# Biodegradation and toxicity of a maize herbicide mixture: mesotrione, nicosulfuron and S-metolachlor

Louis Carles, Muriel Joly, Frédérique Bonnemoy, Martin Leremboure,
Florence Donnadieu, Isabelle Batisson, P Besse-Hoggan

► To cite this version:

Louis Carles, Muriel Joly, Frédérique Bonnemoy, Martin Leremboure, Florence Donnadieu, et al.  
Biodegradation and toxicity of a maize herbicide mixture: mesotrione, nicosulfuron and S-metolachlor.  
Journal of Hazardous Materials, 2018, 354, pp.42 - 53. 10.1016/j.jhazmat.2018.04.045. hal-01785204

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Distributed under a Creative Commons Attribution 4.0 International License
Title: Biodegradation and toxicity of a maize herbicide
mixture: mesotrione, nicosulfuron and S-metolachlor

Authors: Louis Carles, Muriel Joly, Frédérique Bonnemoy,
Martin Leremboure, Florence Donnadieu, Isabelle Batisson,
Pascale Besse-Hoggan

PII: S0304-3894(18)30277-2

DOI: https://doi.org/10.1016/j.jhazmat.2018.04.045

Reference: HAZMAT 19328

To appear in: *Journal of Hazardous Materials*

Received date: 24-10-2017

Revised date: 18-4-2018

Accepted date: 19-4-2018

Please cite this article as: Carles L, Joly M, Bonnemoy F, Leremboure M, Donnadieu F, Batisson I, Besse-Hoggan P, Biodegradation and toxicity of a maize herbicide mixture: mesotrione, nicosulfuron and S-metolachlor, *Journal of Hazardous Materials* (2010), https://doi.org/10.1016/j.jhazmat.2018.04.045

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# Biodegradation and toxicity of a maize herbicide mixture: mesotrione, nicosulfuron and S-metolachlor

Louis Carles<sup>a,b*</sup>, Muriel Joly<sup>a,b</sup>, Frédérique Bonnemoy<sup>a</sup>, Martin Leremboure<sup>b</sup>, Florence

Donnadieu<sup>a</sup>, Isabelle Batisson<sup>§a</sup> and Pascale Besse-Hoggan<sup>§b</sup>

<sup>a</sup> Université Clermont Auvergne, CNRS, Laboratoire Microorganismes : Génome et Environnement (LMGE), F-63000 Clermont-Ferrand, France.

<sup>b</sup> Université Clermont Auvergne, CNRS, Sigma Clermont, Institut de Chimie de Clermont-Ferrand (ICCF), F-63000 Clermont-Ferrand, France.

§ Both authors contributed equally to the work

* Corresponding author:

Louis Carles

Laboratoire Microorganismes : Génome et Environnement
1 Impasse Amélie Murat,
TSA 60026, CS 60026
63178 Aubière Cedex, France

Tel: +33 (0)4 73 40 74 53

Fax: +33 (0)4 73 40 76 70

E-mail: louis.carles64@gmail.com

Graphical abstract
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## Highlights

* *B. megaterium* transforms both mesotrione and nicosulfuron, alone or in mixture.

* Biotransformation pathways are not affected in binary and ternary mixtures.

* Nicosulfuron degradation and metabolite formation kinetics is mixture dependent.

* Binary/ternary mixtures of active ingredients show synergistic effects (Microtox®).

* Metabolite mixtures +/- S-metolachlor show synergism, antagonism or additivity.

## Abstract

The prediction of chemical mixture toxicity is a major concern regarding unintentional mixture of pesticides from agricultural lands treated with various such compounds. We focused our work on a mixture of three herbicides commonly applied on maize crops within a fortnight, namely mesotrione (β-triketone), nicosulfuron (sulfonylurea) and S-metolachlor (chloroacetanilide). The metabolic pathways of mesotrione and nicosulfuron were qualitatively and quantitatively determined with a bacterial strain (*Bacillus megaterium* Mes11). This strain was isolated from an agricultural soil and able to biotransform both these herbicides. Although these pathways were unaffected in the case of binary or ternary
herbicide mixtures, kinetics of nicosulfuron disappearance and also of mesotrione and nicosulfuron metabolite formation was strongly modulated. The toxicity of the parent compounds and metabolites was evaluated for individual compounds and mixtures with the standardized Microtox® test. Synergistic interactions were evidenced for all the parent compound mixtures. Synergistic, antagonistic or additive toxicity was obtained depending on the metabolite mixture. Overall, these results emphasize the need to take into account the active ingredient and metabolites all together for the determination of environmental fate and toxicity of pesticide mixtures.

**Keywords:** Herbicide mixture; Simultaneous biotransformation; *Bacillus megaterium* Mes11; Microbial toxicity

# 1. Introduction

Agricultural practices have considerably evolved over the last few decades, adopting Integrated Pest Management. This includes various management practices to favour growth of healthy crops and minimize pesticide use. This trend has triggered use of new-generation pesticides applied at lower doses but often in mixture, in order to improve their efficiency and the range of weeds treated. These changes lead to potential contamination of surface and ground water by a variety of compounds from different sources. The prediction of the toxicity of such unintentional mixtures is a major challenge because of their complex and variable composition. However, many of the parent compounds and their metabolites are still unidentified and toxicity data are lacking [1].

The toxicity of a given mixture is predicted with mathematical models combining (eco)toxicological data of individual compounds and their chemical concentration. This approach does not consider potential synergistic effects of pesticides and metabolites mixtures
[2]. Identifying biodegradation pathways of chemicals in mixtures is therefore essential to include all relevant molecules in monitoring studies (i.e. synergistic, co-occurring and highly hazardous compounds).

We focused our work on three herbicides, commonly applied within a fortnight on maize crops: in pre-emergence S-metolachlor (chloroacetanilide) and in post-emergence, a mixture of mesotrione (β-triketone) and nicosulfuron (sulfonylurea). Mesotrione (2-(4-methylsulfonyl-2-nitrobenzoyl)cyclohexane-1,3-dione) is a selective herbicide inhibiting carotenoid biosynthesis in a wide range of broad-leaved and grass weeds. An application rate (AR) of 150 g active ingredient (a.i.)/ha is adopted [3]. Two major metabolites, AMBA (2-amino-4-methylsulfonylbenzoic acid) and MNBA (4-methylsulfonyl-2-nitrobenzoic acid), are often detected in the environment [4,5] and are more persistent in the soil than the parent compound [6]. The Microtox® test also showed that AMBA was more toxic than mesotrione towards *Aliivibrio fischeri* (formerly *Vibrio fischeri*) [7]. Nicosulfuron (2-[(4,6-dimethoxypyrimidin-2-ylcarbamoyl)sulfamoyl]-N,N-dimethylnicotinamide) is a selective herbicide inhibiting the branched-chain amino acid biosynthesis of annual grass, broad-leaved and perennial weeds, with an AR of 60 g a.i./ha [8]. Its major transformation pathway is the hydrolysis of the sulfonylurea bridge, leading to ADMP (2-amino-4,6-dimethoxypyrimidine) and ASDM (2-(aminosulfonyl)-N,N-dimethyl-3-pyridinecarboxamide). ADMP is more toxic towards *A. fischeri* than the parent compound [9]. S-metolachlor, the active enantiomer of (2-chloro-N-(2-ethyl-6-methylphenyl)-N-[2-methoxy-1-methylethyl]acetamide) inhibits the very long chain fatty acid biosynthesis of annual grass and broad-leaved weeds, with an AR of 1920 g a.i./ha [10]. Its major metabolites (metolachlor oxalinic acid and ethanesulfonic acid) are often detected in higher concentration in water than the parent compound [11,12].

