Impact of solid-phase extraction on the performance of a suspect analysis method for the screening of organic micropollutants in surface waters

Pedro A. Segura*, Mathieu Racine, Alexia Gravel, Emmanuel Eysseric, Anne-Marie Grégoire, Diane Rawach, François-Xavier Teysseire

* Tel: 1-(819) 821-7922. Fax: 1-(819) 821-8019. E-mail: pa.segura@usherbrooke.caDepartment of Chemistry, Université de Sherbrooke, Sherbrooke, QC J1K 2R1Keywords:

Supplementary material

1. Detailed explanation of procedure used in the choice of model compounds

1.1 Pharmaceuticals and personal care products (PPCPs)

Data from Monteiro and Boxall 1 on the occurrence of pharmaceuticals and their metabolites in surface waters and wastewater treatment plant (WTTP) effluents around the world was used to establish a preliminary list of suspect water micropollutants. From those compounds, we selected structurally different micropolluntats, i.e. not analogues, having different functional groups and previously reported in surface waters and wastewaters concentrations > 500 ng L -1. The selected compounds were the following: acetaminophen (ACT), carbamazepine (CBZ), cyclophosphamide (CYC), diatrizoate (DIA), dimethylaminophenazone (DAP), diclofenac (DCF), fluoxetine (FLX), gemfibrozil (GEM), ibuprofen (IBU), metoprolol (MEP), naproxen (NAP), ofloxacin (OFX), roxithromycin (ROX), salicylic acid (SCA), sulfamethoxazole (SMX). The synthetic estrogen 17α-ethinylestradiol (EE2) which has been detected at maximum concentration of 831 ng L-1 in surface waters 1 was selected as a representative of estrogenic compounds.

For the selection of active ingredients in personal care products, only UV filters were preselected. Two main information sources were used to identify the most relevant compounds : the list of UV filters allowed in cosmetic products in the European Union 2 and the sunscreen monograph of Health Canada 3. A selection of compounds present in both lists, having a log Kow

< 5 and being structurally different was performed. The selected compounds were: avobenzone (AVB), ensulizole (ESZ), oxybenzone (OXB) and sulizobenzone (SLB).

1.1 Pesticides and consumer product additives

Structurally different and frequently used pesticides in the Estrie region (Québec, Canada) where the sampling was done were selected such as atrazine (ATZ), carbaryl (CAR), chlorpyrifos (CPF), 2,4-dichlorophenoxyacetic acid (2,4-D), glyphosate (GLY), linuron (LIN) and thiabendazole (TBZ).

A preliminary preselection list of the phthalates according to Wypych 4 was done in order to identify the most used compounds. Other plasticizers of the citrate, sebacate and adipate class which have been suggested as replacement of phthalates in industrial products 5 were also added to the preselection list. Then, a selection of compounds having a log Kow < 5 and being structurally different was performed. The selected compounds were: benzylbutyl phthalate (BBP), dibutyl phthalate (DBP), o-toluenesulfonamide (OTSA), tributyl O-acetylcitrate (ATBC) and 2,2,4-trimethyl-1,3-pentanediol diisobutyrate (TXIB). Also, four parabens were preselected (methylparaben, ethylparaben, propylparaben and butylparaben) according to their presence in WWTP effluents and surface waters 6,7. Since these compounds were structurally very similar, only proprylparaben (PPB) was selected as a model compound for this class of micropollutant.

Regarding flame retardants, compounds were preselected based on their structural diversity, their presence in surface waters and log Kow 8,9. Four compounds were finally selected: tris(2-butoxyethyl) phosphate (TBEP), tributyl phosphate (TBP), tris (1-chloro-2-propyl) phosphate (TCPP) and tris(1,3-dichloro-2-propyl)phosphate (TDCPP). Sucralose (SUC), an artificial sweetener that has been proposed as a molecular marker of contamination of surface waters by domestic wastewaters 10 was also included in this study.Home-made libraryThe home-made library is available as an Excel File: SupplementaryMaterial(Databases.xls)

2. Validation of the procedure to predict retention times

The compounds used as a training set and their retention times are available in the Excel File: SupplementaryMaterial(Databases.xls). With the aim to obtain a better correlation between the experimental and the calculated retention times, different parameters were tested. The first step was to compare different similarity coefficients, such as the Tanamito, the Dice, and the Cosine coefficient, by calculating the presence of fingerprints (binary strings) like structural fragments, or physicochemical properties.

The Tanimoto, the Dice, and the Cosine coefficient are defined by equation 1, 2 and 3 respectively.

