Articles

Special Issues May 2019

Click the title above to open the Current Trends In Mass Spectrometry May 2019 special issue in an interactive PDF format.

Vol 37 No 3 CTMS March 2019 Special Issue

Lurasidone is an atypical antipsychotic that was approved by the FDA in 2010 to treat bipolar depression and schizophrenia. Like other antipsychotics, adherence to lurasidone is critical for successful disease treatment. Thus, therapeutic drug monitoring (blood testing) is often employed by clinicians to monitor adherence. Urine drug testing, with its advantages over blood testing, is another method used to confirm medication adherence. However, analytes used in blood testing are often very different than those used for testing in urine, where nonactive metabolites are often most prevalent. Choosing metabolites in urine that are relatively prevalent affords optimal method sensitivity, and thus improved testing results for adherence. To ensure optimal lurasidone adherence testing, an untargeted high-resolution mass spectrometry method was employed, using known positive human urine samples to identify the lurasidone metabolites and their relative abundance in urine. This testing identified a different primary urine metabolite from what has been reported in blood. The higher prevalence of this metabolite will improve lurasidone urine adherence monitoring.

	Medication monitoring has become increasingly important for successful treatment of patients with mental health diseases because adherence to treatment is generally poor, especially in the schizophrenic population (1–8). Urine has become an alternative to blood or plasma medication monitoring due to its noninvasive nature and ease of collection. Whereas blood or plasma drug testing usually involves the identification and quantitation of the parent compound or active metabolites, or both, the success of urine drug testing (UDT) is largely dependent on analysis of any metabolites of the parent compound. Although the parent compounds may be present in urine, often they are at very low concentrations relative to metabolites, and therefore do not provide the sensitivity required for medication monitoring. Urine metabolites are often predicted from identification in blood, plasma, or specific testing methods, such as gas chromatography-mass spectrometry (GC-MS), extractions, radioactivity, and using in vitro or animal samples. However, it has been shown that these methods are not always successful in identifying the most abundant urinary metabolite (9–12). Without suitable metabolites to test, a negative UDT result could prompt a clinician to alter treatment for a patient when treatment need not be altered. Therefore, a generic, untargeted approach is useful for the successful identification of urinary metabolites suitable for highly sensitive medication monitoring. Liquid chromatography–high resolution mass spectrometry (LC–HRMS) provides a sensitive and nonspecific detection method for setting up such an experiment.

	Lurasidone (Latuda) is an atypical antipsychotic that was approved for the treatment of acute symptoms of schizophrenia (13,14) and bipolar depression (15,16) in 2010 and 2013, respectively. It is commercially available as 20 mg, 40 mg, 60 mg, 80 mg, and 120 mg tablets, and is typically prescribed or administered at 40 or 80 mg per day. It is absorbed after oral administration with a bioavailability of 9–19%. Dosing is designed to be with food, which can increase the bioavailability by 100%. The mean elimination half-life is 18 h. Steady state serum concentrations for lurasidone are typically achieved after seven days of dosing (17–20). Lurasidone is metabolized in the liver primarily by CYP3A4. Metabolism includes oxidative N-dealkylation, hydroxylation of the norborane ring, S-oxidation, and reductive cleavage of the isothiazole ring, followed by S-methylation. Nearly two dozen metabolites of lurasidone have been previously identified, and only ~9% of the dose is excreted in urine (17–20). Typically, adherence to lurasidone therapy is monitored by evaluating levels of lurasidone and M11/ID-20219 (one of its metabolites) that were each predicted to be present in urine at approximately 12 and 24%, respectively. The structures for lurasidone and many of the confirmed metabolite structures can be seen in Table I.

	Previously, we reported the identification of novel metabolites for monitoring aripiprazole, brexpiprazole, haloperidol, and quetiapine in urine that were not originally predicted (9–12). Because there are some similarities of these antipsychotics to lurasidone, we decided to determine if the urinary lurasidone compound(s) predicted from plasma studies were indeed the most abundant prior to development of a confirmation method. This work reports the identification of lurasidone and prevalent lurasidone metabolites in urine using LC–HRMS from patients prescribed lurasidone. Additionally, confirmation of the most prominent metabolites was tested in a validated, targeted, quantitative liquid chromatography–tandem mass spectrometry method (LC–MS/MS), which are at odds with current reports of urine metabolites (17–20).

 were purchased from Cerilliant (Round Rock, Texas). Hydroxylurasidone was a custom synthesis product purchased from 

C Molecular (Greensboro, North Carolina). All solvents, including methanol (optima grade), formic acid (88%), acetonitrile (optima grade), ammonium acetate (optima grade), and isopropanol (optima grade), were purchased from VWR (Radnor, Pennsylvania, USA). Drug-free human urine was acquired from UTAK Laboratories (Valencia, California). Standards for S-methyl lurasidone and S-methyl hydroxylurasidone were not commercially available, and synthesis requests were unsuccessful.

	Identification of lurasidone metabolites using LC–HRMS was completed on 13 authentic urine samples from patients who were prescribed the medication. After metabolite identification was complete, an LC–MS/MS confirmation was validated. An additional 56 patients were prescribed lurasidone at different doses, with specimens collected over three separate days for each patient used to confirm the accuracy of the method. These samples were provided voluntarily, and anonymously, to assist with the development of a lurasidone confirmation method. No identifying or demographic information was collected on these volunteers, other than the prescribed lurasidone dose. There was an alphanumeric code from the clinic that was provided to track the patients who provided samples over the course of the three separate days. None of the results were shared with the clinician to assist with treatment. Ameritox is accredited by the College of American Pathologists (CAP) and abides by CAP, Clinical Laboratory Improvement Amendments (CLIA), and Health Insurance Portability and Accountability Act (HIPAA) requirements. Due to the secondary analysis nature of this work and the absence of clinical conclusions, neither the United States Food and Drug Administration (FDA) nor other clinical trial review or approval was obtained by Ameritox. Writing this manuscript did not involve human subjects, as defined by the U.S. Code of Federal Regulations (45 

46.102); thus, an Institutional Review Board (IRB) approval of these specific research activities was unnecessary.

