples.
Dilution water: The test water (dilution water) was aerated non-chlorinated drinking water obtained from the municipal water works of the city of Frankenthal (67227, Germany), additionally purified through a charcoal filter and diluted with deionized water to achieve a hardness of approximately 100 mg/L CaCO3. The mixed test water was sanitized by UV treatment prior to entering the aquaria. Generally the total organic carbon (TOC) content of the dilution water is <2.0 mg/L. The drinking water used to prepare the test water is regularly assayed for chemical contaminants by the municipal authorities of Frankenthal (67227, Germany) and the department Environmental Analytics Water / Steam Monitoring of BASF SE as well as for presence of microbes by a contract laboratory. On the basis of the analytical findings, the water was found to be suitable. The German Drinking Water Regulation (Trinkwasserverordnung) served as guideline for maximum tolerable contaminants. The results of periodic water quality analyses of the drinking water and the dilution water are to be found in the archives of the testing facility. The analytical data for the drinking water used for the preparation of the dilution water are archived with the raw data.
Feeding: Same as during acclimatization, however no Artemia were provided as food during the test.
Amount of food: Approx. 1% of the mean body weight per day, in 1 application per day. The amount of food was adapted to the number of fish in the test vessel after each sampling.
Food analysis for contaminants: The food is regularly analyzed for contaminants and the results are stored with the raw data. In view of the aim and duration of this study, the contaminants contained in commercial feed should have no influence on the results.
Cleaning of test vessels: The test vessels were cleaned at least once daily, generally no sooner than 30 minutes after the feeding, to remove residual food and excretion.

Each test group consisted of 1 aquarium (approx. 100 L) with 70 fish randomly inserted at test initiation. Each test group aquaria was uniquely identified with appropriate labels. In the interest of animal welfare, this study used only one concentration of test substance to determine bioconcentration as recommended in the 2012 revised OECD 305 test guideline for non-polar organic chemicals. Recent scientific publications provide compelling evidence that BCF values do not differ when multiple concentrations are tested. The test organisms were introduced into the test vessels according to a randomization plan prepared by using a program of the laboratory data evaluation group of the testing facility.

Nominal and measured concentrations:

nominal: 0.2 μg/L
measured: 0.19 ±0.01 μg/L

During uptake, the concentration of the test substance in water remained within ±20% of the nominal concentration based on radioactivity measurement, except on day 7 when the concentration dropped to 0.079 μg/L (39% of nominal) at 9:45 am. In response to this low value, the entire system was checked and a technical malfunction was discovered with the metering pump and immediately corrected. A second set of samples was collected on day 7 at 11:00 am and the measured value was 0.218 μg/L (109% of nominal), confirming that the exposure system was again functioning normally. A system function control sample taken on day 6 demonstrated concentration in the required range, so the fish were exposed to the decreased concentration for <24-h.

Reference substance (positive control):

no

Details on estimation of bioconcentration:

The radioactivity in the test solutions was reported as μg substance equivalent/kg (L) water. For the determination of the radioactivity in the fish the measured radioactivity of each combusted tissue sample is corrected for the background, burning factor and control fish. The radioactivity in the tissue is reported as weight equivalent in μg substance equivalent/kg fish. Since fish were too large to be combusted whole, they were dissected in up to 5 parts: head, viscera and bones, skin and fins (non edible) and fillet (1- 2, edible). Measured tissue concentration values from the separate parts were used to identify differences in bioaccumulation between tissue compartments. Whole fish concentration values are based on the sum of the concentrations in the individually measured dissected parts and normalized to the weight of the entire fish.

Calculation of the bioconcentration factor
Generally, the bioconcentration factor (BCF) at a specific sampling time was calculated by dividing the concentration in fish at this time, CF(t), by the mean value of the concentration in water (CW) during the uptake period. BCF values in this study were calculated based on the steady state concentration in fish from the plateau phase of the uptake period (BCFss). According to the OECD 305 (2012), a steady-state is reached when three successive analyses of CF made on samples taken at intervals of at least two days are within ± 20% of each other, and there is no significant increase of CF in time between the first and last successive analysis. The steady state bioconcentration factor (BCFss) was calculated according to the following formula:
BCFss = CFss / CWss
Where, CFss: Steady state concentration of the test substance in fish (μg/kg wet weight), mean of all concentration values in fish of samples taken after steady state was reached.
CWss: Steady state concentration of the test substance in water (μg/L), mean of all concentration values in water.

First order kinetic BCF values were based on all data collected during uptake and depuration periods by using both one- and two-compartment biokinetic models.
First order kinetics, one compartment model.
The kinetic bioconcentration factor (BCFK) is usually adequately described with a one compartment model (BCFK1) as described in the OECD 305 test guideline. BCFK1 was calculated using measured data from the uptake and depuration curves according to the following formula:
BCFK1 = k1 / k2
Where,
k1: Uptake rate constant from water (day -1)
k2: Depuration rate constant (day -1)

The uptake and depuration rate constants (k1 and k2) were derived by simultaneously fitting the measured concentrations in fish over time to the following function:
Uptake period: CF (t) / CW = (k1/k2)*(1-e-k2t), for 0 < t < tc
Depuration period: CF (t) / CW = (k1/k2)*(e-k2(t- tc)-e-k2t) for t > tc
Where,
CF (t): Concentration in fish as a function of time (μg test substance/kg wet weight)
Cw : Concentration in water during uptake period (μg test substance/L)
t: Time from start of exposure (day)
tc: Time at the end of the uptake phase (day), in this study day 28
k1: Uptake rate constant (day -1)
k2: Depuration rate constant (day -1)
Both formulas were combined to fit the measured data using the individual fish and water values at each measurement time with the SAS-Procedure NLIN. The time to reach 50% depuration (or depuration half-life, t1/2) is calculated according to the formula: t1/2 = ln 0.5 / -k2. Similarly the time to reach 95% of the steady state concentration during uptake was calculated as: ln 0.05 / -k2.

First order kinetics, two-compartment model.
For some substance classes, bioaccumulation data may not be adequately described by a one compartment model. If depuration data is initially rapid then declines to a slower constant rate, the semi-logarithmic plot of CF versus time yields a curve. In such cases the biphasic measured
data may have a better fit in a two compartment model.
In a two compartment model, the uptake and depuration concentrations in fish over time are described by the following functions:
Uptake period: CF (t) / CW = A(1-e-α*t) + B(1-e-β*t), for 0 < t < tc
Depuration period: CF (t) / CW = A(e-α*(t- tc)) - e-α*t + B(e-β*(t- tc) - e-β*t) for t > tc
Where,
CF (t): Concentration in fish as a function of time (μg test substance/kg wet weight)
Cw : Concentration in water during uptake period (μg test substance/L)
t: Time from start of exposure (day)
tc: Time at the end of the uptake phase (day), in this study day 28
A: intercept of α phase (L/kg)
B: intercept of β phase (L/kg)
α: Distribution rate constant (day -1)
β: Terminal depuration rate constant (day -1)
Both formulas were combined to fit the measured data using the individual fish and water values at each measurement time with the SAS-Procedure NLIN. The rate constants for uptake and transfer between compartments are calculated using the estimated two-compartment parameters as follows: k12 = (A*α+B*β) Uptake rate constant
k21 = (α+β-((α*β)/k12))*(A+B)) Overall depuration rate constant
k32 = (α*β)/k21 Transfer rate constant from fish compartment 2 to compartment 1
k23 = α+β-k21-k32 Transfer rate constant from fish compartment 1 to compartment 2
The two-compartment bioconcentration factor is calculated from either the rate constants or the intercepts:
BCFK2 = (k12/(α*β))*(k23+k32) = A+B
The BCF values were further normalized to 5% fish lipid content and corrected for growth during the
experiment.

Growth correction and lipid normalization
Since growth leads to a “dilution” of the test substance in the fish, the kinetic BCF calculation should be corrected for the growth rate. The depuration rate constant(s) (k2 for one compartment, k21, k32, and k23 for two-compartment model) consists of the true depuration rate constant and the growth rate constant. For the evaluation of the growth rate constants the individual weight data were converted to natural logarithms and plotted vs. time (day) separately for each test group, including data from the start of the uptake. A linear least squares correlation was calculated for each group and the variances of the slopes (growth rates) were statistically evaluated using student t-test (or ANOVA). Since there was no statistically significant difference between the slopes, the test and control data were pooled and an overall fish growth rate constant for the study (kg) was calculated as the overall slope of the linear correlation.