Despite their sequential application on soil surface, these three herbicides are detected in surface and ground water, S-metolachlor being the most frequently detected [13,14]. Their
monitoring carried over the past 10 years in France revealed the combined presence of these
herbicides, either in binary or ternary mixtures [13,14]. However, the combinations of these
herbicides have not been studied extensively, in particular regarding their biotransformation,
the predominant process involved in their environmental fate. Several isolated microbial
strains, able to biotransform each herbicide separately, have already been described for
mesotrione [15–20], nicosulfuron [9,21–26] and to a lesser extend for S-metolachlor [27].

Some studies have also investigated the microbial toxicity (Microtox®) of pure active
ingredients and formulations (alone or in mixtures) of the three herbicides [7,28]. However,
no data are available concerning the effect of mixing these herbicides on their
biotransformation kinetics and the toxicity of parent compounds and/or metabolites.

A preliminary screening of bacterial strains for their ability to transform the three
herbicides revealed that *Bacillus megaterium* Mes11 (noteworthy for its mesotrione-degrading
activity [15]) was also able to transform nicosulfuron. The aims of this study were therefore to
determine: (i) the biotransformation pathways of each of these three herbicides and the effects
of each compound on the biotransformation of the others for this model strain and (ii) the
toxicity of the parent compounds and metabolites, alone or in mixtures toward *A. fischeri*
using the standardized Microtox® test. The present study will therefore provide relevant
information on the biotransformation (e.g. identity of metabolites, biotransformation kinetics)
and toxicity of the pesticide mixtures studied (parent compounds and metabolites).

**2. Materials and Methods**

**2.1. Bacterial strain, chemicals and media**

The bacterial strain *Bacillus megaterium* Mes11 was previously isolated from a maize
cultivated soil [15]. Nicosulfuron (Pestanal, purity 99.6%), mesotrione (Pestanal, purity
99.9%), S-metolachlor (Pestanal, purity 98.4%), AMBA, ADMP (purity 98.0%) and Tryptic
Soy broth (TS) medium were purchased from Sigma Aldrich (France) and ASDM (purity 98%) from J and K Scientific (Germany). The composition of Glucose-mineral salt medium (GSM), pH 6.7, was previously described by Carles et al. [9].

## 2.2. Biotransformation assays

The strain Mes11 was grown in TS broth (750 mL) supplemented with 0.1 mM mesotrione, at 28 °C on a rotary shaker (150 rpm). The cells were harvested after 24 h of culture and centrifuged at 8,000 g for 15 min at 4 °C. The bacterial pellet was washed in NaCl (8 g/L). The resting cells were then resuspended in 150 mL of GSM supplemented with 1 mM of mesotrione, nicosulfuron and S-metolachlor, either alone, in binary or ternary mixtures. The same media without inoculation served as abiotic controls. Each condition was carried out in triplicate. The cultures were incubated in the dark at 28 °C under agitation at 150 rpm and sampled periodically (3 x 1 mL). After centrifugation, the supernatants were frozen at -25 °C until analysis by HPLC, ¹H NMR and LC/ESI-MS.

One millilitre of Mes11 culture was used to extract parent compounds/metabolites adsorbed on biomass. After centrifugation (13,000 g, 5 min), the pellet was suspended in 500 µL absolute ethanol (purity > 99.5%, v/v). The suspension was stirred overnight at room temperature and centrifuged (13,000 g, 5 min). The extraction was performed twice.

## 2.3. Analysis of the herbicides and their metabolites

### 2.3.1. Analysis by HPLC

The herbicide analyses were monitored by HPLC on an Agilent Series 1100 chromatograph, equipped with a Diode Array Detector (DAD) and a reverse phase column (Zorbax Eclipse XDB-C18, 3.5 µm, 75 mm x 4.6 mm) at 22 °C. The mobile phase was composed of acetonitrile (Solvent A) and acidified water ($H_3PO_4$, 0.01% v/v; pH 2.9)
(Solvent B) used at a flow rate of 1 mL/min and with different linear gradients according to the herbicide analysed (Table S1). Injection volume: 5 µL. Each sample was injected twice. The measure of precision was less than 2%. The limits of quantification and detection of the different molecules tested are given in the Table S2.

### 2.3.2. Analysis by ¹H NMR

¹H Nuclear Magnetic Resonance (NMR) analyses were performed at 25 °C at 500.134 MHz on an Avance 500 Bruker spectrometer (Bruker Biospin, Wissembourg, France) equipped with a triple-resonance (¹H, ¹³C, ¹⁵N) inverse probe with 5-mm-diameter tubes containing 600 µL of sample (540 µL of supernatant with 60 µL of a 5 mM of tetra-deuterated sodium trimethylsilylpropionate solution in D₂O), with water being suppressed by a classical two phase-shifted pulse saturation sequence (double pulsed field gradient echo sequence WATERGATE) according to Durand et al. [16].

### 2.3.3. Analysis by LC-MS

LC/ESI-MS analyses were performed on a ThermoScientific UHPLC Ultimate 3000 RSLC coupled with an Orbitrap Q-Exactive analyser. The crude supernatants were harvested (13,000 g, 5 min) before LC-MS analyses and directly injected without any further treatment. The analyses were carried out in positive mode. The UHPLC was equipped with a Kinetex EVO C18; 100 x 2.1 mm; 1.7 µm (Phenomenex) at 30 °C with a gradient acetonitrile + 0.1% formic acid (Solvent A) and water + 0.1% formic acid (Solvent B): 0-7.5 min: 5-99% A (linear); 7.5-8.5 min: 99% A; 8.5-9 min: 99-5% A; 9-11 min: 5% A. Flow: 0.45 mL/min. For the mass spectrometer, gaseous N₂ was used as nebulizer gas (50 L/h). The spray voltage was 3.2 kV.
## 2.4. Modelling of the herbicide degradation and metabolite formation/disappearance kinetics

The transformation kinetics of nicosulfuron and those of mesotrione and nicosulfuron metabolites were fitted with the DFOP (Double First-Order in Parallel) model [29] using the CAKE (Computer Assisted Kinetic Evaluation) V3.2 software (Tessella Technology & Consulting, UK), characterized by the following equation:

$$C_t = C_0 g^{-k_1 t} + C_0 (1 - g)^{-k_2 t} \quad (\text{Equation 1})$$

where $t$ is the incubation time, $C_t$ the chemical concentration at time $t$, $C_0$ the initial concentration of the parent herbicide or the maximal concentration of the intermediate metabolites, $g$ the fraction of $C_0$ applied to the first term of the equation 1 (i.e. the fraction of the initial chemical that degrades at the fastest rate), $k_1$ and $k_2$ the rate constants for the first and the second terms of the equation 1, respectively (in day$^{-1}$). The dissipation time $x$ (DT$_x$) value is the time required for the concentration to decline to x% of the initial value.

The kinetics of mesotrione transformation and those of mesotrione/nicosulfuron metabolite production (intermediate metabolites and end-products) were fitted with the DoseResp function of the OriginPro V8 software (Origin Lab Corporation, USA), characterized by the following equation:

$$C_t = \frac{C_{max}}{1 + 10^{(h_{50} - t)(p)}} \quad (\text{Equation 2})$$

where $t$ is the incubation time, $C_t$ the chemical concentration at time $t$, $C_{max}$ the maximal concentration of this chemical, $h_{50}$ the time required for the concentration to reach/decrease by 50% of the final value and $p$ the rate constant.