	Thirteen patient urine specimens (100 µL) were diluted 5X with 400 µL of a reference standard, (0.25 µg/mL of hydrocodone-d

 was used as an internal reference standard for all LC–HRMS injections, to guarantee successful injection of the sample, and provide a retention time marker. Prepared samples were injected (5 µL) and separated on a Phenomenex Kinetex Phenyl-Hexyl, 2.1 x 50-mm, 2.6-µm column (Torrance, California) at 50 °C, and analyzed on an Agilent 6530 Q-TOF (quadrupole time-of-flight mass spectrometer) with an Agilent 1290 LC system (Santa Clara, California). The LC–QTOF method conditions are detailed in a previous publication (12). A lurasidone control in drug-free urine (75 ng/mL) was run, along with the patient samples, to assist in positive identification of the parent compound, if present. No other standards were available or purchased to assist in identification, until a confirmation method was developed. Each sample was injected and analyzed twice.

	The MS-only data were processed using Agilent Mass Hunter Qualitative Analysis and PCDL (Personal Compound Database and Library) manager software. A database of lurasidone and 11 of its possible metabolites' chemical formulas (Table I) was compiled, and used to search against the samples. The software matched compounds based on retention time (if available), mass (±20 parts per million or ppm), the isotopic distribution pattern, and the isotopic spacing theoretically derived from the chemical formula. To be identified as positive and a potential lurasidone metabolite, a compound had to have consistent retention times across multiple patient samples when a known retention time was lacking; otherwise, the retention times had to be within ±0.05 minutes of a control. The mass accuracy had to be within ±20 ppm; and the composite score of the mass accuracy and isotopic features had to be ≥70 (out of a possible 100). Compounds that had the highest area counts were also ranked and noted as the most abundant. To assist with differentiation of structural isomers, such as M8/M9 and M10, fragmentation spectra were obtained and reviewed to identify which isomer was present at an identified metabolite peak, as needed.

	Hydroxylurasidone was received as a neat solid that was dissolved into methanol at a concentration of 1 mg/mL, and lurasidone was received as a 100 µg/mL methanolic standard. Hydroxylurasidone and lurasidone were combined and diluted into a methanolic stock that was then further diluted into normal, drug-free human urine, to reach the appropriate calibrator (5, 25, 100, 500, and 1000 ng/mL) and quality control levels (75 ng/mL). Lurasidone-d8, 1 mg/mL methanolic stock, was diluted to 900 ng/mL in 0.1% formic acid in water solution. A 100 µL aliquot of the sample (patient sample, calibrator, or quality control stock) and 400 µL of lurasidone-d8 internal standard in 0.1% formic acid were added to a vial. Vials were then capped and vortexed for 10 s prior to injection of 5 µL.

	Samples were analyzed by LC–MS/MS on a Waters Acquity UPLC Xevo TQ-MS system (Waters Corporation, Milford, Massachusetts), a Waters Acquity UPLC CSH Phenyl-Hexyl 2.1 x 50-mm, 1.7-µm UPLC column. The LC method and MS conditions can be found in Strickland and associates (12). Analyte transitions are listed in Table II. The acquisition method was run in dynamic multiple reaction monitoring (MRM) mode, in order to maximize the number of points across the various analyte peaks. The validation of this method followed CAP and CLIA guidelines (21–25), and an internal SOP (standard operating procedure) that has been described in detail elsewhere (26). It should be noted that, due to the lack of standards for S-methyl lurasidone and S-methyl hydroxylurasidone, they were unable to be validated, and estimates of their concentration were made by comparing the quantiative peak area ratio to the lurasidone calibration curve. These compounds were included as a proof of concept, to show their estimated prevalence and relative importance for lurasidone compliance in UDT for when standards might be available. Also, due to the lack of standards, the transition parameters were estimated from hydroxylurasidone and are not optimized.

	The metabolite identification from the 13 patients analyzed by LC-QTOF can be seen in Table III. Compounds identified with the highest confidence (>90%) are highlighted in green, while less confident (>70% but <90%) compounds are highlighted in yellow. The three most abundant compounds for each specimen and replicate are noted with a star-asterisk in the respective square. For unidentified or not confidently identified compounds (<70%) for a given specimen replicate, the field is blank. It is clear that, although lurasidone was identified in almost all of the samples, it was not consistently among the most abundantly identified compounds. Metabolite M11, the predicted major metabolite, was rarely confidently detected in these samples. Instead of lurasidone and M11, metabolites M21 (S-methyl lurasidone), M22 (S-methyl hydroxylurasidone), and isomer M8/M9 (hydroxylurasidone), or isomer M10 (lurasidone sulfoxide), were frequently detected. To determine whether hydroxylurasidone or the lurasidone sulfoxide (isomers) was present, the collected fragmentation data from the QTOF were analyzed, and are shown in Figure 1. The identification of a peak at 

182 (red circle in Figure 1) confirmed the isomer as hydroxylurasidone by indicating a fragmentation of the hydroxylated norborane ring. If the identity was the lurasidone sulfoxide, expected fragmentation peaks of 

 152 or 237 from the oxidized sulfur atom on the isothiazole ring structure would be present. Additionally, the unhydroxylated norborane ring would have an expected 

 of 166. The absence of those expected peaks (

 152, 237, and 166) in the spectra confirms the identity of the metabolite as hydroxylurasidone, and a custom synthesis of the molecule was requested to validate a confirmation method. S-methyl lurasidone and S-methyl hydroxylurasidone were also requested as custom synthesis products, but attempts to synthesize for the method were unsuccessful.

	Upon receiving the hydroxylurasidone standard, an LC–MS/MS method was developed and validated. The results of validation of lurasidone and hydroxylurasidone are shown in Table IV. Although S-methyl lurasidone and S-methyl hydroxylurasidone were included in the method, without standards, validation was unable to be completed, and is the reason for their exclusion from Table IV. To ensure the ability to successfully detect and quantify lurasidone and hydroxylurasidone in patient specimens, samples from 56 additional patients (from three separate collection days) were provided for testing with the validated method. The results of these patient analyses are summarized in Table V, and separated by the prescribed dose. It is clear that testing for hydroxylurasidone helps with positive confirmation of taking lurasidone medication. It also appears, from the estimated concentrations of the S-methyl lurasidone and S-methyl hydroxylurasidone, that confirmation would be easier with these metabolites, because they are more abundant than both lurasidone and hydroxylurasidone. However, the lack of standards for these compounds makes it impossible to currently validate a method for reporting UDT results for these compounds.