The one compartment BCFK1 was corrected by subtracting the calculated growth rate constant from the depuration rate constant (k2) to give the growth-corrected depuration rate constant (k2g)

Similarly, for the two compartment BCFK2 the overall depuration rate constant (k21) was growth corrected (k21g) by subtracting the kg and used to derive growth-corrected transfer rate constants k32g, and k23g.

BCF results were adjusted to a guideline standard fish lipid content of 5% (w/w).

Lipid content:

3.7 %

Time point:

other: day 3

Lipid content:

4.1 %

Time point:

other: day 28

Lipid content:

4.5 %

Time point:

other: day 59

Key result

Conc. / dose:

0.19 µg/L

Temp.:

13 °C

pH:

7.8

Type:

BCF

Value:

461 L/kg

Basis:

whole body w.w.

Time of plateau:

7 d

Calculation basis:

steady state

Remarks on result:

other:

Remarks:

lipid normalized to 5%

Conc. / dose:

0.19 µg/L

Temp.:

13 °C

pH:

7.8

Type:

BCF

Value:

361 L/kg

Basis:

whole body w.w.

Time of plateau:

7 d

Calculation basis:

kinetic, corrected for growth

Remarks on result:

other:

Remarks:

lipid normalized to 5%; one compartment model

Conc. / dose:

0.19 µg/L

Temp.:

13 °C

pH:

7.8

Type:

BCF

Value:

458 L/kg

Basis:

whole body w.w.

Time of plateau:

7 d

Calculation basis:

kinetic, corrected for growth

Remarks on result:

other:

Remarks:

lipid normalized to 5%; two compartment model

Elimination:

yes

Parameter:

DT50

Depuration time (DT):

3.8 d

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

growth rate constant (d-1)

Value:

0.013

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

overall uptake rate constant (L kg-1 d-1)

Value:

49.8

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

overall depuration rate constant (d-1)

Value:

0.182

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

growth-corrected depuration rate constant (d-1)

Value:

0.168

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

growth-corrected half-life (d)

Value:

4.1

Remarks on result:

other:

Remarks:

one compartment model

Rate constant:

overall uptake rate constant (L kg-1 d-1)

Remarks:

uptake rate constant k12

Value:

64.14

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

overall depuration rate constant (d-1)

Remarks:

k21

Value:

0.28

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

growth-corrected depuration rate constant (d-1)

Remarks:

k21g

Value:

0.267

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

other:

Remarks:

k32: transfer rate constant from fish compartment 2 to compartment 1

Value:

0.047

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

other:

Remarks:

k32g: growth-corrected k32

Value:

0.049

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

other:

Remarks:

k23: transfer rate constant from fish compartment 1 to compartment 2

Value:

0.017

Remarks on result:

other:

Remarks:

two compartment model

Rate constant:

other:

Remarks:

K23g: growth-corrected k23

Value:

0.028

Remarks on result:

other:

Remarks:

two compartment model

Validity criteria fulfilled:

yes

Conclusions:

This study determined the bioconcentration potential of 14C-2-(2H-benztriazol-2-yl)-4-(1,1,3,3- tetramethylbutyl)phenol in rainbow trout (Oncorhynchus mykiss) exposed via water in a flowthrough- system at test concentration 0.2 μg/L and a dilution water control 0 (Control) over an uptake period of 28 days followed by a depuration period in clean water of 28 days. Concentrations in fish and water were determined on 7 occasions during uptake by measuring the total radioactivity. The mean measured concentrations of the test substance during the uptake period in water was 0.19 ±0.01 μg/L. The water concentration was kept constant within ±20% of the nominal concentration, except on day 7 when the concentration briefly dropped to 0.079 μg/L due to a pump malfunction. During depuration the concentrations in water were measured on 3 occasions and in fish on 6 occasions. Over the entire test all water quality parameters were maintained within acceptable limits. No toxic effects (i.e. mortality) or changes in behavior or appearance were observed in the test treatment organisms in comparison to the control group. There was no statistically significant difference in fish growth rate between control and treatment group during the experiment, therefore data from both groups were combined to determine the overall growth rate (kg) for “growth-corrected” calculations. The lipid content of control fish sampled over the test period remained constant considering the variability of individual values and the lipid content from the end of the uptake period (4.1%) was used for lipid normalization calculations. Total radioactive residues in fish were measured separately in edible (e.g. fillet) and non-edible (e.g. remaining carcass) portions and the whole fish value was calculated from the weight normalized sum of the individually measured portions. The measured values in whole fish were within ±20% of each other from days 7 – 24 of the uptake period satisfying the steady state criterion in OECD 305. However, the measured concentration in fish at the start of depuration on day 28 fish was unexpectedly 53% higher than the day 24 fish. No technical or biological condition could be identified to explain the concentration increase of the day 28 fish. Consequently, the day 28 value, although unusual, must be accepted as plausible and was used to determine the steady state bioconcentration factor (BCFss) of 378. The uptake and depuration data were fit against one- and two-compartment first order kinetic models to derive uptake and depuration rate constants. Both models predict that steady state conditions were achieved during the uptake phase; however the calculated parameters had high confidence intervals due to the data variability. Nevertheless the lipid normalized and growth corrected two compartment BCF (BCFK2Lg =458) was nearly identical to the lipid normalized BCFss (BCFssL =461). Given the ensuing uncertainty of the predictions from the kinetic models, the BCFSSL based on the day 28 Cf was used as the worst case condition and considered the most relevant BCF in this study. In conclusion the bioconcentration factor BCFSSL was 461 for the whole fish based on total radioactive residues of 14C-2-(2Hbenztriazol- 2-yl)-4-(1,1,3,3-tetramethylbutyl)phenol. The results in this study are consistent with all validity criteria and the test is valid according to the guidelines of this study. No deviations from test guidelines or other incidents occurred during the course of the reported test which may have influenced the results.

Reference 2

Endpoint:

bioaccumulation in aquatic species: fish

Type of information:

(Q)SAR

Adequacy of study:

supporting study

Study period:

2017

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
OASIS Catalogic v5.11.19

2. MODEL (incl. version number)
BCF base-line model v02.09 - July 2016

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See section 'Test Material'.

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached QMRF.

5. APPLICABILITY DOMAIN
See attached QPRF.

6. ADEQUACY OF THE RESULT
- The model is scientifically valid (see attached QMRF).
- The model estimates the Bioconcentration factor (BCF) as required information point according to Regulation (EC) No 1907/2006 [REACH], Annex IX, 9.3.2 Bioaccumulation in aquatic species (preferably fish);
further related predictions: Apparent effect of mitigating factors / Maximum bioconcentration factor (BCFmax) / Maximum diameter of energetically stable conformers / Whole body primary biotransformation half-life / Metabolic biotransformation rate constant Km / Metabolites and their quantitative distribution
- See attached QPRF for reliability assessment.

Principles of method if other than guideline:

Calculation using Catalogic v.5.11.19, BCF base-line model v.02.09

GLP compliance:

no

Details on estimation of bioconcentration:

BASIS FOR CALCULATION OF BCF
- Estimation software: OASIS Catalogic v5.11.19 [BCF base line model - v.02.09]

Type:

BCF

Value:

120.2 L/kg

Remarks on result:

other: considering all mitigating factors; the substance is not within the applicability domain of the model.

Type:

BCF

Value:

16 330.5 L/kg

Remarks on result:

other: without considering any mitigating factors; the substance is not within the applicability domain of the model.

Reference 3

Endpoint:

bioaccumulation in aquatic species: fish

Remarks:

The BCF for metabolites >=0.1% were modeled.

Type of information:

(Q)SAR

Adequacy of study:

supporting study

Study period:

2017

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
OASIS Catalogic v5.11.19

2. MODEL (incl. version number)
BCF base-line model v02.09 - July 2016

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
See section 'Test Material'.

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See attached QMRF.

5. APPLICABILITY DOMAIN
See attached QPRF.

6. ADEQUACY OF THE RESULT
- The model is scientifically valid (see attached QMRF).
- The model estimates the Bioconcentration factor (BCF) as required information point according to Regulation (EC) No 1907/2006 [REACH], Annex IX, 9.3.2 Bioaccumulation in aquatic species (preferably fish);
further related predictions: Apparent effect of mitigating factors / Maximum bioconcentration factor (BCFmax) / Maximum diameter of energetically stable conformers / Whole body primary biotransformation half-life / Metabolic biotransformation rate constant Km / Metabolites and their quantitative distribution
- See attached QPRF for reliability assessment.

