Deriving in Vivo Bioconcentration Factors of a Mixture of Fragrance

Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Deriving in Vivo Bioconcentration Factors of a Mixture of Fragrance Ingredients Using a Single Dietary Exposure and Internal Benchmarking Chang-Er L. Chen,*,†,‡ Karin Löfstrand,‡ Margaretha Adolfsson-Erici, Michael S. McLachlan, and Matthew MacLeod Department of Environmental Science and Analytical Chemistry (ACES), Stockholm University, SE-106 91 Stockholm, Sweden S Supporting Information *

ABSTRACT: Chemicals in mixtures that are hydrophobic with Log KOW > 4 are potentially bioaccumulative. Here, we evaluate an abbreviated and benchmarked in vivo BCF measurement methodology by exposing rainbow trout to a mixture of eight test chemicals found in fragrance substances and three benchmark chemicals (musk xylene (MX), hexachlorobenzene (HCB) and PCB52) via a single contaminated feeding event followed by a 28-day depuration period. Concentrations of HCB and PCB52 in fish did not decline significantly (their apparent depuration rate constants, kT, were close to zero), whereas kT for MX was 0.022 d−1. The test chemicals were eliminated much more rapidly than the benchmark chemicals (kT > 0.117 d−1). The bioconcentration factors (BCFA) for the test chemicals were in the range of 273 L kg−1 (8-cyclohexadecen-1-one (globanone)) to 1183 L kg−1 (α-pinene); the benchmarked BCFs (BCFG) calculated relative to HCB ranged from 238 L kg−1 (globanone) to 1147 L kg−1 (α-pinene). BCFG were not significantly different from BCFA but had smaller standard errors. BCFs derived here agreed well with values previously measured using the OECD 305 test protocol. We conclude that it will be feasible to derive BCFs of chemicals in mixtures using a single dietary exposure and chemical benchmarking.

1. INTRODUCTION Essential oils are mixtures of chemical substances derived from plants1 that are used for flavouring foods and beverages, as aroma additives for cosmetics and household products, as masking agents for unpleasant odors and as therapeutic agents.2,3 Essential oils can be registered as either substances of unknown or variable composition, complex reaction products or biological materials (UVCB) or as multiconstituent substances (MCS) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) legislation. Composing dossiers of information about UVCBs and MCSs under REACH can be challenging, particularly in the case of UVCBs when the composition and components of the mixtures are not well characterized. Many components in essential oils would be screened as potentially B for hazard classification (criterion is log KOW ≥ 4)4 and PBT assessment (log KOW > 4.5).5 However, whether the BCF of UVCBs and MCSs such as essential oils can be determined from the mixtures rather than studying each component individually in in vivo experiments remains unclear. Previous research has shown that it is feasible to measure the BCFs of multiple chemicals in one in vivo experiment.6−8 Therefore, it might be possible to measure the BCFs of the main constituents of essential oils together in one experiment. The standard method for determining the BCF is the OECD 305 guideline, in which fish are first placed in water containing a constant concentration of the test chemical(s) for several © XXXX American Chemical Society

weeks and then transferred to chemical-free water for several weeks.6 The different components of MCSs like essential oils often have a wide range of physical chemical properties like volatility, hydrophobicity, and water solubility. This can make it difficult to maintain constant and defined water concentrations of all components of the mixture during the exposure phase. Therefore, determination of the BCF of essential oils cannot be readily accomplished using the standard OECD 305 test protocol9 involving aqueous exposure. The 2012 revision of the OECD 305 test guideline6 offers the option to estimate BCFs of complex mixtures using a dietary exposure approach in which a comparable exposure to all components of the mixture is more likely than in the aqueous exposure. This was also documented recently in Gobas and co-workers’ studies.8,10 The dietary exposure and depuration protocol specified in the updated OECD 305 for BCF estimation typically requires constant exposure conditions for 7−14 days and three sampling points during the uptake phase and up to 28 days and typically six sampling points during the depuration phase. At each sampling point in the OECD protocol, a minimum of five fish are sacrificed. In addition, control fish fed with uncontaminated food are also required. A previous study11 demonstrated that Received: Revised: Accepted: Published: A

January 9, 2018 March 21, 2018 March 31, 2018 March 31, 2018 DOI: 10.1021/acs.est.8b00144 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology the time, cost and the number of fish used in an in vivo BCF test with aqueous exposure can be strongly reduced by employing abbreviated uptake and depuration periods and using internal benchmarking chemicals. This study further builds on these findings to demonstrate an abbreviated BCF measurement using dietary exposure and internal benchmarking. Bioconcentration tests that use the internal benchmarking technique have many potential advantages over nonbenchmarked tests. Benchmarking can correct for dilution of chemicals due to growth of fish during the test,7 improve chemical recovery statistics12 and interstudy comparability, and improve the precision of BCF measurements by correcting for sources of variability in chemical concentrations in fish that are covariant among all chemicals in the test.7,11 Interindividual variability in the amount of chemical consumed by each fish is an example of a covariant factor that can be corrected by benchmarking. Variability in the ingested dose of chemical is expected to be largest in a single dose experiment, and reducing this source of variability is one of the reasons why long uptake periods with multiple dosing events have been used in previous work and are recommended in the revised OECD 305 protocol.6,8,10,13,14 We hypothesize that test duration and fish use can be reduced by using a single dietary dosing and exploiting internal benchmarking to account for interindividual variability in the ingested dose of chemicals. Here, we report an evaluation of our proposed single dietary exposure event, benchmarked BCF measurement methodology using a mixture of eight fragrance chemicals that were selected to cover a range of physicochemical properties, and for most of which high quality measured BCF values are available. We dosed fish kept in clean water with a mixture made up of the test compounds and three benchmark chemicals via a single contaminated dietary exposure followed by a depuration period in which fish remained in clean water and received clean food. Our objective was to test the applicability and utility of the benchmarking technique in an abbreviated dietary exposure experiment by (1) measuring the benchmarked depuration rate constant (kTG) for the chemicals, and (2) estimating the BCF of the chemicals in fish and evaluating our results against previous BCF measurements for the test chemicals.