	Figure 1: Fragmentation data from sample 8 Identified M8-M9-M10 Peak.

	The advantages of HRMS analysis have been reviewed in the literature, including the extreme selectivity of such methods (10–12,27–30). Using this method, authentic urine samples of human subjects who were known to be taking chronic doses of lurasidone were tested for the presence of lurasidone and 11 possible metabolites. Due to the high mass resolving power and low mass error on the QTOF, compounds that have similar mass to charge ratios, but different chemical formulas, were differentiated with the searching algorithm (hydroxylurasidone and S-methyl lurasidone). Also, by eliminating extraction preparation methods, compound loss was mitigated. Using liquid as opposed to gas chromatography also helped ensure that compounds with low volatility can still be accurately analyzed. Surprisingly, neither of the predicted major urinary metabolites M5 nor M11 were found to be consistently excreted through human urine in large detectable amounts. This result could be due to the fact that M5 and M11 both have a carboxylic acid moiety that would be a possible glucuronidation target. This was considered, and the glucuronidated versions were searched for in patient samples and poorly identified. While this may indicate that the glucuronide metabolites of M5 and M11 are not present in urine in any appreciable amount, it could also be due to the poor ionizability of glucuronidated compounds. To confirm, an additional hydrolysis study could be completed to see if there is an increase in the prevalence of M5 and M11 in patients after hydrolysis. However, with the significant presence of hydroxylurasidone (M8/M9), S-methyl lurasidone (M21), and S-methyl hydroxylurasidone (M22), it seemed unnecessary to pursue hydrolysis as a means for analyzing for lurasidone compliance.

	The presence of these metabolites in urine was not well predicted from results in blood. Hydroxylurasidone was estimated to have a prevalence of 2.8% as the M8 isomer, and 0.4% as the M9 isomer; S-methyl lurasidone and S-methyl hydroxylurasidone were not predicted at any measurable amount (17–20). It does appear that the estimate of lurasidone at 12% might be reasonable, as all but one of the 13 patients in metabolite discovery had detectable amounts of the parent compound (17–20).

	To better understand the relative amounts of each metabolite, lurasidone, hydroxylurasidone, S-methyl lurasidone, and S-methyl hydroxylurasidone present in urine, the results from the 56 patients used during method validation were analyzed. These results in Table V show that hydroxylurasidone, S-methyl lurasidone, and S-methyl hydroxylurasidone are present at approximately 2x, 7x, and 5x times, respectively, relative to lurasidone. All of the compounds show a general increase in concentration and percent positivity rate with increasing doses. It is clear that S-methyl lurasidone and S-methyl hydroxylurasidone provide slightly better positivity correlations at lower doses, but hydroxylurasidone does appear to provide enough benefit to help compensate for lower prevalence of lurasidone. Therefore, with the lack of available standards for S-methyl lurasidone and S-methyl hydroxylurasidone, hydroxylurasidone was validated to assist in UDT for lurasidone compliance.

	We successfully identified prevalent lurasidone metabolites in urine. Based on those identifications, we successfully validated a method for the purpose of UDT monitoring of lurasidone. The hydroxylurasidone metabolite provides benefit for lurasidone UDT monitoring by being more prevalent in the urine than lurasidone by ~2x, and providing more consistent positivity correlation at lower lurasidone doses. Although other metabolites are present in the urine in large concentrations, standards for those compounds are not available at this time. However, the proof-of-concept work with S-methyl lurasidone and S-methyl hydroxylurasidone shows that they are ~5x and ~7x greater in abundance than lurasidone, respectively, and would provide even better positivity correlation at low doses.

	(1) M. Ko, and T. Smith, Urine Drug Monitoring in Patients on Prescribed Antipsychotic Medications. 29th Annual US Psychiatric and Mental Health Congress Oct 21-24 (

http://www.ingenuityhealth.com/wpcontent/uploads/2016/11/Mental_Health_Populations_Study_US_Psych_2016_Poster.pdf

	(2) A.E. Cooper, P. Hanrahan, and D.J. Luchins, 

	(3) C.R. Dolder, J.P. Lacro, L.B. Dunn, and D.V. Jeste, 

	(4) S. Offord, J. Lin, D. Mirski, and B. Wong, I 

	(5) D.I. Velligan, F. Lam, L. Ereshefsky, and A.L. Miller, 

	(6) D.I. Velligan, Y-W. F. Lam, D.C. Glahn, J.A. Barrett, N.J. Maples, L. Ereshefsky, and A.L. Miller., 

	(8) R.A. Millet, P. Woster, M. Ko, M. DeGeorge, and T. Smith, Adherence to Treatment with Antipsychotic Medications Among Patients with Schizophrenia, Major Depressive Disorder, or Biopolar Disorder. Poster Presentation. (US Psychiatric and Mental Heal Congress (USPMHC) San Diego, CA,2015).

	(9) J. McEvoy, R.A. Millet, K. Dretchen, A.A. Morris, M.J. Corwin, and P. Buckley, 

	(10) J.R. Enders, S.G. Reddy, E.C. Strickland, and G.L. McIntire, 

	(11) O.T. Cummings, E.C. Strickland, J.R. Enders, and G.L. McIntire, 

	(12) E.C. Strickland, O.T. Cummings, A,A. Morris, A. Clinkscales, and G.L. McIntire, 

	(14) S. Caccia, L. Pasina, and A. Nobili, 

	(15) R. Franklin, S. Zorowitz, A.K. Corse, A.S. Widge, and T. Deckersbach, 

	(16) M. Ostacher, D. Ng-Mak, P. Patel, D. Ntais, M. Schlueter, and A. Loebel, 

The World Journal of Biological Psychiatry

http://doi.org/10.1080/15622975.2017.1285050

	(17) Product Information: LATUDA oral tablets, lurasidone hydrochloride oral tablets. Sunovion Pharmaceuticals Inc. Marlborough, MA (2018).

Disposition of Toxic Drugs and Chemicals in Man 

(Biomedical Publications, Seal Beach, CA, 11th Ed., 2010), pp 1232–1233.