## 2.5. Mixture toxicity assays

### 2.5.1. Mixture ratio
Mixture toxicity assays were carried out in parallel to biotransformation tests. The active ingredients and/or metabolites were mixed according to the initial molar concentration of the parent compounds (starting condition) and the final molar concentration of the metabolites (after 20 days of incubation). In our study, all the ratios were 1:1 (binary mixture), 1:1:1 (ternary mixture), and 1:1:1:1 (quaternary mixture), except for mixtures containing AMBA which has a proportion of 0.31.

### 2.5.2. Microtox® assays

The herbicides and their metabolites were dissolved in DMSO and serial-diluted (8 solutions ranging from 0 to 800 µM). The dose response curve obtained was used to determine the median inhibitory concentration (IC₅₀) of each compound (alone or in mixture), which is the concentration required to induce a 50% decrease of *Aliivibrio fischeri* bioluminescence compared to the untreated bacteria after 15 min of incubation, as previously described [7,28,30]. The calculations to estimate the IC₅₀ were carried out with the Omni™ software packages (version 1.18, AZUR Environmental Ltd.) as described by Doe et al. [31].

### 2.5.3. Modelling of mixture toxicity

The mixture toxicities were compared with the expected toxicity values predicted by the Concentration Addition (CA) and the Independent Action (IA) models [32]. The prediction of combined effects of mixture-components based on CA can be expressed as:

$$IC_{50, mix} = \left( \sum_{i=1}^{n} \frac{p_i}{IC_{50, i}} \right)^{-1} \quad (\text{Equation 3})$$

where *IC*₅₀, *mix* is the total concentration of the mixture that causes 50% inhibition, *p*ᵢ the proportion of the *i*th component in the mixture, *n* the number of components in the mixture, and *IC*₅₀, *i* the median inhibitory concentration of the *i*th compound when applied alone.
The prediction of combined effects of mixture-components based on IA can be expressed as:

$$E(c_{mix}) = 1 - \prod_{i=1}^{n} (1 - E(C_i)) \quad (\text{Equation 4})$$

where $E(C_{mix})$ is the total effect of the mixture, $n$ the number of components in the mixture and $E(C_i)$ is the fractional effect (x%) caused by the $i^{th}$ compound when applied alone. $E(C_i)$ was calculated from concentration response functions determined for each single substance. The value of $E(C_{mix})$ satisfying the equation for a given effect level x was computed by an iterative procedure using the OriginPro 8 software.

The synergy was evaluated with the ratio of predicted versus observed IC$_{50}$ values, called Model Deviation Ratio (MDR) and defined as:

$$MDR = \frac{Expected}{Observed} \quad (\text{Equation 5})$$

where “Expected” corresponds to the IC$_{50, mix}$ predicted by the model (CA or IA), and “Observed” is the IC$_{50, mix}$ observed with the Microtox® test (i.e. the concentration of each component in the mixture causing 50% inhibition of A. fischeri bioluminescence). The compounds were then classified as synergistic (MDR ≥ 2), additive (0.5 < MDR < 2), or antagonistic (MDR ≤ 0.5).

## 2.6. Statistical analyses

Statistical analyses were carried out using the OriginPro 8 software. The differences in IC$_{50}$ values of the parent compounds and metabolites, alone or in mixtures, were assessed using one-way ANOVA. The differences in degradation kinetic parameters and DT$_{50}$ values of parent compounds and metabolites, alone or in mixture, were assessed in the same manner. The normality and homogeneity of variance were checked prior to ANOVA analysis
(Kolmogorov Smirnov's and Levene's tests, respectively, P < 0.05). Separate *post hoc* comparisons (Tukey's test, P < 0.05) were applied to determine differences in IC₅₀ values of the parent compounds and metabolites, alone or in combination, as well as for the kinetic parameters and DTₓ of each compound, separately.

**3. Results and Discussion**

3.1. Biotransformation kinetics of each herbicide by *Bacillus megaterium* Mes11

3.1.1. Nicosulfuron

Preliminary tests showed that Mes11 strain requires glucose under our conditions (resting
cells) to completely biotransform nicosulfuron within 20 days in GSM (Figure 1A), indicating
that nicosulfuron is not used as a carbon source. It is co-metabolically transformed, as
described for all the other nicosulfuron-transforming strains isolated to date [9,22–24,26,33].
A two-step dissipation of nicosulfuron was observed (Figure 1A). The first step corresponded
to about 80% of nicosulfuron dissipation within 19 h of incubation, mainly ascribed to
adsorption/absorption of nicosulfuron on bacterial biomass (Table S3). The second step (from
20 h to 20 days) corresponded mainly to the quantitative transformation of nicosulfuron into
three major metabolites (Figures 1B-D and 2A) identified by LC-MS and ¹H NMR analyses.
Two of them, **ASDM** and **ADMP**, resulted from cleavage of the sulfonylurea bridge C-N
bond. A third metabolite (**N3**), resulting from cleavage of the C-S bond of the sulfonylurea
bridge and contraction following elimination of the sulfur dioxide group, was identified as 2-
(1-(4,6-dimethoxy-pyrimidin-2-yl)-ureido)-N,N-dimethyl-nicotinamide, as already described
[9,23,24]. A very minor metabolite (**N4**, 2-(4,6-dimethoxy-pyrimidin-2-yl)-N,N-dimethyl-
nicotinamide) was also detected by LC-(+)ESI-MS, corresponding to the hydrolysis of the **N3**
urea function (Figure 2A). **N3** has been already identified with other strains [9,23,24], as well
as **N4** [9].
Both ASDM and ADMP reached the highest concentrations (about 0.9 mM after only 8 days of incubation; Figures 1B-C), indicating that this transformation pathway dominates as commonly described [9,34–38]. ADMP, also identified with all the nicosulfuron-transforming strains reported in the literature, can be therefore described as a common metabolite.

### 3.1.2. Mesotrione

Both HPLC and ¹H NMR analyses revealed complete removal of mesotrione (1 mM) within 5 h of incubation (Figure 3A). The dissipation of mesotrione was ascribed to biotransformation processes because its concentration remained stable in the abiotic control and no significant amount of mesotrione was adsorbed on the bacterial biomass (data not shown). In our study, the biotransformation of mesotrione was four to five-times faster than that observed previously in minimal medium [15], probably due to the presence of glucose (5 g/L), that could lead to a different physiological state of the Mes11 strain. The metabolites of mesotrione identified by ¹H NMR and LC-MS were the same as those previously described [15] (Figure 2B): two transitory metabolites (a hydroxylamino derivative (M2) obtained by reduction of the nitro group of mesotrione in equilibrium with its cyclized form isoxazolol (M3) due to an intramolecular cyclization of the hydroxyl moiety of the hydroxylamine on the nearest carbonyl group), subsequently being transformed into AMBA, which accumulated in the medium (Figure 3B). Therefore, the biotransformation pathway of mesotrione by the Mes11 strain was unchanged by glucose.

The M2 and M3 transitory metabolites reached their maximum concentrations (M2 0.5, M3 0.65 mM) after 8 and 19 h of incubation, respectively, and completely disappeared after 8 and 20 days of incubation (Figures 3C-D). The concentration of AMBA was higher than 0.2 mM after 19 h of incubation, which was about twice the concentration found without glucose (i.e. compared to Batisson et al. [15]), which is consistent with the faster transformation of
mesotrione observed in our study. The presence of glucose has therefore not only accelerated
the transformation of mesotrione, but also increased the final concentration of its end-product
AMBA.

3.1.3. *S*-metolachlor

S-metolachlor was not transformed by the strain Mes11 since its initial and final (after 20
days of incubation) concentrations were similar.

3.2. Biotransformation kinetics of the herbicide mixtures by *B. megaterium* Mes11

The same mesotrione and nicosulfuron metabolites were identified with or without the
other herbicides, indicating no effect of the herbicides studied on the biotransformation
pathway of nicosulfuron (Figure 2A) and mesotrione (Figure 2B). However, some differences
could be identified in terms of transformation kinetics.

The kinetics of nicosulfuron disappearance was well fitted by the DFOP model (r² > 0.93),
which corresponds to a bi-phasic exponential decrease (Figure 1A; Table 1). This model
corresponds to the sum of two first order terms (see Materials and Methods section, Equation
1). The term with the faster rate dominates the early degradation step whereas the term with
the slower rate dominates the later part of the degradation curve. The DFOP model (also
suitable for mono degradation curves [39]) was therefore applied in our study for the
transformation of metabolites.

3.2.1. *Effects of mesotrione and/or S-metolachlor on the biotransformation of nicosulfuron*

The first rate constant *k*₁ of nicosulfuron in the presence of mesotrione (with or without S-metolachlor) was 4 to 13-times less than those of nicosulfuron alone or associated with S-metolachlor, showing that nicosulfuron dissipation was considerably slowed down by
mesotrione during the first phase of dissipation (Table 1, Figure 1A, Tukey's test, P < 0.05), corresponding mainly to biosorption (Table S3). We assume that mesotrione modified the bacterial cell wall properties, leading to a reduction of nicosulfuron adsorption/absorption into the cell, as described in the case of chlorothalonil and atrazine [40]. Lower degradation efficiency of mixed pesticides (methyl-parathion, lindane and carbofuran) compared to individual pesticides was also observed in a previous study [41]. This resulted in lower DT₅₀ values for nicosulfuron without mesotrione (Table 1, Figure S1A, Tukey's test, P < 0.05).

Conversely, the second rate constant $k_2$ of nicosulfuron in the presence of mesotrione (with or without S-metolachlor) was higher than those of nicosulfuron alone or with S-metolachlor during the second phase of dissipation (from 19 h to 20 days of incubation) (Table 1, Figure 1A, Tukey's test, P < 0.05). This stimulation indicates a positive effect of mesotrione on the nicosulfuron biotransformation, which is predominant during this phase. Nevertheless, as mesotrione disappeared very quickly (within 5 h), the effect observed is probably due to mesotrione metabolites rather than to the parent compound.

The DT₉₀ value of nicosulfuron alone was the lowest one, showing an overall inhibitory effect of the other two herbicides on the biotransformation of nicosulfuron (Table 1, Figure S1B, Tukey's test, P < 0.05).

Some differences have also been observed for production kinetics of metabolites in the mixtures, as in the case of ASDM formation, which was accelerated in the presence of S-metolachlor (independently of mesotrione), as illustrated by the *p* rate constant (Table 1, Figure 1B, Tukey's test, P < 0.05). The maximum concentrations of ASDM and ADMP were also lowest in the case of the ternary mixture (Table 1, Figure 1C, Tukey's test, P < 0.05). Overall, these results did not show any significant effect of mesotrione and/or S-metolachlor on the balance between the two identified biotransformation pathways of nicosulfuron leading to ADMP/ASDM or N3.
### 3.2.2. Effects of nicosulfuron and/or S-metolachlor on the biotransformation of mesotrione

The disappearance of mesotrione was not modified in the presence of nicosulfuron and/or S-metolachlor (Figure 3A, Table 2, Tukey's test, $P < 0.05$). However, some significant differences were observed for the kinetic parameters of the formation/disappearance of M2 and M3 (Table 2, Figures 3C-D, Tukey's test, $P < 0.05$) for different conditions. It is difficult to conclude on these differences, because these compounds are transitory metabolites and their kinetic parameters depend on many unknown phenomena, such as dynamic equilibrium between M2 and M3, flux from mesotrione and to AMBA.

Interestingly, the formation of the end-product AMBA was faster in the presence of S-metolachlor than in its absence, as shown by the *p* rate constant and $h_{50}$ values (Table 2, Figure 3B, Tukey's test, $P < 0.05$). The production of AMBA results from a nitroreduction of the nitro group of mesotrione. S-metolachlor may thus induce the expression of the NfrA1 and NfrA2 nitroreductases [42], since previous studies have shown an activation of the nitroreductase regulatory system by pesticides such as paraquat [43]. However, although it accelerates the transformation of mesotrione into AMBA, S-metolachlor led to a lower final AMBA concentration ($C_{max}$), mainly with the ternary mixture (Table 2, Figure 3B, Tukey's test, $P < 0.05$). These results showed that S-metolachlor can modify the formation kinetics of AMBA, which is more persistent in soil than mesotrione [6].

The identification and quantification of metabolites are thus of prime concern, mainly if they are toxic compounds causing adverse effects to the environment. Then, new data on the biodegradation and toxicity of pesticide (or other compounds) mixtures could be added in databases hosted by national (e.g. E-Phy of ANSES (Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail) in France) or European institutions such as EFSA (European Food and Safety Authority). To our knowledge, such databases
regrouping biodegradation and toxicity data of mixtures do not exist so far, and have to be
created in order to improve knowledges on the impact of hazardous compound mixtures
towards target and non-target organisms in the environment.

3.3. Toxicity of herbicide and/or metabolite mixtures

The experimental data of mixture toxicity were compared with those predicted by the CA
and IA models. CA model is known to generate larger predicted mixture effects than IA, and
is therefore considered as more conservative [44–46] and adapted to multicomponent (> two
molecules) mixtures [47,48]. However, the combined variances of toxicity data for individual
compounds and mixtures render detection of small deviations from the reference models (CA
and IA) difficult. Biologically significant synergy was therefore previously defined as a more
than two fold deviation from CA or IA [44,45,49] and evaluated with the MDR values chosen
as the reference scale because of the simplicity of adjusting values from the predictive model.
Risk assessors can easily divide the level of potential toxicity described by a mixture model
by the MDR to adjust empirical data for the mixture of interest [44].

3.3.1. *Toxicity of the herbicide mixtures (starting conditions)*

The MDR values of all the parent herbicide mixtures tested were ≥ 2 whatever the model
tested (CA and IA). These results suggest synergistic effects of these compounds for the
toxicity towards *A. fischeri* (Table 3). Moreover, IC₅₀ values of the single parent compounds
were about 3-fold (mesotrione, Figure S2A), 4 to 10-fold (nicosulfuron, Figure S2B) and 6 to
15-fold (S-metolachlor, Figure S2C) higher than those of their binary or ternary mixtures
(Tukey's test, P < 0.05). This was already emphasized by Cedergreen [45] who showed five
groups of pesticides over-represented in the synergistic mixtures (azole fungicides, triazine
herbicides and organophosphate, carbamate and pyrethroid insecticides). The results of the
present study indicated that β-triketone, sulfonylurea and chloroacetanilide herbicides, which have different modes of action in plants, are potential synergistic compounds at high concentration on the bioluminescent marine bacteria *A. fischeri*, a model strain in microbial ecotoxicology. However, the mode of action of these herbicides in microorganisms is still unknown. The $IC_{50}$ found in our study were relatively high (of µM order) compared to the environmental concentrations of mesotrione, nicosulfuron and S-metolachlor detected in aquatic environments (of nM order, [13,14]). Nevertheless, these concentrations are of the order of those potentially found in soil during or just after the herbicide treatment, indicating a potential risk for edaphic microorganisms. These results may also permit the mechanism of action of these compounds in microorganisms to be identified in the future. This has been very poorly documented so far. Conversely, in higher organisms, several mechanisms have been proposed (change of bioavailability, uptake, internal transportation, metabolization, binding at the target site and excretion of each compound) [45].

### 3.3.2. Toxicity of metabolite and/or S-metolachlor mixtures (final conditions)

The toxicity of metabolite mixtures was determined with and without S-metolachlor because this herbicide was not degraded by the strain Mes11 and was therefore present at the end of the experiment. According to CA prediction, synergistic effects were observed in the case of a mixture composed of AMBA + ASDM (independently of the presence of S-metolachlor), as shown by $MDR \ge 2$ (Table 3). On the contrary, the effect of the other mixtures was mainly antagonistic or additive ($MDR < 0.5$ or 1), depending on each mixture (Table 3). Overall, the MDR values calculated with IA model were similar or lower than those obtained with CA. Consequently, synergism and additivity were most often predicted by CA than IA, whereas antagonism was found for a higher number of metabolite mixtures with IA model compared to CA (Figure S3). These results are in accordance with other studies
describing CA as more conservative than IA [44–46]. Moreover, the IA model is based on the
Bliss Independence approach which presents some limitations. First, the determination of
mixture effects implies the knowledge of the action mechanism of each component in the
mixture, which is not the case for the majority of compounds that can possess complex,
multiple and possibly unknown action mechanisms. Then, Bliss Independence approach
assumes that dose response curves are exponential, which is not necessary the case. In the
present study, dose response curves well fitted with a sigmoid function, allowing the
determination of IC₅₀ values. The dose response curves obtained were parallel on a log-dose
scale (i.e. constant ratio of dose at each level of effect) and have equal maximum effect for an
individual compound, which conditions the application of Loewe Additivity (CA model).
Therefore, CA model seems to be more suitable for the prediction of
antagonism/additivity/synergism in the case of pesticide/metabolite mixtures in the present
study.

The IC₅₀ values of a given compound in each mixture were also compared. There was no
effect of the other compounds on the IC₅₀ value of AMBA, except for two mixtures (AMBA
+ S-metolachlor + ADMP or ASDM) being about twice as toxic as the single compound
(Figure S2D, Tukey's test, P < 0.05). The IC₅₀ values of ADMP in all the tested mixtures
were about 6.5-fold (ADMP + AMBA + S-metolachlor) to 14-fold (ADMP + ASDM) higher
than that of the single compound (Figure S2E, Tukey's test, P < 0.05). On the contrary, the
IC₅₀ value of ASDM (> 3900 µM) indicates extremely low toxicity towards *A. fischeri* that
was considerably increased in all mixtures tested (up to 100 to 200 µM), and to a lesser extent
in the mixture composed of ASDM + ADMP (about 400 µM) (Figure S2F, Tukey's test, P <
0.05).

To date, studies related to the toxicity of pesticide metabolites towards *A. fischeri* were
only performed with individual compounds (e.g. [7,50,51]). The present work showed
synergism and additivity for AMBA and ASDM mixtures, and antagonism for the majority of the other metabolite mixtures tested. For more complete toxicity analyses, it would be interesting in the future to include the other metabolites, although they are present in very low concentrations and/or as intermediates.

3.4. Conclusions

The present work characterizes mixture effects on the biotransformation and toxicity of three herbicides (mesotrione, nicosulfuron, S-metolachlor) applied on maize crops. Our results indicate that *B. megaterium* Mes11 was able to simultaneously biotransform two herbicides belonging to different chemical families (β-triketone and sulfonylurea), which is poorly described so far. Unless the biotransformation pathways of each herbicide (i.e. the identified metabolites) were not modified in the presence of the other herbicides studied, differences in active ingredient biotransformation and metabolite production kinetics were observed. The toxicity tests also revealed mixture effects, leading to synergistic interactions for parent compounds and synergistic/additive/antagonistic effects for metabolites.

The present study gives new data on the toxicity of mesotrione, nicosulfuron (and their metabolites) and S-metolachlor, alone or in binary/ternary mixtures towards *A. fischeri*, allowing a better understanding of their potential impact on microorganisms. New toxicity data on the herbicides studied and their metabolites can also be used to identify synergistic compounds, and thus should perhaps be considered even if active ingredients occur at concentrations below toxic level in the environment. In a general way, our study emphasizes the importance to take into account the ‘pesticidome’ (mixture including the active ingredients, metabolites and adjuvants potentially co-occurring in the environment) and not only the active ingredient, to assess the overall fate and toxicity of pesticides in the
environment. New data could then be used to improve the global risk assessment of pesticide mixtures.

## Funding

This work was supported by the Agence Nationale de la Recherche [grant number ANR-13-CESA-0002 TRICETOX] (programme CESA), the Région Auvergne, the French Ministry for Higher Education and Research and the European Regional Development Fund.

## Acknowledgements

The authors acknowledge Philip Hoggan for English reviewing and thank the Clermont Environmental Research Federation FR 3467 CNRS-UCA-INRA for its support.

## Conflict of Interest

The authors declare no competing financial interest.
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**Figure captions**

**Figure 1.** Comparative biotransformation kinetics of nicosulfuron (alone or in mixtures) by *Bacillus megaterium* Mes11. Nicosulfuron alone (—■—), with mesotrione (—●—), S-metolachlor (—▲—) or mesotrione and S-metolachlor (—▼—). (A) nicosulfuron, (B) ASDM, 2-(aminosulfonyl)-N,N-dimethyl-3-pyridinecarboxamide (C) ADMP, 2-amino-4,6-dimethoxypyrimidine and (D) N3, 2-(1-(4,6-dimethoxy-pyrimidin-2-yl)-ureido)-N,N-dimethyl-nicotinamide. The values are means ± standard errors (n ≥ 3) of experimental data. Fitting curves are also indicated (decreasing concentration = DFOP model, increasing concentration = DoseResp model).

**Figure 2.** Proposed scheme of (A) nicosulfuron and (B) mesotrione biotransformation by *Bacillus megaterium* Mes11 in GSM. The thick and thin arrows show the main and the minor pathways, respectively. ADMP, 2-amino-4,6-dimethoxypyrimidine; ASDM, 2-(aminosulfonyl)-N,N-dimethyl-3-pyridinecarboxamide; N3, 2-(1-(4,6-dimethoxy-pyrimidin-2-yl)-ureido)-N,N-dimethyl-nicotinamide; N4, 2-(4,6-dimethoxy-pyrimidin-2-yl)-N,N-dimethyl-nicotinamide; AMBA, 2-Amino-4-(methylsulfonyl)benzoic acid; M2 and M3, hydroxylamino and isoxazolol derivatives of mesotrione, respectively.

**Figure 3.** Comparative biotransformation kinetics of mesotrione (alone or in mixtures) by *Bacillus megaterium* Mes11. Mesotrione alone (—■—), with nicosulfuron (—●—), S-metolachlor (—▲—) or nicosulfuron and S-metolachlor (—▼—). (A) mesotrione, (B) AMBA, 2-Amino-4-(methylsulfonyl)benzoic acid (C) M2 and (D) M3, hydroxylamino and isoxazolol derivatives of mesotrione, respectively. The values are means ± standard errors (n
≥ 3) of experimental data. Fitting curves are also indicated (decreasing concentration = DFOP model, increasing concentration = DoseResp model)
<figure><img src="image_4.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3</figcaption></figure>
Tables

<table><caption>**Table 1.** Kinetics of nicosulfuron biotransformation and production of its metabolites. The experimental data were fitted with the DFOP and DoseResp function using CAKE and OriginPro, respectively, as described in the Materials and Methods section. The values of model parameters, coefficient of determination (r²) and Dissipation Time x% (DTx) are reported as the mean ± standard error (SE), n = 3. Significant differences between conditions for the parameters of each compound are indicated by lowercase letters a < b < c < d (Tukey's test, P < 0.05).</caption><thead><tr><th rowspan="2">Compound</th><th rowspan="2">Condition</th><th rowspan="2">Model</th><th colspan="4">Model parameters</th><th rowspan="2">DT<sub>50</sub> (day)</th><th rowspan="2">DT<sub>90</sub> (day)</th><th rowspan="2">r<sup>2</sup></th></tr><tr><th>C<sub>0</sub> (mM)</th><th>k<sub>1</sub> (day<sup>-1</sup>)</th><th>k<sub>2</sub> (day<sup>-1</sup>)</th><th>g</th></tr></thead><tbody><tr><td rowspan="4">Nicosulfuron</td><td>Nicosulfuron</td><td>DFOP</td><td>1.02 ± 4 10<sup>-3</sup></td><td>5.35 ± 0.02 (b)</td><td>0.09 ± 1 10<sup>-3</sup> (a)</td><td>0.80 ± 0.01</td><td>0.18 ± 3 10<sup>-3</sup> (a)</td><td>7.41 ± 0.32 (a)</td><td>0.937 ± 0.002</td></tr><tr><td>Nicosulfuron + Mesotrione</td><td>DFOP</td><td>0.91 ± 0.01</td><td>0.41 ± 0.11 (a)</td><td>0.23 ± 3 10<sup>-3</sup> (c)</td><td>0.30 ± 0.22</td><td>2.72 ± 0.08 (b)</td><td>9.26 ± 0.34 (b)</td><td>0.994 ± 0.002</td></tr><tr><td>Nicosulfuron + S-metolachlor</td><td>DFOP</td><td>1.05 ± 2 10<sup>-3</sup></td><td>5.02 ± 0.10 (b)</td><td>0.09 ± 2 10<sup>-3</sup> (a)</td><td>0.72 ± 1 10<sup>-3</sup></td><td>0.23 ± 4 10<sup>-3</sup> (a)</td><td>11.73 ± 0.23 (c)</td><td>0.965 ± 0.002</td></tr><tr><td>Nicosulfuron + Mesotrione + S-metolachlor</td><td>DFOP</td><td>0.94 ± 0.01</td><td>1.38 ± 0.41 (a)</td><td>0.16 ± 4 10<sup>-3</sup> (b)</td><td>0.23 ± 0.02</td><td>2.83 ± 0.18 (b)</td><td>12.47 ± 0.15 (c)</td><td>0.993 ± 0.003</td></tr><tr><td colspan="10" style="text-align: left; font-weight: bold;">Model parameters</td></tr><tr><td colspan="2"></td><td></td><th>C<sub>max</sub> (mM)</th><th>p (day<sup>-1</sup>)</th><th>h<sub>50</sub> (day)</th><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">ASDM</td><td>Nicosulfuron</td><td>DoseResp</td><td>0.96 ± 0.02 (b)</td><td>0.26 ± 0.03 (a)</td><td>3.59 ± 0.25 (a)</td><td>-</td><td>-</td><td>-</td><td>0.852 ± 0.017</td></tr><tr><td>Nicosulfuron + Mesotrione</td><td>DoseResp</td><td>0.96 ± 0.01 (b)</td><td>0.27 ± 2 10<sup>-4</sup> (a)</td><td>4.22 ± 0.01 (a)</td><td>-</td><td>-</td><td>-</td><td>0.966 ± 0.001</td></tr><tr><td>Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.96 ± 0.02 (b)</td><td>0.37 ± 8 10<sup>-4</sup> (b)</td><td>3.71 ± 0.12 (a)</td><td>-</td><td>-</td><td>-</td><td>0.982 ± 0.003</td></tr><tr><td>Nicosulfuron + Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.84 ± 0.03 (a)</td><td>0.37 ± 0.01 (b)</td><td>3.50 ± 0.12 (a)</td><td>-</td><td>-</td><td>-</td><td>0.978 ± 0.002</td></tr><tr><td rowspan="4">ADMP</td><td>Nicosulfuron</td><td>DoseResp</td><td>0.89 ± 0.01 (b)</td><td>0.33 ± 3 10<sup>-3</sup> (ab)</td><td>3.31 ± 0.03 (a)</td><td>-</td><td>-</td><td>-</td><td>0.966 ± 0.001</td></tr><tr><td>Nicosulfuron + Mesotrione</td><td>DoseResp</td><td>0.82 ± 0.01 (ab)</td><td>0.21 ± 0.02 (a)</td><td>4.44 ± 0.22 (b)</td><td>-</td><td>-</td><td>-</td><td>0.901 ± 0.019</td></tr><tr><td>Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.92 ± 0.02 (b)</td><td>0.27 ± 0.01 (a)</td><td>4.04 ± 0.08 (b)</td><td>-</td><td>-</td><td>-</td><td>0.904 ± 0.014</td></tr><tr><td>Nicosulfuron + Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.76 ± 0.03 (a)</td><td>0.43 ± 0.04 (b)</td><td>3.36 ± 0.21 (a)</td><td>-</td><td>-</td><td>-</td><td>0.975 ± 0.008</td></tr><tr><td rowspan="4">N3</td><td>Nicosulfuron</td><td>DoseResp</td><td>0.17 ± 0.01 (b)</td><td>0.26 ± 0.01 (a)</td><td>4.69 ± 0.09 (b)</td><td>-</td><td>-</td><td>-</td><td>0.969 ± 0.004</td></tr><tr><td>Nicosulfuron + Mesotrione</td><td>DoseResp</td><td>0.22 ± 0.02 (c)</td><td>0.24 ± 0.04 (a)</td><td>4.61 ± 0.54 (b)</td><td>-</td><td>-</td><td>-</td><td>0.941 ± 0.009</td></tr><tr><td>Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.13 ± 3 10<sup>-3</sup> (a)</td><td>0.36 ± 0.01 (b)</td><td>3.51 ± 0.11 (a)</td><td>-</td><td>-</td><td>-</td><td>0.943 ± 0.011</td></tr><tr><td>Nicosulfuron + Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.14 ± 2 10<sup>-3</sup> (a)</td><td>0.34 ± 3 10<sup>-3</sup> (b)</td><td>3.26 ± 0.03 (a)</td><td>-</td><td>-</td><td>-</td><td>0.946 ± 0.003</td></tr></tbody></table>