Figure 1. Processes that determine the extent of bioaccumulation of chemicals by fish. Uptake rate constants from water via gills (k1) and diet (kD), and depuration rate constants to water via gills (k2), and by faecal egestion (kE), metabolism (kM), and growth dilution (kG).

k T = k 2 + kE + kM + k G

The depuration via growth is a pseudodepuration process since it reduces the concentration, but not the amount of chemical in the fish. Growth rates of fish are highly variable in the wild, and tend to be faster in test systems than in the wild. Thus, it is desirable to know the overall rate constant for depuration excluding growth dilution (kTG), which is given by k TG = k 2 + kE + kM = k T − k G

(3)

At steady state, the bioconcentration factor (BCF) that refers only to aqueous exposure can be calculated as BCF =

CF k = 1 CW kT

(4)

eq 4 provides the basis for deriving the BCF of chemicals in fish from rate constants without the need to measure CW. Instead, the key parameter to measure experimentally is kT, which is the slope of a linear relationship between the natural logarithm of chemical concentration in fish and time determined in an in vivo experiment. BCF values determined this way are referred to as “kinetic BCFs”. A corresponding growth-corrected bioconcentration factor (BCFG) can be obtained from BCFG =

2. THEORY Bioaccumulation. Bioaccumulation of chemicals by fish is the net result of competing uptake and depuration processes that can be modeled using first order kinetic rate constants, k (Figure 1). The intake processes include accumulating chemicals from water via gills (rate constant k1) and/or via diet (rate constant kD ). Depuration processes include depuration to water through gills (rate constant k2), fecal egestion (rate constant kE), biotransformation (rate constant kM) and growth dilution (rate constant kG). The concentration of chemical in the fish as a function of time can be calculated from concentrations in water and diet with a one-compartment mass balance model: dC F = k1C W + kDCD − (k 2 + kE + kM + kG)C F dt

(2)

k1 k TG

(5)

In order to calculate kinetic BCFs from a dietary study, the uptake rate constant from water (k1) is required. Various models are available for estimating k1 if an experimental value is not available.15,16 Many of these methods perform similarly in prediction of k1 for a given chemical and fish size.15 In consideration of more recent insights into bioaccumulation and improved model parametrization, Arnot and Gobas17 developed an improved model for estimating k1 based on fish size and the KOW value of the target chemical, which was later further updated to take the effect of temperature into consideration.18 We chose this method to estimate k1 in our study. According to Arnot and Gobas,17 k1 can be estimated as

(1)

k1 =

where CW is the freely dissolved concentration of chemical in water, CD represents concentration in fish feed and CF denotes the concentration in fish. The sum of k2, kE, kM and kG is the overall rate constant for depuration, kT:10

EW × GV W

(6)

where EW is the chemical transfer efficiency at the gill (unit less), GV is the gill ventilation rate (L/d), and W is the wet weight of the fish (kg). EW can be approximated as,19 B

DOI: 10.1021/acs.est.8b00144 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Table 1. Names, Abbreviation, CAS Number, Molecular Weight, log KOW and Vapour Pressure and Chemical Structures of the Test and Benchmark Compounds name Test Chemicals α-pinene camphene D-limonene 2-t-butylcyclohexyl acetate 6,7-dihydro-1,1,2,3,3-pentamethyl-4(5H)-indanone cyclohexyl salicylate acetyl cedrene 8-cyclohexadecen-1-one Benchmarks Chemicals musk xylene hexachlorobenzene 2,2′,5,5′-tetrachlorobiphenyl a

vapor pressure (Pa, 25 °C)

abbr.

CAS no.

molecular weight (g/mol)

log KOW

APN CAM LIM Verdox DPMI CHS AC GLO

80−56−8 79−92−5 138−86−3 88−41−5 33704−61−9 25485−88−5 32388−55−9 3100−36−5

136.24 136.24 136.24 198.31 206.33 220.27 246.40 236.44

4.6a 4.2a 4.5a 4.4b 4.5b 4.9b 5.0b 5.8b

6.3 × 3.3 × 1.9 × 7.1b 5.4 × 2.1 × 7.6 × 3.2 ×

MX HCB PCB52

81−15−2 118−74−1 35693−99−3

297.26 284.80 291.99

4.9a 5.7a 6.1a

3.0 × 10−5a 2.3 × 10−3a 1.1 × 10−3b

102a 102a 102a 10−1b 10−3b 10−3b 10−2b

The average of experimental values available from EPI Suite. bEstimated from the structure in EPI Suite.