	(19) Sunovion Pharmaceuticals Inc. Center for Drug Evaluation and Research Approval Package (Lurasidone). 

https://www.accessdata.fda.gov/drugsatfda_docs/nda/2013/200603Orig1s010.pdf

	(20) T. Ishibashi, T. Horisawa, K. Tokuda, T. Ishiyama, M. Ogasa, R. Tagashira, K. Matsumoto, H. Nishikawa, Y. Ueda, S. Toma, H. Oki, N. Tanno, I. Saji, A. Ito, Y. Ohno, and M. Nakamura, 

	(21) Association of Public Health Laboratories. CLIA-Compliant Analytical Method Validation Plan and Template for LRN-C Laboratories. December 2013.

	(22) F.T. Peters, O.H. Drummer, and F. Musshoff, 

 (AACC Press, Washington, DC, 2nd edition 2003) pp 114–115.

	(24) U.S. Department of Health and Human Services, Food and Drug Administration. Guidance for Industry-Bioanalytical Method Validation (2001).

	(25) National Laboratory Certification Program (NLCP). Manual for Urine Laboratories. October 2010.

	(27) J.M. Colby, K.L. Thoren, and K.L. Lynch, 

	(28) E. Partridge, S. Trobbiani, P. Stockham, T. Scott, and C.A. Kostakis, 

	(29) J.M. Colby,K.L. Thoren, and K.L. Lynch, 

	(30) S.K. Manier, A. Keller, J. Schaper, and M.R. Meyer, 

 is with Ameritox, LLC, in Greensboro, North Carolina. 

 is with the Molecular Education, Technology and Research Innovation Center of North Carolina State University, in Raleigh, North Carolina. 

 is with Premier Biotech, in Minneapolis, Minnestota. Direct correspondence to: 

For lurasidone treatment adherence testing, an untargeted high-resolution mass spectrometry method was employed, using known positive human urine samples to identify the lurasidone metabolites and their relative abundance in urine.

Determination of the Relative Prevalence of Lurasidone Metabolites in Urine Using Untargeted HRMS

Accurate detection, identification, and quantitation of compounds in high matrix food extracts often proves challenging, even to experienced analysts. This work becomes more challenging as regulatory agencies drive limits of detection (LODs) lower, while simultaneously increasing the number and types of compounds that must be targeted. Selected ion monitoring and tandem mass spectrometry (MS/MS) techniques can help mitigate matrix interferences, but they may not be selective enough for all compounds in the most challenging matrices. Furthermore, these types of targeted analysis techniques remove the possibility for retrospective nontargeted analysis of the data, preventing analysts from detecting new or emerging contaminants. In contrast, comprehensive two-dimensional gas chromatography (GC×GC) dramatically improves chromatographic resolution of analytes within a sample, often completely separating target compounds from would-be matrix interferences. Additionally, new time-of-flight mass spectrometers (TOF-MS) allow for full scan collection at selected ion monitoring (SIM)-level sensitivities, obviating the need for quadrupole–based systems. In this article, we demonstrate the use of GC×GC–TOF-MS as a methodology to combat matrix interferences, and quickly target and quantify suspected contaminants, while still allowing nontargeted analyte detection in a single sample injection.

	The "quick, easy, cheap, effective, rugged, and safe" (QuEChERS) technique has become the predominant method to extract pesticides from a variety of food products (1). Since its introduction, many improvements have been made to the extraction chemistries, not only to improve pesticide recoveries, but also to decrease the amount of coextracted commodity matrix. Even so, particularly problematic matrices still exist. In the case of samples that contain high levels of fat (fish, avocados, and nuts) or pigmentation (spinach and blueberries), large amounts of unwanted matrix still pass into the final extract. These coextracted compounds often negatively affect pesticide detection and quantitation, challenging the efforts of analysts worldwide, especially as limits of detection (LODs) are decreased by numerous regulatory entities.

	In most analyses, a mass spectrometer is coupled to a single dimension of chromatographic separation. In the case of high matrix commodities, the likelihood of coextracted interferences is high, and the system becomes dependent on complex mass transitions, their corresponding retention windows, and peak picking routines (deconvolution, if available) to do the heavy lifting of fully resolving and identifying the target analytes from the ubiquitous background signals. Furthermore, these selective sample data are collected in limited mass windows, and known to be completely ill-suited for nontargeted interrogation. To examine the samples for new or emerging contaminants, samples must be retained for future analyses on an independent analysis system capable of nontargeted work.

	Alternatively, one could utilize the additional separation efficiency delivered by two-dimensional gas chromatography (GC×GC) to better chromatographically separate target analytes from matrix interferences and a mass spectrometer (such as time-of-flight [TOF]-MS) capable of collecting full scan data. These separations, combined with the full scan data at the sensitivity available with modern TOF-MS systems (low femtograms on column), allow for successful quantitation of target compounds, plus accurate identification of nontargeted analytes in a single injection.

	In this article, we demonstrate improvement in experimental metrics (identification, limit of detection [LOD], and linearity) when performing both quantitative and nontargeted analysis with GC×GC–TOF-MS on spiked extracts from spinach, which is known to be a challenging food matrix.

	Bagged spinach was purchased from a local grocery chain. Using a commercially available QuEChERS extraction kit (Restek PNs 25852 and 26225), a bulk QuEChERS extract was created, and subsequent dSPE cleanup of the spinach was performed. Following the kit instructions (1), 15 g of leaf spinach was homogenized, and combined with 15 mL of 1% acetic acid in acetonitrile in a 50 mL conical tipped tube. The contents of the prepared salt packet (6 g anhydrous MgSO

) were added, the tube immediately capped and then shaken, by hand, for 1 min. After shaking, the mixture was centrifuged for 5 min at 3500 RPM, separating the organic layer from the spinach solids, water, and unbound salts mixture. Post centrifugation, 6 mL of the organic layer was added to a dSPE tube containing 900 mg MgSO

, 15 mg primary and secondary amine (PSA), and 45 mg graphitized carbon black (GCB). This second clean-up step is responsible for the primary removal of pigments (GCB), sugars, organic and fatty acids (PSA), and any remaining water (MgSO

O), though attention must be paid not to overemploy these compounds, as they may also bind pesticides and lower recovery efficiencies. The dSPE tube was immediately capped, shaken for 2 min, and then centrifuged for 5 min at 3500 rpm. The supernatant was removed from the dSPE material by pipette, and stored in a clean, conical tipped tube. Extracts from duplicate, concurrent preparations were pooled, and centrifuged a final time. This final step is not specifically called for in the kit instructions, though we have found it useful, because it helps ensure that any accidentally pipetted dSPE material is removed from the final extract.