Principles of method if other than guideline:

Calculation using Catalogic v.5.11.19, BCF base-line model v.02.09

GLP compliance:

no

Details on estimation of bioconcentration:

BASIS FOR CALCULATION OF BCF
- Estimation software: OASIS Catalogic v5.11.19 [BCF base line model - v.02.09]

Type:

BCF

Value:

>= 4.8 - <= 9.1 L/kg

Remarks on result:

other: BCF corrected (considering all mitigating factors); range for all metabolites

Type:

BCF

Value:

>= 1 230.3 - <= 8 128.3 L/kg

Remarks on result:

other: BCF max; range for all metabolites

Reference 4

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

(Q)SAR

Adequacy of study:

supporting study

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

Please refer to QMRF and QPRF in the section "Overall remarks" and "Executive summary", respectively.

Principles of method if other than guideline:

Estimation of BCF, BAF and biotransformation rate using BCFBAF v3.01

GLP compliance:

no

Radiolabelling:

no

Test organisms (species):

other: fish

Details on estimation of bioconcentration:

BASIS INFORMATION
- Measured/calculated logPow: calculated

BASIS FOR CALCULATION OF BCF
- Estimation software: BCFBAF Program (v3.01) (part of EPI Suite v4.11)
- Result based on calculated log Pow of: 6.21 (EPI Suite v4.11)

Key result

Type:

BCF

Value:

3.77 dimensionless

Remarks on result:

other: The substance is within the applicability domain of the BCFBAF submodel: Bioconcentration factor (BCF; Meylan et al., 1997/1999).

Key result

Type:

BCF

Value:

3.356 dimensionless

Remarks on result:

other: Upper trophic, incl. biotransformation estimates; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Key result

Type:

BCF

Value:

4.319 dimensionless

Remarks on result:

other: Upper trophic, incl. biotransformation rate of zero; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Key result

Type:

BAF

Value:

4.067 dimensionless

Remarks on result:

other: Upper trophic, incl. biotransformation estimates; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Type:

BAF

Value:

6.621 dimensionless

Remarks on result:

other: Upper trophic, incl. biotransformation rate of zero; The substance is within the applicability domain of the BCFBAF submodel: Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003).

Details on kinetic parameters:

Biotransformation half-life (days): 8.03 (normalised to 10 g fish)
Biotransformation rate (kM, normalised to 10 g fish at 15 °C): 0.08628 /day

BCFBAF Program (v3.01) Results:

==============================

SMILES : c32c(cccc3)nn(c1c(O)ccc(C(C)(C)CC(C)(C)C)c1)n2

MOL FOR: C20 H25 N3 O1

MOL WT : 323.44

--------------------------------- BCFBAF v3.01 --------------------------------

Summary Results:

Log BCF (regression-based estimate): 3.77 (BCF = 5.84e+003 L/kg wet-wt)

Biotransformation Half-Life (days) : 8.03 (normalized to 10 g fish)

Log BAF (Arnot-Gobas upper trophic): 4.07 (BAF = 1.17e+004 L/kg wet-wt)

 

Log Kow (experimental): not available from database

Log Kow used by BCF estimates: 6.21

 

Equation Used to Make BCF estimate:

Log BCF = 0.6598 log Kow - 0.333 + Correction

 

Correction(s):                   Value

No Applicable Correction Factors

 

Estimated Log BCF = 3.767 (BCF = 5843 L/kg wet-wt)

   

Whole Body Primary Biotransformation Rate Estimate for Fish:
TYPE	NUM	LOG BIOTRANSFORMATION FRAGMENT DESCRIPTION	COEFF	VALUE
Frag	1	Aromatic alcohol [-OH]	-0.4727	-0.4727
Frag	2	Carbon with 4 single bonds & no hydrogens	-0.2984	-0.5969
Frag	1	Triazole Ring	0.3225	0.3225
Frag	7	Aromatic-H	0.2664	1.8646
Frag	5	Methyl [-CH3]	0.2451	1.2255
Frag	1	-CH2- [linear]	0.0242	0.0242
Frag	1	Number of fused 6-carbon aromatic rings	-0.5779	-0.5779
Frag	1	Number of fused 5-carbon aromatic rings	0.0000	0.0000
Frag	1	Benzene	-0.4277	-0.4277
L Kow	*	Log Kow =  6.21 (KowWin estimate)	0.3073	1.9097
MolWt	*	Molecular Weight Parameter		-0.8294
Const	*	Equation Constant		-1.5371
RESULT	LOG Bio Half-Life (days)			0.9049
RESULT	Bio Half-Life (days)			8.034
NOTE	Bio Half-Life Normalized to 10 g fish at 15 deg C

 

Biotransformation Rate Constant:

kM (Rate Constant): 0.08628 /day (10 gram fish)

kM (Rate Constant): 0.04852 /day (100 gram fish)

kM (Rate Constant): 0.02728 /day (1 kg fish)

kM (Rate Constant): 0.01534 /day (10 kg fish)

 

Arnot-Gobas BCF & BAF Methods (including biotransformation rate estimates):

Estimated Log BCF (upper trophic) = 3.356 (BCF = 2268 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 4.067 (BAF = 1.167e+004 L/kg wet-wt)

Estimated Log BCF (mid trophic)  = 3.492 (BCF = 3103 L/kg wet-wt)

Estimated Log BAF (mid trophic)  = 4.419 (BAF = 2.627e+004 L/kg wet-wt)

Estimated Log BCF (lower trophic) = 3.532 (BCF = 3402 L/kg wet-wt)

Estimated Log BAF (lower trophic) = 4.634 (BAF = 4.308e+004 L/kg wet-wt)

 

Arnot-Gobas BCF & BAF Methods (assuming a biotransformation rate of zero):

Estimated Log BCF (upper trophic) = 4.319 (BCF = 2.083e+004 L/kg wet-wt)

Estimated Log BAF (upper trophic) = 6.621 (BAF = 4.178e+006 L/kg wet-wt)

Executive summary:

