Comparative Toxicokinetics of Organic Micropollutants in

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Comparative toxicokinetics of organic micropollutants in freshwater crustaceans Junho Jeon, Denise Kurth, Roman Ashauer, and Juliane Hollender Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es400833g • Publication Date (Web): 11 Jun 2013 Downloaded from http://pubs.acs.org on June 17, 2013

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Environmental Science & Technology

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Comparative toxicokinetics of organic micropollutants in

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freshwater crustaceans

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Junho Jeon†*, Denise Kurth†‡, Roman Ashauer†§, and Juliane Hollender†‡

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†

Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf,

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Switzerland ‡

Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, CH-8092, Zürich,

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Switzerland §

Environment Department, University of York, Heslington, York, United Kingdom

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* Corresponding author

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Tel: +41 58 765 5042

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Fax: +41 58 765 5893

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E-mail: [email protected]

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Number of words: 5,150

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Number of figures: 3 (3 large, 1,800 words equivalents)

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Number of tables: 1 (1 large, 600 words equivalents)

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7,550 words in total

ACS Paragon Plus Environment


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Abstract

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Exposure and depuration experiments for Gammarus pulex and Daphnia magna were

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conducted to quantitatively analyze biotransformation products (BTPs) of organic

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micropollutants (tramadol, irgarol, and terbutryn). Quantification for BTPs without

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available standards was performed using an estimation method based on physico-

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chemical properties. Time-series of internal concentrations of micropollutants and BTPs

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were used to estimate the toxicokinetic rates describing uptake, elimination, and

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biotransformation processes. Bioaccumulation factors (BAF) for the parents and retention

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potential factors (RPF), representing the ratio of the internal amount of BTPs to the

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parent at steady state, were calculated. Nonlinear correlation of excretion rates with

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hydrophobicity indicates that BTPs with lower hydrophobicity are not always excreted

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faster than the parent compound. For irgarol, G.pulex showed comparable elimination,

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but greater uptake and BAF/RPF values than D.magna. Further, G. pulex had a whole set

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of secondary transformations that D. magna lacked. Tramadol was transformed more and

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faster than irgarol and there were large differences in toxicokinetic rates for the

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structurally similar compounds, irgarol and terbutryn. Thus, predictability of

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toxicokinetics across species and compounds needs to consider biotransformation and

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may be more challenging than previously thought because we found large differences in

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closely related species and similar chemical structures.


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Introduction Xenobiotic compounds, such as pesticides, personal care products, pharmaceuticals, and

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industrial chemicals are detected in freshwater environments worldwide. Freshwater

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organisms accumulate contaminants via active or passive uptake routes and may suffer

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from adverse effects if concentrations in the organisms reach critical levels.1, 2 The extent

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of bioaccumulation, quantified by the bioaccumulation factor (BAF), is a key index in

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assessing chemical risks to the environment.3, 4 Organisms eliminate accumulated

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chemicals to reduce their internal concentrations in a number of ways e.g., excretion via

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gills, dermal tissue, urine and feces, and biotransformation. Biotransformation is a

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biological process aimed at modifying xenobiotics to derivatives with generally increased

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hydrophilicity to promote the excretion from the body and thus reduce bioaccumulation

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and potential toxic effects. Since the modifications are mediated by enzymes in most

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cases and different organisms have different kinds and amounts of enzymes,

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biotransformation pathways and kinetics vary considerably across species5-7 as well as

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compounds.8, 9 The processes of uptake, internal distribution, biotransformation and

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elimination of xenobiotic substances in organisms are termed toxicokinetics (TK) and

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each process can be quantified by rate constants.

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Cytochrome P450 isoenzymes (P450) are the heme-thiolate superfamily of enzymes and

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perform extensive oxidative transformations to form hydroxylation, oxidation,

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deamination, dealkylation, and dehalogenation products.10 These transformations play an

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important role in regulating the overall bioaccumulation (or body burden) and potential

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toxicity of a substance.11-14



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There have been many studies on TK of pharmaceuticals and biocides in their target

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organisms (e.g., mammalians for pharmaceuticals,15-18 algae for biocides19) but limited

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information is available on non-target organisms such as the freshwater crustaceans,

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Gammarus pulex and Daphnia magna. G.pulex is a key species in flowing freshwater

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bodies, where it acts as a shredder of detritus and is consumed by fish. D.magna feeds on

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algae in lakes and is an important food source for fish and is thus a key species in lake

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ecosystems. Information on qualitative and quantitative analysis of biotransformation

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products (BTPs or metabolites) in those organisms is also lacking because conventional

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analytical techniques such as liquid chromatography coupled with mass spectrometry

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(LC/MS) or radioactivity detection (LC/RAD), were not sensitive nor selective enough to

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identify BTPs present at trace concentrations in such small organisms. Recently, liquid

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chromatography high resolution mass spectrometry (LC-HRMS, e.g., Time-of-flight and

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Orbitrap) has become a promising technique for metabolite profiling studies due to its

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sensitivity, selectivity and speed of analysis.20-23 The major obstacle in metabolite