	From the pooled extract, a small aliquot was set aside, and the remainder used to create a series of nine matrix-matched quantitation standards, spiked at concentrations from 0.05 to 200 ng/g with a commercially available chlorinated pesticide mix (Restek PN 32564). Dilutions of the stock standard were made so the ratio of spiking standard (20 µL) to matrix extract (180 µL) was consistent in all the experimental standards. The chlorinated mix was chosen because it is less likely that any of the pesticides in this mix would already be present in the spinach, and thus bias the quantitation results. Data from both the matrix-matched standards and unspiked extract were collected in both traditional, single dimension GC as well as GC×GC modes, using conditions described in Table I. Target peak detection, identification, and quantitation limits for each analyte were determined following the criteria for unit mass resolution TOF-MS systems as described in SANTE/11813/2017 (2). Table II shows a reproduction of Table 4 from SANTE/11813/2017, where these criteria are summarized. Following data collection, files were analyzed with ChromaTOF BT software, using both Target Analyte Find (TAF) for quantitative purposes and NonTarget Deconvolution (NTD) for qualitative investigation for incurred contaminants.


	Figure 1: Section of contour plot for the spinach QuEChERS extract with dSPE cleanup (inset upper left), spiked with pesticides at 20 ng/g. In this example, the second dimension of separation effectively moved Fenson and other pesticides away from high concentration matrix interferences. The chromatographic separation of GC×GC significantly improves both analyte detection and quantitation.

	Figure 1 shows an example of data collected using a GC×GC–TOF-MS system. In this plot, areas of increased signal are shown with increasing color intensity (red being the highest), and the location of individual pesticides are indicated with labels and peak markers (black dots). In this sample, it is demonstrated that many of the pesticides are chromatographically resolved from the much more abundant matrix background. Additionally, fenson is highlighted with a red box as an example, since it was effectively separated from a very large area of matrix.


	Figure 2: Example GC and GC×GC quantitation curves. The axes are scaled logarithmically for better visualization of the low concentration section of each curve. Shown in the figure are: (a) chloroneb by GC, (b) chloroneb by GC×GC, (c) mirex by GC, and (d) mirex by GC×GC. 

	With LC–MS experiments, it is well known that high levels of matrix tend to suppress target compound ionization (3), leading to various challenges. In GC–MS experiments, the matrix poses its own set of hurdles, as the matrix tends to interfere by adding spurious signals, sometimes referred to paradoxically as 

. Far from improving the target signal, matrix noise may skew the lower ends of quantitation curves, or unequally affect ion ratio masses. If, by chromatography, one can separate these targeted compounds from the matrix, interference effects are properly mitigated before detection by the mass spectrometer. As a result, effects on targeted signals are decreased, leading to improvements in LODs and quantitation linearity. As one will see, these improvements are entirely due to both the effect of the thermal modulation process, and secondary chromatographic separation when performing GC×GC.


	Figure 3: GC×GC resolution of chlorbenside from the matrix interference. The GC×GC separation allows for a linear and sensitive quantitation curve. In the traditional, single-dimension GC separation the coeluting matrix completely obscures the pesticide below 20 ng/g making consistent, accurate integration impossible.

	Figures 2 and 3 show examples of calibration curves comparing GC to GC×GC data for the compounds chloroneb, mirex, and chlorbenside. When examining the curves in Figure 2, one can see a significant decrease in LOD (factor of 10x) by using GC×GC with nearly equivalent linearity. Also worth noting is the range of the calibration curves. The entire dynamic range has shifted to lower concentrations. The improvements in the calibration curves and LODs for these analytes are due to both the increased selectivity from enhanced chromatographic resolution, and the improved sensitivity afforded by cryogenic zone compression in GC×GC. In the left side of Figure 3, chlorbenside has very significant matrix coelution in the first dimension (

 axis). The calibration curve is improved dramatically by the chromatographic separation of compounds (specifically the second dimension retention time) versus separation by mass only with one-dimensional GC systems. For GC×GC–TOF-MS systems, it is the separation of mass combined with the separation of time (chromatographic) that leads to these advantages. The advantages of the technique become very distinct, as shown in the right side of Figure 3, where the linearity is improved (note the quadratic fit on the simple GC experiment) as well as the limit of detection. Note further that the matrix interference for chlorbenside contains isobaric co-elutions, which would limit quantitation with techniques using GC with quadrupole MS. This trend is further illustrated in Table III, which shows marked improvement in LOD and linearity for GC×GC data compared to the single dimension separation for a variety of spiked pesticides.

	In Figure 4, one can observe standard deconvolution results comparing GC and GC×GC data. Mathematical identification of true signal components over noise and GC co-elutions are handled with the NonTarget Deconvolution algorithm provided by ChromaTOF for Pegasus BT. The identification of these known components was performed by comparison of the deconvoluted spectra to the spectra in the NIST GC–MS library (2017). The initial result lead us to several pesticides and an ultraviolet (UV) stabilizer in both the traditional GC and GC×GC data files (Figure 4). However, in the figure, there is no distinct difference between the capabilities of GC and GC×GC in these cases, since the components are well resolved in the primary GC dimension. Perfect co-elutions do frequently exist in nontargeted analysis of complex matrices, and are beyond the capabilities of mathematical deconvolution. These situations benefit greatly from the use of GC×GC.


	Figure 4: Initially identified incurred pesticides and ultraviolet (UV) stabilizer (Bumetrizole) shown as both (a) traditional GC chromatogram and (b) GC×GC surface plot.

	Figures 5 and 6 show an example of the advantages of GC×GC for nontargeted analysis. Review of the GC×GC data lead to the discovery of an unexpected pesticide, chlorantraniliprole, that was not readily apparent in the single dimension GC data. In the GC×GC contour plot (left) of Figure 5, the pesticide is cleanly resolved in the second dimension, whereas perfectly co-eluting with a matrix component in the first dimension. Contrast this result with Figure 6, where the compound was initially missed, due to a nearly perfect coelution with the abundant matrix compound. In Figure 6, the deconvoluted spectrum obtained from GC analysis is actually a combination of chlorantraniliprole and the interfering component resulting in an awful similarity score. The deconvoluted spectra of Figure 5 show the two compounds successfully separated chromatographically, leading to a clean spectrum and a high similarity score for chlorantraniliprole.


	Figure 5: GC×GC contour and spectral plots of chlorantraniliprole and the interfering matrix compound. The two compound signals have been normalized to allow for easier viewing. Note the chromatographic separation from the column bleed (horizontal, orange band) and the improvement in the deconvolution of both compound spectra are compared to the traditional, single dimension GC separation results as shown in Figure 6.

	High levels of matrix interference can directly affect the ability to accurately and reliably quantitate low levels of pesticides, and further hamper nontargeted workflows. This study was designed to evaluate and demonstrate the effectiveness of GC×GC separations to mitigate these matrix effects, compared to a traditional, single dimension separation for both targeted, quantitative and nontargeted, qualitative workflows. As shown in these examples, the additional level of chromatographic resolution achievable through GC×GC can indeed reduce matrix interferences, and improve the effectiveness of both types of analyses. By decreasing the level of matrix-induced noise, quantitation becomes both more accurate and more sensitive, leading to dramatic improvements in non-target peak detection, identification, and quantitation.


	Figure 6: GC extracted ion chromatogram (XIC) and spectra plots of chlorantraniliprole (orange) and matrix (green). The two compound signals have been normalized to allow for easier viewing. The top, raw spectra plot shows the intensity of both compounds relative to the overriding column bleed signal. The GC-MS library spectrum for chlorantraniliprole (bottom) is shown for reference. In the middle deconvoluted spectra you can see the most prevalent ions from chlorantraniliprole al though, they are obviously dwarfed by ions from the coeluting matrix compound.

	(1) M. Anastassiades, S.J. Lehotay, D. Stajnbaher, and F.J. Schenck, 

	(2) European Commission: Directorate General For Health And Food Safety. SANTE/22813/2017, Guidance document on analytical quality control and method validation procedures for pesticide residues and analysis in food and feed. URL: 

https://ec.europa.eu/food/sites/food/files/plant/docs/pesticides_mrl_guidelines_wrkdoc_2017-11813.pdf

(7) 1041–1044 (2003). DOI: 10.1373/49.7.1041

are with LECO Corporation, in Saint Joseph, Michigan. Direct correspondence to: 

In this study of pesticides in spinach extract, the use of GC×GC–TOF-MS is demonstrated as a methodology to overcome matrix interferences and quickly quantify suspected contaminants. The approach also allows nontargeted analysis using a single sample injection.

Quantitation and Nontargeted Identification of Pesticides in Spinach Extract with GC×GC–TOF-MS

One of the most widely encountered challenges in untargeted metabolomics is how to identify and annotate unknown compounds. Many classes of compounds, such as flavonoids, endocannabinoids, steroids, and phospholipids, are difficult to confidently identify and annotate, due to their structural diversity and the limited availability of reference standards. This study applies a novel mass spectrometry-based flavonoid profiling workflow to characterize and structurally annotate a large number of unknown flavonoids in fruit juice and vegetable juice samples.

	Widely found in fruits and vegetables, as well as plant-derived products such as tea, cocoa, and wine, flavonoids are powerful antioxidants with anti-inflammatory and immune system benefits (1). With diverse and important biological roles, flavonoids have been the focus of much research interest.

	Untargeted flavonoid profiling using high-resolution mass spectrometry (MS) is one of the most widely used approaches for flavonoid analysis, because the resulting data can provide insight into the biological functions and potential health benefits of these compounds. However, the comprehensive identification of flavonoids remains challenging, due to their structural diversity. Because flavonoids are involved in a broad range of secondary metabolic pathways that involve modifications such as acylation, hydroxylation, methylation, prenylation, and glycosylation, large numbers of isomeric and isobaric structures may exist in the same sample. Indeed, over 10,000 flavonoid structures have been isolated (2).

	Despite the vast number of reported flavonoids, the limited availability of authentic flavonoid reference standards, and therefore reference spectra, means that many unknown flavonoid compounds encountered in profiling studies do not have an exact match in MS spectral libraries. This is particularly true for flavonoids with multiple glycoside modifications, which can be very challenging to characterize. Consequently, many flavonoid structural characterization studies published to date have involved the manual assignment of fragment ions generated from tandem mass spectrometry (MS

) (3,4). This painstaking analysis requires in-depth knowledge of flavonoid fragmentation rules, and is both labor- and time-intensive. Moreover, for the majority of flavonoid glycoconjugates, MS

 does not generate sufficient diagnostic fragment ion information to annotate aglycone structures (5), or differentiate between isomers.

	Multiple stage mass spectrometry can be used to systematically fragment analytes to generate more structurally relevant fragment ion information. This approach can be used to generate a so-called "spectral tree" to support the annotation of unknown compounds. Here, we report a novel structure-based flavonoid profiling workflow for the detection and identification of unknown flavonoids in fruit and vegetable juices. The method uses comprehensive fragment ion information generated from higher-energy collisional dissociation (HCD) and collisional induced dissociation (CID) Fourier transform (FT) MS

, for rapid flavonoid annotation. We demonstrate this workflow for the annotation of flavonoid glycoconjugates, although the approach may be applied to other transformation products of secondary metabolism.

	Three commercially available fruit and vegetable juice samples (kale juice; berries juice mixture, consisting of apple, orange, cherry, peach, mango strawberry, and blackberry juices; and a "red" juice mixture, consisting of apple, strawberry, banana, beet, and raspberry juices) were analyzed in this study. Each juice sample was filtered and diluted two-fold with methanol prior to analysis.

	Separations were performed on a Thermo Scientific Vanquish ultrahigh-pressure liquid chromatography (UHPLC) system. The gradient was as follows: 0.5% to 10% B in 1 min, 10 to 30% B in 9 min, 30 to 50% B in 8 min, 50 to 99% B in 4 min, hold at 99% B for 3 min, 99 to 0.5% B in 4.99 min. Mobile phase A was water with 0.1% formic acid, and mobile phase B was methanol with 0.1% formic acid, operating at a flow rate of 200 µL/min. A Thermo Scientific Hypersil Gold (2.1 × 150 mm, 1.9 µm) column, operating at 45 °C, was employed. Each sample (2 µL injection volume) was analyzed in triplicate.

	MS data were collected on a Thermo Scientific Orbitrap ID-X Tribrid mass spectrometer using electrospray ionization (ESI). A default acquisition template was used to collect the maximum amount of MS

 spectral tree data to enable the structure annotation of unknown flavonoid compounds. A short cycle time of 1.2 s was chosen to permit sufficient MS scan points across each peak for precise quantitation, while delivering high resolution spectral data. Because HCD MS

 provides sufficient fragment ions for structure annotation when the flavonoid compounds do not have glycol modifications, only HCD MS

 data were collected for precursor ions in the mass range 150–420 

. For precursor ions in the mass range 420–1200 

, glycol modifications were anticipated, and product ion-dependent MS

 method was employed. This approach involved a high-resolution accurate mass (HRAM) full MS scan, followed by CID MS

 scans. Product ions generated from each MS

 scan were monitored by the mass spectrometer, and an MS

 scan was triggered if one or more predefined neutral sugar losses were detected. An additional MS

 scan was triggered if predefined neutral sugar losses were detected from the MS

 scan. The product ion dependent method and predefined neutral sugar loss scheme are shown in Figure 1.


	Figure 1: Flowchart visualizing the intelligent, automated product ion-dependent MS

 method, and table detailing the targeted sugar neutral loss scheme.

 spectral tree data were initially processed using Thermo Scientific Mass Frontier 8.0 software to determine which compounds included the basic flavonoid structure. Detected flavonoid-related compounds were subsequently annotated using a flavonoid structure database and structural ranking tools within the Thermo Scientific Compound Discoverer 3.0 software.

 approach described in the experimental section was used to systematically fragment flavonoids, generating spectral trees. A representative MS

 spectral tree, generated from an unknown compound detected in the kale juice sample, is shown in Figure 2. The MS

 641.1720 did not return an exact match against the cloud-based mass spectral database (mzCloud) spectral library (Figure 2a). However, fragmenting the MS

 317.0657 resulted in the detection of more structurally relevant fragment ions, which matched with the reference flavonoid isohamnetin (Figure 2b). Thanks to this confident substructure match using MS

 spectral data, we established that part of the structure of the unknown compound had the same structure as the reference, confirming that this unknown compound belongs to the same flavonoid class.

 spectral trees for an unknown compound (M + H: 641.1720) detected in the kale juice sample.

	The Mass Frontier 8.0 software was used to process the MS

 spectral tree data for each juice sample. The software's Joint Components Detection (JCD) algorithm was used to detect unknown compounds from the raw data for each juice, with detected compounds and associated spectral trees then queried against mzCloud's MS

 spectral library containing mass spectra generated from authentic reference material. Using the "subtree search" functionality, experimental MS

	For each unknown compound, the greatest overlap between the spectral tree and the library was identified when performing a subtree search. Exact compound matches were made where MS

 tree matches were found, whereas substructure/subtree matches were made when the compound did not exist in the reference library. These outcomes depended on whether there was an exact or partial MS

 precursors of the unknown compound and library reference matched, and the spectral tree match between the unknown compound and reference yielded a confidence score of greater than 60, full spectral annotation was achieved. Typically, however, the MS

 spectra of the unknown compound did not match any library references, due to the limited availability of reference flavonoid standards. The subtree search was able to overcome this challenge by using the substructure information from the partial MS

 spectral tree match for true unknown compounds. When a subtree match between an unknown compound and a reference was found, the substructure of the unknown compound was identified to match the reference structure or its substructure. In this way, the software was able to detect true unknown flavonoid compounds using molecular weight, retention time, and substructural data.

	The detected compounds that matched both mass lists were selected for further flavonoid structure annotation using the Compound Discoverer 3.0 software. A detected compound with a molecular weight of 742.2320, which matched both mass lists, is shown in Figure 3. Two isomeric flavonoid structures from the Arita Lab 6549 flavonoid structural database, and three isomeric flavonoid structures from the ChemSpider database, were selected as candidate structures for this compound. These five structure candidates were ranked using the Fragment Ion Search (FISh) scoring algorithm; the software first predicted the fragmentation of the five structural candidates based on known fragmentation rules, before calculating the FISh scores through matching predicted fragment ions with observed fragment ions from MS

 data. The structure with the highest FISh score was the best proposed match with the observed fragment ions from the MS

 data, and was the best structure candidate for the unknown flavonoid class compound. For the flavonoid highlighted in Figure 3, the FISh scoring algorithm annotated the compound as narirutin 4'-glucoside.


	Figure 3: (a) An unknown flavonoid compound (MW = 742.23274) that matched both mass lists; (b) candidate structures proposed using the Arita Lab 6549 flavonoid structure database and the ChemSpider database for the identified compound.

 spectral tree data has previously been used to generate detailed fragmentation pathways for flavonoid annotation (5), MS

 workflows have traditionally been limited by issues around ease of use. Establishing instrument methods has historically been challenging for nonexpert users, a challenge that has been further compounded by the fact that MS

 spectral tree data processing has required manual fragment ion assignment. This has proved to be a major process bottleneck, and has required specialist knowledge around flavonoid chemical structure and fragmentation rules.

 instrument template used in this study enabled the acquisition of high-quality MS

 data without the need for any specialist expertise. Furthermore, the analysis tools applied in this workflow, including the subtree search function in the Mass Frontier 8.0 software and the FISh scoring algorithm in the Compound Discoverer 3.0 software, allow fragment ion information from the MS

 spectral tree to be processed automatically, without the need for knowledge of specific fragmentation rules.

	The new workflow presented here makes full use of the deeper and more structurally relevant fragment ion information generated through MS

 analysis, enabling more flavonoid compounds to be annotated relative to an MS

 spectral tree match results provided valuable substructural information for true unknown compounds; with subtree search, the software identified unknown compounds belonging to the flavonoid compound class that did not have exact references in the mzCloud library, but partial matches of the extensive high resolution fragmentation information within mzCloud.

	Tables I and II highlight some of the flavonoids identified by the novel MS

 workflow, and compare these to the compounds identified using an MS

-only approach. Table I demonstrates that, although both methods identified the flavonoid rutin in the juice samples, the MS

 method was able to identify five additional unknown secondary metabolites of this compound. Similarly, Table II shows that an additional three secondary metabolites of the flavonoid isorhamnetin could be identified using the MS

 spectral tree workflow was able to identify a total of 129 flavonoid compounds in the three fruit and vegetable juice samples analyzed in this study. All 62 flavonoid structures identified by the MS

 spectral tree workflow, together with an additional 67 flavonoids that were only detected using the new technique (Figure 4). This represents a twofold increase in the number of annotations relative to the MS


	Figure 4: Number of annotated flavonoid compounds detected by MS

 approach presented here also enables simultaneous quantitation of identified flavonoid compounds and statistical analysis. The instrument template was deliberately designed with a short cycle time of 1.2 s to achieve sufficient scan points across the chromatographic peak. This strategy enabled both precise quantitation, while facilitating the acquisition of detailed MS

 spectral tree data in the same LC–MS run.

