Thomaidis, Nikolaos S.

Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

11/9/20141Development of a LC-HRMS workflow for the target, suspect and non-target screening of contaminants of emerging concern in environmental water samplesNikolaos S. ThomaidisLaboratory of Analytical ChemistryDepartment of ChemistryUniversity of AthensPharmaceuticalsIllicit drugsPersonal care productsEndocrine disruptive compounds (EDCs)Flame retardantsFood additivesDisinfection by-products Pesticides +Metabolites & Transformation Products (TPs)Emerging pollutants (EPs)aquatic environment 11/9/20142Target screening• Known EPs• Analytical standards availableSuspect screening• List of possible EPs and their TPs (literature & prediction models)Non-target screening• Unknown compounds• post-acquisition data tools Identification ApproachesWorkflows11/9/20143IDENTIFICATION LEVELSSchymanski et al. Environmental Science and Technology (2014) 48(4):2097Location: WWTP of Athens, GreecePeriod: March 2014Samples:• 24-h composite flow-proportional samples ofinfluent wastewaters & effluent wastewatersover a week (7 consecutive days)• 2-h composite flow-proportional samples of influent wastewaters(Thursday & Saturday, 12 samples per day, from 02:00 to 00:00)Sampling11/9/20144Sample Preparation - Analysis• 200 mL filtered wastewater (pH adjusted to 6.5)• Isotopically labelled internal standards were added (100 ng/L)• Mixed SPE with 4 sorbents:(Strata X copolymer, Strata-X-AW, Strata-X-CW, IsoluteENV+)• Extraction: Neutral, Basic & Acidic Compounds• Evaporation/reconstitution to a final volume of 200 μLHPLC-HRMS-QTOF-MS/MSNon-target screening: AutoMS/MSTarget & Suspect Analysis:bbCIDI. Target Screening• more than 700 pesticides• more than 800 EPs & TPs…including information over:1500 compounds for positive ESI screening500 compounds for negative ESI target screening~200 common compoundsin-house database:11/9/20145I. Target ScreeningNorvenlafaxine10Lamotrigine13Caf feine5Terbuthylazine29A trazine-2-hydrox y8c arbendazim15sulfamethox zole62-A mi o-Benzothiazole18Atrazine25Prometry n32Metoprolol tratrate11Metolac hlor33A mikacin2Norephedrine3A trazine- desethyl17Isoproturon27Prometryn30Carbamazepine21Oxad xy l23B nzothiazole24Schrad n16Topi amate (NH4)0V alsartan28chloridazon4Benzotriaz ole (BTR)7Me-Benzotriaz ole19Metformin2DEET (Diethyltoluamide)26cotinine4tram dol9melamine1v e lafaxine22terbutryn1Nigericin342 4 6 8 10 12 14 Time [min]0.00.51.01.52.02.55x10Intens.Criteria• deltaRT ≤ 0.05 min• Accuracy: Error ≤ 5 ppm• Isotopic fit: ≤ 20 mSigma• MS/MS fragments, ion ratio• Ion Intensity > 500 (+ESI) / 200 (-ESI)• Area > 2000 (+ESI) / 800 (-ESI)83 % of analytes 0204060801000.025 0.05 0.25 0.5 1% AnalytesC (μg/L)positive ESInegative ESI5.811.625.041.316.301020304050% AnalytesI. Target ScreeningValidation ü Linearity in stds, spiked samples & matrix-matched samplesR2> 0.92- 0.9999ü Repeatability:   %RSD <20%ü% Recoveriesü LODs200 target compounds− 170 positive ESI− 50 negative ESIOver the whole range of database11/9/20146I. Target Screening4.0 ng/L (Primidone) –26.1 μg/L (Caffeine)0.5 mg/L (Metformin)Resultseffluent influent123 Compounds detected 17675 pharmaceuticals & drugs of abuse 10323 pesticides 396 PFCs 64 sweeteners 410 Disinfection by-products & PCP 195 Aminoacids 5Sat. 15/03/1424-h composite wastewaterSat. 