Table 2. List of Test and Reference Chemicals Showing Overall Depuration Rate Constant (kT), Growth Corrected Depuration Rate Constant (kTG), Apparent Bioconcentration Factor (BCFA), Growth Corrected BCF (BCFG) (Based on k1 = 138 L kg ww−1 d−1), Literature BCF (BCFA,LT) and Benchmarked BCF (BCFA-BM) (Based on Benchmarking BCFA to Musk Xylene) kT ± SE name Test APN CAM LIM Verdox DPMI CHS AC GLO Benchmarks MX HCB PCB52

kTG ± SE

BCFA ± SE

BCFG ± SE

−1

(d ) 0.117 0.151 0.121 0.421 0.317 0.313 0.138 0.509

± ± ± ± ± ± ± ±

0.020 0.035 0.031 0.052 0.041 0.208 0.013 0.288

0.022 ± 0.011 0.004 ± 0.006 0.006 ± 0.013

BCFA,LTa

BCFA-BM ± SEb

−1

(L kg ww ) 0.120 0.153 0.120 0.421 0.382 0.386 0.134 0.581

± ± ± ± ± ± ± ±

0.015 0.028 0.019 0.035 0.047 0.191 0.010 0.245

0.018 ± 0.005 0.003 ± 0.006

1183 912 1145 342 435 440 1003 273

± ± ± ± ± ± ± ±

223 209 296 44 56 292 95 154

1147 900 1145 342 361 358 1031 238

6265 ± 3016 36 935 ± 60 837 21 810 ± 43 210

a

± ± ± ± ± ± ± ±

143 165 181 31 44 177 77 100

810 180 104 750 1982 244

7547 ± 2064

6500 28 500 78 600

53 246 ± 13 5447

1226 945 1187 354 451 456 1039 282

± ± ± ± ± ± ± ±

231 217 306 46 58 303 99 160

6500 ± 3126 38 283 ± 63 061 22 606 ± 44 790

9

Mean values of in vivo BCFs determined using OECD test guideline 305. BCFs were wet weight based (not normalized to lipid), see SI Table 3 for details. bBenchmarked to musk xylene (MX) using its BCFA,LT via eq 11. −1 ⎛ R ⎞ E W = ⎜R WW + LW ⎟ K OW ⎠ ⎝

k1 =

EOX

M × COX

155 K OW

)C

OX

(10)

In a specific experiment, T, W, and COX are the same for all chemicals, so k1 varies with the KOW of the test chemical. For chemicals with KOW > 1000, k1 is approximately constant because the value of the term including KOW in eq 10 tends to a constant value of 1.85 ≈ 1.85 + 155/KOW. Benchmarking Method. The benchmarking method measures the relative behavior of chemicals instead of the absolute value for a given behavioral characteristic, similar to the internal standard method commonly employed in analytical chemistry.20 The benchmarked amount of a test substance X (NXBM) is calculated as the ratio of the amount of test substance (NX) and the amount of a selected benchmarking chemical (NBM):

(8)

where M is temperature T (°C) dependent, described by18 log10 M = 2.8 + 0.786log10 W + 0.017T

(

0.65W 1.85 +

(7)

where RWW (=1.85) is a coefficient representing diffusive resistance in the water phase; RLW (=155) is a coefficient representing diffusive resistance in the lipid phase and KOW is the octanol−water partition coefficient of the chemical. GV can be estimated from the metabolic oxygen requirements, M (mg d−1), of the fish, the dissolved oxygen concentration in the water COX (mg L−1) and the efficiency of oxygen transfer across the gills EOX (default = 0.65):18

GV =

102.8 + 0.786log10W + 0.017T

NXBM =

(9)

Combining eq 6−9) and substituting the known values for RWW and RLW gives

NX NBM

(11)

In the context of in vivo bioaccumulation studies, internal chemical benchmarking has proven useful to reduce variability C


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chemicals) in toluene (500 μL). The mixture was rotated in a closed bottle for 14 h to allow the chemicals to be adsorbed. It was then dried on a filter paper in the fume hood for 1.5 h. The chemical concentrations in the contaminated food were measured in triplicate using the same method as employed for analysis of fish samples (see below). Nominal concentrations of the model chemicals that correspond to the amount spiked and the actual concentrations measured in the prepared feed are given in SI Table S1. In Vivo Experiment. The rainbow trout (Oncorhynchus mykiss) used in this study were purchased from Vilstena and Näs fish farms, Sweden and were kept in accordance with the Swedish National Agriculture Board’s guidance on research animals (Statens Jordbruksverks Föreskrifter och Allmänna Räd om Försöksdjur, SJVFS 2015:24). Ethical approval for the experiments was obtained from Stockholms Norra Djurförsöksetiska Namnd (permit N144/12). The in vivo study was carried out in two 50 L aquaria with 12 rainbow trout in each. The fish densities in the two aquaria were 29.1 and 26.7 kg/m3. The aquaria were equipped with a pump to simulate a stream and had an aerated water flow of 2 L/min giving a water exchange rate in the aquaria of 2.4/h or 58 times the water volume in the aquarium per day. The oxygen concentration was (9.57 ± 0.4) mg/L throughout the experiment. The room and water were kept at (12.8 ± 0.2) °C. The fish were fed one contaminated meal (6 g per aquarium). Two fish from each aquarium were sacrificed on days 1, 4, 8, 15, 22, and 29. During the depuration phase, the fish were fed clean food corresponding to 0.5% of their body weight. In parallel, reference fish were fed clean food throughout the experiment as the control. Feces residues were siphoned off daily to clean the tank. The weight of each fish was recorded after sampling. Sample Preparation and Analysis. The methods for sample preparation and analysis have been described in detail elsewhere.21 In brief, whole rainbow trout were homogenized with a Büchi B-400 laboratory mixer and subsamples (about 5 g, wet weight) were spiked with surrogate standards as listed above. All fish were analyzed in duplicate. The samples were extracted four times with acetonitrile (10 mL each time) under ultrasonication (15 min). The combined extracts were solvent exchanged to hexane by liquid−liquid extraction. The analytes were transferred from the combined extracts to a SPE column via a stream of nitrogen gas in a purpose-built purge and trap system (operated for 24 h at 70 °C).21 The analytes were then eluted from the SPE column with 1 mL of n-hexane to GC vials. The eluent was spiked with the volumetric standard PCB53 before instrumental analysis. Sample analysis was performed using a Trace 1300 Series gas chromatograph (GC) (Thermo Scientific) equipped with a programmed temperature vaporizing (PTV) injector and a AI 1310 Autosampler (Thermo Scientific), coupled to a single quadrupole ISQ mass spectrophotometer (MS) (Thermo Scientific). The PTV injector was run in splitless mode (1.5 min) and the temperature started at 60 °C, was held constant for 0.5 min, increased by 14.5 °C/s to 300 °C, where it was held constant for 1 min. A Thermo TG-5SIL MS capillary column (30 m × 0.25 mm i.d., 0.25 μm film) was used with helium (at a constant flow 1 mL/min) as the carrier gas. The oven temperature started at 60 °C, was held constant for 2 min, increased by 10 °C/min to 250 °C and then by 30 °C/min to 300 °C, where it was held constant for 2 min. The transfer line was set to 300 °C and the ion source to 260 °C. The MS was operated in electron

that is covariant between chemicals (e.g., from interindividual differences in intake) and to correct for growth dilution in fish bioaccumulation experiments.7 To correct for growth dilution, a benchmark chemical with very slow depuration by all process except growth is employed. For such a benchmarking chemical, kT = kG and hence, kG for other chemicals can be approximated as the kT measured for the benchmark. Mathematically:7 kT = −

kG = −

ln Cx , t 2 − ln Cx, t1 (12)

t 2 − t1

ln C BM, t 2 − ln C BM, t1 (13)

t 2 − t1

where C is the concentration in fish at time t. Combing eqs 3, 12, and 13 results in

( ) − ln( )

ln k TG =

CX , t 2

CX , t 1

C BM, t 2

C BM, t1

t 2 − t1

(14)

Thus, the growth-corrected depuration rate constant kTG can be directly calculated from results of a benchmarked in vivo test as the slope of a plot of the natural logarithm of the benchmarked concentrations of test chemicals in fish versus time if the benchmark chemical can be assumed to be eliminated only by growth dilution.

3. METHODS AND MATERIALS Chemicals and Reagents. Eight test compounds, that is, α-pinene (APN), camphene (CAM), limonene (LIM), 2-tbutylcyclohexyl acetate (Verdox), 6,7-dihydro-1,1,2,3,3-pentamethyl-4(5H)-indanone (DPMI), cyclohexyl salicylate (CHS), acetyl cedrene (AC), and 8-cyclohexadecen-1-one (GLO) and three benchmark compounds, musk xylene (MX), hexachlorobenzene (HCB) and 2,2′,5,5′-tetrachlorobiphenyl (PCB52) were selected for study (Table 1, the chemical structures are given in Figure S1 in the Supporting Information (SI)). The test chemicals cover a range of physicochemical properties and in vivo BCF data are available for all chemicals except for APN and LIM (Table 2). APN, CAM, and LIM are commonly found in essential oils, whereas the other chemicals are fragrance ingredients commonly used in consumer products. The benchmark chemicals were chosen because they have slow depuration rates and they could be readily analyzed with the same method as the test chemicals. D2Limonene (LIM-D2), D4-acetyl cedrene (AC-D4), 13C6hexachlorobenzene (HCB-C13), D15-musk xylene (MX-D15), 13 C12-2, 2′, 5, 5′-tetrachlorobiphenyl (PCB52-C13) were used as surrogate standards for the analytical method. PCB53 was used as the volumetric standard. Toluene, acetonitrile and n-hexane, all from Merck (Darmstadt, Germany), were of the highest available quality. High-purified water was generated from a Milli-Q ultrapure water system (Milli-Q PLUS 185 from Millipore Stockholm, Sweden). Solid phase extraction (SPE) cartridges (Isolute ENV+ (10 mg/1 mL) for trapping target chemicals and Isolute ENV+ (50 mg/1 mL) for cleaning the nitrogen gas) were obtained from Biotage Sweden (Uppsala, Sweden). Preparation of Feed. Fish feed granulates (14 g, 2 mm, EFICO Alpha from BioMar, Wernberg-Koblitz, Germany) were first spiked with 5 mL of soy sauce (Wanjashan, China, purchased at a local supermarket in Stockholm, Sweden) containing all the test and reference substances (in total 11 D