QPRF: BCFBAF v3.01 

1.	Substance	See “Test material identity”
2.	General information
2.1	Date of QPRF	See “Data Source (Reference)”
2.2	QPRF author and contact details	See “Data Source (Reference)”
3.	Prediction
3.1	Endpoint (OECD Principle 1)	Endpoint	Bioaccumulation (aquatic)
		Dependent variable	- Bioconcentration factor (BCF) - Bioaccumulation factor (BAF; 15 °C) - Biotransformation rate (kM) and half-life
3.2	Algorithm (OECD Principle 2)	Model or submodel name	BCFBAF Submodels: 1) Bioconcentration factor (BCF; Meylan et al., 1997/1999) 2) Biotransformation rate in fish (kM; Arnot et al., 2008a/b) 3) Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003)
		Model version	v. 3.01
		Reference to QMRF	Estimation of Bioconcentration, bioaccumulation and biotransformation in fish using BCFBAF v3.01 (EPI Suite v4.11)
		Predicted value (model result)	See “Results and discussion”
		Input for prediction	Chemical structure via CAS number or SMILES; log Kow (optional)
		Descriptor values	- SMILES: structure of the compound as SMILES notation - log Kow - Molecular weight
3.3	Applicability domain (OECD principle 3)	Domains:
		1) Bioconcentration factor (BCF; Meylan et al., 1997/1999)
		a) Ionic/non-Ionic	The substance is non-ionic.
		b) Molecular weight (range of test data set): - Ionic: 68.08 to 991.80 - Non-ionic: 68.08 to 959.17 (On-Line BCFBAF Help File, Ch. 7.1.3 Estimation Domain and Appendix G)	The substance is within range (323.44 g/mol).
		c) log Kow (range of test data set): - Ionic: -6.50 to 11.26 - Non-ionic: -1.37 to 11.26 (On-Line BCFBAF Help File, Ch. 7.1.3 Estimation Domain and Appendix G)	The substance is within range (6.21).
		d) Maximum number of instances of correction factor in any of the training set compounds (On-Line BCFBAF Help File, Appendix E)	Not applicable as correction factors were not used.
		2) Biotransformation rate in fish (kM; Arnot et al., 2008a/b)
		a) The substance does not appreciably ionize at physiological pH. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		b) Molecular weight (range of test data set): 68.08 to 959.17 (On-Line BCFBAF Help File, Ch. 7.2.3)	The substance is within range (323.44g/mol).
		c) The molecular weight is ≤ 600 g/mol. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		d) Log Kow: 0.31 to 8.70 (On-Line BCFBAF Help File, Ch. 7.2.3)	The substance is within range (6.21).
		e) The substance is no metal or organometal, pigment or dye, or a perfluorinated substance. (On-Line BCFBAF Help File, Ch. 7.2.3)	fulfilled
		f) Maximum number of instances of biotransformation fragments in any of the training set compounds (On-Line BCFBAF Help File, Appendix F)	Not exceeded.
		3) Arnot & Gobas BAF and steady-state BCF Arnot & Gobas, 2003)
		a) Log Kow ≤ 9 (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
		b) The substance does not appreciably ionize. (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
		c) The substance is no pigment, dye, or perfluorinated substance. (On-Line BCFBAF Help File, Ch. 7.3.1)	fulfilled
3.4	The uncertainty of the prediction (OECD principle 4)	1. Bioconcentration factor (BCF; Meylan et al., 1997/1999) Statistical accuracy of the training data set (non-ionic plus ionic data): - Correlation coefficient (r^2) = 0.833 - Standard deviation = 0.502 log units - Absolute mean error = 0.382 log units   2. Biotransformation Rate in Fish (kM) Statistical accuracy (training set): - Correlation coefficient (r^2) = 0.821 - Correlation coefficient (Q^2) = 0.753 - Standard deviation = 0.494 log units - Absolute mean error = 0.383 log units   3. Arnot-Gobas BAF/BCF model No information on the statistical accuracy given in the documentation.
3.5	The chemical mechanisms according to the model underpinning the predicted result (OECD principle 5)	1. The BCF model is mainly based on the relationship between bioconcentration and hydrophobicity. The model also takes into account the chemical structure and the ionic/non-ionic character of the substance.   2. Bioaccumulation is the net result of relative rates of chemical inputs to an organism from multimedia exposures (e.g., air, food, and water) and chemical outputs (or elimination) from the organism.   3. The model includes mechanistic processes for bioconcentration and bioaccumulation such as chemical uptake from the water at the gill surface (BCFs and BAFs) and the diet (BAFs only), and chemical elimination at the gill surface, fecal egestion, growth dilution and metabolic biotransformation (Arnot and Gobas 2003). Other processes included in the calculations are bioavailability in the water column (only the freely dissolved fraction can bioconcentrate) and absorption efficiencies at the gill and in the gastrointestinal tract.

References

- Arnot JA, Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR and Combinatorial Science 22: 337-345.

- Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008a. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry 27(11), 2263-2270.

- Arnot JA, Mackay D, Bonnell M. 2008b.Estimating metabolic biotransformation rates in fish from laboratory data. Environmental Toxicology and Chemistry 27: 341-351.

- Meylan, W.M., Howard, P.H, Aronson, D., Printup, H. and S. Gouchie. 1997. "Improved Method for Estimating Bioconcentration Factor (BCF) from Octanol-Water Partition Coefficient", SRC TR-97-006 (2nd Update), July 22, 1997; prepared for: Robert S. Boethling, EPA-OPPT, Washington, DC; Contract No. 68-D5-0012; prepared by: ; Syracuse Research Corp., Environmental Science Center, 6225 Running Ridge Road, North Syracuse, NY 13212.

- Meylan, WM, Howard, PH, Boethling, RS et al. 1999. Improved Method for Estimating Bioconcentration / Bioaccumulation Factor from Octanol/Water Partition Coefficient. Environ. Toxicol. Chem. 18(4): 664-672 (1999). 

- US EPA (2012). On-Line BCFBAF Help File.

 

 

Identified Correction Factors (Appendix E), Biotransformation Fragments and Coefficient values (Appendix F)

Model:	BCFBAF v3.01
Substance:	Phenol, 2-(2H-benzotriazol-2-yl)-4-(1,1,3,3-tetramethylbutyl)-
CAS:	3147-75-9
SMILES:	c32c(cccc3)nn(c1c(O)ccc(C(C)(C)CC(C)(C)C)c1)n2
Ionic/Non-ionic	non-ionic
Ionisation at physiological relevant pH	Substance does not ionise.
Molecular Weight:	323.44
Log Kow:	6.21 (calculated by KOWWIN Program v1.68)

 

No correction factors identified

 

The Training Set used to derive the Coefficient Values listed below contained a total of 421 compounds (see Appendix I for the compound list).

Fragment Description	Coefficient value	No. compounds containing fragment in total training set	Maximum number of each fragment in any individual compound	No. of instances of each fragment for the current substance
Aromatic alcohol  [-OH]	-0.47273947	26	2	1
Carbon with 4 single bonds & no hydrogens	-0.29842827	47	10	2
Triazole Ring	0.32253333	4	1	1
Aromatic-H	0.26637806	305	15	7
Methyl  [-CH3]	0.24510529	170	12	5
-CH2-  [linear]	0.02418707	109	28	1
Number of fused 6-carbon aromatic rings	-0.577854	67	5	1
Benzene	-0.427728	197	3	1

 

 

Assessment of Applicability Domain Based on Molecular Weight and log Kow 

1. Bioconcentration Factor (BCF; Meylan et al., 1997/1999)
Training set: Molecular weights	Ionic	Non-ionic
Minimum	68.08	68.08
Maximum	991.80	959.17
Average	244.00	244.00
Assessment of molecular weight	Molecular weight within range of training set.
Training set: Log Kow	Ionic	Non-ionic
Minimum	-6.50	-1.37
Maximum	11.26	11.26
Assessment of log Kow	log Kow within the range of training set.

2. Biotransformation Rate in Fish (kM; Arnot et al., 2008a/b)
Training set: Molecular weights
Minimum	68.08
Maximum	959.17
Average	259.75
Assessment of molecular weight	Molecular weight within range of training set.
Training set: Log Kow
Minimum	0.31
Maximum	8.70
Assessment of log Kow	log Kow within the range of training set.
3. Arnot-Gobas BAF/BCF (Arnot & Gobas, 2003)
Assessment of log Kow	Log Kow within acceptable range (loh Kow <= 9).

 Remarks: The identified fragment "Number of fused 5-carbon aromatic rings" is not listed in Appendix F.

Reference 5

Endpoint:

bioaccumulation: aquatic / sediment

Type of information:

(Q)SAR

Adequacy of study:

supporting study

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification

Justification for type of information:

1. SOFTWARE
T.E.S.T. (version 4.2.1) (Toxicity Estimation Software Tool). US EPA, 2012.

2. MODEL (incl. version number)
T.E.S.T. (version 4.2.1)

3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
c32c(cccc3)nn(c1c(O)ccc(C(C)(C)CC(C)(C)C)c1)n2

4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
See QMRF in section "Overall remarks, attachments".

5. APPLICABILITY DOMAIN
See QPRF in section "Executive summary".

6. ADEQUACY OF THE RESULT
- The model is scientifically valid (see QMRF).
- The model estimates the bioconcentration factor (BCF) as required information point under Regulation (EC) No 1907/2006 [REACH], Annex IX, 9.3.2 Bioaccumulation in aquatic species, preferably fish (see also QPRF).
- See QPRF for reliability assessment.

Principles of method if other than guideline:

T.E.S.T. is a toxicity estimation software tool. The program requires only the molecular structure of the test item, all other molecular descriptors which are required to estimate the toxicity are calculated within the tool itself. The molecular descriptors describe physical characteristics of the molecule (e.g. E-state values and E-state counts, constitutional descriptors, topological descriptors, walk and path counts, connectivity, information content, 2d autocorrelation, Burden eigenvalue, molecular property (such as the octanol-water partition coefficient), Kappa, hydrogen bond acceptor/donor counts, molecular distance edge, and molecular fragment counts). Each of the available methods uses a different set of these descriptors to estimate the toxicity. The bioaccumulation factor (BCF) was estimated using several available methods: hierarchical clustering method; FDA method, single model method; group contribution method; nearest neighbor method; consensus method. The methods were validated using statistical external validation using separate training and test data sets. The experimental data set was obtained from several different databases (Dimitrov et al., 2005; Arnot and Gobas, 2006; EURAS; Zhao, 2008). From the available data set salts, mixtures and ambiguous compoun ds were removed. The final data set contained 676 chemicals.