$k_1$, $k_2$ and $p$: rate constants
<table><caption>**Table 2.** Kinetics of mesotrione biotransformation and production of its metabolites. The experimental data were fitted with the DFOP and DoseResp function using CAKE and OriginPro, respectively, as described in the Materials and Methods section. The values of model parameters, coefficient of determination (r²) and Dissipation Time x% (DTx) are reported as the mean ± standard error (SE), n = 3. Significant differences between conditions for the parameters of each compound are indicated by lowercase letters a < b < c (Tukey's test, P < 0.05).</caption><thead><tr><th rowspan="2">Compound</th><th rowspan="2">Condition</th><th rowspan="2">Model</th><th colspan="4">Model parameters</th><th rowspan="2">DT<sub>50</sub> (day)</th><th rowspan="2">DT<sub>90</sub> (day)</th><th rowspan="2">r<sup>2</sup></th></tr><tr><th>C<sub>0</sub> (mM)</th><th>k<sub>1</sub> (day<sup>-1</sup>)</th><th>k<sub>2</sub> (day<sup>-1</sup>)</th><th>g</th></tr></thead><tbody><tr><td rowspan="4">M2 <sup>(1)</sup></td><td>Mesotrione</td><td>DFOP</td><td>0.51 ± 0.10</td><td>0.78 ± 0.07 (b)</td><td>0.01 ± 8 10<sup>-4</sup> (a)</td><td>1.00 ± 0.00</td><td>0.91 ± 0.09 (a)</td><td>3.00 ± 0.30 (a)</td><td>0.955 ± 0.021</td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DFOP</td><td>0.33 ± 0.03</td><td>0.51 ± 0.01 (a)</td><td>0.01 ± 3 10<sup>-4</sup> (a)</td><td>1.00 ± 0.00</td><td>1.36 ± 0.02 (b)</td><td>4.51 ± 0.07 (b)</td><td>0.931 ± 0.016</td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DFOP</td><td>0.43 ± 0.10</td><td>0.51 ± 0.00 (a)</td><td>0.01 ± 5 10<sup>-4</sup> (a)</td><td>1.00 ± 0.00</td><td>1.36 ± 0.00 (b)</td><td>4.53 ± 0.00 (b)</td><td>0.900 ± 0.000</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DFOP</td><td>0.61 ± 0.16</td><td>0.85 ± 0.01 (b)</td><td>0.01 ± 9 10<sup>-4</sup> (a)</td><td>1.00 ± 0.00</td><td>0.82 ± 0.01 (a)</td><td>2.72 ± 0.04 (a)</td><td>0.928 ± 0.004</td></tr><tr><td rowspan="4">M3 <sup>(1)</sup></td><td>Mesotrione</td><td>DFOP</td><td>0.65 ± 0.01</td><td>0.18 ± 0.01 (b)</td><td>0.06 ± 0.06 (a)</td><td>0.99 ± 7 10<sup>-3</sup></td><td>3.97 ± 0.29 (b)</td><td>13.17 ± 0.96 (b)</td><td>0.966 ± 0.009</td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DFOP</td><td>0.64 ± 0.01</td><td>0.31 ± 0.01 (c)</td><td>0.11 ± 0.10 (a)</td><td>0.68 ± 0.32</td><td>2.27 ± 0.05 (a)</td><td>7.54 ± 0.15 (a)</td><td>0.979 ± 0.005</td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DFOP</td><td>0.58 ± 0.02</td><td>0.12 ± 4 10<sup>-4</sup> (a)</td><td>0.11 ± 4 10<sup>-4</sup> (a)</td><td>0.73 ± 1 10<sup>-3</sup></td><td>5.84 ± 0.02 (c)</td><td>19.40 ± 0.06 (c)</td><td>0.931 ± 0.001</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DFOP</td><td>0.38 ± 4 10<sup>-3</sup></td><td>0.17 ± 2 10<sup>-3</sup> (b)</td><td>3 10<sup>-11</sup> ± 2 10<sup>-11</sup> (a)</td><td>0.99 ± 3 10<sup>-3</sup></td><td>4.14 ± 0.06 (b)</td><td>13.90 ± 0.26 (b)</td><td>0.976 ± 0.002</td></tr><tr><td colspan="10" style="text-align: left; font-weight: bold;">Model parameters</td></tr><tr><td colspan="2"></td><td></td><th>C<sub>max</sub> (mM)</th><th>p*</th><th>h<sub>50</sub> (day)</th><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Mesotrione</td><td>Mesotrione</td><td>DoseResp</td><td>0.98 ± 0.02</td><td>-0.65 ± 0.06(a)</td><td>2.43 ± 0.13 (a)</td><td>-</td><td>-</td><td>-</td><td>0.989 ± 0.005</td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DoseResp</td><td>0.95 ± 0.01</td><td>-0.59 ± 0.02(a)</td><td>2.22 ± 0.05 (a)</td><td>-</td><td>-</td><td>-</td><td>0.995 ± 0.001</td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.96 ± 0.01</td><td>-0.65 ± 0.01(a)</td><td>2.35 ± 0.06 (a)</td><td>-</td><td>-</td><td>-</td><td>0.997 ± 0.001</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.96 ± 0.01</td><td>-0.62 ± 5 10<sup>-4</sup> (a)</td><td>2.20 ± 0.02 (a)</td><td>-</td><td>-</td><td>-</td><td>0.996 ± 0.001</td></tr><tr><td rowspan="4">AMBA</td><td>Mesotrione</td><td>DoseResp</td><td>0.45 ± 0.01 (c)</td><td>0.25 ± 0.04 (a)</td><td>3.17 ± 0.37 (b)</td><td>-</td><td>-</td><td>-</td><td>0.880 ± 0.01</td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DoseResp</td><td>0.48 ± 0.02 (c)</td><td>0.15 ± 0.01 (a)</td><td>5.74 ± 0.28 (c)</td><td>-</td><td>-</td><td>-</td><td>0.865 ± 0.01</td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.37 ± 0.01 (b)</td><td>0.61 ± 0.05 (b)</td><td>1.92 ± 0.06 (a)</td><td>-</td><td>-</td><td>-</td><td>0.922 ± 0.02</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.26 ± 0.01 (a)</td><td>0.53 ± 0.06 (b)</td><td>2.09 ± 0.17 (ab)</td><td>-</td><td>-</td><td>-</td><td>0.912 ± 0.02</td></tr><tr><td rowspan="4">M2 <sup>(2)</sup></td><td>Mesotrione</td><td>DoseResp</td><td>0.51 ± 0.10 (a)</td><td>57.49 ± 20.46 (b)</td><td>0.13 ± 0.04 (a)</td><td>-</td><td>-</td><td>-</td><td>0.979 ± 0.02</td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DoseResp</td><td>0.33 ± 0.03 (a)</td><td>10.83 ± 1.02 (a)</td><td>0.20 ± 0.01 (a)</td><td>-</td><td>-</td><td>-</td><td>0.997 ± 2 10<sup>-3</sup></td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.43 ± 0.10 (a)</td><td>3.54 ± 0.64 (a)</td><td>0.31 ± 0.08 (a)</td><td>-</td><td>-</td><td>-</td><td>0.931 ± 0.02</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.43 ± 0.03 (a)</td><td>6.97 ± 0.23 (a)</td><td>0.19 ± 0.01 (a)</td><td>-</td><td>-</td><td>-</td><td>0.969 ± 0.01</td></tr><tr><td rowspan="4">M3 <sup>(2)</sup></td><td>Mesotrione</td><td>DoseResp</td><td>0.65 ± 0.01 (c)</td><td>5.26 ± 0.46 (a)</td><td>0.22 ± 0.01 (b)</td><td>-</td><td>-</td><td>-</td><td>0.985 ± 4 10<sup>-3</sup></td></tr><tr><td>Mesotrione + Nicosulfuron</td><td>DoseResp</td><td>0.65 ± 0.01 (c)</td><td>14.16 ± 1.39 (b)</td><td>0.14 ± 3 10<sup>-3</sup> (a)</td><td>-</td><td>-</td><td>-</td><td>0.999 ± 2 10<sup>-4</sup></td></tr><tr><td>Mesotrione + S-metolachlor</td><td>DoseResp</td><td>0.58 ± 0.02 (b)</td><td>3.89 ± 0.40 (a)</td><td>0.21 ± 0.02 (b)</td><td>-</td><td>-</td><td>-</td><td>0.871 ± 0.03</td></tr><tr><td>Mesotrione + Nicosulfuron + S-metolachlor</td><td>DoseResp</td><td>0.38 ± 0.01 (a)</td><td>11.21 ± 0.11 (b)</td><td>0.14 ± 0.01 (a)</td><td>-</td><td>-</td><td>-</td><td>0.998 ± 1 10<sup>-3</sup></td></tr></tbody></table>

