Talanta 143 (2015) 50–55
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Rapid label-free determination of ketamine in whole blood using secondary ion mass spectrometry Hua-Yang Liao a, Jung-Hsuan Chen b,c, Jing-Jong Shyue a,d, Chia-Tung Shun b,e, Huei-Wen Chen c, Su-Wei Liao e,1, Chih-Kang Hong e,1, Pai-Shan Chen b,e,n a
Research Center for Applied Sciences, Academia Sinica, Taipei 115, Taiwan Forensic and Clinical Toxicology Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei 100, