Accurate Mass Screening and Data Evaluation Approaches for

Chapter 1

Downloaded by 80.82.78.170 on December 17, 2016 | http://pubs.acs.org Publication Date (Web): December 12, 2016 | doi: 10.1021/bk-2016-1242.ch001

Accurate Mass Screening and Data Evaluation Approaches for Ozonation By-Products in Wastewater Treatment Plant Effluents S. Merel and C. Zwiener* University of Tuebingen, Environmental Analytical Chemistry, Hoelderlinstrasse 12, 72074 Tuebingen, Germany *E-mail: [email protected].

The potential risks of micropollutants (MPs) for aquatic ecosystems and human health require measures to reduce their input into receiving waters. While ozonation is increasingly considered in order to upgrade conventional wastewater treatment plants due to its demonstrated ability to react with trace organic contaminants, little is known about its overall impact on the complex compound mixture of a secondary wastewater effluent. Therefore, we applied a non-target screening (NTS) approach to assess the transformation processes of MPs and other wastewater constituents during ozonation and subsequent biofiltration. The workflow includes accurate mass measurement, data processing by data filtering, statistical analysis and visualization tools. Compound identification was based on matches with accurate mass fragmentation spectra from libraries and with authentic standards. The data reveal for example that from 1796 compounds detected in wastewater, only 506 have been completely removed during ozonation, while 277 compounds have been formed, from which the most part (242 compounds) could not be removed in a subsequent biofiltration process. Among typical wastewater contaminants (carbamazepine) and transformation products (valsartan acid), specific ozonation byproducts could be identified using the NTS approach. Kendrick mass analysis revealed that 99 precursor compounds are closely related to 60 oxidized transformation products from

© 2016 American Chemical Society Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

which 27 could be assigned to N-oxides, typical oxidation products of compounds with a tertiary amine group. A further substructure search in a chemical database revealed more than 1,300 compounds with the potential to form N-oxides during oxidation processes and which likely occur in wastewater treatment.


Introduction The use of synthetic organic compounds poses a big challenge for the protection of water resources and ecosystems. Today we have more than 100,000 chemcials in the EC inventory of the Euorpean Chemicals Agency (ECHA) (1). An estimated number of more than 30,000 compounds are in daily use. Among them are pesticides, industrial chemicals, pharmaceuticals and personal care products, from which a part is finally released to the environment. Surface water run-off and effluents from wastewater treatment plants are major sources of input into surface waters. Groundwater quality can be affected directly by leaking sewer systems and by surface water - groundwater exchange, for example where bankfiltration is forced by groundwater abstraction for water supply near rivers. Growing population density and variations in precipitation events due to climate change are further factors which may intensify pollutant burden of receiving water bodies and therefore increase the challenge to maintain and improve water quality. As a consequence a considerably large number of micropollutants (MPs) in the range of µg/l to ng/L are found in surface waters in particular in densely populated areas. Among these compounds, pharmeceuticals are often the most considered. For instance, with several tons prescribed per year in Germany (2), the beta blockers metoprolol and atenolol are common wastewater contaminants which are also among the most frequently detected compounds in freshwater ecosystems (3). Other common pharmaceuticals frequently detected in wastewater (4) and freshwater (3) include the antiepileptics carbamazepine, gabapentin and primidone; the painkillers diclofenac, naproxen, tramadol and ibuprofen; the blood lipid regulator gemfibrozil, the antihypertensive and antiarrhythmic diltiazem; the antihistamine diphenhydramine; and the antidepressants citalopram and fluoxetine. The large consumption of antibiotics is also reflected in wastewater composition with the frequent detection of ciprofloxacin, clarithromycin, clindamycin and sulfamethoxazole which are of particular concern as there occurrence in the environment could contribute to the development of microorganisms resistant to antibiotics (5, 6). Morevoer the contaminants of concern released to the environment through the discharge of wastewater also include a wide range of industrial compounds such as perfluorinated compounds (PFCs) and flame retardants, or personal care products such as insect repellents (7). Finally, the main metabolites of these compounds should also be considered as major environmental contaminants, particularly those pharmaceuticals which may remain biologically active. 4 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


So far only a few of these compounds have been included into the list of priority substances in the field of water policy, which have to be monitored according to the Water Framewaork Directive (WFD) of the European Commission (EC 2000/60/EC). The amended directive for priority substances (Directive 2013/39/EC) introduced a new Watch List of emerging pollutants or other chemicals for which the available monitoring data are either insufficient or of insufficient quality to assess the environmental risk. The Watch List also includes pharmaceuticals like the nonsteroidal anti-inflammatory drug diclofenac, the macrolide antibiotics erythromycin, clarithromycin and azithromycin and the estrogen in contraceptives 17-alpha-ethinylestradiol (E2) and the natural and pharmaceutical hormone 17-beta-estradiol (EE2). Environmental Quality Standards (EQS) have been defined for priority substances by the Directive on Environmental Quality Standards (Directive 2008/105/EC) of the European Parliament and the Council of the European Union with the aim to protect the aquatic environment from adverse effects of chemicals substances. EQS for annual averages (AA-EQS) for inland surface waters can be rather low for some priority hazardous substances like for the perfluorinated surfactant perfluoroctanesulfonic acid (PFOS) with 0.65 ng/L or for the herbicide trifluralin with 30 ng/L. Consequently, trifluralin has been banned in the European Union in 2008 based on its high toxicity to fish and other aquatic life. Due to its persistence, bioaccumulation potential and toxicity, PFOS came into focus of investigations of the U.S Environmental Protection Agency (EPA) which lead to a phaseout of PFOS production in 2000 by the producer. PFOS and PFOS-related chemicals are still produced in China. In 2009 PFOS was added to Annex B of the Stockholm Convention on Persistent Organic Pollutants, which lists compounds which should be restricted in production and use. These examples clearly demonstrate that the steady input of a large number of MPs can pose a risk for the ecosystem and for human health. Despite the ubiquitous occurrence of MPs in inland waters, their large number in the range of thousands of compounds at low concentrations and in ever changing composition makes it difficult to understand their effects on complex aquatic ecosystems (8). Endocrine disrupting compounds (EDCs) which can mimic or interfere with the natural hormone system are an exception. Adverse effects of EDCs have been shown in lake experiments (9) or exposed fish from rivers (10). With a set of five bioassays it has been demonstrated that WWTP effluents contain MPs of ecotoxicological potential (11). With a classic mixture toxicity concept ecotoxicological risks of pharmaceutical mixtures from WWTP effluents have been modeled (12). The data clearly show potential risks from pharmaceutical mixtures, even though the contribution of a single compound is a factor of 1000 lower. However, risk assessment is still hampered by the lack of data on chronic and in vivo fish toxicity of most pharmaceuticals. Advanced Wastewater Treatment The increasing awareness of potential risks of MPs for aquatic ecosystems and human health fosters the scientific and public discussion and political action for further reduction of the input of MPs to the aquatic environment. Measures to 5

Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


reduce the input of MPs into surface water comprise efficient avoiding of sources (limiting or preventing applications; replacement of chemicals by less harmfull ones; appropriate waste disposal) and end-of-pipe technologies to remove harmfull compounds (further processes in wastewater treatment, drinking water treatment). The processes in WWTP are designed to remove the bulk of organic load measured as BOD (biochemical oxygen demand) and nutrients like nitrogen and phosphorus. Generally the removal of MPs in WWTP is not sufficient for compounds which are not very well biodegraded in the activated sludge process or which are sorbed to sludge. In 2014 the Swiss government decided to implement further technical measures on larger municipal WWTPs to reduce the load of MPs and toxicity in WWTP effluents (adapted Water Protection Act). A comprehensive discussion on various aspects including the scientific background, requirements, technical approaches, expected benefits and effects is presented in a feature article (13). Also in Germany the Federal Environment Agency (Umweltbundesamt) considers a further treatment step in WWTP as one component to reduce pollutant burden of surface waters and published recommendations on the implementation of further MP removal in WWTP (14). First pilot and full-scale studies are already projected or realized for WWTPs discharging to sensitive receiving waters like drinking water reservoirs (e.g. for the city of Duelmen). Also on an European level discussions are aiming at the mandatory implementation of further MP removal in WWTPs. As a frist step this requires a careful selection of plants that allow the most cost-efficient reduction of loads and toxicities of MPs. A pragmatic approach considers the anticipated reduction of MP loads and the dilution capacity of receiving waters. This resulted for Swiss plants in the necessity of upgrading WWTPs with PE > 80,000, plants with PE > 8,000 with discharges of more than 10 % of the dry weather flow of the receiving water and plants with PE > 24,000 PE discharging into sensitive waters (13). In Germany there is also a large potential for pollutant reduction at large WWTPs. From a total of 9,600 municipal WWTPs about 50 % of the total PE capacity is treated in only 230 large plants which treat wastewater with more than 100,000 PE each (14). Ozonation in Wastewater Treatment Among the processes available to upgrade conventional wastewater treatment plants, ozonation is commonly considered. Indeed, ozone is known for more than a decade for its ability to react with trace organic contaminants and degrade them (15). In practice, the installation of an ozone reactor is often associated with the installation of a biofilter, which does not require a large modification of the existing treatment plant and does not have a footprint. Therefore, implementing ozonation in existing infrastructure should not induce major engineering difficulty, even for the largest plants treating effluents from major cities where the land available is often limited. Ozone, already frenquently employed in drinking water production, is known to degrade a large number of wastewater contaminants previsouly mentioned. In fact, numerous publications are available reporting the degradation of MPs by ozone. However, while the efficiency of ozonation is already well established 6



and the apparent removal of the contaminants is well characterized with rather fast kinetics, the main drawback of ozonation is the formation of transformation products. On the one hand, for the most common wastewater contaminants like the beta blocker metoprolol, several studies have already characterized over a dozen transformation products resulting from N-dealkylation, single or multiple hydroxylation, and ring opening (2, 16–18). On the other hand, for the remaining contaminants, little is known regarding their transformation products. Nevertheless, a recent review (19) detailing the general reaction mechanisms of ozone with different types of compound containing either amine groups, sulfur or an aromatic ring, allows anticipating the formation of certain transformation products. For instance, according to this review, it can be expected that ozonation of compounds including a tertiary amine would lead to the formation of the associated N-oxide through formal addition of oxygen on the nitrogen. Indeed, this was confirmed with several compounds including the antidepressant citalopram (20), the painkiller tramadol (21) and the antibiotic clarithromycin (22). Yet, in a real wastewater effluent, where most of the components remain unknown, assessing the formation of ozonation transformation products, remains a major concern and challenge. A large number of MPs being degraded during ozonation, the main concern regarding the associated transformation products mostly arise from their potential and unknown toxicity. For instance, when a MP is degraded by ozone through hydroxylation or N-oxidation, its transformation products have a similar structure and might still be biologically active. In fact, while the formation of clarithromycin N-oxide was shown to inactivate the antibiotic activity of clarithromycin, N-oxides of the antidepressant amitriptyline and the painkiller tramadol were shown to be biologically active compounds (23–25). Therefore, ozonation transformation products should be structurally and toxicologically characterized. Non-Target Approach The characterization of transformation products from ozonation or other oxidative treatment is often performed on single compounds from benchscale experiments. However, the same approach can also be applied directly on a full scale wastewater treatment plant, comparing two sets of samples taken before and after ozonation. Screening for precursors and their transformation products usually involves a first analysis of the samples with liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS), scanning compounds with a mass to charge ratio (m/z) in a specific range. Subsequent data processing relies on the deconvolution of the chromatograms using the high mass resolution to distinguish compounds and to propose tentative identifications, either through a suspect list or the assignment of probable molecular formulas. Additional statistical tools allow comparing the abundances of the compounds before and after treatment in order to distinguish precursors from transformation products. Subsequent data filtration based on the Kendrick mass, the mass defect or the retention time also contribute to the identification of homologue series of compounds. Upon application of relevant filters and restriction of the original 7



dataset to a limited number of significant precursors and their potentially related transformation products, more accurate identification could be attempted through the acquisition of targeted MS/MS spectra. The tentative identity of precursors can be confirmed by comparison of their MS/MS spectra with a spectra library or an analytical standard. However, despite ongoing constant efforts from scientists and vendors to build MS/MS spectra libraries, the availability of MS/MS information and analytical standards still represent a strong limitation that only allows the unambiguous identification of common contaminants. This limitation is even more pronounced for the transformation products, which mostly remain tentatively identified via diagnostic evidence by examining the consistency of their MS/MS spectra with the MS/MS spectra of their previously identified precursors. Finally, in the absence of standard and library spectra, the identity of a chemical and its predicted structure could also be further sustained by comparing the acquired MS/MS spectra to in-silico generated fragmentation patterns. This chapter aims at describing, discussing and applying accurate mass screeening and data evaluation approaches to assess the fate of trace organic contaminants during wastewater ozonation. More particularly, this chapter will describe the data processing workflow, from the general overview of changes in the water composition during ozonation to the examination of N-oxides, a specific subset of ozonation transformation products which might remain biologically active and therefore represent a major concern.

Methods Wastewater Samples The samples analyzed in this study were collected from a large wastewater treatment plant located in the Berlin area, Germany. With a dry weather capacity above 40,000 m3/d, the wastewater influent initially went through bar screens, successively followed by mechanical primary treatment and biological secondary treatment. A part of the secondary wastewater effluent was then further treated using a pilot including an ozonation tank coupled to a dual biofilter. The ozone reactor delivered an ozone dose of 6.4 mg/L therefore achieving a ratio O3/DOC of 0.5 with a 15 min contact time. The dual biofilter comprised 1.2 m of anthracite and 0.6 m sand, allowing a retention time in the range of 15 to 20 min. In order to assess the impact of ozonation on the secondary wastewater composition, 50 mL samples were collected immediately before and after the ozone reactor. In addition, another 50 mL sample of biofilter effluent was collected in order to assess the ability of biofiltration to further remove ozonation transformation products. Sample Analysis by LC-QTOF After collection, wastewater samples were brought to the laboratory and kept at 4°C in darkness. Samples were centrifuged at 6,000 rpm for 10 min and, without any kind of preconcentration, were analyzed in triplicate by liquid chromatography coupled to time of flight mass spectrometry. 8



Liquid chromatography was performed with an Agilent 1260 infinity device including an autosampler, a binary pump, a solvent degaser and column compartment. A sample volume of 100 µL was injected on a reversed phase C18 column (Agilent Poroshell 120) with the following characteristics: 2.1 mm internal diameter, 100 mm length and 2.7 µm particle size. While the column was thermostated at 40°C, the analytes were eluted with 0.4 mL/min of a gradiant of water and acetonitrile, both acidified with 0.1% formic acid. Initially, the fraction of acetonitrile was maintained at 5% for 1 min and linearly increased to 90% over the following 14 min. The fraction was then immediately increased and held at 100% for 3 min. Finally, the fraction of acetonitrile was set at 5% for 7 min in order to allow the column to equilibrate before the next sample analysis. After elution, the analytes were detected by high resolution mass spectrometry, using an Agilent 6550 QTOF with electrospray ionization. Detection was performed in positive mode with nitrogen as sheath gas and drying gas, a nebulizer pressure of 35 psi and a capillary voltage of 3 kV. Initially, data acquisition was performed at a rate of 3 spectra/sec, monitoring compounds with a mass to charge ratio (m/z) between 50 and 1,000. Then, for further identification of some selected compounds of interest, data acquisition consisted of tandem mass spectrometry. The precursor ion [M+H]+ was selected through the quadrupole, with an isolation width of 0.65 amu. Collision induced dissociation of the precursor ion was performed in the collision cell, using a stream of nitrogen as collision gas and a collision energy successively set at 10 V, 20 V and 40 V. Data Evaluation The large amount of data obtained from QTOF analysis was processed through a multiple step approach, allowing initially a general overview of treatment processes on water composition before focusing more specifically on a subgroup of compounds and their subsequent identification. The first step of data processing consisted in the deconvolution of the total ion chromatograms with the “molecular feature extraction” (MFE) algorithm in order to obtain a list of all compounds occurring in each individual sample. At this stage all compounds remain unidentified and are only defined by their accurate mass and retention time, both with a minimum of 4 digits. A second step of the data processing was the alignment of the different compounds in order to pair compounds with the same mass and retention time across the sample and consider them as a single component. While samples where analyzed in triplicate, compounds detected only once were then discarded. The remaining compounds where then used for a recursive analysis. This third step of data processing aimed at searching again the raw data specifically for these remaining compounds (mass and retention time) with the “find by formula” algorithm in order to discard potential false positive from the initial MFE. The fourth step in data processing allowed retaining only the compounds statistically relevant, by discarding all those that were not detected in triplicates and that were not significantly more abundant than in the blank. After such initial cleaning of the dataset, a first overview and comparison of the different samples was obtained through a principal component analysis and a Venn diagram. 9



From the general overview comparing the different samples, focusing on a specific group of compounds implies the application of further statistical analysis and filtration steps. For instance, performing an ANOVA with a p-value < 0.05 allows isolating compounds which abundance increased or decreased significantly during ozonation or biofiltration. These compounds could further be grouped according to their general behavior through a hierarchical clustering analysis. After grouping compounds with decreasing abundance (precursors) and increasing abundance (trans-formation products) after ozonation, these compounds were further filtered according to their Kendrick mass (KM) and Kendrick mass defect (KMD) in order to isolate those differering by one or more atoms of oxygen. Finally, in order to isolate further those compounds where the oxygen was fixed on a tertiary amine during ozonation, a last data filtration based on retention time was applied. Indeed, N-oxides are known to have in most cases a longer retention time than the precursor compound. The compounds isolated through the previous filters were initially identified through a suspect screening approach, matching their measured m/z with the theoretical m/z values of compounds enclosing a tertiary amine group gathered from the STOFF-IDENT database (26). For those with a positive match, further confirmation of their identity was performed through the acquisition of MS/MS spectra and comparison with library spectra and/or an analytical standard.

Results and Discussion A pilot-plant trial was conducted to demonstrate the efficiency of an ozonation process followed by an active filtration step to reduce the pollutant burden of a WWTP effluent. Samples after wastewater treatment (secondary WWTP effluent), after ozonation and after active filtration were measured by LC-ESI-high-resolution mass spectrometry using a generic measurement method and a non-target screening (NTS) approach. The NTS workflow generally includes the steps of accurate mass measurement and subsequent steps of data alignment, data reduction, and finally identification based on information which is inherent in accurate mass data, further MS-MS fragmentation and/or comparison to library spectra or in-silico generated fragmentation patterns. The advantage of a NTS approach is a more comprehensive overview on changes of the chemical composition of the sample by the applied treatment steps. Spectra libraries even allow identifying a small fraction of the unknown compounds and studying there removal while these would have remained undetected with a usual targeted approach. The challenges are the steps of sufficient data reduction without losing too much information and finally the identification of unkown transformation products for which there is typically a large data gap in literature and MS libraries.

10 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


NTS Screening Approach Scan data from LC-QTOF-MS measurements of triplicate samples before and after the two treatment steps ozonation and biofiltration were subjected to molecular feature extraction (MFE), which is a peak picking algorithm and includes also binning of mass data of naturally occurring isotopes and further ion adducts (e.g. [M+Na]+, [M+NH4]+). Since the threshold values during MFE are set to rather low values with the idea not to miss too much information, more than a total of 24,700 features have been found in all measurements (Figure 1). These data still contain only features which are characterized by accurate mass and retention time and which are therefore artificially separated due to inaccuracies of mass and/or retention times. Generally, several features have to be merged and considered as one compound across all samples. Therefore data alignment for all features in all samples and all replicates is a rather helpful process to reduce the number of features and to get comparable data sets for all samples and all replicates. The plots in Figure 1 along with Table 1 demonstrate the effect of the size of retention time window and mass window on the number of individual features found after the data alignment. As it is very unlikely that the same compound could be measured twice with the exact same mass (5 digits provided by MFE) or the exact same retention time (8 digits provided by MFE) across all samples, performing the alignment without any tolerance on the mass or any tolerance on the retention time does not lead to any reduction in the number of features. In fact, without a minimum window on the mass and the retention time none of the feature can be aligned. A retention time window below 0.1 min and a mass window below 2 ppm generates artificially high numbers of features. A rather consistent number of features is obtained at a RT window of 0.2 min and a mass window of 3 ppm, which cannot be considerably further reduced for larger RT and mass windows. This makes sense since it is also the typical measurement accuracy of RT (0.1 min) and accurate mass (between 1 and 5 ppm). However, for low masses for example at 100 Da, a 3 ppm mass window translates to a 0.3 mDa mass accuracy, which is still below the measurement accuracy. In this case the ppm window should be increased up to 10 or 15 ppm at the discretion of the user according to what is considered more critical: having two features aligned when they should be considered individually (excessive alignment), or having two features considered individually when they should be aligned (insufficient alignment). In general, a good practice consists in determining the mass and retention time window based on quality control samples spiked with a set of analytical standards covering the mass range and the retention time range of interest for the analysis. After the alignment, from the initial 24,729 features, those with similar retention time and mass across all samples are merged together and a total of 5,810 features remain for further consideration. Among these remaining features, some were detected only once (Figure 2). However, knowing that samples were analyzed in triplicates, all features detected only once most likely belong to the background and are not statistically relevant. Therefore, they can be discarded while the remaining features will be selected to perform a recursive analysis. 11 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

Table 1. Number of Compounds Considered after Alignment with Different Levels of Tolerance

12


Δm

RT window (min)

(ppm)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

24,729

24,729

24,729

24,729

24,729

24,729

24,729

24,729

24,729

24,729

24,729

1

24,729

8,584

8,456

8,388

8,351

8,301

8,257

8,241

8,229

8,205

8,178

2

24,729

6,682

6,613

6,584

6,562

6,536

6,498

6,490

6,476

6,467

6,456

3

24,729

6,222

6,163

6,134

6,113

6,092

6,060

6,052

6,039

6,031

6,021

4

24,729

6,060

6,005

5,969

5,943

5,926

5,900

5,891

5,879

5,871

5,860

5

24,729

5,982

5,923

5,891

5,866

5,842

5,815

5,808

5,796

5,786

5,776

6

24,729

5,942

5,883

5,849

5,825

5,799

5,773

5,765

5,751

5,741

5,732

7

24,729

5,928

5,866

5,836

5,812

5,783

5,756

5,748

5,735

5,725

5,717

8

24,729

5,913

5,850

5,821

5,797

5,767

5,741

5,731

5,718

5,706

5,697

9

24,729

5,902

5,838

5,809

5,785

5,757

5,730

5,720

5,706

5,695

5,685

10

24,729

5,890

5,827

5,798

5,774

5,746

5,716

5,707

5,692

5,681

5,673

11

24,729

5,885

5,820

5,791

5,767

5,739

5,709

5,700

5,684

5,673

5,663

12

24,729

5,884

5,819

5,788

5,764

5,737

5,705

5,696

5,679

5,669

5,659

13

24,729

5,880

5,815

5,784

5,760

5,733

5,701

5,692

5,675

5,664

5,655

14

24,729

5,877

5,812

5,780

5,756

5,729

5,696

5,686

5,670

5,659

5,649

15

24,729

5,875

5,810

5,778

5,754

5,727

5,695

5,684

5,669

5,658

5,648

16

24,729

5,874

5,808

5,776

5,752

5,725

5,692

5,681

5,666

5,655

5,645


RT window (min)

(ppm)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

17

24,729

5,873

5,807

5,774

5,750

5,723

5,691

5,678

5,663

5,652

5,641

18

24,729

5,873

5,807

5,774

5,750

5,723

5,691

5,678

5,663

5,652

5,641

19

24,729

5,872

5,805

5,772

5,747

5,718

5,686

5,673

5,658

5,647

5,635

20

24,729

5,872

5,804

5,769

5,744

5,715

5,683

5,670

5,655

5,644

5,632

13


Δm



Figure 1. The effect of the parameters retention time window and mass window on the number of resulting features during data alignment.

The recursive analysis is the second step to generate consistent data sets across all samples and replicates by searching specifically for the selected features in all samples with the more sensitive algorithm “find by formula” (extracting ion chromatograms based on accurate mass and RT). The recursive analysis detects features of low abundance in samples, which would have been overlooked by the less sensitive MFE algorithm. Additionally, the recursive analysis helps discarding further artifacts due to the less specific MFE algorithm. Therefore, discarding the features detected only once with MFE and using better algorithm, the recursion decreases the number of features to consider from 5,810 to 4,022. The results in Figure 2 demonstrate that the number of features found in all three samples and the control could be increased considerably and that the data sets of the three replicates are more consistent. On the one hand, the number of features detected three times significantly decreased after recursion. This can be attributed to the better algorithm used for the recursive analysis. Indeed, the MFE algorithm is more prone to artifacts that could be randomly detected on three occurrences across all samples (for instance in a replicate before ozonation, in a replicate after ozonation and in a replicate after biofiltration) while the more specific algorithm used for the recursive analysis tends to exclude them. On the other hand, the number of features detected on twelve occurrences (triplicates before ozonation, triplicates after ozonation, triplicates after biofiltration and blank triplicates) increased after recursion. This is also a consequence of the better algorithm used for the recursive analysis which allows using a lower threshold and therefore leads to the detection of more significant compounds across all samples. 14



Figure 2. Effect of recursive data analysis on the number of compounds found in all three replicates of the three samples and a control.

The detailed results of the feature extraction process are shown in Figure 3 and Table 2. The MFE also includes chromatographic parameters like peak width and signal to noise ratios, which considerably reduces noise during the MFE process: The result is clearly visible in the extracted ion chromatograms. Mass vs. RT plots allow a first visual control of the extracted features and reveal that there is a cluster of rather polar compounds at a mass range between about m/z 100 and m/z 350 which are not or only rarely retained on the LC column. A large fraction of compounds appears in the mass range between m/z 150 and m/z 500 in a RT window between 5 min and 13 min. Further regular patterns in a mass range between m/z 600 and m/z 1000 are characterized by a series of constant mass differences which points to members of surfactants with a different number of a repeating unit in the molecule (e.g. glycol ether sulfate surfactants with different numbers of (-CH2CH2O-) units recently detected in wastewaters) (27). Table 2 demonstrates first that ozone treatment could clearly reduce the number of compounds from about 3,200 to 2,600 and that the biofiltration step finally increased the number again to about 3,000. 15


Table 2. Number of features found in individual samples (replicates A, B, C) with molecular feature extraction (MFE) and recursive analysis. MFE


Sample

A

B

C

Recursion After Align.

A

B

C

After Align.

Untreated wastewater

3,016 2,922 3,111

3,997

2,875 2,888 2,885

3,271

Ozonated wastewater

1,821 1,883 1,826

2,469

2,306 2,308 2,282

2,679

Biofiltered wastewater

2,349 2,265 2,279

3,094

2,624 2,608 2,637

3,031

Tap water

1,068 1,160 1,029

1,469

1,318 1,341 1,298

1,484

Overall

-

-

-

5,810

-

-

Features in 100% replicates of at least one sample and three times more abundant than in the tap water

-

4,022 2,312

Figure 3. Workflow of data extraction from high-resolution MS scans to single compounds (molecular features) characterized by accurate mass and retention time including data alignment and recursive analysis. 16 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


In order to obtain a refined overview of the significant features in each sample, the data set can be further filtered in order to retain only those detected in all replicates of at least one wastewater sample. From the overall 4,022 features aligned after recursion, the data set is then reduced to 3,213. From these features only those significantly more aundant (3 times) than in the blank (tap water) should be considered. Therefore, after applying this fold change the final data set can be further reduced to 2,312 relevant features across all wastewater samples. These results reveal already that the NTS approach gives a rather comprehensive overview on changes of the chemical composition during the treatment processes. However, it has to be kept in mind that also with the NTS approach only a limited analytical window can be used due to restrictions of the chromatographic system and of the electrospray ionization process (in this case only the positive ionization mode has been used). The detected features cannot be assigned to pollutants only, since also components of natural organic matter and biological matter can contribute to the whole compound pattern. Pollutants and their behavior can be finally assessed only after identification.

Fate of Compounds during Ozonation and Biofiltration Further exploration of the complex data sets regarding the effect of treatments on wastewater composition has been performed by principle component analysis and a Venn diagram. In Figure 4, the PCA scores plot for two principal components clearly demonstrates how the three replicates for each type of sample group together, indicating the good reproducibility of the measurement. Moreover, the PCA plot also indicates that the sample composition is clearly different for nontreated, ozonated and biofiltrated water. From the PCA, the first two components explain 62% of the variation, but this can be increased to 72% when considering a third component. The Venn diagram shows all possible logical relations between a limited set of data in a rather concise way. Starting from the sample before treatment, it is evident that 506 out of 1796 compounds are removed during ozonation while 963 compounds appear persistent during both treatment steps (Figure 4). However, the Venn diagram does not consider any variation in concentration so, among the 963 compounds detected in all samples, it is possible that some might still be partially attenuated during water treatment. Nevertheless, the fraction of the whole compound pattern removed by ozone remains apparently much smaller than the anticipated removal of about 80 % for MPs and should be investigated further. At the same time, ozonation produced 277 new compounds from which only 35 are removed in the subsequent filtration step and 242 remained persistent. Since the persistent compounds are discharged to the receiving water, this fraction will be also in the focus of further analysis. Interestingly, also during biofiltration, 239 compounds are formed or added to the samples (e.g. from microbial excretions) and 232 of the compounds which were already present in the untreated sample and then removed by ozone are reformed. For this fraction, enzymatic processes which are able to reverse minor changes from ozone oxidation could be responsible. 17



Figure 4. Data analysis by principal component analysis (PCA) and Venn diagram to explore the effect of treatment on the chemical composition of the samples. The overall information presented in the Venn diagram could be supplemented by more statistical analysis in order to consider the changes in concentration of each compound. Performing an ANOVA with a p-value < 0.05 will allow discriminating compounds without statistically significant change in concentration across all samples (persistent compounds) from those that are at least partially attenuated or formed. After performing such ANOVA, from the 2,312 relevant compounds from wastewater samples presented in the Venn diagram, 620 did not show any statistically significant difference in abundance across all samples and could really be considered as persistent. However, the remaining 1,692 compounds which are significantly altered by ozone and/or biofiltration can be further submitted to a hierarchical cluster analysis and illustrated in a heat map (Figure 5). Each vertical line represents one compound and the colour code shows signal intensity categories. In the heatmap, six clusters of compounds can be easily distinguished; each corresponding to a group of compounds with a specific behaviour throughout ozonation and biofiltration. For instance, cluster 1 gathers compounds that are formed during ozonation and not removed by biofiltration while cluster 2 gathers compounds that are released into the water after biofiltration. However, each cluster could be further examined and divided into subgroups based on colour shade. Indeed, while cluster 3 shows compounds removed by ozonation, compounds partially removed can also be distinguished (left part in cluster 3). Similarly, cluster 4 shows the compounds transformed by ozonation and reformed by biofiltration, but partial reformation of compounds in the biofiltration step can be discriminated (left part in cluster 4). For further analysis and compound identification features lists (accurate mass and RT) can be retrieved for the specific fractions and clusters of the data analysis and visualization techniques discussed above. For instance, each compound (vertical line) from the heat map can be selected individually in order to check its mass and retention time along with its abundance across the different wastewater samples. Then, for each cluster it is possible to select a compound as indicator and examine the respective extracted ion chromatograms (EICs) in order to have a better grasp on the overall behaviour of all the remaining compounds of this cluster during wastewater treatment. For instance, for each one of the six cluters previously defined by the cluster analysis, Figure 6 shows extracted ion 18



chromatograms (EICs) for compounds selected as examples. The chromatograms have all the same scale for the intensity (y-axis). Therefore information on the complete (clusters 3 and 4) or partial removal (cluster 5 and 6) after ozonation and biofiltration (cluster 6) can be directly obtained from Figure 6.

Figure 5. Heatmap of the results of a hierarchical cluster analysis of all features by treatment.

Figure 6. Extracted ion chromatograms for compounds from the six clusters defined by hierarchical cluster analysis. 19 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Figure 7. Kendrick mass plot for oxygen homologues of possible precursors and TPs of ozonation. (A: plot of all precursors and transformation products according to ANOVA; B: plot of precursors and oxides after data filtration based on KM and KMD; C: plot of precursors and potential N-oxides after further filtration based on retention time)



Formation of Transformation Products during Ozonation The products formed during ozonation can be further investigated when applying other filters to the data set, such as a Kendrick mass analysis. This approach was initially developed to simplify data evaluation and interpretation of high-resolution mass spectral data and to identify alkyl homologue series in organic compounds (28). In this case we first focused on ozonation TPs formed by a formal addition of a defined number of oxygen atoms (e.g. hydroxylation), which is the most common degradation pathway during ozonation. For each precursor (significant decrease in concentration after ozonation) and each transformation product (significant increase in concentration after ozonation) isolated from the previous ANOVA, the oxygen-based Kendrick mass (KM) and Kendrick mass defect (KMD) were calculated. Each precursor and transformation product can then be plotted according to its KM and KMD (Figure 7, top). The data set was then further refined to visualize only precursors and their potential oxides. This could be achieved when considering only the pairs precursor/TP with a KM shifted by a multiple of 16 but sharing the same KMD with a 2 mDa tolerance window. This restricted the data set to 99 precursors for 60 oxides (Figure 7, center). Finally, with the objective of focusing more specifically on the formation of N-oxides which might remain biologically active, these last data set was further reduced and limiting the number of formal oxygen addition to one while applying an additional filtration based on retention time. Indeed, despite some minor exceptions (29, 30), N-oxides are tipically characterized by their longer retention times compared to that of the parent compound (31–34). After considering these last criteria, the data set could finally be reduced to 27 precursors for 27 potential N-oxides. Identification of N-Oxides Formed during Ozonation The isolation of a specific group of compounds of interest such as the N-oxides is not nearly the end of the data processing when performing NTS. Indeed, once a group of compounds of interest has been isolated, one major task is their individual identification. Matching the list of accurate masses of interest with a list a masses corresponding to known compounds suspected to occur in the samples analyzed (suspect screening) is usually the starting point of identification. However, this implies the availability of a large but relevant database (35). On the one hand, a restricted database would result in the lack or a limited number of positive matches. On the other hand, an excessively large database would result in a large number of false positive. In the current study, the masses of selected precursors were matched with the STOFF-IDENT database (26) containing a list of expected wastewater contaminants, among which are over 1,300 with a tertiary amine moiety likely to form N-oxides during ozonation. This initial process allowed identifying the neuroactive substances amisulpride, citalopram, sulpiride, tiapride, and venlafaxine; the painkillers lidocaine and tramadol; the antihistamine diphenhydramine; and the antibiotic clarithromycin. However, a matching accurate mass within 5 ppm is necessary but not enough for a truly reliable compound identification. Therefore, the identities of the 21 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


previously mentioned compounds were further confirmed through the acquisition of targeted MS/MS spectra through a second analysis on the QTOF-MS, followed by comparison with a MS/MS spectral library and/or authentic standards when possible. Following the identification of the selected precursor, the identity of the corresponding N-oxide was also confirmed through the same approach of acquisition and comparison of MS/MS spectra with a standard or spectral library (Figure 8).

Figure 8. Occurrence and identification of clarithromycin-N-oxide during ozonation. The lack of analytical standards and MS/MS spectra for transformation products, as previously mentioned, also limited the unambiguous identification of N-oxides. Therefore, the suspected N-oxides for which neither the standard nor the MS/MS spectra were available were tentatively identified by diagnostic evidence. This commonly used approach mostly consists of the detailed study of the fragmentation of the compounds. For instance, when a tentative structure is proposed for the N-oxide, it is possible to examine the product ions on the MS/MS spectrum in order to match their respective accurate mass with a fragment of the proposed molecule. Moreover, the identity of the tentatively identified N-oxide can be further sustained by comparing its MS/MS spectra with the MS/MS spectra obtained for the precursor in order to confirm that the fragmentation pattern of the molecule is consistent with the formal addition of oxygen to a tertiary amine moiety. The detailed examination of MS/MS spectra and the consideration that precursors and transformation products are likely to have common product ions indicates that another approach can be used for tracking and identifying transformation products. This approach called MSE or All-Ions fragmentation (AIF) comprises the acquisition of accurate masses across a defined mass range when applying alternatively different collision energies but without selecting any precursor ion. The low energy level (no collision induced dissociation) provides 22



information on protonated or deprotonated molecules while the high energy level (with collision induced dissociation) provides information on the fragments. Therefore a transformation product of an original contaminant can be detected by common fragments on the high energy level. Then, the mass of the transformation product can be retrieved by co-eluting peaks of the extracted ion chromatograms on the low energy level. Overall, AIF enables to get information on both the molecule mass and its fragments in one analytical run. While this approach was not evaluated in the current study, several peer-reviewed articles describe the application of MSE and AIF for non-target screening and the identification of transformation products (36–38). The chromatograms of the N-oxide presented in Figure 8 show the longer retention time of the transformation product compared to the parent compound, but they also illustrate the stability of clarithromycin N-oxide through the biofiltration process. The same behaviour was observed for all the N-oxides identified. Moreover, the abundance of clarithromycin slightly increases after biofiltration which tends to indicate the potential for the parent compound to reform, for instance due to the reduction of the N-oxide by redox conditions in the biofilter or by enzymatic reaction (39–42) from microorganisms in the biofilter.

Conclusions The analysis of samples through a NTS approach requires significantly more data processing compared to a more usual targeted analysis, but it provides a more complete picture of the effect of processes for a much broader range of compounds. The application of such approach in environmental science could be the more comprehensive optimization of water treatment processes in different aspects. For instance, to have a better assessment of compound removal and of by-product formation. In addition, data processing also allows moving from the broad picture to specific investigation of a limited subset of compounds of interest through the application of successive filters, as shown in this study with the assessment of N-oxide formation during ozonation. Finally, data from accurate mass scan also allow a retrospective analysis, if other contaminants or transformation products may be of interest at a later time. In this context, a new trend consists in performing NTS through the MSE or AIF approach. When such data are available, accurate mass matches of compounds of interest in a retrospective analysis can be sustained by the presence of relevant product ions eluting at the same RT without further MS/MS measurements.

Acknowledgments The authors thank the staff of Berlin Wasserbetriebe and Kompetenz Zentrum Wasser Berlin, Germany, for providing the samples used in this study. 23 Drewes and Letzel; Assessing Transformation Products of Chemicals by Non-Target and Suspect Screening Strategies and ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

References 1.

2.

3.


4.

5.

6.

7.

8.

9.

10. 11.

12. 13.

14.

15.

European Chemicals Agency (ECHA). EC inventory of ECHA; http:// echa.europa.eu/information-on-chemicals/ec-inventory (accessed 05/10/ 2016). Benner, J.; Ternes, T. A. Ozonation of metoprolol: Elucidation of oxidation pathways and major oxidation products. Environ. Sci. Technol. 2009, 43, 5472–5480. Hughes, S. R.; Kay, P.; Brown, L. E. Global synthesis and critical evaluation of pharmaceutical data sets collected from river systems. Environ. Sci. Technol. 2013, 47, 661–677. Merel, S.; Anumol, T.; Park, M.; Snyder, S. A. Application of surrogates, indicators, and high-resolution mass spectrometry to evaluate the efficacy of UV processes for attenuation of emerging contaminants in water. J. Hazard. Mater. 2015, 282, 75–85. Makowska, N.; Koczura, R.; Mokracka, J. Class 1 integrase, sulfonamide and tetracycline resistance genes in wastewater treatment plant and surface water. Chemosphere 2016, 144, 1665–1673. Manaia, C. M.; Macedo, G.; Fatta-Kassinos, D.; Nunes, O. C. Antibiotic resistance in urban aquatic environments: Can it be controlled? Appl. Microbiol. Biotechnol. 2016, 100, 1543–1557. Merel, S.; Nikiforov, A. I.; Snyder, S. A. Potential analytical interferences and seasonal variability in diethyltoluamide environmental monitoring programs. Chemosphere 2015, 127, 238–245. Eggen, R. I. L.; Behra, R.; Burkhardt-Holm, P.; Escher, B. I.; Schweigert, N. Peer reviewed: Challenges in ecotoxicology. Environ. Sci. Technol. 2004, 38, 58A–64A. Kidd, K. A.; Blanchfield, P. J.; Mills, K. H.; Palace, V. P.; Evans, R. E.; Lazorchak, J. M.; Flick, R. W. Collapse of a fish population after exposure to a synthetic estrogen. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 8897–8901. Jobling, S.; Nolan, M.; Tyler, C. R.; Brighty, G.; Sumpter, J. P. Widespread sexual disruption in wild fish. Environ. Sci. Technol. 1998, 32, 2498–2506. Escher, B. I.; Bramaz, N.; Quayle, P.; Rutishauser, S.; Vermeirssen, E. L. M. Monitoring of the ecotoxicological hazard potential by polar organic micropollutants in sewage treatment plants and surface waters using a mode-of-action based test battery. J. Environ. Monit. 2008, 10, 622–631. Backhaus, T.; Karlsson, M. Screening level mixture risk assessment of pharmaceuticals in stp effluents. Water Res. 2014, 49, 157–165. Eggen, R. I. L.; Hollender, J.; Joss, A.; Schaerer, M.; Stamm, C. Reducing the discharge of micropollutants in the aquatic environment: The benefits of upgrading wastewater treatment plants. Environ. Sci. Technol. 2014, 48, 7693–7689. UBA. Organische mikroverunreinigungen in gewässern vierte reinigungsstufe für weniger einträge; Umweltbundesamt: Dessau-Roßlau, 2015; p 26. Zwiener, C.; Frimmel, F. H. Oxidative treatment of pharmaceuticals in water. Water Res. 2000, 34, 1881–1885. 24