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profiling with mass spectrometric methods is the limited availability of reference

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standards for metabolites, leading to the infeasibility of absolute quantification. Semi-

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quantitative evaluation has been established by comparing peak intensity or area of

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metabolite to those of parent compound, assuming that the ionization efficiency (IE)

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during the ion formation in electrospray ionization (ESI) and the response of metabolite

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in the mass detector corresponds to that of the parent compound.24, 25A number of studies

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clearly showed the strong relationship between IE and chemical properties such as

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molecular volume and basicity (pKa),26, 27 which can be used to predict the response in

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the mass detector. Previously, we identified several BTPs of micropollutants and


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elucidated their structures using LC-HRMS/MS (LTQ-Orbitrap).28 It was observed that

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structure similarity resulted in analogous biotransformation pathways for two pairs of

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target compounds, i.e. irgarol and terbutryn as well as tramadol and venlafaxine.

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However, it is still unknown whether TK for the analogous compounds are also similar,

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hampering our ability to predict biotransformation rates as well as concentrations of

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BTPs.

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The present study aimed at better understanding the role of biotransformation in

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toxicokinetics. Specific objectives were quantitative comparisons in toxicokinetic

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processes and rates (i) between two analogous compounds having an identical molecular

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back-bone structure (irgarol and terbutryn), (ii) two dissimilar compounds with different

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functionalities (irgarol and tramadol) and (iii) two different species (G. pulex and D.

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magna). In addition, the role of P450 activity in the TK of D. magna was investigated

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through experiments with and without the presence of a P450 inhibitor. The implications

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for species sensitivity differences and predictability of TK in chemical risk assessment

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are also discussed.



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Materials and Methods

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Chemicals and Test organisms

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Detailed information on chemical standards and the maintenance and culture of test

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organisms, i.e. Gammarus pulex and Daphnia magna are provided in the supporting

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information (SI1, SI2 and Table S1). Physico-chemical properties of the target

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compounds irgarol, terbutryn, and tramadol, and their BTPs identified in our previous

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study28 are described in Table S2. The BTPs were named with a scheme described as

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follows; e.g., MIR327A, ‘M’ stands for metabolite, the following two alphabet letters (IR)

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represent the first two letters of the name of parent compound (irgarol), the following

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three-figures (327) represent [M+H]+ of BTP, A or B at the end is a differentiator for

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BTPs having the same exact mass.

114 115 116

Kinetic experiments The TK study closely followed our previous study28 and Ashauer et al.9 Briefly, G.

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pulex were exposed to the test compounds for 24 h and then transferred into clean media

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for the 4 day-depuration phase. D. magna were exposed for 20 hours to the test

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compounds and then transferred to clean media for a 24 h depuration phase. Samples of

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whole G. pulex and D. magna for tissue residue analysis were taken frequently

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throughout the experiment. The specific conditions of the experiments are described in

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SI3. On each sampling date the sampled organisms were sieved, blotted dry on tissue,

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placed in a 2 ml microcentrifuge tube, weighed and shock frozen in liquid nitrogen. The


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organism and exposure solution samples were kept at -80 °C until extraction and

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chemical analysis. The chemical extraction procedure with Fastprep is described in SI4.

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In order to examine biotransformation catalyzed by P450, a group of D.magna was pre-

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exposed to 500 µg/L of the P450 inhibitor piperonyl-butoxide (PBO), 3 hours before

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adding irgarol to the solution. The remaining procedure was identical to those described

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above, without the depuration phase. Two controls, one without target chemicals

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(chemical-negative) and one without test organisms (organism-negative), were included

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in the experiments. Additionally, a food positive-organism negative control was

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conducted for G. pulex to quantify the formation of transformation products by microbes

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on the food source (inoculated horse chestnut leaves).

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LC- High resolution mass analysis Organism extracts from the kinetic experiments were cleaned up with online solid phase

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extraction and then analyzed with liquid chromatography electrospray ionization -

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tandem mass spectrometry (Q Exactive Orbitrap LC-ESI-MS/MS, Thermo Fisher

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Scientific Inc.). Aside from the mass detector, the analytical procedure is almost identical

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to the previous study to identify the BTPs.28 Detailed descriptions are available in SI5.

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Quantification of target molecules To quantify compounds with a reference standard available (i.e. the parent compounds

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and the BTP (2-methylthio-4-tert-butyl amino-s-triazine), calibration curves were

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established on the basis of the area response ratio of the reference standard to the

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respective deuterated internal standard (IS). For the remaining BTPs where reference



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standards were not commercially available, we used an adjusted response area ratio. The

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ionization efficiency (IE) ratio of BTP to the molar concentration of the parent was

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estimated using physico-chemical properties as described elsewhere26, 27 and the

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concentration of the BTP was calculated based on the calibration curve established for the

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parent. The change in the IE caused by the chromatographic retention time difference of

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parent and BTP was also considered. The concentration was considered as 0 if the signal

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to noise ratio (S/N) of the target peak in chromatogram was below the limit of detection

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(LOD). When the S/N was between LOD and the limit of quantification (LOQ), the

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concentrations were set to (LOD+LOQ)/2. Detailed information on the estimation of BTP

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concentration and LOD/LOQ are provided in the SI6 and Table S3, respectively.

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Evaluation of biotransformation kinetics

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A first-order model was applied for the estimation of uptake, biotransformation and

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elimination rate constants as described below. Ashauer et al.9 reported that the percentage

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of dietary uptake in G. pulex for organic compounds (log KOW range from 1.1 to 5.18)

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was generally less than 1 % of total uptake. We included one combined rate constant to

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estimate the contribution of uptake via food and water in the BAF estimation for G. pulex.

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As the contribution of uptake via food is likely very small the resulting BAF will be close

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to the bioconcentration factor (BCF). The TK model is described by:

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dCorg _ p (t ) 167 168

dt

= C water (t ) × ku − Corg _ p (t ) × k e (1)

And at steady-state:


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BAF (or BCF) =

C org_p (t ) C water (t )

=

ku ku = k e k ex + k bt

(2)

170 171

Where Corg_p(t) is the time course of the concentration of the parent compound in the

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organism [nmol kgwet weight−1], Cwater(t) is the time course of the concentration of the

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parent compound in water [nmol L−1], ku is the uptake rate constant [L kgwet weight−1 d−1],

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ke is the overall elimination rate constant, corresponding to the sum of the direct excretion

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(kex) and the total biotransformation rate constant (kbt) of the parent compound [d−1].

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According to the proposed pathways in our previous study, the biotransformation rate

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depends on the internal concentration of either the parent compound or the respective

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BTP formed upstream in the biotransformation pathway (Figure S1). For the rate

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estimation, BTPs are classified into two groups: primary BTPs formed directly from

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parent compounds and secondary BTPs formed from a preceding BTP. The TK model

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was refined accordingly as follows, assuming that biotransformation can be modeled as a

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first-order process.

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For the primary BTPs:

dCorg_1stBTn(t) 185 186

dt

= Corg_p(t) × k1stBTn − Corg_1stBTn(t) × k2 ndBTn − Corg_1stBTn(t) × kex_1stBTn (3)

187 188

For the secondary BTPs



dC org_ 2 ndBTn (t) 189 190

dt

= Corg_ 1stBTn (t) × k 2 ndBTn − Corg_ 2 ndBTn (t) × k ex_ 2 ndBTn

(4) 191 192

where Corg_1stBTn(t) and Corg_2ndBTn(t) are the time courses of the concentration [nmol kgwet

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weight

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k2ndBTn are the biotransformation rate constants [L kgwet weight−1 d−1] of the primary and

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secondary BTPs, respectively, and kex_1stBTn and kex_2ndBTn are the elimination rate

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constants [d−1] of the primary and secondary BTPs, respectively.

−1

] of the primary and secondary BTPs in the organism, respectively, k1stBTn and

197 198

Model calibration

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The rate constants were estimated for each BTP by first approximating the parameters of

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the primary BTPs and then simultaneously fitting all toxicokinetic parameters (model

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calibration). The model calibration minimized the differences between simulated internal

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concentrations for all parent compounds and BTPs and measured concentrations of all

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parent compounds and BTPs at all sampling dates. The model calibration assumed

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normally distributed, independent errors in the internal concentration data and thus

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employed a least squares minimization using the Levenberg-Marquard algorithm.

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Simulations and parameter estimation were carried out in ModelMaker (v4.0, Cherwell

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Scientific Ltd., Oxford, UK). Further information on model calibration is available in SI7.

208 209 210 211

The Retention Potential Factor We propose the retention potential factor (RPF) as a measure of the relative significance of BTP in bioaccumulation. RPF is the ratio of the concentration of a BTP to the


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concentration of the parent in the organism at steady state, corresponding to the ratio of

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the biotransformation rate constant to the elimination rate constant as described below.

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For the primary BTPs,

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RPF1stBTn =

Corg_1stBTn (t ) Corg_p (t )

k1stBTn kex_1stBTn + k2ndBTn

=

(6)

217 218

For the secondary BTPs,

RPF2ndBTn = 219

=

C org_2ndBTn (t ) C org_p (t )

=

C org_1stBTn (t ) C org_p (t )

×

C org_2ndBTn (t ) C org_1stBTn (t )

k1stBTn k × 2ndBTn k ex_1stBTn + k 2ndBTn kex_2ndBTn

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(7)

221 222

Thus, the relative internal concentration of BTPs to their parent compound at steady state

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can be calculated on the basis of RPF values.

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Results and discussions

226 227

Validation of BTP (semi)quantification method

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BTPs without reference standards were quantified using estimated relative ionization

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efficiencies of BTPs to the parent. To verify the quantification method, the estimated

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concentration of the BTP MIR214 (MTE214) was compared with the concentration

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measured using the available reference standard. Overall, on average, the estimated

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concentration was 81.2 % of the measured concentration (standard deviation (sd), ±23.5).

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This compares favorably with another common (semi)quantification method, which

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simply applies the area ratio of BTP to IS of the parent compound directly to the

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calibration curve of the parent without any conversion factor.24 Applying this method to

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MIR214 significantly underestimates the concentration (average ± sd, 26.4 % ± 6.6). This

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comparison clearly shows that our quantification method can improve the accuracy of the

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quantification at least for structurally similar BTPs. The detailed information on the

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validation of quantification method is available in SI8.

240 241

Kinetic rate estimation

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The bioaccumulation of the parent compounds in G. pulex and D. magna reached a

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steady state within 24h and 20h exposure, respectively (Figure 1). At the end of the 96h

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depuration period, the parent compounds were almost completely eliminated from G.

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pulex (less than 1 % of the highest internal concentration remained), while MIR270A,

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MIR327A, MIR327B, and MTR266 were still present in the organism. In contrast, the

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parent compounds in D. magna and all BTPs except MTE214 were almost completely


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eliminated after 24h depuration. MTE214 concentrations at the end of the depuration

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phase in D. magna were still equivalent to about 5 % of the highest internal concentration

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during the exposure phase.

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Since all kinetic parameters for a parent compound and its BTPs were simultaneously

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estimated in the complete TK model, biotransformation and elimination rate constants are

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mutually related to each other in the model (co-variation). This co-variation is reflected in

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the parameter standard errors (SEs), which are consequently larger than SEs derived from

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estimations for single reaction rates (Table S4). Our simultaneous fit integrates the error

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and uncertainty for all reactions.

257 258

Comparison between species with irgarol

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Toxicokinetics for irgarol and 4 BTPs were compared between G. pulex and D. magna

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(Figure 2(a) and (b)) as MIR214, MIR270A, MIR270B, and MIR327B were successfully

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quantified in both species. The comparison of TK in both species indicates that N-

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dealkylation and hydroxylation are major transformation mechanisms for irgarol in D.

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magna, while the products formed by hydroxylation and glutathione conjugation resulting

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in the cysteine conjugate formation are dominant in G. pulex.

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G. pulex exhibited a higher uptake (ku) and excretion rate (kex) of irgarol compared with

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D. magna, but the overall elimination rate, ke (sum of excretion and biotransformation

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rates, kex+ k1stBT1+ k1stBT2+ k1stBT3+ k1stBT4) was comparable in both species (Table S4),

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resulting in a higher BAF in G. pulex (Table 1). The extremely low kex value (0.0002) and

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relatively high discrepancy between ke and kex (ke- kex=0.35) in D. magna indicate that

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most of the accumulated parent molecules are transformed and thus only few are excreted



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in the unchanged form (Figure 2(b)). Contrarily, the relatively high kex and low sum of

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biotransformation rates for G. pulex indicate that the significance of biotransformation for

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the elimination of irgarol in G. pulex is less than that in D. magna. For the primary BTPs,

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MIR270A, MIR270B, and MIR327B, which all produce the secondary BTPs in G. pulex,

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the overall elimination is regulated by both the transformation to the secondary BTP and

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the direct excretion to surrounding medium. The direct excretion of MIR270A and

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MIR270B was the dominant elimination process in G. pulex, making up about 67 % and

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98 %, respectively, of the overall elimination, whereas almost all MIR327B was

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transformed to MIR327A (Figure 2(a)). On the other hand, no secondary BTPs were

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identified in D. magna, due either to a relatively low transformation or high excretion rate.

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We simulated a maximum internal concentration of a secondary BTP, MIR286 in D.

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magna, by assuming that k2ndBT2 and k2ndBT3 were identical to k1stBT3 and k1stBT2 (0.067 and

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0.259 h-1), respectively, and the excretion rate was identical to kex_2ndBT2 estimated for G.

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pulex (0.346 h-1) (see Table 1). The simulated concentration of approximately 350

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nmol/kg is above the LOQ (about 6 nmol/kg) and indicates that the non-detection of the

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BTP in D. magna is mainly due to faster excretion than that in G. pulex. If this hypothesis

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is true, the calculated excretion rate of MIR286 in D. magna necessary to reduce internal

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concentrations below the LOQ should be greater than 4 h-1, which is about 12 times

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greater than in G. pulex. This faster excretion is consistent with the comparison of the

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elimination kinetics between the two species: the total sum of elimination (or excretion)

291

rate for the primary BTPs in D. magna is about 6.6 (or 7) times greater than that in G.

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pulex.


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Biotransformation rates (k1stBT1, k1stBT2, and k1stBT3) for oxidation and hydroxylation in D.

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magna are greater than those in G. pulex; about 4 times for MIR214, 18 times for

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MIR270A and 4 times greater for MIR270B. Consequently, the elimination rates,

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kex_1stBT1, kex_1stBT2, and kex_1stBT3 in D. magna are about 3 times, 100 times and 2 times

297

greater than the overall elimination rate in G. pulex, respectively. Note that these

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differences in rate constants are larger than what could be explained by the temperature

299

difference in the D.magna and G.pulex studies. For MIR327B we estimated a greater

300

biotransformation and a lower overall elimination rate in G. pulex, resulting in a RPF

301

about 13 times higher than for D. magna (Table 1). In addition to MIR327B, the RPF of

302

MIR270A in G. pulex is greater than that in D. magna, whereas we found a greater RPF

303

for MIR214 and MIR270B in D. magna. Overall, the total sum of RPFs representing the

304

ratio of the total amount of BTPs to the parent at steady state was 0.56 and 0.17 for G.

305

pulex and D. magna, respectively. The greatest RPF in G. pulex was estimated for MIR

306

327B, comparable to MIR270B, followed by >>MIR214 ≥ MIR270B whereas the RPFs

307

in D. magna were all similar, although declining in the order MIR214≥ MIR270B ≥

308

MIR270A>MIR327B (Table 1).

309

The greater RPFs for G. pulex are attributable to the lower elimination rates of the BTPs.

310

However, this does not mean that the overall toxicity of irgarol in G. pulex is less than in

311

D. magna since the absolute amount of irgarol accumulated was higher in G. pulex and

312

the relative toxicity potential of each of the BTPs are not known. In this study, no adverse

313

effects were observed for either organism during the exposure at 200 µg/L of irgarol, so

314

that our insight on the overall toxicity and in particular the contribution of BTPs is

315

limited. Nevertheless, the different amounts and compositions of BTPs in different



316

species as indicated by the RPF values are likely related to differences in species

317

sensitivities to toxicants, which should be further explored.

318 319

Nonlinearity of excretion rate over hydrophobicity

320

In general, the ability to biotransform xenobiotics contributes to organisms’ defenses

321

against toxicants by generating BTPs that lack the toxicophore, or by generating BTPs

322

that are less hydrophobic and thus excreted faster. The faster excretion of BTPs leads to

323

lower internal concentrations and thus to a reduction of toxicity which is a function of the

324

number of accumulated molecules in target sites. We observed that biotransformation in

325

G. pulex and D. magna generally reduces hydrophobicity (see log Dow values, Table 1),

326

but that the excretion of some BTPs is not faster than that of the parents (Table 1 and

327

Table S4). The common assumption that reduced hydrophobicity results in faster

328

excretion of BTPs in aquatic organisms has rarely been tested empirically, because

329

studies with in depth TK characterization such as the one presented here are challenging.

330

To extensively establish the correlation of the excretion rate with hydrophobicity, we

331

combined datasets from both the present study and Ashauer et al. (only for G. pulex).9

332

Interestingly, the hydrophobicity correlates non-linearly with the excretion rates for both

333

organisms (Figure 3). The excretion rate reaches the maximum between log Dow = 1 - 2

334

and decreased with increasing/decreasing hydrophobicity of molecules which is

335

consistent with an observation in fish29. As the hydrophobicity of tramadol BTPs (except

336

MTR280B) in G. pulex, is reduced, the excretion rates decreased, resulting in the

337

increased elimination half-lives (Table 1). The low excretion for hydrophilic (ionized)

338

molecules can be explained by the lower permeability across cell membranes.30 In the


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membrane permeation concept based on fugacity theory,30-32 more hydrophobic

340

compounds (e.g. a parent) can more easily gain access into a cell membrane by

341

hydrophobic interaction. Once the compound permeates the membrane and is

342

biotransformed in the cell, the BTPs have less potential to overcome the membrane due to

343

the reduced hydrophobicity leading to the retarded excretion. Hendriks et al.33 also

344

reported declining elimination rates with decreasing log Kow below 3, while the increase

345

in elimination rates with decreasing log Kow from 7 to 3 was also shown in other

346

studies.33-36 However the authors could not fully confirm declining elimination rates for

347

substances with lower log Kow value due to the limited number of datasets on hydrophilic

348

substances. The present study and Ashauer et al.9 support the nonlinear correlation of

349

excretion rate with hydrophobicity by providing datasets covering hydrophilic

350

compounds and their BTPs in the range between -1 and 5.2 of log Dow. This observation

351

implies that biotransformation of polar compounds does not always induce faster

352

excretion, but can result in increased BTP retention compared with the parent where the

353

retention potential factor (RPF) is greater than 1 (Table 1).

354 355

Comparison between compounds: Irgarol vs. Tramadol

356

Here, the toxicokinetics in G. pulex are compared for substances with differing physico-

357

chemical properties. The BAF for tramadol is much smaller than irgarol, resulting mainly

358

from the significantly lower ku for tramadol due to the slower uptake of cations. In

359

addition, tramadol transformations to primary BTPs (i.e. MTR250, MTR280A, and

360

MTR280B) comprised about 50 % of the total elimination of the parent, while only 11 %

361

of irgarol was transformed. This higher biotransformation rate for tramadol apparently



362

contributed to the lower BAF value. In general, the bioaccumulation potential of organic

363

xenobiotic compounds in aquatic organisms is predicted by quantitative structure-activity

364

relationships (QSARs) considering only specific chemical properties such as

365

hydrophobicity.33, 37 However, since the extent of biotransformation is independent of the

366

molecule’s hydrophobicity, ignoring biotransformation seriously limits the predictive

367

power of such models. Thus, it is desirable to develop novel relationships for QSARs

368

which include the extent of biotransformation, for example, based on the presence of

369

certain functional groups in the molecular structure of xenobiotics.