References:
- Dimitrov, S., N. Dimitrova, T. Parkerton, M. Combers, M. Bonnell, and O. Mekenyan. 2005. Base-line model for identifying the bioaccumulation potential of chemicals. SAR and QSAR in Environmental Research 16:531-554.
- Arnot, J.A., and F.A.P.C. Gobas. 2006. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ. Rev. 14:257-297.
- EURAS. Establishing a bioconcentration factor (BCF) Gold Standard Database. EURAS [cited 5/20/09]. Available from http://www.euras.be/eng/project.asp?ProjectId=92.
- Zhao, C.; Boriani, E.; Chana, A.; Roncaglioni, A.; Benfenati, E. 2008. A new hybrid system of QSAR models for predicting bioconcentration factors (BCF). Chemosphere 73:1701-1707.

GLP compliance:

no

Test organisms (species):

other: fish

Details on estimation of bioconcentration:

BASIS FOR CALCULATION OF BCF
- Estimation software: US EPA T.E.S.T. v4.2.1

Applied estimation methods:
- Hierarchical method : The toxicity for a given query compound is estimated using the weighted average of the predictions from several different cluster models.
- FDA method : The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
- Single model method : Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables).
- Group contribution method : Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables).
- Nearest neighbor method : The predicted toxicity is estimated by taking an average of the 3 chemicals in the training set that are most similar to the test chemical.
- Consensus method : The predicted toxicity is estimated by taking an average of the predicted toxicities from the above QSAR methods (provided the predictions are within the respective applicability domains; recommended method by T.E.S.T. for providing the most accurate predictions).

Key result

Type:

BCF

Value:

146.04 L/kg

Remarks on result:

other: method: consensus (average of reasonable results from all models). Based on the mean absolute error, the confidence in the predicted BCF values is low.

Key result

Type:

other: log BCF

Value:

2.16 L/kg

Remarks on result:

other: method: consensus (average of reasonable results from all models). Based on the mean absolute error, the confidence in the predicted BCF values is low.

Model:	US EPA T.E.S.T. v4.2.1: Bioaccumulation in fish
Substance:	2-(2H-benzotriazol-2-yl)-4-(1,1,3,3-tetramethylbutyl)phenol
CAS-#:	3147-75-9
SMILES:	c32c(cccc3)nn(c1c(O)ccc(C(C)(C)CC(C)(C)C)c1)n2

Method	Predicted value		Model statistics			MAE (in log10)
						External test set		Training set
	log BCF	BCF	r²	q²	No. of chemicals	Entire set	SC >= 0.5	Entire set	SC >= 0.5
Consensus method	2.16	146.04	-	-	-	0.51	0.76	0.42	0.63
Hierarchical clustering	2.90	797.68 (283.78-2242.19)	0.662 - 0.826	0.569 - 0.737	67 - 540 (cluster models: 8)	0.54	0.95	0.23	0.32
Single model	2.38	237.32 (17.47-3224.73)	0.764	0.733	540	0.54	0.60	0.53	0.61
Group contribution	1.72	52.29 (1.51-1813.52)	0.719	0.527	499	0.62	0.59	0.60	0.51
FDA	1.72	52.91 (5.76-486.15)	0.761	0.628	30	0.57	0.69	0.53	1.09
Nearest neighbor	2.10	126.82	-	-	3	0.60	1.35	0.55	0.96

Legend:

MAE = mean absolute error

SC = similarity coefficient

r² = correlation coefficient

q² = leave one out correlation coefficient

Executive summary:

QPRF: Estimation of bioaccumulation in fish using T.E.S.T. 4.2.1

 

1.	Substance	See “Test material identity”
2.	General information
2.1	Date of QPRF	See “Data Source (Reference)”
2.2	QPRF author and contact details	See “Data Source (Reference)”
3.	Prediction
3.1	Endpoint (OECD Principle 1)	Endpoint	Bioaccumulation (aquatic)
		Dependent variable	Bioconcentration factor (BCF)
3.2	Algorithm (OECD Principle 2)	Model or submodel name	US EPA T.E.S.T. 4.2.1: 1)    Hierarchical clustering 2)    FDA method 3)    Single model 4)    Group contribution 5)    Nearest neighbour 6)    Consensus
		Model version	v. 4.2.1
		Reference to QMRF	Estimation of bioaccumulation in fish using T.E.S.T. 4.2.1
		Predicted value (model result)	See “Results and discussion”
		Input for prediction	Chemical structure via CAS number, SMILES, MDL molfile, structure (drawing)
		Descriptor values	Molecular descriptors (calculated by T.E.S.T.)
3.3	Applicability domain (OECD principle 3)	General remarks	Predictions are considered only from valid models. Models which do not meet the constraints are listed in the output with a corresponding remark. If the substance is not within the applicability domain, no BCF is calculated.
		Hierarchical clustering	In domain
		FDA method	In domain
		Single model	In domain
		Group contribution	In domain
		Nearest neighbour	In domain
		Consensus	In domain
3.4	The uncertainty of the prediction (OECD principle 4)	The uncertainty of the predictions can be assessed by comparing the mean absolute error (MAE) of the entire dataset with the MAE of the dataset restricted to substances with a similarity coefficient (SC) of ≥ 0.5. If the MAE for the entire set is lower than the MAE for the similar substances (SC ≥ 0.5), the confidence in the predicted BCF value is high. The table below lists the information on q^2(leave one out correlation coefficient), r^2(correlation coefficient), MAE and SC of the models. Based on the MAE of the external and the training dataset, the confidence in the estimated BCF is assessed as follows.                       Model Confidence in estimated BCF External test set: Training set: Consensus method low low Hierarchical clustering low low Single model low low Group contribution high high FDA low low Nearest neighbor low low
3.5	The chemical mechanisms according to the model underpinning the predicted result (OECD principle 5)	Molecular descriptors are used to develop the models. The overall pool of descriptors in the software contain 797 2-dimensional descriptors of the following classes: E-state values and E-state counts, constitutional descriptors, topological descriptors, walk and path counts, connectivity, information content, 2d autocorrelation, Burden eigenvalue, molecular property (such as the octanol-water partition coefficient), Kappa, hydrogen bond acceptor/donor counts, molecular distance edge, and molecular fragment counts. The descriptors used to describe the compound can be viewed in the model output details.

 

Detailed information on q²(leave one out correlation coefficient), r²(correlation coefficient), MAE and SC: see section ‘Any other information on results incl. tables’.

Description of key information

Does not accumulate in organisms.

Additional information

The bioaccumulative potential of the substance was determined in an experimental study conducted in 2017 according to OECD TG 305 (aqueous exposure). Furthermore, information from different QSAR models (Table 1) and molecular parameters (i.e. logKow, molecular weight and molecular dimensions) were used as supporting information.

Additionally, the relevant metabolites identified under section 5.2 were assessed with information from OASIS Catalogic v5.11.19, BCF base-line model v.02.09 and further lines of evidence, e.g. logKow, general polarity.

 

Table 1: QSAR models used in the weight of evidence approach.

Software	Model	Sub-model
OASIS Catalogic v5.11.19	BCF base-line model v.02.09
T.E.S.T. v4.2.1	Hierarchical clustering
	FDA method
	Single model
	Group contribution
	Nearest neighbor
	Consensus
EPISuite v4.11	BCFBAF model v3.01	Regression-based estimate
		Arnot-Gobas BCF & BAF methods
VEGA v1.1.3	CAESASR v2.1.14
	Meylan v1.0.3
	KNN/Read-Across v1.1.0

 

 

Parent compound (CAS # 3147-75-9)

Experimental study

The experimental study was conducted based on the guidelines OECD 305 and US EPA OPPTS 850.1730. The test was conducted with radiolabeled substance due to the low water solubility and the analytical feasibility. The radiolabel was 2H-benzo-U-C14 and the radiochemical purity was 99.7%.

The test substance is sparingly water soluble. Anorganic solvent-free saturated stock solution was prepared using asolid-liquid (slow-stir) saturator technique. Thetest substancewasdissolved in approx.100 mL acetone. The acetone solution was checked for complete dissolution of the test substance, then the solution wasdistributed onto approx. 20 g of glass wool placed on bottom of the stainless steel stock solution tank.All the acetone was then completely evaporatedto leave athin layer of test substance coating the glass wool. The purpose of the glass wool was to facilitate dissolution by increasing the surface area where the substance was attached.Afterwards, the glass wool was covered by a stainless steel grate and200 L of dilution water were added(taking care to minimize the disturbance of the glass wool).The stock solutionwasstirredwith a paddle stirrer at the test temperature up to 7 days.Since all acetone was evaporated prior to adding water, no solvent was present in the final test solution and no solvent control is required. Potentially undissolved substance or particles of glass wool was separated by filtration via a paper filtration unit.The concentration in the stock solution tank was verified in each newly prepared stock solution without a GLP status. The final nominal concentration in the test was 0.2µg/L which is clearly below the solubility limit of the test substance (2 µg/L). An underestimation of the BCF due to exposure above the solubility limit can therefore be ruled out.

The fish were exposed to one concentration of test substance at 0.2 μg/L in a flow-through-system for an uptake period of 28 days followed by a depuration period in clean water of 28 days. Over the entire test all water quality parameters were maintained within acceptable limits. No toxic effects (i.e. mortality) or changes in behavior or appearance were observed in the test treatment organisms in comparison to the control group. There was no statistically significant difference in fish growth rate between control and treatment group during the experiment, therefore data from both groups were combined to determine the overall growth rate (kg) for “growth-corrected” calculations. The lipid content of control fish sampled over the test period remained relatively stable considering the variability of individual values and the average lipid content from the end of the uptake period (4.1%) was used for lipid normalization calculations.

Test substance concentrations in fish and water were determined on 7 occasions during uptake by measuring the total radioactivity. The mean measured concentration of the test substance during the uptake period in water was 0.19 ±0.01 μg/L. The concentration was kept constant within the range of ±20% of the nominal concentration throughout the exposure period, except on day 7 when the concentration briefly dropped to 0.079 μg/L due to a pump malfunction. During depuration the concentration in test water was measured on 3 occasions and in fish on 6 occasions.

Total radioactive residues in fish were measured separately in edible (e.g. fillet) and non-edible (e.g. remaining carcass) portions and the whole fish value was calculated from the weight normalized sum of the individually measured portions. The measured values in whole fish were within ±20% of each other from days 7 – 24 of the uptake period satisfying the steady state criterion in OECD TG 305. However, the measured concentration in fish at the start of depuration on day 28 fish was unexpectedly 53% higher than the day 24 fish. No technical or biological condition could be identified to explain the concentration increase of the day 28 fish. Consequently, the day 28 value, although unusual, must be accepted as plausible and was used to determine the steady state bioconcentration factor (BCFss) of 378.

The uptake and depuration data were fit against one- and two-compartment first order kinetic models to derive uptake and depuration rate constants. Both models predict that steady state conditions were achieved during the uptake phase; however the calculated parameters had high confidence intervals due to the data variability. Nevertheless the lipid normalized and growth corrected two compartment BCF (BCFK2Lg =458) was nearly identical to the lipid normalized BCFss (BCFssL =461). Given the ensuing uncertainty of the predictions from the kinetic models, the BCFSSL based on the day 28 Cf was used as the worst case condition and considered the most relevant BCF in this study. In conclusion the bioconcentration factor BCFSSL was 461 for the whole fish based on total radioactive residues of 14C-2-(2Hbenztriazol- 2-yl)-4-(1,1,3,3-tetramethylbutyl)phenol.

 

QSAR calculations

As supporting information several QSAR models were used to calculate the BCF (Table 1). The results and further information on the reliability (e.g. applicability domain) can be found in Table 2.

 

Table 2: Results and further details of the supporting QSAR estimations.

Model	Results [L/kg]	Remarks
OASIS Catalogic v5.11.19; BCF base-line model v.02.09	120   [BCFmax = 16331]	- all mitigating factors applied - within parameter and mechanistic domain - outside structural fragment domain
T.E.S.T. v4.2.1; Hierarchical clustering	706	- within applicability domain - Confidence in the estimated BCF: low
T.E.S.T. v4.2.1; FDA method	159	- within applicability domain - Confidence in the estimated BCF: low
T.E.S.T. v4.2.1; Single model	152	- within applicability domain - Confidence in the estimated BCF: low
T.E.S.T. v4.2.1; Group contribution	1343	- within applicability domain - Confidence in the estimated BCF: low (external test set), high (training set)
T.E.S.T. v4.2.1; Nearest neighbor	2644	- within applicability domain - Confidence in the estimated BCF: low
T.E.S.T. v4.2.1; Consensus	571	- within applicability domain - Confidence in the estimated BCF: low
EPISuite v4.11; BCFBAF model v3.01; regression-based estimate	5843	- within applicability domain
EPISuite v4.11; BCFBAF model v3.01; Arnot-Gobas BCF & BAF methods	2268	- within the applicability domain
VEGA v1.1.3; CAESAR v2.1.14	62	- outside of the applicability domain
VEGA v1.1.3; Meylan v1.0.3	1863	- outside of the applicability domain
VEGA v1.1.3; KNN/Read-Across v1.1.0	447	- outside of the applicability domain

 

The BCF base-line model v.02.09 integrated in OASIS Catalogic v5.11.19 reflects the current understanding of the process by which lipophilic organic chemicals are bioaccumulated in fish through the respiratory organs only. Chemicals bioaccumulating by other mechanisms (e.g., binding to proteins) are considered out of the mechanistic domain of the model. The model consists of two major components: a model for predicting the maximum potential for bioaccumulation based solely on chemicals’ lipophilicity (i.e., BCFmax model), and a set of mitigating factors that account for the reduction of the bioaccumulation potential of chemicals based on chemical (e.g., molecular size and ionization) and organism-dependent factors (e.g., metabolism). BCFmax model is a theoretical model based on the assumption that the only driving force of bioconcentration is lipophilicity and the effect of any other factors are insignificant. It mathematical formalism is derived considering multi-compartment diffusion. The bioconcentration predicted by BCFmax model could be limited by variety of mitigating factors that account for the reduction of the bioaccumulation potential of chemicals based on chemical and organism-dependent factors. The effect of mitigating factors mathematically is quantified by probabilities: to penetrate through the cell membrane, to be ionized, to be metabolised, etc. In the BCF base-line model the tissue metabolism simulator is used to account for the effect of metabolism. It consists of a consequence of spontaneous abiotic and enzyme controlled steps. Probabilities of these molecular transformations are assessed by fitting the training set data. The CATALOGIC platform utilizes a multi-stage applicability domain that has been described by Dimitrov et al. (2005). The applicability domain of the BCF base-line model contains three layers: (1) General properties requirements. These requirements specify in the domain only those chemicals that fall in the range of variation of physicochemical properties that may affect significantly the quality of the measured endpoint. For the BCF base-line model attention is focused on lipophilicity (log KOW), molecular weight (MW) and water solubility (WS). Only correctly predicted chemicals from the training set are used to determine the range of variation of these properties. (2) The structural domain. It determines the maximum structural similarity between the target chemical and chemicals from the training set. The structural neighborhood of atom-centered fragments (ACF) accounting for 1st neighbors, atom type, hybridization and attached hydrogen atoms are used to determine this similarity. The target chemical could contain the following types of ACF:

-      Fragments present in correctly predicted training chemicals only (i.e. correct fragments)

-      Fragments found both in correctly and non-correctly predicted training chemicals (i.e. fuzzy fragments). These fragments are treated as correct fragments

-      Fragments present in non-correctly predicted training chemicals only (i.e. incorrect fragments),

-      Fragments not present in the training chemicals (i.e. unknown fragments).

(3) The mechanistic domain.It discriminates between modes of bioaccumulation - passive (partitioning in lipid phase) or active (based on protein binding). Only chemicals with expected passive diffusion driven bioaccumulation are considered to be in the mechanistic domain of the model.

In the present case, the substance fulfills the general properties requirements, i.e. its logKow, molecular weight and water solubility are within the ranges of the model. Furthermore, the substance is within the mechanistic domain, i.e. it is expected to be taken up by passive diffusion only. However, the substance is not in the structural domain. Only 57.14% of its fragments could be found in correctly predicted training set chemicals. The remaining 42.86% are not present in the training set chemicals. If a chemical is out of at least one of the specified layers mentioned above, it will be classified as out of the applicability domain. This classification means that the prediction falls in the extrapolation space but the prediction still could be reliable. The estimated BCF (all mitigating factors applied) was determined to be 120 L/kg. This results is slightly below the experimentally derived BCFss values of 378 L/kg (not lipid-normalized) and 461 L/kg (lipid-normalized to 5%). The slight deviation to the experimentally derived BCF values might be explained with differing lipid contents of the training set of the model compared to the lipid contents of the fish used in the experimental study (4.1% was used for the lipid normalization calculations). The training set of the BCF base-line model consists of 705 chemicals. Out of these, 560 chemicals are from MITI/NITE and 145 from ExxonMobil. At least the underlying experimental studies from MITI/NITE of the training set are not lipid-normalized to 5% which might be an explanation for the deviation of the calculated result and the experimentally derived BCF values. However, as the experimental results are far from any regulatory threshold relevant for the PBT/vPvB assessment, the predicted results from Catalogic are regarded as a reliable supporting information for the assessment of the bioaccumulative potential. 