15/03/142-h influent wastewater01020304050607080901002 4 6 8 10 12 14 16 18 20 22 24hoursamisulprideBenzoylecgoninetramadolLamotrigineI. Target Screening108.0439116.9706 127.1255142.1601147.0564156.0108167.1541 180.0099 194.0295 201.1462 211.0284+bbCID MS, 25.0eV, 5.82min #679, Background Subtracted, Background Subtracted0200040006000Intens.120 140 160 180 200 220 m/z6 8.04 9479 .0 17 392 .0 49 410 8.0 45714 7.0 78 11 56 .0 11 01 88 .080 5 2 54 .0 59 4 29 2.01 52+bbCID MS, 2 5.0e V, 5.80 min #67 5, Ba ckground Subtra cted , Background Subtracted0123454x1 0Inten s.7 5 100 12 5 1 50 17 5 20 0 2 25 25 0 27 5 m/zReference StandardMS spectraMS/MS spectra247.1455254.0587261.1091271.0935 284.2066292.0149305.1377+MS, 5.81min #678, Background Subtracted, Background Subtracted0.00.51.01.52.04x10Intens.250 260 270 280 290 300 310 m/z25 4.05 9029 2.01 48+MS, 5.81 min #6 76 , Background Subtra cted , Background Subtra cted0.00.51.01.52.05x 10Inten s.10 0 1 50 2 00 25 0 3 00 3 50 400 4 50 m/zSamplesulfamethoxazole5.2 5.4 5.6 5.8 6.0 6.2 Time [min]0.00.20.40.60.81.04x10Intens.Sulfamethoxazole (K)Chromatogram (EICs)11/9/20147II. Suspect Screening• more than 10000 EPs and TPs…including information over:from literature from prediction models (UM-PPS, Metabolite Predict) from regulation bodies(REACH)1. in-house database2. Retention time prediction toolKNN-GA-SVM3. High Resolution Mass Spectral Librariesfor MS/MS data (MassBank, MetFrag)Retention Time Prediction ModelsQSAR/QSPR procedure:1. Optimization by HyperChem / MOPAC2. Molecular descriptors by Dragon (zero, constant and near-constant, and collinear descriptors were removed)3. Division of dataset to training and test datasets by clustering (KNN) or PCA4. Selection of the relevant descriptors by Stepwise or Genetic algorithm 5. Build of models by MLR, ANNs, and SVM and their comparison11/9/20148                                      Data set was gathered and drawn then optimized using Hyperchem package. Cluster Analysis  PCA  Genetic algorithm  Stepwise Genetic algorithm  Stepwise KNN-GA-MLR KNN-GASVM KNN-GA-ANN  KNN-SW-MLR KNN-SW-SVM KNN-SW-ANN  KNN-GA-MLR compare with PCA-GA-MLR          KNN-SW-MLR compare with PCA-SW-MLR KNN-GASVM compare with PCA-GA-SVM           KNN-SW-SVM compare with PCA-SW-SVM KNN-GA-ANN compare with PCA-GA-ANN          KNN-SW-ANN compare with PCA-SW-ANN  KNN-GA-MLR compare with KNN-SW-MLR KNN-GA-SVM compare with KNN-SW-SVM KNN-GA-ANN compare with KNN-SW-ANN PCA-GA-MLR PCA -GASVM PCA -GA-ANN  PCA -SW-MLR PCA –SW-SVM PCA -SW-ANN  Interpretation of Descriptors  Retention Time Predictor software (RTP)  R² = 0.950802468101214160 2 4 6 8 10 12 14 16Predicted Retention timeExperimental Retention timeTestTrainingR² = 0.963402468101214160 2 4 6 8 10 12 14 16Predicted Retention timeExperimental Retention timeTestTrainingKNN-GA-SVM – (-) ESI compounds KNN-GA-SVM plot – (+) ESI CompoundsThe best prediction accuracy was achieved by KNN-GA-SVM model for both positive and negative ESI compounds.11/9/20149II. Suspect ScreeningOptimization & Validation ü Peak Area/Intensity Ratio > 4ü False Negative Results <10%Criteria…in order exclude too many irrelevant peaks !Peak Area: 20,000Intensity: 5,000Thresholds+ ESIPeak Area: 2,000Intensity: 500- ESIApplication to “artificial” suspectat 5 concentration levels (1 – 0.025 μg/L) C (μg/L) +ESI -ESI1 9.8 00.5 11 6.20.25 19 270.05 61 400.025 88 50% False Negative11/9/201410II. Suspect ScreeningIon Intensity > 20,000 (+ESI) / 2,000 (-ESI)Area > 5,000 (+ESI) / 500 (-ESI)Database of 10,000 compounds 3590 hits (+ESI) / 1493 hits (-ESI)Retention time prediction model &(+)ESI or (-)ESI amenableMS/MS data in on-line spectral libraries4438 hits (+ESI) / 3245 hits (-ESI)Isotopic fit: ≤ 100 mSigma2612 hits (+ESI) / 943 hits (-ESI) 3325 hits (+ESI) / 2727 hits (-ESI)Accuracy: Error ≤ 5 ppm2061 hits (+ESI) / 703 hits (-ESI) 2997 hits (+ESI) / 2249 hits (-ESI)Area/Intensity : 7-25 *610 hits (+ESI) / 150 hits (-ESI) 663 hits (+ESI) / 564 hits (-ESI)…in progress* Validationmean ±2 SD2 3 4 5 6 7 8 Time [min]0.00.20.40.60.85x10Intens.II. Suspect Screeningfocus on.. METABOLITES & TPsDatabase of 1,500 human metabolitesof consumed pharmaceuticals o 2 generations• Phase I• Phase II• Cytochrome P450274 hits (+ESI) 254 hits (-ESI)Acetylsalicylic acid Met12 EIC tR= 4.43 minAccuracy: 1.3 ppmIsotopic fit: 0.6 mSigmaPredicted tR= 4.39 min1 07 .0 36 8 11 0. 03 631 16 .0 49 41 26 .0 10 4 13 1 .07 04 13 7.0 25 71 51 .0 40 01 56 .0 12 2-bbCID  MS, 4 .4 2min #5 11 , Ba ckground Subtra cted, Background Subtra cte d0.00.20.40.60.84x10Intens.100 110 120 130 140 150 m/zMS/MS spectraHydroxyphenyl acetate(-ESI)Retention time predictionReject ~30% suspect compounds11/9/201411Theophylline Met8 tR= 3.0 minAccuracy: 0.3 ppmIsotopic fit: 10 mSigmaPredicted tR= 3.9 mintR= 3.6 minAccuracy: 3.2 ppmIsotopic fit: 10.7 mSigmaPredicted tR= 3.5 minMS/MS spectraMS spectra100.1124111.0447115.0389133.0497149.0801173.1644182.1389199.0818+MS, 3.56min #408, Background Subtracted, Background Subtracted0.000.250.500.751.001.255x10Intens.100 120 140 160 180 200 m/z77.0394121.029617 2.9914197.0680279.017 6-MS, 2.98min #338, Background Subtracted, Background Subtracted0.000.250.500.751.005x10Int ens.0 50 100 1 50 200 250 300 m/z57.03 3375.044 0 86.09 65100.11261 12.0512 128 .0486146.0 594156.063 4171.0 868 187.0936 211 .1076+bbC ID MS,  25.0eV, 3.49-3.57min #399 -409, B ackground Subtracted,  Bac kgrou nd Subtrac ted02464x10Intens.60 8 0 100 120 1 40 160 180 20 0 220 m/ z58.0280 110.03 63154.04 95172.99 12197.068 2237.0 094 2 79.0189-bbCI D MS,  2.97 -2.99mi n #337-339 , Bac kground Subtrac te d, Ba ckground Sub trac ted0.00.51.01.54x 10Inten s.0 50 10 0 150 200 2 50 300 350 m/zSuspect Screening – Human Metabolites+ ESI- ESIWhat proportion of substances present in the samples are actually detected with target and suspect screening?TARGET SCREENINGSUSPECT SCREENINGü Known substanceü Reference standard availableü Unequivocal identificationü Possible quantificationü Suspect substance  ü No reference standard availableü Qualitative detection possibleWHY NON-TARGET? III. Non-target Screening11/9/201412ü Usually, many of the most intense peaks do not correspond tosubstances included in the target and suspect screening lists.ü These substances are potentially relevant, due to their highconcentration.ü Identification of these substances is environmentally relevant ü Nevertheless, full identification of unknown compounds is often difficult &there is no guarantee of a successful outcome NON-TARGET SCREENINGü No former information on the analytesü Molecular structures can be assigned on the basis of the exactmass, isotopic pattern and fragmentation informationIII. Non-target ScreeningDetermination of the Elemental compositions of the unknownsAutomatic peak detection using Algorithms(High number of peaks)Full scan (MS) and Product ion spectra (MS/MS)Accurate mass measurements•Interpretation of the fragmentation pathway•Chromatographic retention time plausibility Determination and evaluation of candidates(Tentative) Identification of TPsSTANDARD SCREENING WORKFLOWConfirmation: RT and MS/MS of chemical standards, when availableü Large effort on manual data evaluationü Systematic strategies with automated approaches are required to prioritize relevant peaks on which the identification efforts should focusIII. Non-target Screening11/9/201413PROPOSED APPROACHü Analyses are carried out in the same way previously describedfor target and suspect screening, except that AutoMSMS isperformed (MS/MS data of the 5 most intense peaks per scanevent).Non-target steps:ü Blank subtractionü Peak peaking procedureü Prioritization of peaks for further evaluationü Determination of elemental compositionü Evaluation of possible candidates → Tentative identificationIII. Non-target ScreeningüUse of metabolomics tools BLANK SUBTRACTION Sample chromatogramProcedural blank chromatogramBlank-subtracted chromatogramIII. Non-target Screening11/9/201414ü Peak peaking: Molecular features Algorithm• Using Data analysis and Target analysis (Bruker)• Threshold: Signal/Noise >10A high number of peaks (> 3500) was obtainedPEAK PEAKING PROCEDUREIII. Non-target ScreeningNon-target identification was performed on selected masses from the top most intense peaksPRIORITIZATION OF PEAKS FOR FURTHER EVALUATIONü Selection of the most relevant from the large peak list(Not included either in the target or the suspect screening)Criteria:• Intensity• Presence of a distinctive isotopic patternIII. Non-target Screening11/9/2014151st step: Generation of possible molecular formula(s)Criteria:• Mass accuracy → threshold: 5 ppm• Agreement of the theoretical and measured isotopic patternDETERMINATION OF ELEMENTAL COMPOSITIONIII. Non-target ScreeningüPlausibility of the generated molecules → Use of the Seven Golden Rules software“Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry”i. Element number restrictionsii. Lewis and Senior chemical rules checkiii. Isotopic pattern filteriv. Hydrogen/carbon ratio checkv. Element ratio of nitrogen, oxygen, phosphorus and sulphur vs carbon checkvi. Element ratio probability checkvii. Check of the presence of trimethylsilylated compoundsü The correct molecular formula is assigned with a probability of 98%,if the formula exists in a compound database30 million compounds database → Great reduction of the possibilitiesDETERMINATION OF ELEMENTAL COMPOSITION:  SEVEN GOLDEN RULES (SGR)Kind and Fiehn. BMC Bioinformatics 8:105 (2007)11/9/201416ü Number of candidates to one molecular formula: 1 - >2000 (Chemspider, Pubmed databases)üDatabases (e.g. MassBank) →Still very limited number of compounds (not very useful for non-target screening)üDeep study of the MS/MS spectra (AutoMSMS analysis)ü In-silico fragmentation software§ Smart formula 3D (Bruker)§ Metfragü Chromatographic retention time plausibility → Application of modelsü Number of data sources and references in different data bases (e.g. Chemspider)EVALUATION OF POSSIBLE CANDIDATESApproaches for tentative identification:ü To confirm the identity of a substance, purchase of reference standard is required (if available)ü Peak peaking: Molecular features Algorithm• Threshold: Signal/Noise >10A large amount of peaks (> 3500) obtainedNon-target identification was performed on 16 selected masses from the top most intense peaksPEAK PEAKING PRIORIZATIONCompound detected using the TARGET ANALYSIS approachMetforminEXAMPLE 1: TREATING METFORMIN AS UNKNOWN11/9/201417EXAMPLE 1: TREATING METFORMIN AS UNKNOWN• Experimental accurate mass:  130.1088• Retention time: 1.4 minMS spectraNumber of possible formulas → 1 (Threshold of 5 ppm and 50 mSigma)Seven Golden Rules1 Plausible Molecular formulaC4H12N5EXAMPLE 1: ELEMENTAL COMPOSITION DETERMINATION11/9/201418üC19H28N4MS/MS Spectraü Hits Chemspider: 12ü Compounds with score > 0.8 → 4EXAMPLE 1: TENTATIVE IDENTIFICATIONüC19H28N4EXAMPLE 1: TENTATIVE IDENTIFICATIONü Metfrag Score: 1 0.86 0.81 0.81ü Explained Fragments: 5 3 2 2ü RT Pred. Model: x xü Chemspider data sources: 59 5 4 4ü Chemspider References: 293 6 4 4Metformin11/9/201419EXAMPLE 1: TENTATIVE IDENTIFICATIONMetforminAPPLICATION OF RETENTION TIME PREDICTION MODEL:Experimental RT = 1.38 minPredicted RT = 2.5 minMetfrag peak explanation• The developed workflow was applied successfully to identify unambiguously this compound as MetforminEXAMPLE 2: APPLICATION OF THE WORKFLOW TO A REAL UNKNOWN• Experimental accurate mass:  145.0977• Retention time: 1.9 minNumber of possible formulas (Threshold of 5 ppm, 50 mSigma) and Seven Golden Rules1 Plausible Molecular formulaC6H12N2O2146.0987145.097711/9/201420145.097798.0606103.087786.060785.0760MS/MS Spectraü Hits Chemspider: 336ü Compounds with score > 0.9→ 28ü Only few with more than 3 fragment