370

Hydroxylation was the most rapid biotransformation mechanism for irgarol, whereas O-

371

demethylation and N-oxidation were key mechanisms for tramadol in G. pulex. The

372

higher sum of RPFs for tramadol BTPs than for irgarol indicates that a relatively large

373

proportion of tramadol is transformed and the BTPs are retained longer in the body than

374

the parent (Table 1). Tramadol is designed to be easily oxidized by P450 in humans to

375

form the active metabolite, O-desmethyltramadol (MTR250) which has a 200 times

376

greater affinity to the target site than the parent.38 Obviously, this fast biotransformation

377

kinetics of tramadol is also similar in freshwater organisms. Except for the glutathione

378

conjugation products, all BTPs identified for both compounds were formed through

379

oxidative reactions mainly mediated by P450 isoenzymes. Hence, the different

380

transformation kinetics for oxidation products can be attributed to the dissimilarity either

381

in physico-chemical properties affecting the affinity of the molecule to the activation site

382

of enzymes or in the amount of activated enzyme affecting the probability for the binding.

383 384

Comparison between compounds: Irgarol vs. Terbutryn


Page 18 of 37

Page 19 of 37


385

Irgarol and terbutryn are both triazine compounds and used as biocides. Both have a

386

similar hydrophobicity, which is reflected in their similar BAF values. The difference in

387

the structure is a branch moiety bound to an amine: the cyclopropyl in irgarol and the

388

ethyl in terbutryn. Interestingly, this different structural moiety led to a dissimilarity in

389

the biotransformation kinetics. N-dealkylation resulted in an identical BTP (MIR214 and

390

MTE214), but different leaving groups led to a higher kinetic rate in terbutryn. This

391

means that the cleavage of the amine-ethyl bond is faster than that of amine-cyclopropyl.

392

Supposing the enzymes involved in the reaction and the quantity of the enzyme are

393

identical, the discrepancy in the formation rate is likely due to different steric hindrance

394

of the moiety. The ethyl group is smaller and more flexible and thus activated enzymes

395

can easily access the amine-ethyl bond. In contrast, the hydroxylation (k1stBT3) at

396

cyclopropyl and ethyl and the glutathione conjugation rate (k1stBT4) for both compounds

397

were almost the same.

398 399

Significance of P450

400

Internal concentrations of irgarol and terbutryn in D. magna concurrently exposed with

401

the P450 inhibitor PBO were greater than those in PBO-free D. magna. As shown in

402

Figure S2, the time courses of the concentration of the parents and their oxidation

403

products (MIR270A, MIR270B, MTE258A and MTE258B) were affected by PBO

404

leading to the higher bioconcentration of the parent at the end of the exposure phase.

405

About 2-fold (irgarol) and 1.5-fold (terbutryn) greater BAF for PBO-exposed D. magna

406

indicates that more than half of the irgarol and one third of the terbutryn accumulated in

407

D. magna were oxidatively biotransformed by P450. No significant changes for MIR214,



408

MIR327B, and MTE315B were observed. MIR327B and MTE315B are cysteine

409

conjugates formed by removal of glycyl and glutamyl moieties catalyzed by peptidases

410

and gamma-glutamyltransferase after their predecessors, GSH conjugates are formed by

411

glutathione-s-transferases.39, 40 Thus, the unaffected kinetics of cysteine conjugates prove

412

that all enzymes involved in the formation of cysteine conjugates are activated

413

independently even when the activity of P450 is inhibited. In the case of MIR214, we

414

expected that the increase of internal concentration with time would be retarded in the

415

presence of PBO as observed for MTE214, because general dealkylation reactions are

416

also mediated by CYP450s. However, no PBO effect was observed for MIR214. A

417

possible explanation is that the increasing internal concentration is achieved not only by

418

the transformation of irgarol, but also by the aqueous uptake of MIR214 originated from

419

impurity of the irgarol standard. We could not detect MIR214 in the exposure medium at

420

the beginning of exposure, but observed a small peak of MIR214 in the measurement of

421

the irgarol standard, corresponding to less than 1 % of irgarol. Therefore, the estimated

422

kinetic rates for MIR214 should be interpreted with caution because the rate constants

423

were calculated assuming the absence of MIR214 in the medium.

424 425

Comparison with other species and substances

426

Information on the toxicokinetics of irgarol in different species is currently limited, but

427

some knowledge about the mammalian metabolism of terbutryn and other triazines (e.g.,

428

atrazine) is available. Interestingly, major metabolites of triazines identified in previous

429

studies are inconsistent even when identical species were used in assays.41-43 This might

430

be due to the different exposure schemes, sample treatments and analytical methods.