S= ca+b−c (1)

S= 2Ca+b (2)

S= c√ab

(3)

In each equation, S represents the similarity coefficient, c is the number of bit-strings present in both molecule “a” and “b”, while a and b are the number of bit set present in molecule “a” and “b” respectively. For the three equations, the limits of the coefficients S are found to be from 0 to 1. The closer the coefficient is to one, the more similar both molecules are. The Tanimoto and the Dice coefficient are very similar, but the first equation has the advantage of including normalization correction in the denominator to eliminate the dependence with the size of the molecule. Otherwise, the probability for a reference molecule to show similarities with a large molecule is higher, while the probability to show similarities with a small molecule is lower.

Another important parameter to evaluate is the number of similar compounds selected from the database for the calculation of the similarity coefficient, and for the prediction of the retention time. As explained before, the software selects a certain number of compounds that are the most similar to the reference molecule, and then calculates physicochemical parameters that are correlated to the retention time. If a small number of molecules is used, the software will have

less information to predict the retention mechanism of the molecule in the column. In the other hand, if the software uses too many molecules, then the calculation will be based on molecules with lower similarity coefficient, hence misrepresenting the predicted retention time. The last parameter tested was the algorithm for the estimation of the partition constant “P”. ACD/ChromGenius propose two different models, which are the Classic and Consensus logP.

3. Results and discussion

3.1 Optimization of the model for prediction of retention time

The predicted retention time of a subset compounds in the database (compounds 1 to 189) was evaluated using the three similarity coefficients discussed previously, all using a training set composed of 25, 35, and 50 most similar compounds. All calculations were made with the Classic and Consensus logP mathematical model. Table SM-1 shows the determination coefficient (R2) of the calculated retention time as a function of the experimental retention time, while Table SM-2 shows the average deviation between the predicted and calculated retention time.

Table SM-1. Determination coefficient (R2) of the regression of the predicted retention time as a function of and calculated retention time.

Number of similar

compounds

Classic logP Consensus logP

Dice Tanimoto Cosine Dice Tanimoto Cosine

25 0.7168 0.7168 0.7313 0.7271 0.7271 0.743535 0.7374 0.7374 0.7544 0.8105 0.8105 0.785950 0.6467 0.6467 0.7318 0.7141 0.7141 0.8107

Table SM-2. Average difference between predicted and calculated retention times.

Number of similar

compounds

Classic logP Consensus logP

Dice Tanimoto Cosine Dice Tanimoto Cosine

25 1.8 ± 1.7 1.8 ± 1.7 1.7 ± 1.9 1.7 ± 1.7 1.7 ± 1.7 1.6 ± 1.9 35 1.8 ± 1.6 1.8 ± 1.6 1.7 ± 1.7 1.7 ± 1.4 1.7 ± 1.4 1.5 ± 1.5 50 1.9 ± 2.0 1.9 ± 2.0 1.7 ± 1.8 1.8 ± 1.8 1.8 ± 1.8 1.5 ± 1.4

According to these results, Consensus logP algorithm with both Tanimoto and Dice similarity coefficients based on 35 compounds gave the best compromise between the lowest number of similar compounds required as well as higher determination coefficient. The average difference between predicted and experimetnal retention times was also among the lowest. Since the Tanimoto similarity coefficient considers a correction factor due to molecular size, it was decided to use it instead of the Dice coefficient to predict the retention time of the test compounds.

3.1 Testing the prediction of retention time model

Once the optimum parameters have been selected (Consesus LogP, Tanimoto similarity coefficient, 35 compounds), 26 additional compounds were added to the knowledge base (compounds 190 to 215) and the performance of the prediction model was tested by estimating the retention time of ten compounds that were not present in the database (Figure SM-3). The predicted and calculated retention time, as well as the value of the similarity coefficient of the test compounds is shown in Table SM-3.

12

(4R,4aR,7aR)-3-oxo-4-[(1H-pyrrol-1-yl)methyl]octahydro-2H-

cyclopenta[c]pyridin-2-yl}methanesulfonate

1,5a-dimethyl-1,2,3,4,5,5a,6,7-octahydro-8H-1-benzazepin-8-one

34

4a-butyl-1-methyl-2,3,4,4a,5,6-hexahydroquinolin-7(1H)-one

methyl (2E)-7-[(4S)-4-benzyl-2-oxo-1,3-oxazolidin-3-yl]-7-oxohept-2-enoate

56

Metolachlor N-(5-hydroxyhexyl)-N-[2-(1H-pyrrol-1-yl)ethyl]acetamide

7 8Trimethoprim Caffeine

910

Chloramphenicol AzithromycinFigure SM-1. Name and structure of the10 test molecules used to validate the model of prediction of retention time.

Table SM-4. Predicted and calculated retention time of the ten test molecules with their average deviation and value of similarity coefficient.