	By obtaining wider annotation coverage of flavonoid compounds using this approach, a greater number of data points could be obtained for more precise statistical analysis. A hierarchical cluster analysis (HCA) of the detected flavonoids revealed that the kale and berries juice samples contained a greater number of high abundance flavonoids. In contrast, most flavonoids detected from the "red"

juice sample were present in low concentrations. The principal component analysis (PCA) shown in Figure 5 reveals that the three juice samples are well differentiated. The proximity of the points for each replicate analyses highlights the precision of the method. This approach could potentially be used in food analysis workflows to support juice adulteration testing.


	Figure 5: Principal components analysis (PCA) of flavonoid compounds identified from the three juice samples.

	The limited availability of authentic flavonoid reference standards has proven to be a major challenge for flavonoid structure characterization workflows, with existing profiling efforts largely reliant upon manual and time-consuming assignment of MS

 and higher-order MS fragmentation data. The novel structure-based MS

 flavonoid profiling workflow presented overcomes these challenges to deliver comprehensive unknown compound annotation, without the need for in-depth knowledge of flavonoid fragmentation rules. Using this approach to analyze three juice samples, over twice as many flavonoids were annotated compared to an MS

-only method. This broad coverage enabled PCA to be performed, highlighting distinct differences in the flavonoid composition of the three juices. This workflow is well-suited for the analysis of juices for food integrity applications.

	(1) A.N. Panche, A.D. Diwan, and S.R. Chandra, 

	(2) V.C. George, G. Dellaire, and H.P. Vasantha Rupasinghe, 

	(3) P. Kachlicki, A. Piasecka, M. Stobiecki, and L. Marczak, 

	(4) D. Tsimogiannis, M. Samiotaki, G. Panayotou, and V. Oreopoulou, 

	(5) J.J.J. van der Hooft, J. Vervoort, R. J. Bino, J. Beekwilder, and R.C.H. de Vos, 

 is a Senior Global Marketing and Strategy Manager for connected laboratory and software at Thermo Fisher Scientific. Direct correspondence to: 

A novel mass spectrometry-based flavonoid profiling workflow is applied to characterize and structurally
annotate a large number of unknown flavonoids in fruit juice and vegetable juice samples.

High-Throughput Structure-Based Profiling and Annotation of Flavonoids

	The 67th Conference on Mass Spectrometry and Allied Topics is set to take place June 2–6 at the Georgia World Congress Center in Atlanta, Georgia.

	On Sunday, four tutorial lectures will be given, in two sessions, both starting at 5:00 pm. Tutorial Session I will be chaired by Susan Richardson of the University of South Carolina. Stephen Blanksby of Queensland University of Technology. Gavin Reid of the University of Melbourne will present the first lecture, on lipodomics. At 5:45 pm, Enrico Davoli of the Mario Negri Institute will present the second tutorial, on targeted imaging.

	Erin Baker of North Carolina University will chair Tutorial Session II from 5:00 pm to 6:30 pm. "Native Mass Spectrometry" is the topic of the first tutorial, presented by Michal Sharon of the Weizmann Institute. Following that talk, "Data-Independent Acquisition" will be presented by Birgit Schilling of The Buck Institute.

	The tutorials are followed by the conference opening plenary lecture at 6:45 pm. Mark Z. Jacobson of Stanford University will present a talk titled "Transitioning the World Energy for All Purposes to Stable Electricity Powered by 100% Wind, Water, and Sunlight."

	A welcome reception will follow the plenary lecture, taking place from 7:45 pm until 9:00 pm in the Poster and Exhibit Hall.

	Jefferey Shabanowitz will receive the Al Yergey Mass Spectrometry Scientist Award Monday at 4:45 pm. The award recognizes dedication and significant contributions to mass spectrometry-based science by "unsung heroes." Shabanowitz played a major role in the development of peptide sequence analysis by tandem mass spectrometry.

	The John B. Fenn Award for a Distinguished Contribution in Mass Spectrometry will then be presented to John R. Yates III of The Scripps Research Institute. The award recognizes Yates for his development of automated, large-scale interpretation of peptide tandem mass spectral data. His SEQUEST algorithm laid a critical foundation for the field of proteomics and has enhanced the accuracy and effectiveness of mass spectrometry for understanding important biological and clinical questions. Yates will then give an award lecture.

	The Biemann Medal will be awarded to Sarah Trimpin of Wayne State University at 4:45 p.m. The Biemann Medal is awarded to an individual early in his or her career to recognize significant achievement in basic or applied mass spectrometry. Trimpin's award is for unusual observation of highly charged protein ions in an atmospheric pressure MALDI experiment that led to her discovery that ionization occurs simply bypassing compounds through the inlet of a mass spectrometer.

	On Thursday, Lilly D'Angelo of Global Food & Beverage Technology Associates will give a plenary lecture at 4:45 p.m. titled "Chemistry of Food and Soft Drinks."

	A closing event at the Georgia Aquarium gets under way at 6:30 pm on Thursday. Tickets must be purchased in advance by noon Monday. The ticket cost is $40 per person and includes a dinner buffet, open until 8:00 pm, with dessert and a cash bar available until the close of the event, at 9:30 pm. Tickets also include one drink per ticket for soda, beer, or wine.

	The 68th Annual ASMS Conference will be held May 31–June 4 in Houston, Texas. For more information, visit 

We present a brief preview of this year’s ASMS conference, taking place June 2–6, 2019, in Atlanta, Georgia.

Special Issues-05-01-2019

Vol 37 No 3 CTMS March 2019 Special Issue

Determination of the Relative Prevalence of Lurasidone Metabolites in Urine Using Untargeted HRMS

Quantitation and Nontargeted Identification of Pesticides in Spinach Extract with GC×GC–TOF-MS

High-Throughput Structure-Based Profiling and Annotation of Flavonoids

Preview of the 67th Conference on Mass Spectrometry and Allied Topics