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Ecotoxicology

, Volume 27, Issue 7, pp 936–944 | Cite as

Is there synergistic interaction between fungicides inhibiting different enzymes in the ergosterol biosynthesis pathway in toxicity tests with the green alga Raphidocelis subcapitata?

  • Anja CoorsEmail author
  • Pia Vollmar
  • Frank Sacher
  • Anja Kehrer
Article

Abstract

Products used for plant protection or as biocides often contain more than one active substance together with numerous formulation additives. The environmental risk assessment for such commercial mixtures applies as default the concept of concentration addition. There is remaining regulatory concern, however, that underestimation of risks can occur if components in the mixture interact synergistically, i.e., elicit effects greater than those predicted by concentration addition. While cases of true synergism appear to be rare, the combination of substances targeting different steps in the same biosynthesis pathway was pointed out as one potential case of synergistic interaction although mechanistic explanations are lacking. The present study aimed to verify this hypothesis using the green alga Raphidocelis subcapitata as the regulatory standard test organism for which such synergism had been indicated earlier. Algal growth inhibition tests were conducted with mixtures of ergosterol biosynthesis inhibitors (tebuconazole, fenpropidin, and fenpropimorph). The fungicides were first tested individually to derive reliable data for a mixture toxicity prediction. The here determined toxicity estimates for two of the fungicides were considerably lower than the endpoints in the regulatory dossiers, which had been used for earlier mixture toxicity predictions. Experimentally observed toxicity estimates for the mixtures deviated <2.6-fold from the predicted values. Hence, the hypothesis of synergistic interaction between fungicides targeting different enzymes in the ergosterol biosynthesis was clearly not confirmed for the green alga R. subcapitata. Overall, the present study demonstrates the importance of reliable and correct input data for mixture toxicity predictions in order to avoid erroneous conclusions on non-additive (synergistic) interactions.

Keywords

Mixture Mixture assessment Synergistic interaction Fungicides Algae Mode of Action 

Abbreviations

a.s.

Active substance

CA

Concentration addition

DMI

C14-Demethylase inhibitors

DNA

Deoxyribonucleic acid

EbC50

Median effect concentration for biomass

EyC50

Median effect concentration for yield

EBI

Ergosterol biosynthesis inhibitor

EDTA

Ethylenediaminetetraacetic acid

ErC50

Median effect concentration for growth rate

EU

European Union

LOEC

Lowest observed effect concentration

MDR

Model deviation ratio

NOEC

No observed effect concentration

OECD

Organization for Economic Co-operation and Development

Introduction

Commercial plant protection and biocidal products are formulated preparations that frequently contain more than one active substance (a.s.) together with numerous formulation additives. The environmental risk assessment within the authorization process in the European Union (EU) includes for such intentionally applied mixtures a mixture toxicity assessment based by default on the concept of concentration addition (CA). There is remaining regulatory concern, however, that such component-based mixture predictions can lead to underestimation of risks if components in the mixture interact synergistically, i.e., elicit effects greater than those predicted by CA (Frische et al. 2014; ECHA 2017). Reviews have demonstrated that overall cases of true synergism appear to be rare (Kortenkamp et al. 2009; Cedergreen 2014), but there are no systematic frameworks to reliably foresee them. Spurgeon et al. (2010) discussed the different possible types of mechanisms for synergistic interactions, and sorted them into three groups: one substance can enhance another substance’s (i) external exposure concentration, (ii) internal exposure concentration (toxicokinetics), or (iii) receptor interaction (toxicodynamics). An opinion paper of three scientific committees of the EU lists a combination of substances that ‘‘act on different enzymes in an important metabolic pathway’’ as a feature that may indicate potential for synergistic interaction (SCHER/SCENIHR/SCCS 2011, p 15). Such a kind of synergistic interaction does not strictly fit into the grouping system developed by Spurgeon et al. (2010), but may be seen in a wider sense as toxicodynamic interaction.

While the opinion paper (SCHER/SCENIHR/SCCS 2011) provides no explanation or further reference for this mechanism of synergistic interaction, there are some examples described in the literature. One example is the combination of the two antibiotics sulfamethoxazole and trimethoprim that inhibit different enzymes in the bacterial biosynthesis of tetrahydrofolate, which is an essential factor in the production of bacterial DNA. These two antibiotics were shown to interact synergistically in laboratory tests with clinically relevant bacteria (Bushby and Hitchings 1968), and they were subsequently often prescribed together in combination products. However, synergistic interaction was not confirmed in clinical studies, which is likely due to the different pharmacokinetics of the two antibiotics in humans (Howe and Spencer 1996). Another example of substances inhibiting different enzymes in the same biosynthesis pathway are fungicides that interfere with the biosynthesis of ergosterol (ergosterol biosynthesis inhibitors, EBI fungicides). These fungicides are specifically C14-demethylase inhibitors (DMI fungicides, G1 or EBI class I fungicides, FRAC 2017) and Δ14 reductase inhibitors (G2 or EBI class II fungicides, FRAC 2017). Ergosterol is a main component of the cell membranes of fungi, but also of green algae (Miller et al. 2012; Brumfield et al. 2017). A meta-study on plant protection products authorized in Germany (Coors and Frische 2011) reported that the joint toxicity of EBI class I and II fungicides was up to 100-fold underestimated by CA predictions in green algae, but not in fish or Daphnia. This evidence for synergistic interaction was solely based on theoretical CA calculations that used the single-substance and product toxicity data compiled from the dossiers submitted within the product authorization process.

The present study aimed to experimentally verify if EBI class I and II fungicides indeed show synergistic interaction regarding their toxicity towards green algae, using R. subcapitata as representative. This question is of regulatory relevance because representatives from these two fungicide groups are often combined in commercial plant protection products as well as in biocidal products (specifically wood preservative products). There is no cross-resistance reported for class I and class II EBI fungicides (FRAC 2017), which is why such combinations provide a suitable tool to prevent resistance development for the individual fungicides. Yet, it is not clear whether the combination of EBI fungicides provides added value also due to synergistic interactions towards fungi as the target organisms. Effects on non-target fungi are currently not listed as data requirement for the environmental risk assessment of biocides or plant protection products in the respective legal documents (ECHA 2014; EU 2013). Green algae are often the most sensitive non-target standard test organisms for EBI fungicides, thereby providing the key regulatory endpoint. Therefore, applying CA to this key endpoint in the environmental risk assessment of such products could in case of synergistic interactions toward green algae result in under-protective decisions. Beyond direct regulatory relevance, the verification of the hypothesis that inhibition of different enzymes in the same biochemical pathway is in general an indication for synergistic interaction potential would inform theories of mechanisms of synergistic interactions and support the development of reliable indicators for synergistic interactions.

Material and methods

The EBI class I fungicide tebuconazole and the EBI class II fungicides fenpropidin and fenpropimorph were selected for the present study. The fungicides were first tested individually in algal growth inhibition tests. Subsequently, two binary mixtures (Mixture 1: fenpropimorph and tebuconazole and Mixture 2: fenpropidin and tebuconazole) were investigated as fixed-ratio mixture dilution series at an equipotent ratio of the two components. All tests were conducted according to OECD guideline 201 (OECD 2006).

Growth inhibition tests

All growth inhibition tests were conducted over an exposure period of 72 h using the unicellular freshwater green alga Raphidocelis subcapitata (SAG 61.81, formerly known as Pseudokirchneriella subcapitata), obtained from the culture collection of algae at Göttingen University. The medium used for pre-culture and tests consisted of trace elements and macronutrients in the final concentrations according to OECD guideline 201, except that concentrations of Na2EDTA and FeCl3 were increased by a factor of 10, which enables better growth based on many years of experience. Pre-culture and test conditions were constant temperature (23 ± 2 °C) in a climate-controlled chamber and permanent light provided by fluorescent tubes of universal white type (Osram Lumilux 58 W/865) at a light intensity between 60 and 120 µE/m2/s. The vessels containing algae were constantly shaken with 100 ± 5 oscillations/min. All tests were conducted with six replicate vessels for the control and three replicate vessels for each test concentration level. Each replicate glass vessel contained 100 ml algal growth medium and was inoculated with 0.5 × 104 cells/ml from a pre-culture of R. subcapitata in its exponential growth phase. The pH of the test solutions was 8.0 ± 0.1 at test start and increased up to 8.7 during the test. Only in the tebuconazole test, the increase in pH was with 1.8 units in the control (due to the depletion of CO2 by well-growing algae) slightly greater than the 1.5 units allowed by the guideline. Algal cell density was determined by measuring fluorescence (Multiple Plate Reader Tecan ULTRA). The results (relative fluorescence units, RFU, corrected for fluorescence of blank measurements) were converted into biomass concentration (cells/ml) based on a calibration curve that was generated individually for each test. The two response variables yield and growth rate were calculated according to the guideline for each replicate vessel.

Tebuconazole (CAS 107534-96-3), fenpropidin (CAS 67306-00-7), and fenpropimorph (CAS 67564-91-4) were obtained from Sigma-Aldrich, Germany, at a purity of at least 96.9%. To prepare the binary equipotent mixtures, two of the fungicides were combined at a fixed identical proportion of their respective EyC50 (median effect concentration for yield, i.e., biomass at end minus inoculated nominal biomass) so that the two components would contribute equally to the expected effects on yield. Note that the EyC50 determined according to the OECD 201 guideline is practically identical to the EbC50 (the endpoint often listen in regulatory dossiers) since the subtraction of the very small inoculum cell number has very little influence on the concentration-response curve for yield in comparison to that for biomass. All tests were set up as geometric dilution series with a spacing factor not exceeding the maximum of 3.2 prescribed by the guideline. In all tests, the fungicides were directly dissolved in the test medium, i.e., without using solvents. When testing fenpropimorph, problems were encountered in ensuring constant concentrations in line with the intended nominal concentrations. This was most likely due to the limited water solubility and high sorption tendency (log Kow of 3.5) of this compound. The final tests with fenpropimorph reported here were therefore all conducted with glass vessels that had been pre-conditioned with the test solutions to saturate sorption capacity.

The validity criterion of an at least 16-fold induction of yield was fulfilled in all tests (at least 256-fold induction observed). Similarly, the validity criterion of a coefficient of variation of the average specific growth rate in the control equal to or below 7% was met in all tests. The coefficient of variation for the section-by-section specific growth rate was not determined in the tests as no cell density measurements were made on day 1 and 2. However, from other algae tests run in parallel it is known that the pre-cultures were exponentially growing without showing a lag-period after inoculation.

Verification of exposure concentrations

Samples for the chemical analysis were taken from freshly prepared test solutions and from test solutions at the end of the exposure period (i.e., day 0 and day 3). Samples were analyzed by direct injection into a liquid chromatographic system (HPLC 1260 Infinity from Agilent Technologies, Waldbronn, Germany) that was coupled via an electrospray interface to an API 5500 tandem mass spectrometer (AB Sciex, Langen, Germany). Quantification was done against a calibration in algae test medium. Limits of quantification were 0.005, 0.001, and 0.030 µg/l for tebuconazole, fenpropidin and fenpropimorph, respectively. The geometric mean of concentrations measured at day 0 and day 3 was calculated for each concentration level and related to the nominal concentration (% recovery).

Data analysis

For the response variables yield and growth rate, LOEC (lowest observed effect concentration) and NOEC (no observed effect concentration) were determined by hypothesis testing using the software ToxRat Professional, version 2.10, release 20.02.2010 (ToxRat Solutions GmbH, Alsdorf, Germany). Applied statistical tests were selected based on the fulfillment of the assumptions for parametric tests (i.e., normal distribution of errors and variance homogeneity) as well as monotonicity of responses. Hypothesis testing included the parametric William’s multiple sequential t-test, the parametric Dunnett’s test, and the non-parametric Welch t-test for inhomogeneous variances with Bonferroni-Holm adjustment (all tests conducted one-sided with alpha = 0.05).

Median effect concentrations (EC50), i.e., the estimated concentration causing 50% effect were estimated by means of concentration-response modeling based on the geometric mean of the measured concentrations. Concentration-response modeling was done in the free software R, version 3.1.3 (R Development Core Team 2013) using the most recent version of the package “drc” (Ritz et al. 2015). In each case, the implemented function “mselect” was applied to identify the model with the best fit based on Akaike’s criterion among log-logistic and Weibull models with three to five parameters. Confidence intervals (95% CI) for the EC50 values were obtained with the implemented function “ED” of the “drc” package using the delta method and the t-distribution.

Mixture toxicity (EC50,mix) was predicted according to the CA concept as
$${\rm EC}_{50,{\rm mix}} = \frac{1}{{{\sum} {\frac{{P_{\rm i}}}{{{\rm EC}_{50,{\rm i}}}}} }}$$
with Pi being the concentration of mixture component i in relation to the summed concentration of all considered mixture components. As measure for the agreement between predicted and observed toxicity, the Model Deviation Ratio (MDR, Belden et al. 2007) was calculated as
$${\rm MDR} = \frac{{{\rm predicted}\,{\rm EC}_{50,{\rm mix}}}}{{{\rm observed}\,{\rm EC}_{50,{\rm mix}}}}$$

An MDR above 1 indicates that the toxicity of the mixture is underestimated by the CA prediction, while an MDR below 1 indicates that it is overestimated.

In addition to calculating MDR values for the here tested mixtures, previously reported MDR values (Coors and Frische 2011) of plant protection products were re-calculated using the here determined toxicity estimates for the individual fungicides.

Results and discussion

Effects of the individual fungicides

Concentration-dependent effects of the three individual fungicides on algal yield and growth rate are shown in Fig. 1. Effect concentrations estimated from these curves as well as the NOECs are summarized in Table 1. They all relate to measured concentrations.
Fig. 1

Yield (left column) and growth rate (right column) in dependence of increasing concentrations of tebuconazole (a, b), fenpropidin (c, d), and fenpropimorph (e, f). Shown are responses for individual replicates in one (tebuconazole, fenpropimorph) or two (fenpropidin) independent tests with the fitted response function. Concentrations are measured initial concentrations (tebuconazole) or the geometric mean of measured concentrations at test start and test end (fenpropidin, fenpropimorph)

Table 1

Median effect concentrations of the three fungicides and their equipotent mixtures determined for yield and growth rate (EyC50 and ErC50, respectively, with their 95% confidence intervals, CI) of R. subcapitata after 72 h exposure

Substance

EyC50 (95% CI)

ErC50 (95% CI)

NOEC (yield and growth rate)

Tebuconazole

2.56 mg/l (2.09–3.01)

4.54 mg/l (4.27–4.80)

0.43 mg/l

Fenpropimorph

6.61 µg/l (1.74–11.5)

1.71 mg/l (1.23–2.19)

 < 5.0 µg/l

Fenpropidin

0.225 µg/l (0.077–0.372)

 > 0.07 mg/l

0.045 µg/l

Mixture 1 (tebuconazole and fenpropimorph)

0.062 mg/l (0.015–0.109)

1.593 mg/l (1.303–1.882)

 < 0.083 mg/l

Mixture 2 (tebuconazole and fenpropidin)

0.601 mg/l (0.401–0.800)

3.687 mg/l (3.306–4.068)

0.243 mg/l

In addition, no observed effect concentrations (NOEC) are given. All values are based on measured concentrations

Measured concentrations of tebuconazole in freshly prepared test solutions amounted on average to 136.6% of the nominal concentrations (see supplements for details). Determined effect concentrations relate accordingly to the measured initial concentrations. Analytical results from the mixture tests (see below) confirmed that tebuconazole is stable during the 72 h static exposure in the algal test, which justifies using measured initial concentrations. The here determined EyC50 and ErC50 values for tebuconazole are close to the EbC50 and ErC50 of 1.96 and 5.3 mg/l, respectively, listed as regulatory endpoint in the EU regulatory dossier (72 h test with Desmodesmus subspicatus, EC 2007a, b).

For fenpropidin and fenpropimorph, the concentration-response curves for inhibition of yield were very flat, resulting in rather wide confidence intervals. In the case of fenpropidin, the maximum inhibition of growth rate (25.3%) was observed at the highest test concentration, resulting in an ErC50 as censored value ( > 0.07 mg/l). Therefore, mixture compositions were based on EyC50 values of the individual fungicides. The recovery of fenpropidin (geometric mean of measured in relation to nominal concentrations) decreased from lower to higher concentrations levels (135.9 to 58.5%). In the case of fenpropimorph, the recovery ranged from 99.9 to 122.5%, showing no dependence on nominal concentration level. In earlier tests without pre-conditioning of test vessels, however, deviation between nominal and measured concentrations was greatest at low and high concentration levels (<50% recovery). This may be explained by the limited water solubility of the test compounds (affecting recovery at high test concentration levels) and the high tendency for sorption to glassware (particularly relevant at low test concentration levels). Given the results of analytical measurements and biological results of the final tests with fenpropidin and fenpropimorph, these problems could be solved by using glassware throughout the test that had been pre-conditioned with test solutions.

The here determined 72 h EyC50 for fenpropidin is about 25-fold lower than the 96 h EbC50 for D. subspicatus of 0.0057 mg/l (nominal) reported for the technical material in the environmental risk assessment of fenpropidin as pesticide (EFSA 2007). This difference could in principle be due to difference in sensitivity of the two species of green algae, but the problematic of ensuring constant concentrations of properly dissolved fenpropidin may also have contributed. As endpoint actually used in the risk assessment of fenpropidin, a 72 h EbC50 for R. subcapitata (formerly: S. capricornutum) of 0.00026 mg/l is listed (EFSA 2007) that was derived with a formulated product. The here determined 72 h EyC50 is very similar to this value, which suggests that at least co-formulants in the product did not cause the 25-fold difference. Hence, it can be concluded that the value for the technical material in the regulatory dossier is questionable.

For fenpropimorph, the here determined 72 h EyC50 of 0.00661 mg/l is about 50-fold lower than the 72 h EbC50 of 0.327 mg/l reported as endpoint in the environmental risk assessment of fenpropimorph (EC 2005, 2009), while the NOEC for yield (0.005 mg/l) is in a similar range. The here determined ErC50 of 1.71 mg/l does not contradict the value in the regulatory dossiers reported as > 1 mg/l (EC 2005, 2009), while the here determined NOEC for growth rate was at least factor 10 lower than the NOEC value of 0.058 mg/l reported in regulatory dossiers (EC 2005, 2009c). The tested species of green algae is the same in the present study and for the regulatory endpoints in the dossiers (R. subcapitata, formerly known as Pseudokirchneriella subcapitata). These results steer doubt on the reliability of the endpoints for algal growth inhibition used in the regulatory assessment of fenpropimorph in the EU.

The high toxicity of the three fungicides, particularly of fenpropidin and fenpropimorph, in R. subcapitata, points at a potential specificity of the toxic mode of action. This may be related to the synthesis of ergosterol as discussed for the marine algae Dunaliella tertiolecta (Baird and DeLorenzo 2010). The biosynthesis pathway for ergosterol in green algae has been proposed to involve as intermediate rather cycloartenol than lanosterol and to be therefore more similar to that of higher plants than to that of fungi (Miller et al. 2012; Brumfield et al. 2017). While this would imply a different mode of action in green algae than in fungi, the synthesis of sterols, including ergosterol, in various phyla involves a great number of different enzymes along multiple possible pathways (Nes 2011). Hence, a specific inhibition of different enzymes in these pathways can still be assumed likely for green algae, which would indicate potential for synergistic interaction according to the hypothesis triggering the present study.

Experimentally determined toxicity of binary mixtures

The geometric mean of the measured concentrations of tebuconazole and fenpropimorph in mixture 1 ranged from 79.2 to 93.4% of the nominal concentrations (mean recovery of 84.6% for fenpropimorph and 82.7% for tebuconazole). Given these similar recoveries of the two mixture components, the actual proportions of the two fungicides in the mixture were indeed equipotent (measured Pi of 0.723 and 0.277 for tebuconazole and fenpropimorph, respectively). For mixture 2, the recovery ranged from 81.5 to 125% with a mean of 91.2 and 103% for tebuconazole and fenpropidin, respectively. The proportions in the mixture were equipotent (measured Pi of 0.000099 for fenpropidin and 0.999901 for tebuconazole). All measured concentrations are provided in the supplements.

Observed concentration-response curves for the two mixtures are shown in Fig. 2 together with the CA-predicted curves. Overall, the difference between predicted and observed response curves appeared to be small. This observation is quantified in the MDR values (Table 2): the predicted EC50 values for both response variables deviate at most 2.6-fold from the experimentally determined values. The greatest deviation from prediction occurred for the endpoint yield of mixture 1, which indicated with a MDR of 0.38 actually a trend towards antagonistic interaction rather than synergism. The censored MDR value for mixture 2 resulted from the censored ErC50 estimate for fenpropidin. A grossly extrapolated value for this ErC50 (used also for constructing the CA-predicted concentration-response curve in Fig. 2d) resulted in an MDR of 1.23, i.e., still well below 2. Hence, the experimental testing of the two binary mixtures provided no evidence for synergistic interaction between fungicides from EBI class I and class II in green algae.
Fig. 2

Yield (left column) and growth rate (right column) in dependence of increasing concentrations of Mixture 1 (a, b) and Mixture 2 (c, d). Concentrations the geometric mean of measured concentrations at test start and test end, summed for the two mixture components (tebuconazole and fenpropimorph for Mixture 1 and tebuconazole and fenpropidin for Mixture 2). The curves represent the log-logistic function fitted to the observed data (full line) or to the responses predicted by concentration addition (dotted line)

Table 2

Model deviation ratios (MDR) for the two equipotent mixtures tested in the present study

Mixture (mass proportion of components, Pi)

MDR for yield or biomass (a)

MDR for growth rate (a)

Fenpropimorph (0.277) and tebuconazole (0.723); Mixture 1 present study

0.38

1.96

Fenpropidin (0.000099) and tebuconazole (0.999901); Mixture 2 present study

2.00

 > 1.22

Fenpropidin (0.60), tebuconazole (0.20), and propiconazole (0.20)

4.26 (107.9)

n.d.

Fenpropidin (0.783) and propiconazole (0.217)

1.50 (37.9)

n.d.

Fenpropidin (0.789) and difenoconazole (0.211)

1.80 (45.6)

n.d.

Fenpropimorph (0.749) and epoxiconazole (0.251)

n.d.

>0.92 (>2.0)

Fenpropimorph (0.577), epoxiconazole (0.116), and pyraclostrobin (0.307)

0.10 (2.41)

n.d.

Fenpropimorph (0.375), epoxiconazole (0.313), and kresoxim-methyl (0.313)

0.67 (6.48)

n.d.

Fenpropimorph (0.656), epoxiconazole (0.172), and kresoxim-methyl (0.172)

0.20 (4.08)

n.d.

Fenpropimorph (0.555), epoxiconazole (0.148), kresoxim-methyl (0.148), and quinoxifen (0.148)

2.18 (22.3)

n.d.