<footnote><sup>(1)</sup>: data for the second kinetic phase of the intermediate metabolites M2 and M3, (i.e. decrease of metabolite concentration)</footnote>

<footnote><sup>(2)</sup>: data for the first kinetic phase of the intermediate metabolites M2 and M3 (i.e. increase of metabolite concentration)
$k_1$, $k_2$ and $p$: rate constants. * $p$ in hour<sup>-1</sup> (mesotrione) or day<sup>-1</sup> (AMBA, M2, M3).</footnote>
<table><caption>**Table 3.** Toxicity values of chemicals towards *A. fischeri* (Microtox®). Experimental data were compared to predicted toxicity values according to the Concentration Addition (CA) and the Independent Action (IA) models. The effect of the other components of the mixture on each compound were qualified of synergistic, additive and antagonistic according to MDR (Model Deviation Ratio) values (see Materials and Methods section). The results are reported as the mean ± standard error (SE), n = 3.</caption><thead><tr><th rowspan="2">Chemicals and mixtures (ratio)</th><th colspan="3">Microtox® IC<sub>50</sub>, 15 min (µM)</th><th rowspan="2">MDR (CA)</th><th rowspan="2">MDR (IA)</th><th rowspan="2">Effect (predicted by CA)</th><th rowspan="2">Effect (predicted by IA)</th></tr><tr><th>Observed (experimental data)</th><th>Predicted (CA model)</th><th>Predicted (IA model)</th></tr></thead><tbody><tr><td>Mesotrione</td><td>128.5 ± 7.1<sup>a</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Nicosulfuron</td><td>420.3 ± 47.7<sup>c</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>S-metolachlor</td><td>628.6 ± 80.3<sup>b</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>AMBA</td><td>82.7 ± 18.6<sup>a</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ADMP</td><td>22.4 ± 8.4<sup>c</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ASDM</td><td>> 3900<sup>c</sup></td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Mesotrione + nicosulfuron (1:1)</td><td>42.4 ± 4.0</td><td>196.8 ± 12.4</td><td>239.1 ± 1.9</td><td>4.6</td><td>5.6</td><td>Synergistic</td><td>Synergistic</td></tr><tr><td>Mesotrione + S-metolachlor (1:1)</td><td>42.4 ± 2.3</td><td>213.4 ± 13.1</td><td>146.8 ± 1.7</td><td>5.0</td><td>3.5</td><td>Synergistic</td><td>Synergistic</td></tr><tr><td>Nicosulfuron + S-metolachlor (1:1)</td><td>110 ± 0.7</td><td>503.8 ± 59.8</td><td>213.4 ± 0.7</td><td>4.6</td><td>2</td><td>Synergistic</td><td>Synergistic</td></tr><tr><td>Mesotrione + nicosulfuron + S-metolachlor (1:1:1)</td><td>51.6 ± 2.1</td><td>255.3 ± 17.2</td><td>122.8 ± 2.2</td><td>4.9</td><td>2.4</td><td>Synergistic</td><td>Synergistic</td></tr><tr><td>ADMP + ASDM (1:1)</td><td>391.3 ± 25.2<sup>c</sup></td><td>44.5 ± 1.8</td><td>24.1 ± 0.4</td><td>0.1</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>AMBA + ADMP (0.31:1)</td><td>97.8 ± 2.2 (AMBA)</td><td>27.1 ± 9.7</td><td>24.8 ± 0.7</td><td>0.3</td><td>0.3</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>AMBA + ASDM (0.31:1)</td><td>312.8 ± 6.9 (ADMP)</td><td>327.1 ± 1.3</td><td>82.6 ± 0.3</td><td>0.1</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>AMBA + ADMP + ASDM (0.31:1:1)</td><td>68.3 ± 0.9 (AMBA)</td><td>218.4 ± 2.8 (ASDM)</td><td></td><td>4.8</td><td>1.2</td><td>Synergistic</td><td>Additive</td></tr><tr><td>S-metolachlor + AMBA + ADMP (1:0.31:1)</td><td>69.1 ± 1.5 (AMBA)</td><td>47.5 ± 2.1</td><td>24.8 ± 0.7</td><td>0.7</td><td>0.4</td><td>Additive</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ASDM (1:0.31:1)</td><td>147.6 ± 8.5 (S-metolachlor)</td><td>46.2 ± 15.6</td><td>14.3 ± 0.9</td><td>0.2</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ASDM (1:0.31:1:1)</td><td>147.6 ± 8.5 (S-metolachlor)</td><td>46.2 ± 15.6</td><td>14.3 ± 0.9</td><td>0.2</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>135.6 ± 5.8 (S-metolachlor)</td><td>412.8 ± 2.2</td><td>150.4 ± 0.6</td><td>0.3</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>135.6 ± 5.8 (S-metolachlor)</td><td>412.8 ± 2.2</td><td>150.4 ± 0.6</td><td>1.0</td><td>0.3</td><td>Additive</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>135.6 ± 5.8 (S-metolachlor)</td><td>412.8 ± 2.2</td><td>150.4 ± 0.6</td><td>0.3</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>3.0</td><td>1.1</td><td>Synergistic</td><td>Additive</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>9.7</td><td>3.6</td><td>Synergistic</td><td>Synergistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>3.0</td><td>1.1</td><td>Synergistic</td><td>Additive</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>0.3</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>1.0</td><td>0.2</td><td>Additive</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>0.3</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr><tr><td>S-metolachlor + AMBA + ADMP + ASDM (1:0.31:1:1)</td><td>213.6 ± 20.3 (S-metolachlor)</td><td>65.9 ± 2.9</td><td>14.3 ± 0.9</td><td>0.3</td><td>0.1</td><td>Antagonistic</td><td>Antagonistic</td></tr></tbody></table>

IC<sub>50</sub> = Inhibitory Concentration 50%; the IC<sub>50</sub> values obtained in the present study were similar to those found by <sup>a</sup> Bonnet et al. [7], <sup>b</sup> Joly et al. [28] and <sup>c</sup> Carles et al. [9]. The predicted (CA and IA) IC<sub>50</sub>, mix values of mixtures containing ASDM were calculated with an IC<sub>50</sub> value of ASDM = 3900 µM.