Page 20 of 37

Page 21 of 37


431

Despite this, dealkylation products (i.e., desethyl-, desisopropyl, and didealkylatrazine)

432

and conjugates (e.g., mercapturate) were the most frequently identified metabolites in

433

rodent and human samples at moderate levels, whereas hydroxylation products were

434

measured at trace levels or undetectable.42-45 This is different to our observations

435

revealing that hydroxylation was the most significant transformation mechanism for

436

irgarol and terbutryn in both crustaceans tested. In addition to triazines, hydroxylation

437

and oxidation products of tramadol were also predominantly formed in G. pulex. These

438

metabolites were considered minor products in humans and other mammal samples,

439

where O- and N-desmethytramadol were identified as the major metabolites.46, 47 Thus, it

440

is likely that the significance of hydroxylation (or oxidation) in detoxification processes

441

in freshwater crustaceans is greater than in mammals. This characteristic might have

442

evolved because it may be beneficial for responding to naturally occurring chemical

443

stressors such as algal toxins. For example, a number of studies reported that the

444

formation of oxidation products of microcystins by ozonation48 and ultrasonication49, 50

445

apparently decreased toxicity. On the other hand, Ufelmann et al. 51 demonstrated that

446

demethylated derivatives of cyanobacterial toxins, microcystins and nodularins exhibited

447

higher hepatocyte toxicity than their parent molecules. This supports the hypothesis that

448

hydroxylation or oxidation is more important for freshwater crustaceans than

449

demethylation.

450

Comparative studies on toxicokinetics of organophosphates in both test organisms have

451

been conducted. Kretschmann et al.52 showed that biotranformation rates of diazinon,

452

forming diazoxon and dearyl products, were more than 2 time and 6 times greater,

453

respectively, in D. magna than in G. pulex, which is consistent with our observations.



454

Meanwhile, Rubach et al.5 compared uptake and elimination rates and bioconcentration

455

factors for chlorpyrifos and reported about 2 times greater uptake rates but slightly

456

smaller elimination rates in D. magna compared to G. pulex. Thus, for chlorpyrifos, the

457

pattern seems to deviate from that observed in our study, but the underlying causes

458

remain unclear because the biotransformation kinetics were not quantified in the study.

459

The resulting BAFs of chlorpyrifos for G. pulex and D. magna were two to three orders

460

of magnitude higher than for any of the compounds tested in this study, mainly due to the

461

greater hydrophobicity of chlorpyrifos.

462 463

Implications for the study of species sensitivity differences

464

Internal exposure is the biologically relevant dose and was heavily modified by

465

biotransformation kinetics in both species. The extent of biotransformation differed

466

according to species and chemicals, leading to the dissimilarity in the profile of internal

467

BTPs. Thus, we should expect large differences in biotransformation even amongst fairly

468

closely related species and extrapolation of toxic effects from one species to others must

469

account for biotransformation differences, for example in risk assessment for chemicals.

470

This is currently challenging as the causes of these biotransformation differences are not

471

well understood. In general, the higher the uptake rate, the higher the sensitivity (e.g.,

472

lower EC50),5 as the internal dose at the target site increases faster. However, Rubach et

473

al.5 observed that BAF was not significantly correlated with arthropod sensitivity

474

(R2=0.054), implying that the ability to perform biotransformation and the susceptibility

475

of target sites to the parent and its BTPs may vary across species. This indicates that a

476

simple quantitative analysis for the parent compound (i.e. BAF) is insufficient to fully


Page 22 of 37

Page 23 of 37


477

understand the observed toxicity and species sensitivity. Thus, the quantitative estimates

478

for biotransformation and elimination rate of BTPs are essential information for the better

479

mechanistic elucidation of toxicity and species sensitivity.

480



481

Acknowledgement

482

We thank to Heinz Singer, Matthias Ruff, and Philipp Longree for Q Exactive Orbitrap

483

analysis, to Anita Hintermeister for collection and maintenance of G. pulex, to Jelena

484

Simovic for providing data on responses of molecules in mass spectrometry, and to

485

Emma Schymanski and Michael Stravs for comments on the manuscript. This study was

486

supported by funding from the Swiss National Science foundation (Grant No. 315230-

487

141190).

488 489

Supporting information available

490

Details on chemicals and solutions, maintenance and culture of test organisms, kinetic

491

experiments, procedure of extract sample (Fastprep method), LC-HRMS/MS analysis,

492

(semi)quantification of BTPs, model calibration, and validation of quantification method.

493

This material is available free of charge via the Internet at http://pubs.acs.org.