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Figure 2. Results of the depuration experiment comparing nonbenchmarked concentrations (blue) and concentrations benchmarked to HCB (red). Ln concentration is plotted against time for α-pinene (APN), camphene (CAM), limonene (LIM), 2-t-butylcyclohexyl acetate (Verdox), 6,7dihydro-1,1,2,3,3-pentamethyl-4(5H)-indanone (DPMI), cyclohexyl salicylate (CHS), acetyl cedrene (AC), musk xylene (MX), 8-cyclohexadecen-1one (GLO), hexachlorobenzene (HCB) and 2,2′,5,5′-tetrachlorobiphenyl (PCB52). The dotted green line is the limitation of quantification (LOQ).

ionization (EI) mode (70 eV), and selective ion monitoring

4. RESULTS

(SIM) mode we used to scan for ions of target compounds with

Fish Health. No signs of health problems were observed for the test and control fish during the experiment. The weight of the fish analyzed varied from 85 to 223 g with a median value of 130 g (mean value 132 g). The semilogarithmic plot of the wet weight of analyzed fish versus sampling time is shown in SI Figure S2. Assuming that the fish were sampled randomly with respect to size, the fish grew during the experiment, though not

a dwelling time of 0.01s each. The internal standard method was employed to quantify the target chemicals in the samples. The data were blank and recovery corrected. For further details, please refer to our previous work.21 E


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Environmental Science & Technology significantly, at a rate of 0.4 g d−1. For a fish with the median weight of 130 g, the corresponding estimated rate constant for growth dilution (kG) is 0.004 d−1. Chemical Concentrations in Fish. None of the test or benchmark chemicals were observed in the control fish at a concentration above the LOQ. The wet weight (ww) concentrations of test and benchmark chemicals in the test fish are illustrated in Figure 2 and are presented in SI Table S2. Fish 4, 8, 18, 19, and 24 were not analyzed because they were too small to be homogenized properly with the mixer. Many of the test chemicals were above the LOQ only at the first several sampling points, with DPMI (52.7−178 ng g−1 ww), CHS (7.0−43.2 ng g−1 ww) and GLO (9.2−133 ng g−1 ww) only above the LOQ at the first two sampling points. AC (3.3−349 ng g−1 ww) was above the LOQ in samples collected at all time points although not in every individual fish after 15 days. The benchmark chemicals, MX (17.7−99.2 ng g−1 ww), HCB (26.6−69.7 ng g−1 ww) and PCB52 (13.6−92.2 ng g−1 ww) could be quantified in all analyzed samples except one fish (Fish 17 collected on day 22 of the experiment). No chemicals could be quantified in fish 17 for unknown reasons, however, it is possible that this particular fish did not ingest any of the food on the day of the dosing. The concentrations of all the chemicals in the fish decreased with increasing depuration time except for HCB and PCB52 for which no significant decline was observed (Figure 2). The concentrations of the test chemicals in fish were also benchmarked to the concentrations of HCB, PCB52 and MX. The depuration kinetics of the benchmarked concentrations were plotted (Figure 2, SI Figures S4 and S5, respectively) to explore the benefit of benchmarking. Depuration Kinetics. Semilogarithmic plots of concentration in fish against sampling time yielded linear relationships for most compounds, indicating first order depuration kinetics (Figure 2 in blue). Generally, fast depuration was observed for all the test chemicals, while three benchmark chemicals MX, HCB and PCB52 were resistant to depuration as expected. The derived kT values from Figure 2 are presented in Table 2. They range from 0.117 d−1 (APN) to 0.509 d−1 (GLO) for the test chemicals. The kT values for HCB (0.004 d−1) and PCB52 (0.006 d−1) were not statistically different from zero, and very close to the rate constant for fish growth (0.003 d−1), while it was 0.022 d−1 for MX which was also much slower than the test chemicals. The concentration data were benchmarked to HCB (the chemical with lowest kT) and plotted against the sampling time (Figure 2 in red). The slope of this plot gave the growth corrected depuration rate constant kTG (see eq 14), which ranged from 0.120 d−1 (APN and LIM) to 0.581 d−1 (GLO) (Table 2). The differences between kT and kTG were small, which is consistent with the small value of kG. However, the benchmarking improved the correlation coefficients of the regression for all test chemicals (Figure 2). SI Figures S4 and S5 show the data benchmarked to PCB52 and MX, respectively. The kTG determined with different benchmarks (HCB, PCB52 or MX) were very similar (SI Figure S6). Therefore, only kTG from benchmarking to HCB is further used to calculate the BCFs. Uptake Rate Constant. The k1 values were estimated according to eq 8 by taking the mean values of fish weight W (140 g) and COX (9.57 mg/L) measured in this experiment at 12.8 °C. SI Figure S3 shows k1 as a function of log KOW for conditions representative of our experiment. The rate constant

for uptake of chemicals from water via the gills is nearly constant for chemicals with log KOW > 4 under our test conditions. All chemicals in this study have log KOW > 4 (Table 1) and had a k1 value of 138 (L kg ww−1 d−1). Estimated BCFs. The growth corrected bioconcentration factors (BCFG) were in the range of 238 (GLO) − 1147 (APN) for all the test chemicals. These BCF values are compared with literature apparent BCF values (BCFA,LT, Table 2 and Figure 3). These BCFA,LT values were determined with