The T.E.S.T. v4.2.1 package encompasses five separate methods to estimate the BCF. The sixth method is the consensus method which simply averages the results of the prediction from the other QSAR methodologies (taking into account the applicability domain of each method). This method typically provides the highest prediction accuracy since errant predictions are dampened by the predictions from the other methods. In addition, this method provides the highest prediction coverage because several methods with slightly different applicability domains are used to make a prediction. For the five separate methodologies, the results range from 152 to 2644. The averaged consensus result is 571. The models only make predictions if the substance is within the respective applicability domains. For the present case, the substance is within the applicability domains of the five models. However, the mean absolute errors regarding the similarity coefficients for both the external test sets and the training sets are above the respective thresholds. Therefore, the confidence in the estimated thresholds is low. Nevertheless, the result of the consensus method delivered a BCF of 571 which matches the experimentally derived BCFss of 378 and 461 L/kg, respectively. Therefore, the model is regarded as reliable for the present substance.

EPISuite v4.11 includes the BCFBAF model v3.01 which encompasses two BCF estimation methodologies. The regression-based model is based on the work from Meylan et al. (1997 and 1999). The second model is based on the works from Arnot and Gobas (2003) and includes estimates on the biotransformation rate in fish. The regression-based model derived a BCF value of 5843 L/kg based on a calculated logKow of 6.21 (a precise experimental value for logKow is not available). The model did not apply any correction factors and derived the BCF by applying common statistical regression methodology (n = 396; r²= 0.792). The high logKow of the compound and the absence of any correction factor is responsible for the extremely high calculated result which is clearly above the experimentally derived value. Although the substance is within the applicability domain of the model (molecular weight and logKow ranges) the result is not regarded as reliable supporting information. At very high logKow values a decreasing correlation of the logKow and the logBCF can be observed. According to ECHA’s Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment draft version 3.0 (March 2017), the relationship between logKow and logBCF decreases at very high logKow (>6).The present compound has a calculated logKow of 6.21. Experimentally, the logKow was determined both according to OECD 117 and the single solubilities in water and octanol. The HPLC method revealed a logKow of >6.5. Based on the single solubilities the logKow was determined to be >6.8 <7.4. For substances with these extremely high logKow values, the correlation of the logKow and the logBCF is highly uncertain and probably the reason for the unrealistically high BCF value estimated by the regression-based model compared to the valid experimental result. Therefore, although the substance is within the applicability domain of the model the result is not regarded as reliable and not taken into account for the assessment of the bioaccumulative potential of the present substance.

The model based on the works of Arnot and Gobas (2003) takes the biotransformation rate of the compound into account and calculates BCF values for the upper, mid and lower trophic levels. The values for the present compound range from 2268 (upper trophic level) to 3402 (lower trophic level). These values clearly overestimate the bioaccumulative potential of the substance as shown in the experimental study. A reason for the overestimation could be the higher default lipid contents of the model. It assumes default lipid contents of 10.7%, 6.85% and 5.98% for the upper, middle and lower trophic levels, respectively. Usually, in the context of REACH a default lipid value of 5% is assumed which represents the average lipid content of the small fish used in OECD 305 studies.The high default lipid contents in the model are at least partially responsible for the high predicted BCF values. A normalization to 5% lipid results in BCF values of 1059, 2264, and 2844 L/kg for the upper, mid and lower trophic level. Furthermore, the model used a calculated logKow of 6.21 for the predictions. This value is too low for the present compound and was only used in the absence of clear experimental data. However, if higher logKow are used for the prediction the result gets more realistic. Table 3 gives an overview of the logKow influencing the result of the model.

 

Table 3: Influence of the logKow on the outcome of the Arnot-Gobas model.

logKow	BCF [upper trophic level; including biotransformation rate estimates]	BCF normalized to 5% lipid [upper trophic level; including biotransformation rate estimates]	Remarks
6.21	2268 L/kg	1059 L/kg	logKow calculated by the model
6.5	2211 L/kg	1033 L/kg	logKow derived by the HPLC method; the result was logKow >6.5
6.8	1922 L/kg	898 L/kg	logKow derived by the single solubilities; the result was logKow >6.8 <7.4
7.4	1064 L/kg	497 L/kg	logKow derived by the single solubilities; the result was logKow >6.8 <7.4

 

The higher logKow values derived in the experimental logKow studies reveal more realistic BCF values, especially after normalization to a lipid content of 5%. In general, the substance is within the applicability domain of the model and taking the higher logKow values and the normalization to 5% lipid into account the results can be regarded as reliable as supporting information.

The current VEGA package v1.1.3 comprises three different models. (1) CAESAR v2.1.14, (2) Meylan v1.0.3 and (3) KNN/Read-Across v1.1.0. The applicability domain of the predictions is assessed using an Applicability Domain Index (ADI) that has values from 0 (worst case) to 1 (best case). The ADI is calculated by grouping several other indices, each one taking into account a particular issue of the applicability domain. Most of the indices are based on the calculation of the most similar compounds found in the training and test set of the model, calculated by a similarity index that consider molecule's fingerprint and structural aspects (count of atoms, rings and relevant fragments). In regards to the CAESAR model the following indices are checked: (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) atom centered fragments similarity check, (6) model descriptors range check and (7) global AD index which takes into account all the previous indices in order to give a general global assessment on the applicability domain. The model predicted a BCF of 62 L/kg but the substance was out of the applicability domain. Therefore, the model was not used as further supporting information for the assessment of the bioaccumulative potential.

The Meylan model is a reconstruction of the regression-based model integrated in EPISuite (Meylan et al. 1997, 1999). The original dataset from EPISuite has been processed and cleared from duplicates and compounds provided with structures that had problems. The final dataset has 662 compounds. Similar to the CAESAR model, the applicability domain is assessed with several indices. (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) logP reliability, (6) model descriptors range check and (7) global AD index. The model predicted a BCF of 1863 but the substance was out of the applicability domain. The reason for the high BCF (cf. to EPISuite) is probably the bad correlation between logKow and logBCF for substances with a high logKow value. The result is not regarded as reliable.

The KNN/Read-Across model performs a read-across on a dataset of 860 chemicals. The applicability domain takes the following indices into account: (1) Similar molecules with known experimental value, (2) accuracy (average error) of prediction for similar molecules, (3) concordance with similar molecules (average difference between target compound prediction and experimental values of similar molecules), (4) maximum error of prediction among similar molecules, (5) atom centered fragments similarity check, (6) global AD index. The model predicted a BCF of 447 L/kg but the substance is out of the applicability domain. Although the result is in the same range as the experimentally derived BCF it is not regarded as reliable and not used as supporting information.

 

Further information

The size of the molecule can be used to strengthen the evidence for a limited bioaccumulative potential of a substance. A parameter that directly reflects the molecular size of a substance is the average maximum diameter (DiamMax average). Very bulky molecules will less easily pass through the cell membranes. InECHA’s Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment draft version 3.0 (March 2017), it is stated that it appeared that for compounds with a DiamMax average larger than 1.7 nm the BCF will be less than 2000. The present compound has a DiamMax average of 1.53 nm. Its respective DiamMax Min and Max values are 1.27 and 1.75 nm. Although the proposed value of 1.7 nm is only exceeded by very bulky conformers it can be assumed that the compound is at least hindered in its uptake behavior due to its size.

Another line of evidence is the tissue absorption potential. According to ECHA’s Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment draft version 3.0 (March 2017), the ‘leakiness’ of a tissue, or its ability to allow a substance to passively diffuse through it, can be measured using trans-epithelial electrical resistance (TEER) and can be used to compare tissue capabilities. A low TEER value indicates the tissue has greater absorption potential. Data indicate that fish and mammalian intestines are equally ‘leaky’ and that fish gills are more restrictive, similar to the mammalian blood brain barrier. The TEER value for fish gills is given with 3500 ohm cm²compared to 400-2000 ohm cm²for the human blood-brain barrier. It can therefore be assumed that the gills as the main uptake barrier are less easily permeable for molecules.