Page 24 of 37

Page 25 of 37


Table 1. Bioaccumulation factor (BAF), retention potential factor (RPF), and elimination half-life (t1/2) with standard error (SE). Parent log Dow compounds and at pH 7.9* their BTP Irgarol MIR214 MIR270A MIR270B MIR284 MIR286 MIR327A MIR327B Tramadol MTR236 MTR250 MTR266 MTR280A MTR280B Terbutryn MTE214 MTE258A MTE258B MTE315B

2.94 2.21 1.94 2.07 -1.00 1.08 -0.53 -0.68 1.10 0.03 0.95 1.16 0.00 1.33 2.79 2.21 1.83 1.83 -0.79

BAF(L/kg) or RPF(mol/mol) , ±SE D. magna G. pulex without PBO with PBO 19.20±1.89 12.84±25.28 29.20±40.53 0.03±0.05 0.06±0.08 0.03±0.03 0.27±0.40 0.05±0.37 0.02±0.12 0.02±0.09 0.05±0.14 0.02±0.05 0.01±0.13 0.01±0.07 0.03±0.18 0.18±1.03 0.01±0.06 0.01±0.03 0.55±0.09 0.65±0.82 1.12±0.48 4.58±1.57 0.42±0.38 0.32±0.06 12.17±11.31 17.68±11.81 0.58±0.08 0.33±0.04 0.03±0.19 0.00±0.02 0.02±0.30 0.00±0.01 0.02±0.07 0.01±0.05

t1/2 (h)** , ±SE D. magna G. pulex (without PBO) 1.8±0.4 2.0±1.7 3.9±2.1 1.5±0.7 13.1±9.0 0.1±0.1 0.8±0.6 0.5±0.4 32.6±27.7 2.0±0.4 3.4±2.9 20.1±19.6 3.6±2.7 1.1±0.3 7.6±2.4 4.4±1.6 18.2±1.7 12.6±5.3 2.1±0.7 1.6±1.2 2.5±0.2 0.5±0.4 0.2±0.2 4.9±3.6

* Octanol-water partitioning coefficient at pH 7.9 predicted by MarvinSketch Ver. 5.10, **Calculated by t1/2=ln(2)/ke



Figure 1. Uptake, biotransformation, elimination kinetics of a) irgarol and b) tramadol in G. pulex and c) irgarol and d) terbutryn in D. magna. The exposure and depuration time for G. pulex are 24h and 96h, respectively, whereas 20h and 24h for D. magna. The kinetics of the low concentrated BTPs are depicted in the panel cut-outs in more detail. a) 2500

Irgarol MIR214 MIR270A MIR270B MIR284 MIR286 MIR327A MIR327B

2000 16000

nmol/kg

14000 12000

1500

1000

nmol/kg

10000 8000

500

6000 0

4000

0

20

40

60

80

100

time (h)

2000 0 0

20

40

60

80

100

120

time (h)


120

Page 26 of 37

Page 27 of 37


b) 1800 1600 Tramadol MTR236 MTR250 MTR266 MTR280A MTR280B

1400

nmol/kg

1200 1000 800 600 400 200 0 0

20

40

60

80

100

120

time (h)



Page 28 of 37

c) 500

400

nmol/kg

10000

nmol/kg

8000

Irgarol MIR214 MIR270A MIR270B MIR327B

300

200

6000

100

4000

0 0

10

20

30

time (h)

2000

0 0

10

20

30

40

time (h)


40

Page 29 of 37


d) 300

250 Terbutryn MTE214 MTE258A MTE258B MTE315B

200

nmol/kg

12000

10000

150

100

nmol/kg

8000 50 6000 0 0

10

20

30

4000

time (h) 2000

0 0

10

20

30

40

time (h)


40


Figure 2. Comparison in toxicokinetics of irgarol between a) G. pulex and b) D. magna. The number of cubes corresponds to the relative internal concentration of the metabolite to the parent whereas the red/blue indicators represent the magnitude of biotransformation/excretion rate, respectively. a)


Page 30 of 37

Page 31 of 37


b)



Page 32 of 37

Figure 3. Relationship between hydrophobicity (log Dow) and excretion rate (log kex). Datasets shown here are collected from results of both the present study and Ashauer et al.9 (only for G.pulex). For logarithm conversion, the excretion rates estimated as zero in Ashauer et al. were considered as 0.0001 to avoid negative rate constants in the model estimation. Non-linear curve fitting was performed by adopting an equation described in Hendriks et al.33 The equation is the following. ݇௘௫ ൌ ௉

ଵ ಴ಹమ ൉ሺ஽೚ೢ

ൈ ିଵሻାଵ

ௐ షೖ ഐ భ ఘಹమೀ ା ಴ಹమ ା ವ೚ೢ

ം

Where w = weight of organism (kg), k = rate exponent, PCH2 = lipid fraction of organism, ρH2O = water layer diffusion resistance (d·kgk ), ρCH2 = lipid layer permeation resistance (d·kg-k), γ=water absorption-excretion coefficient(kgk ·d-1). Parameter values used are w=0.00003 and 0.000001 for G. pulex and D. magna, respectively, k=0.25, PCH2=0.01·w-0.04 and γ = 200 kgk ·d-1 for both organisms. The remaining parameters, ρH2O and ρCH2 are optimized to obtain the best fitting curves. 6

4

G.pulex D.magna Reg. for G.pulex Reg. for D.magna

log kex

2

0

-2

-4

-6 0

2

4

6

log Dow


Page 33 of 37


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Comparative Toxicokinetics of Organic Micropollutants in

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