Figure 3. Apparent BCF (BCFA) and growth corrected BCF (BCFG) plotted against BCF values from the literature (BCFA,LT). The diagonal line represents a perfect agreement between measured BCFs in this study and BCFs from literature.

single chemicals following the OECD 305 protocol with aqueous exposure; they were not growth corrected. No BCFA,LT values were found for APN or LIM. The references for the in vivo BCFs available in literature used as comparison are given in Table 2 and more details in SI Table S3. Measuring BCF using the dietary exposure method is particularly subject to systematic bias, as k1 is estimated, not measured. In order to take potential bias from the estimation of k1 into consideration, the BCF data were benchmarked directly to the values of MX in the literature (BCFA-BM). Therefore, we also quantified BCFA by directly benchmarking to BCFA,LT (rather than through kT as for the determination of BCFG) according to eq 11. MX was chosen as the benchmark chemical for BCFA due to the large number of high quality BCF studies. The resulting benchmarked BCF (BCF-BM) ranged from 282 L kg ww−1 (GLO) − 1226 L kg ww−1 (APN) for the test chemicals (Table 2).

5. DISCUSSION Depuration Kinetics. The linear relationship between ln C against time indicates that it is appropriate to fit the data with the first order depuration model. It should be noted, however, that some of the test substances were eliminated rapidly, for example GLO, DPMI, and CHS (Figure 2) and were detected only at the first two sampling points; this made the results for these chemicals more uncertain, but on the other hand, indicates that these chemicals are not bioaccumulative. MX has a similar kTG value in this dietary exposure experiment (0.018 d−1) compared to that in an aqueous exposure study in rainbow trout conducted in our laboratory (0.015 d−1),7 which suggests F


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feed ingested. However, as Figure 2 (for HCB) and SI Figure S4 (for PCB 52) indicate, the variation within the data is larger for almost all of the test compounds than for HCB. Furthermore, benchmarking does not eliminate all of the variability. This indicates that there are other important sources of variability in the concentrations of the test compounds in the fish. Other sources of variability may be individual differences in chemical uptake during digestion or in the primary depuration processes, gill ventilation and metabolism, as well as analytical error. The relative importance of these sources of variability is likely to be chemical specific, which means that the benchmarking will not eliminate all the variability. Bioconcentration Factor. The BCF values (both BCFA and BCFG) for all the test chemicals (Table 2) are below the REACH regulatory threshold (i.e., BCF 2000). As expected and also documented in previous studies, musk xylene is bioaccumulative with both BCFA (6265) and BCFG (7547) higher than 2000. No significant difference was observed between BCFA and BCFG in this study due to the low growth rate of fish during the test. The BCF data from our study (either BCFA or BCFG) agreed well with values generated previously using the standard OCED TG 305 bioaccumulation test with aqueous exposure to single chemicals (Figure 3). To our knowledge, these are the first reported BCFs for APN and LIM since no data could be found in our literature review of previous studies. The BCF values (both BCFA and BCFG) for the rest of test chemicals and HCB in this study are different from those in the literature by factors that range from 0.6−1.7, except DPMI, for which our measured value based on observations at just two time points above quantification limits, is 3−4 times higher than its BCFA,LT, but still well below the regulatory threshold. The BCFA and BCFG determined in this study for PCB52 are factors of 0.3 and 0.7, respectively, of BCFA,LT but are associated with large uncertainties due to slow depuration kinetics for this compound. The overall agreement between our results and previous studies suggests that our method using a single dietary dosing with multiple chemicals together with internal chemical benchmarking delivers BCF values that are consistent with the standard OECD TG 305 test. The feasibility of measuring the BCF by exposing fish to a diet containing multiple chemicals simultaneously has been also documented in previous studies,8,10,13 and confirms that an opportunity exists for assessing the bioaccumulation of components of chemical mixutures such as UVCBs and MCSs. Although the BCFA determined in this study are not significantly different from BCFG, the calculated uncertainties in BCFG are lower than for BCFA (Table 2). This is a consequence of the benchmarking of the data against HCB which corrected for interindividual variability in the quantity of contaminated feed ingested, and other sources of variability that are covariant for all chemicals. Benchmarking will be advantageous in future BCF testing of mixtures, especially in cases where fish grow significantly during the test, and when delivering chemical to fish with a single oral dose leads to higher interindividual variability in the delivered dose. The relative standard error of BCFG is ≤14% for all test chemicals except CHS and GLO (probably due to the higher variation of relative recovery of CHS and GLO during the extraction21). While this can be considered low, it is not a comprehensive measure of uncertainty in BCF because it does not include the uncertainty in k1, which was estimated with a model. The available models for predicting k1 give a wide range