Fenpropimorph (0.593), epoxiconazole (0.185), and metrafenone (0.222)

0.07 (2.73)

n.d.

In addition, re-calculated MDR values are shown for the plant protection products with EBI fungicides class I and class II assessed in Coors and Frische (2011) using the here determined single-substance toxicity estimates for fenpropidin, fenpropimorph, and tebuconazole, together with the previously determined MDR (in brackets)

aMDR calculated in Coors and Frische (2011)

Re-calculated toxicity of plant protection products

Mixture toxicity predictions and resulting MDR values for plant protection products containing EBI fungicides from class I and class II in the study of Coors and Frische (2011) had been based on the endpoints listed in the regulatory dossiers, i.e., an EbC50 of 1.96 mg/l for tebuconazole, an EbC50 of 5.7 µg/l for fenpropidin, and an EbC50 of 0.327 mg/l for fenpropimorph. Product toxicity and MDR values of that study were re-calculated using the here determined single-substance estimates for tebuconazole, fenpropidin, and fenpropimorph. The resulting MDR values (Table 2) indicated in no case a more than fivefold underestimation of product toxicity, and only in two cases a more than twofold underestimation. Hence, the earlier discussed evidence for strong synergistic interaction of EBI class I and class II fungicides with an up to about 100-fold underestimation of mixture toxicity by CA vanished. The deviation could be explained almost completely by the apparently incorrect toxicity data of individual fungicides used as input data for the predictions.

Conclusions

The present study could falsify the hypothesis that substances inhibiting different enzymes in the same biochemical pathway are generally suspect of exhibiting synergistic interaction. Earlier evidence observed in green algae for such an interaction from an assessment of plant protection products containing fungicides that inhibit different enzymes in the ergosterol biosynthesis pathway could be traced back to incorrect input data for individual mixture components. It still remains open whether EBI fungicides would show synergistic interaction in fungi as the target organisms similar to what has been reported for antibiotics in bacteria as target organism. However, this reported evidence for synergistic interaction of the two antibiotics sulfamethoxazole and trimethoprim (inhibiting different enzymes in the bacterial biosynthesis of tetrahydrofolate) may be questionable as well, since in vitro observed mixture effects would fit with additive response prediction and were not reproducible in vivo (Howe and Spencer 1996). The indication for ‘‘potentiation’’ reported by the initial study (Bushby and Hitchings 1968) and interpreted as synergism could be due to inconsistent definition of ‘‘synergy’’ in the literature as discussed by Berenbaum (1989). Overall, the lack of evidence for synergistic interaction among EBI fungicides should reduce regulatory concern about underestimation of environmental risk because of synergistic interactions when applying component-based approaches for mixtures of EBI fungicides. Hence, the present study supports using CA as default approach when it comes to a component-based environmental risk assessment of mixtures, in particular mixtures of EBI fungicides as represented in formulated products. Beyond this, the present study underlines the importance of reliable and correct input data for mixture toxicity predictions in order to avoid erroneous conclusions on non-additive (synergistic) interactions.

Availability of data and material

Additional data on measured test concentrations are available in the supplements.

Disclaimer

The views expressed herein are those of the authors and do not necessarily represent the opinion or policy of the Agency.

Notes

Acknowledgements

Technical support by Theodora Volovei and Elena Heusner (ECT) and Pia Gerstner (TZW) is gratefully acknowledged.

Author contributions

AC and AK designed the research. PV and AC performed the experiments and analyzed the data. FS was responsible for the chemical analysis. AC wrote the first draft of the manuscript, and all authors contributed to editing and revisions.

Funding

This study was financially supported by the German Federal Environment Agency through the project FKZ 3713 64 417.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10646_2018_1917_MOESM1_ESM.pdf (12 kb)
Supplementary Information

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ECT Oekotoxikologie GmbHFlörsheimGermany
  2. 2.TZW: DVGW-Technologiezentrum WasserKarlsruheGermany
  3. 3.Federal Environment AgencyDessau-RoßlauGermany

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