16. Faber, H.; Lutze, H.; Lareo, P. L.; Frensemeier, L.; Vogel, M.; Schmidt, T. C.; Karst, U. Liquid chromatography/mass spectrometry to study oxidative degradation of environmentally relevant pharmaceuticals by electrochemistry and ozonation. J. Chromatogr. A 2014, 1343, 152–159. 17. Sein, M. M.; Schmidt, T. C.; Golloch, A.; von Sonntag, C. Oxidation of some typical wastewater contaminants (tributyltin, clarithromycin, metoprolol and diclofenac) by ozone. Water Sci. Technol. 2009, 59, 1479–1485. 18. Tay, K. S.; Rahman, N. A.; Abas, M. R. B. Ozonation of metoprolol in aqueous solution: Ozonation by-products and mechanisms of degradation. Environ. Sci. Pollut. Res. 2012, 20, 3115–3121. 19. Hübner, U.; von Gunten, U.; Jekel, M. Evaluation of the persistence of transformation products from ozonation of trace organic compounds – a critical review. Water Res. 2015, 68, 150–170. 20. Hörsing, M.; Kosjek, T.; Andersen, H. R.; Heath, E.; Ledin, A. Fate of citalopram during water treatment with O3, ClO2, UV and fenton oxidation. Chemosphere 2012, 89, 129–135. 21. Zimmermann, S. G.; Schmukat, A.; Schulz, M.; Benner, J.; Gunten, U. v.; Ternes, T. A. Kinetic and mechanistic investigations of the oxidation of tramadol by ferrate and ozone. Environ. Sci. Technol. 2012, 46, 876–884. 22. Lange, F.; Cornelissen, S.; Kubac, D.; Sein, M. M.; von Sonntag, J.; Hannich, C. B.; Golloch, A.; Heipieper, H. J.; Möder, M.; von Sonntag, C. Degradation of macrolide antibiotics by ozone: A mechanistic case study with clarithromycin. Chemosphere 2006, 65, 17–23. 23. Dencker, S. J. Clinical trial with imipramine N-oxide and amitriptyline Noxide. Nordisk Psykiatrisk Tidsskrift 1971, 25, 463–470. 24. Godt, H. H.; Fredslund-Andersen, K.; Edlund, A. H. Amitriptyline N-oxide. A new antidepressant: A clinicall double-blind trial in comparison with amitriptyline. Nordisk Psykiatrisk Tidsskrift 1971, 25, 237–246. 25. Raffa, R. B.; Haslego, M. L.; Maryanoff, C. A.; Villani, F. J.; Codd, E. E.; Connelly, C. D.; Martinez, R. P.; Schupsky, J. J.; Buben, J. A.; Wu, W. N.; Takacs, A. N.; Mckown, L. A. Unexpected antinociceptive effect of the Noxide (rwj 38705) of tramadol hydrochloride. J. Pharmacol. Exp. Ther. 1996, 278, 1098–104. 26. Norman network, STOFF-IDENT database; http://www.normannetwork.com/?q=node/236 (accessed 07/25/2016). 27. Gago-Ferrero, P.; Schymanski, E. L.; Bletsou, A. A.; Aalizadeh, R.; Hollender, J.; Thomaidis, N. S. Extended suspect and non-target strategies to characterize emerging polar organic contaminants in raw wastewater with lc-hrms/ms. Environ. Sci. Technol. 2015, 49, 12333–12341. 28. Kendrick, E. A mass scale based on CH2 = 14.0000 for high resolution mass spectrometry of organic compounds. Anal. Chem. 1963, 35, 2146–2154. 29. Chen, H.; Chen, Y.; Du, P.; Han, F.; Wang, H.; Zhang, H. Sensitive and specific liquid chromatographic–tandem mass spectrometric assay for atropine and its eleven metabolites in rat urine. J. Pharm. Biomed. Anal. 2006, 40, 142–150. 30. Ramanathan, R.; Su, A. D.; Alvarez, N.; Blumenkrantz, N.; Chowdhury, S. K.; Alton, K.; Patrick, J. Liquid chromatography/mass spectrometry methods 25


31.


32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

for distinguishing N-oxides from hydroxylated compounds. Anal. Chem. 2000, 72, 1352–1359. Christopher, L. J.; Cui, D.; Li, W.; Barros, A.; Arora, V. K.; Zhang, H.; Wang, L.; Zhang, D.; Manning, J. A.; He, K.; Fletcher, A. M.; Ogan, M.; Lago, M.; Bonacorsi, S. J.; Humphreys, W. G.; Iyer, R. A. Biotransformation of [14c]dasatinib: In vitro studies in rat, monkey, and human and disposition after administration to rats and monkeys. Drug Metab. Dispos. 2008, 36, 1341–1356. Dumasia, M. C.; Teale, P. N-deethylation and N-oxidation of etamiphylline: Identification of etamiphylline-n-oxide in greyhound urine by high performance liquid chromatography-mass spectrometry. J. Pharm. Biomed. Anal. 2005, 36, 1085–1091. Sun, X.; Niu, L.; Li, X.; Lu, X.; Li, F. Characterization of metabolic profile of mosapride citrate in rat and identification of two new metabolites: Mosapride N-oxide and morpholine ring-opened mosapride by UPLC–ESI-MS/MS. J. Pharm. Biomed. Anal. 2009, 50, 27–34. Tomar, R. S.; Joseph, T. J.; Murthy, A. S. R.; Yadav, D. V.; Subbaiah, G.; Krishna Reddy, K. V. S. R. Identification and characterization of major degradation products of risperidone in bulk drug and pharmaceutical dosage forms. J. Pharm. Biomed. Anal. 2004, 36, 231–235. Zedda, M.; Zwiener, C. Is nontarget screening of emerging contaminants by LC-HRMS successful? A plea for compound libraries and computer tools. Anal. Bioanal. Chem. 2012, 403, 2493–2502. Hernández, F.; Bijlsma, L.; Sancho, J. V.; Díaz, R.; Ibáñez, M. Rapid wide-scope screening of drugs of abuse, prescription drugs with potential for abuse and their metabolites in influent and effluent urban wastewater by ultrahigh pressure liquid chromatography–quadrupole-time-of-flight-mass spectrometry. Anal. Chim. Acta 2011, 684, 96–106. Hernández, F.; Ibáñez, M.; Bade, R.; Bijlsma, L.; Sancho, J. V. Investigation of pharmaceuticals and illicit drugs in waters by liquid chromatography-high-resolution mass spectrometry. TrAC, Trends Anal. Chem. 2014, 63, 140–157. Hernández, F.; Ibáñez, M.; Botero-Coy, A.-M.; Bade, R.; Bustos-López, M. C.; Rincón, J.; Moncayo, A.; Bijlsma, L. Lc-qtof ms screening of more than 1,000 licit and illicit drugs and their metabolites in wastewater and surface waters from the area of bogotá, colombia. Anal. Bioanal. Chem. 2015, 407, 6405–6416. Chen, C.; Cheng, G.; Hao, H.; Dai, M.; Wang, X.; Huang, L.; Liu, Z.; Yuan, Z. Mechanism of porcine liver xanthine oxidoreductase mediated n-oxide reduction of cyadox as revealed by docking and mutagenesis studies. PLoS One 2013, 8, e73912. Iobbi-Nivol, C.; Pommier, J.; Simala-Grant, J.; Méjean, V.; Giordano, G. High substrate specificity and induction characteristics of trimethylamine-Noxide reductase of Escherichia coli. Biochim. Biophys. Acta, Protein Struct. Mol. Enzymol. 1996, 1294, 77–82. Kitamura, S.; Wada, Y.; Tatsumi, K. NAD (P) H-dependent reduction of nicotinamide n-oxide by an unique enzyme system consisting of liver 26



microsomal NADPH-cytochrome c reductase and cytosolic aldehyde oxidase. Biochem. Biophys. Res. Commun. 1984, 125, 1117–1122. 42. Zheng, M.; Jiang, J.; Wang, J.; Tang, X.; Ouyang, M.; Deng, Y. The mechanism of enzymatic and non-enzymatic N-oxide reductive metabolism of cyadox in pig liver. Xenobiotica 2011, 41, 964–971.


Accurate Mass Screening and Data Evaluation Approaches for

Recommend Documents