that the overall depuration is similar for different exposure routes (aqueous vs dietary). The overall depuration rate constants (kT) of the test chemicals (0.117−0.509 d−1) were much larger than the rate constant for growth dilution (kG = 0.003 d−1) (Figure 2 and Table 2). Clearly, holding the feeding rate to 0.5% of fish body weight per day (the minimum feeding rate recommended in OECD TG305) effectively controlled the fish growth during the experiment. The overall depuration rate of the two benchmark chemicals PCB52 and HCB (0.006 d−1 and 0.004 d−1, respectively) was similar to the rate of growth dilution. Therefore, they were appropriate benchmark chemicals for growth dilution. Because the fish grew very slowly during the study, benchmarking against HCB had little impact on the magnitude of the depuration rate constants of the test chemicals (compare kT and kTG in Table 2). However, benchmarking against HCB improved the correlation coefficients for the regressions of concentration against time for all test chemicals (Figure 2), and the standard error of the depuration rate constant was reduced for all test chemicals except DPMI (Table 2). Comparable kT values were obtained when benchmarking to PCB52 (SI Figure S4) or MX (SI Figure S5), suggesting benchmarking to these chemicals accounted for the same sources of variability in this experiment. The ability of benchmarking to reduce the variability in bioaccumulation data has been previously demonstrated in water exposure experiments.11 Here we show this useful feature of benchmarking in dietary exposure experiments. As discussed earlier, multiple dosing can reduce the interindividual variability in concentrations in fish arising from differences in food ingestion. Substituting multiple dosing with a single dietary exposure using internal benchmarking would be of interest because it can simplify the dosing procedure, save costs for chemical use and reduce animal use. Improvement of R2 for the regressions of concentration against time following benchmarking was observed in this study (Figure 2), although differences were not large. This can be attributed to small interindividual differences in dietary ingestion of contaminant in this experiment, but we do not expect the differences to always be so small. When the interindividual differences are larger, benchmarking will be more important, especially in studies that use a single dose of chemicals. The depuration rate constants of PCB52 and HCB determined here are close to those determined by Gobas and Lo10 in a study where a multiple dosing protocol was employed. As well, the kT values for HCB and MX are similar to those determined in a previous study in our laboratory11 that used dosing from the water. The similar depuration kinetics observed in these studies indicate that the single diet dosing approach does not produce different overall depuration kinetics compared to other dosing strategies. A caveat is that the combined levels of the chemicals used in the test should not approach or exceed the toxic level to ensure good fish health during the experiment. One should also avoid including chemicals that could induce metabolism of other chemicals in the mixture. As discussed in Adolfsson-Erici et al.,11 when applying benchmarking to reduce data variability one must consider what sources of variability the benchmarking chemical captures. In this experiment, we believe that the primary source of variability captured by benchmarking to HCB or PCB52 was the interindividual variability in the quantity of contaminated G


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Environmental Science & Technology of values, which suggests that the uncertainty in k1 could be large. However, as k1 is expected to be similar for the chemicals in this study, an error in k1 would lead to a bias in the BCF results, which is not evident in our comparison of BCF values with literature values determined in standard tests. One way to compensate for an error in k1 is to benchmark BCF against a chemical with a well-established BCF that can serve as a reference. Of the studied chemicals, MX was the most appropriate for this purpose since there are well-established BCF for MX from the gold standard database. However, as our measured BCF for MX is very close to the averaged gold standard value for MX, the benchmarked BCF values (BCFABM) for other chemicals are comparable with BCFA (Table 2) and BCFA,LT. This might indicate that uncertainty of k1 in this experiment is relatively small. It has been documented and is reflected in eq 10 that k1 is fish size dependent,16 therefore, effectively controlling the fish growth would limit the uncertainty of k1 resulting from increasing fish size. Environmental Implications. This study demonstrates an abbreviated method to determine BCF. The effect of growth dilution for in vivo bioaccumulation studies can be reduced through limiting fish growth (by feeding fish with minimal feed) and/or using the internal benchmarking technique. The benchmarking technique reduces variability in in vivo bioaccumulation studies and can also allow one to decrease the dosing frequency via food ingestion to a single dose. The number of fish required and total costs may therefore be reduced. The variability from food ingestion is more important for chemicals where the variability contributed by depuration over time is low (i.e., slowly eliminated compounds). It is thus particularly important to include a benchmark chemical for assessing bioconcentration factors in an in vivo study of slowly eliminated chemicals that are most interesting from a regulatory perspective. The method developed here should also be applicable to measure BCF values of the major constituents in real UVCB and MCS mixtures.

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Michael S. McLachlan: 0000-0001-9159-6652 Matthew MacLeod: 0000-0003-2562-7339 Present Address †

Environmental Research Institute, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China. Author Contributions ‡

C.E.L.C. and K.L. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Test chemicals were a kind gift from Givaudan, Ashford, United Kingdom; International Flavours and Fragrances (IFF), Hazlet and Symrise, Holzminden, Germany. This study was funded by the Research Institute for Fragrance Materials (RIFM, http:// www.rifm.org/).