matchesEXAMPLE 2: TENTATIVE IDENTIFICATIONüC6H12N2O2EXAMPLE 2: TENTATIVE IDENTIFICATIONü Metfrag Score: 1 0.97 0.95 0.95 0.95ü Explained Fragments: 4 4 4 4 3ü RT Pred. Model: xü Chemspider data sources: 1 4 5 17 2ü Chemspider References: 1 4 5 18 2145.097798.0606 103.087786.060785.0760ü C6H12N2O2TENTATIVE CANDIDATES11/9/201421EXAMPLE 3: TENTATIVE IDENTIFICATION• Experimental accurate mass:  195.1233• Retention time: 4.2 minNumber of possible formulas (Threshold of 5 ppm, 50 mSigma) and Seven Golden Rules1 Plausible Molecular formulaC8H18O5163.0861151.0953107.0707 195.1233133.084789.059645.0338ü Metfrag Score: 1 1 0.95ü Explained Fragments: 5 5 5ü RT Pred. Model:ü Chemspider data sources: 1 67 2ü Chemspider References: 1 379 2ü Hits Chemspider: 13ü 3 compounds with Metfrag score > 0.95 and the others below 0.5ü C8H18O5TetraethyleneglycolEXAMPLE 3: TENTATIVE IDENTIFICATION11/9/201422SUMMARY OF THE LEVELS OF IDENTIFICATIONRetentiontime (min)Mass of ion [m/z] (peak of component) Ion type IntensityMolecular formula Proposed identification nameLevel of confirmation of identification1.28 164.1282 [M+H]+ 1508655 C7H17NO3 Unequivocal molecular formula1.91 145.0977 [M+H]+ 2186079 C6H12N2O2e.g. 4-(2-Hydroxyethyl)-2-piperazinone Tentative candidates2.27 96.0452 [M+H]+ 1145713 C5H5NO 2-Formyl-1H-pyrrole Probable structure4.19 195.1233 [M+H]+ 1405658 C8H18O5 tetraethyleneglycol Tentative candidate4.68 135.1018 [M+H]+ 1122821 C6H14O3 Unequivocal molecular formula4.98 424.1857 [M+H]+ 1263654 Exact mass of interest5.09 358.2078 [M+NH4]+ 1264684 C15H24N4O5 Unequivocal molecular formula5.16 283.1753 [M+H]+ 1262520 C13H22N4O3 Unequivocal molecular formula5.2 468.2108 [M+H]+ 1263126 Exact mass of interest5.24 374.239 [M+H]+ 1184473 C16H31N5O5 Unequivocal molecular formula5.73 149.1176 [M+H]+ 1688072 C7H16O3 Unequivocal molecular formula6.13 520.333 [M+H]+ 1262524 Exact mass of interest6.44 608.3854 [M+H]+ 1262588 Exact mass of interest9.1 232.1913 [M+H]+ 1160646 C12H25NO3e.g. N,N-Bis(2-hydroxyethyl)octanamide Tentative candidates9.4 191.1647 [M+H]+ 1410087 C10H22O3 Unequivocal molecular formula12.69 316.1955 [M+H]+ 1137576 C16H29NO3Se.g. 1-{(2-Methoxyethyl)[(5-methyl-2-thienyl)methyl] amino}-3-[(2-methyl-2-propanyl)oxy]-2-propanolTentative candidatesü 16 evaluated top intense peaks in +ESI modeü 5 Tentatively candidatesü 7 Unequivocal molecular formula ü 4 Exact mass of interest Conclusions• Target and suspect HRMS screening workflows were developed and validated• Target screening can identify app. 10% of the obtained peaks from a LC-QTOFMS analysis• Suspect screening can explained app. 20% of the obtained peaks• Non-target workflows are needed for the tentative identification of the highly abundant peaks 11/9/201423http://trams.chem.uoa.gr/National and KapodistrianUNIVERSITY OF ATHENSFaculty of ChemistryThank you for your attention!Acknowledgments  Pablo Gago Ferrero Anna BletsouKaterina Psoma Reza AalizadehThis research has been co-financed by the European Union and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)

Development of a LC-HRMS workflow for the target, suspect and non-target screening of contaminants of emerging concern in environmental water samples

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Development of a LC-HRMS workflow for the target, suspect and non-target screening of contaminants of emerging concern in environmental water samples

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Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)

Development of a LC-HRMS workflow for the target, suspect and non-target screening of contaminants of emerging concern in environmental water samples

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Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)