Compound number

tR(predicted)(min)

tR(experimental)(min)

ΔtR

(min)

Retention time difference of

similiar compounds

(min)

Similarity coefficient of

compounds used for estimation of

retention time1 6.70 11.46 4.76 1.5 ± 1.3 0.55 ± 0.042 7.50 6.60 0.90 1.1 ± 0.9 0.56 ± 0.033 9.38 9.13 0.25 1.2 ± 0.9 0.58 ± 0.034 11.46 12.65 1.19 1.3 ± 1.2 0.65± 0.035 13.42 13.65 0.23 0.9 ± 0.8 0.70 ± 0.066 9.46 8.70 0.76 1.2 ± 1.3 0.61 ± 0.047 3.11 4.48 1.37 1.2 ± 1.0 0.67 ± 0.048 5.87 4.77 1.10 1.1 ± 1.2 0.53 ± 0.049 10.42 6.50 3.92 1.2 ± 1.0 0.55 ± 0.0510 11.10 7.99 3.11 1.9 ±1.2 0.54 ± 0.07

The third column of Table SM-4 represents the average deviation ± standard deviation between the predicted and calculated retention time for the 35 molecules selected for the calculation of the similarity coefficient. For 7 out of the 10 test molecules, the absolute error between the predicted and calculated retention time is found between 0.23 and 1.37 minutes. For three compounds (1, 9 and 10) retention time was difference > 3.0 min.

As it can be seen, the use of predicted retention times in suspect screening workflows, is not free errors and the threshold of 2.9 min as maximum accepted difference between predicted and experimental retention times may lead to wrongly rejecting a potential correct tentative structure. However, this approach is fast, easy to implement and represents a good compromise between false positives and false negatives.

4. Parameters for the determination of spectral accuracy

Ion m/z 267.1727 eluted at 20.3 min (extracted with WAX cartridges)

Parameter Value

Electron state EvenCharge +1Mass tolerance 10 ppmDouble bond equivalents

1.5 to 20

Elemental constraints C1-16, H0-40, N0-7, O0-11, F0-9, P0-3, S0-5, Cl0-5, S0-10, Br0-3, I0-2, Na0-12

5. Analysis of unknown oligomers

Figure SM-2. Top: Survey View showing a series of abundant peaks observed in the WAX cartridges (from 8.6 to 13 min). Bottom: extracted ion chromatograms of the main ions. The m/z ratios of each peak are listed in Table SM-5.

Table SM-5. Mass-to-charge ratio of the unidentified compounds. The average mass difference between the z=1 and z=2 ions was 44.0256 and 22.0130, respectively.

m/z

PeakRetention

time(min)

z=2 z=1

1 8.9 348.7221 696.43382 9.2 370.7353 740.45983 9.5 392.7483 784.48574 9.7 414.7614 828.51145 10.0 436.7742 872.53666 10.2 458.7872 916.56307 10.4 480.8002 960.58848 10.6 502.8129 1004.61329 10.7 524.8259  

10 10.9 546.8388  11 11.1 568.8520  12 11.2 590.8649  13 11.4 612.8782  14 11.5 634.8908  15 11.6 656.9036  16 11.7 678.9170  17 11.9 700.9298  18 12.0 722.9427  19 12.1 744.9560  20 12.2 766.9685  

6. Library tandem mass spectra of level 2a compounds from mzCloud

Figure SM-6. Tandem mass spectrum of gabapentin from mzCloud.

Figure SM-7. Tandem mass spectrum of N,N-diethyl-m-toluamide from mzCloud.

Figure SM-8. Tandem mass spectrum of caffeine from mzCloud.

Figure SM-9. Tandem mass spectrum of metoprolol from mzCloud.

Figure SM-10. Tandem mass spectrum of venlafaxine from mzCloud.

7. References

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L342, 22.12.2009.(3) Health Canada 2013.(4) Wypych, G. Handbook of plasticizers; ChemTec Publishing: Toronto, ON, 2004.(5) Lowell Center for Sustainable Production Phatalates and Their Alternatives : Health and

Environmental Concerns; University of Massachussetts Lowell: Lowell, MA, 2011.(6) Lee, H.-B.; Peart, T. E.; Svoboda, M. L. J. Chromatogr. A 2005, 1094, 122.(7) Jonkers, N.; Sousa, A.; Galante-Oliveira, S.; Barroso, C. M.; Kohler, H.-P. E.; Giger, W.

Environ. Sci. Poll. Res. 2010, 17, 834.(8) van der Veen, I.; de Boer, J. Chemosphere 2012, 88, 1119.(9) Reemtsma, T.; Quintana, J. B.; Rodil, R.; Garcı, M.; Rodrı, I. TrAC Trends in Analytical

Chemistry 2008, 27, 727.(10) Harwood, J. J. Chemosphere 2014, 95, 3.