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(1) Amorati, R.; Foti, M. C.; Valgimigli, L. Antioxidant Activity of Essential Oils. J. Agric. Food Chem. 2013, 61 (46), 10835−10847. (2) Wei, A.; Shibamoto, T. Antioxidant activities and volatile constituents of various essential oils. J. Agric. Food Chem. 2007, 55 (5), 1737−1742. (3) Edris, A. E. Pharmaceutical and therapeutic potentials of essential oils and their individual volatile constituents: A review. Phytother. Res. 2007, 21 (4), 308−323. (4) EU. Reach Annex Ix: Standard Information Requirements for Substances Manufactured or Imported in Quantities of 100 Tonnes or More [68]. In EC, Ed. Off. J. Eur. Union, 2006; Vol. 1907/2006. (5) ECHA. Guidance on Information Requirements and Chemical Safety Assessment Chapter R.11: PBT/vPvB Assessment, 3rd ed.; Agency, E. C., Ed.; Helsinki, Finland, 2017. (6) OECD, Test No. 305: Bioaccumulation in Fish: Aqueous and Dietary Exposure; OECD Publishing: Paris, 2012; p 72. (7) Adolfsson-Erici, M.; Akerman, G.; McLachlan, M. S. Measuring bioconcentration factors in fish using exposure to multiple chemicals and internal benchmarking to correct for growth dilution. Environ. Toxicol. Chem. 2012, 31 (8), 1853−1860. (8) Lo, J. C.; Campbell, D. A.; Kennedy, C. J.; Gobas, F. A. P. C. Somatic and gastrointestinal in vivo biotransformation rates of hydrophobic chemicals in fish. Environ. Toxicol. Chem. 2015, 34 (10), 2282−2294. (9) OECD. Test No. 305: Bioconcentration: Flow-through Fish Test; OECD Publishing: Paris, 1996. (10) Gobas, F. A. P. C.; Lo, J. C. Deriving bioconcentration factors and somatic biotransformation rates from dietary bioaccumulation and depuration tests. Environ. Toxicol. Chem. 2016, 35 (12), 2968−2976. (11) Adolfsson-Erici, M.; Akerman, G.; McLachlan, M. S. Internal Benchmarking Improves Precision and Reduces Animal Requirements for Determination of Fish Bioconcentration Factors. Environ. Sci. Technol. 2012, 46 (15), 8205−8211. (12) Xiao, R. Y.; Adolfsson-Erici, M.; Akerman, G.; McLachlan, M. S.; MacLeod, M. A Benchmarking Method to Measure Dietary Absorption Efficiency of Chemicals by Fish. Environ. Toxicol. Chem. 2013, 32 (12), 2695−2700. (13) Lo, J. C.; Letinski, D. J.; Parkerton, T. F.; Campbell, D. A.; Gobas, F. A. P. C. In Vivo Biotransformation Rates of Organic Chemicals in Fish: Relationship with Bioconcentration and Biomagnification Factors. Environ. Sci. Technol. 2016, 50 (24), 13299− 13308. (14) Arnot, J. A.; Quinn, C. L. Development and Evaluation of a Database of Dietary Bioaccumulation Test Data for Organic Chemicals in Fish. Environ. Sci. Technol. 2015, 49 (8), 4783−4796.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b00144. Table S1. Comparison of theoretical and measured concentrations of the chemicals in feed; Table S2. Concentration of target chemicals in the fish; Table S3. Details of the BCFA,LT values; Figure S1. Chemical structures of the compounds in this study; Figure S2. Weights (g) of fish plotted against sampling day; Figure S3. Rate constant for uptake (k1) as a function of log KOW; Figure S4. Depuration plots for all the target chemicals benchmarked to PCB52: Figure S5. Depuration plots for all chemicals benchmarked to MX; Figure S6. Comparison of the overall depuration rate constant (kT) with the growth corrected rate constants (kTG) determined by benchmarking with HCB, PCB52, and MX (PDF)

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Phone: +862039311529; +8615505384235; e-mail: changer. [email protected] or [email protected]. ORCID

Chang-Er L. Chen: 0000-0002-2069-4076 H


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Environmental Science & Technology (15) Crookes, M.; Brooke, D. Estimation of Fish Bioconcentration Factor (BCF) from Depuration Data; Environment Agency, U.K.: Bristol, U.K., 2011. (16) Brooke, D. N.; Crookes, M. J.; Merckel, D. A. S. Methods for predicting the rate constant for uptake of organic chemicals from water by fish. Environ. Toxicol. Chem. 2012, 31 (11), 2465−2471. (17) Arnot, J. A.; Gobas, F. A. P. C. A food web bioaccumulation model for organic chemicals in aquatic ecosystems. Environ. Toxicol. Chem. 2004, 23 (10), 2343−2355. (18) Arnot, J. A.; Mackay, D.; Bonnell, M. Estimating metabolic biotransformation rates in fish from laboratory data. Environ. Toxicol. Chem. 2008, 27 (2), 341−351. (19) Gobas, F. A. P. C.; Mackay, D. Dynamics of Hydrophobic Organic-Chemical Bioconcentration in Fish. Environ. Toxicol. Chem. 1987, 6 (7), 495−504. (20) McLachlan, M. S.; Zou, H.; Gouin, T. Using Benchmarking To Strengthen the Assessment of Persistence. Environ. Sci. Technol. 2017, 51 (1), 4−11. (21) Chen, C. E. L.; Lofstrand, K.; Adolfsson-Erici, M.; MacLeod, M. Determination of fragrance ingredients in fish by ultrasound-assisted extraction followed by purge & trap. Anal. Methods 2017, 9 (15), 2237−2245.

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Deriving in Vivo Bioconcentration Factors of a Mixture of Fragrance

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