As the uptake of an organic substance in aquatic organisms is driven by its hydrophobicity the logKow is a useful criterion to conclude on the bioaccumulative potential. According to ECHA’s R.11 guidance at logKow values between 4 and 5 the logBCF increases linearly with logKow. However, at very high logKow values (>6) a decreasing relationship between the two parameters is observed. Thus, a direct conclusion on the bioaccumulative potential based on the logKow is not possible for the present substance. Furthermore, as mentioned above the decreasing relationship between the two parameters is probably the reason why some of the QSAR models predicted very high BCF values. Another uptake mechanisms than passive diffusion driven by hydrophobicity is not expected for the present substance.

Conclusion for the parent substance

The parent substance is clearly not bioaccumulative. A valid and highly reliable experimental study determined a BCF value of 461 L/kg. This result could be confirmed by different QSAR predictions which predicted BCF values in the same range. Furthermore, data like DiamMax average, TEER and mammalian toxicological data are clear evidence of the substance not being bioaccumulative. Assessing all lines of evidence it can be concluded that the substance is not bioaccumulative. The BCF of the substance is clearly below the PBT/vPvB threshold values of 2000 and 5000, respectively

 

Metabolites

In addition to the parent compound (CAS # 3147-75-9) the relevant metabolites were assessed regarding their potential to bioaccumulate. In the absence of experimental data, the metabolites were identified with CATALOGIC 301C v09.13 integrated in OASIS Catalogic v5.11.19. Only metabolites at the PBT-relevant quantity of 0.1% were taken into account. Out of these metabolites only metabolites with a logKow ≥ 4 have been further taken into account. According to ECHA’s Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.11: PBT/vPvB assessment draft version 3.0 (March 2017), for the PBT and vPvB assessment a screening threshold value has been established, which is logKow greater than 4.5. The assumption behind this is that the uptake of an organic substance in aquatic organisms is driven by its hydrophobicity. For organic substances with a logKow value below 4.5 it is assumed that the B criterion, i.e. a BCF value of 2000 (based on wet weight of the organism, which refers to fish in most cases), is not exceeded. Therefore, the decision to take only metabolites with a logKow ≥ 4 presents a reasonable worst case. Ultimately, 4 metabolites could be identified which were further evaluated (Table 4). As a first step, the OECD QSAR Toolbox v3.4 was used to identify existing experimental data both on the degradability and the bioaccumulative potential for the identified metabolites. However, no experimental data could be found in the Toolbox for any of the metabolites. Therefore, in the absence of experimental studies, calculated BCF values from the BCF base-line model v.02.09 integrated in OASIS Catalogic v5.11.19 were used for the assessment. This model proved to be suitable and reliable for the prediction of the BCF for the parent compound. Its modeled BCF value was in the range of the experimentally derived BCF value for the parent compound (cf. to the respective paragraph above). Therefore, it is regarded as reliable for the prediction of the BCF for the metabolites as well.

 

Table 4: Overview of the relevant metabolites identified with CATALOGIC 301C v09.13 and their calculated BCF values. Furthermore, information on the transformation processes and the amount of polar groups is listed.

SMILES	Quantity [%]	logKow^(1)	BCF^(2)	Transformation process	Remarks
CC(C)(C)CC(C)(C)c1cc(O)c(O)c(N2Nc3ccccc3N2)c1	2.33	5.3	9	Phase I: Aromatic ring oxidation	2 hydroxyl groups
CC(C)(C)CC(C)(C)c1ccc(O)c(N(N)Nc2ccccc2O)c1	0.16	4.8	7	Phase I: Oxidative deamination and N-dealkylation	2 hydroxyl groups
CC(C)(CC(C)(C)c1ccc(O)c(N2Nc3ccccc3N2)c1)C(O)=O	12.51	4.5	5	Phase I: Aldehyde oxidation	1 hydroxyl, 1 carboxyl group
CC(C)(C)CC(C)(c1ccc(O)c(N2Nc3ccccc3N2)c1)C(O)=O	2.90	4.1	5	Phase I: Aldehyde oxidation	1 hydroxyl, 1 carboxyl group

⁽¹⁾Calculated within Catalogic.

⁽²⁾Calculated with the BCF base-line model v.02.09, Catalogic v5.11.19.

 

As for the parent substance, the metabolites fulfill the general properties requirements, i.e. their logKow values, molecular weights and water solubilities were within the ranges of the model. Furthermore, they were within the mechanistic domain, i.e. it is expected that they are taken up by passive diffusion only. However, as for the parent substance, the metabolites were not in the structural domain. The model identified 53 to 62% correct fragments, 0% unknown fragments and 38 to 47% incorrect fragments depending on the specific metabolite. As for the parent substance, none of the metabolites is therefore classified as “within domain” by the model. Nevertheless, the predictions are regarded as reliable due to the high correlation between the predicted and the experimental result for the parent compound. As the metabolites are structurally similar to the parent compound a similar correlation can be expected. Furthermore, no “borderline” cases could be identified were a metabolite is near a regulatory threshold, i.e. a BCF of 2000 or 5000. The identified BCF values are well below the PBT trigger of 2000 which provides a reasonable margin of safety. The predicted BCF values range from 9 down to only 5 L/kg. Depending on the specific metabolite, the influence of the mitigating factors differed. Details can be found in the QPRF attached to the endpoint study record. The DiamMax average values for the metabolites derived by the model ranged from 1.5 to 1.6 nm. In conclusion, according to the results from the BCF base-line model v.02.09, Catalogic v5.11.19 none of the metabolites can be regarded as bioaccumulative.

In addition to the predicted BCF values further information on the transformation processes derived by CATALOGIC 301C v09.13 were analyzed and considered to adequately conclude on the bioaccumulative potential. The general biotransformation process consists of phase I reactions followed by phase II reactions. Phase I reactions include oxidative, reductive and hydrolytic reactions and its goal is to transfer the substrate to a more soluble substance to increase water solubility. In the present case Catalogic predicted three phase I reactions relevant for the biotransformation/biodegradation of the parent substance and its metabolites. (1) Aromatic ring oxidation, (2) oxidative deamination and N-dealkylation, and (3) aldehyde oxidation. These reactions lead to additional polar hydroxyl, aldehyde and/or carboxyl groups. These additional functional groups are responsible for the increased solubility and the decreasing logKow values. Subsequently, the decreasing logKow has an immediate effect on the bioaccumulative potential because the decreased lipophilicity leads to a reduced uptake by passive diffusion. Furthermore, the additional functional groups increase the polarity of the metabolites and consequently its hydration shell which further hinders the uptake through cell membranes. In Table 4 it can be clearly seen that both the logKow and the predicted BCF value strongly correlates with the polarity of the metabolite. Both the amount and the oxidative level have a direct impact on the predicted BCF as well as the logKow.

In conclusion, none of the relevant metabolites is regarded to be bioaccumulative. Their predicted BCF values are well below the PBT threshold of 2000. This assumption is further strengthened by the decreased lipophilicity (decreasing logKow values) as well as the increasing amount of polar functional groups causing an increased hydration shell which clearly hinders the uptake by passive diffusion through cell membranes.

 

 

 

 

 

References

Meylan, W.M., Howard, P.H, Aronson, D., Printup, H. and S. Gouchie.  1997.  "Improved Method for Estimating Bioconcentration Factor (BCF) from Octanol-Water Partition Coefficient", SRC TR-97-006 (2nd Update), July 22, 1997; prepared for: Robert S. Boethling, EPA-OPPT, Washington, DC; Contract No. 68-D5-0012; prepared by: ; Syracuse Research Corp., Environmental Science Center, 6225 Running Ridge Road, North Syracuse, NY 13212.

 Meylan,WM, Howard,PH, Boethling,RS et al. 1999.  Improved Method for Estimating Bioconcentration / Bioaccumulation Factor from Octanol/Water Partition Coefficient. Environ. Toxicol. Chem. 18(4): 664-672 (1999).

Arnot JA, Gobas FAPC. 2003. A generic QSAR for assessing the bioaccumulation potential of organic chemicals in aquatic food webs. QSAR and Combinatorial Science 22: 337-345.

Dimitrov S, Dimitrova N, Parkerton T, Comber M, Bonnell M and Mekenyan O. “Base-line model for identifying the bioaccumulation potential of chemicals”, SAR and QSAR in Environmental Research 16(6), 2005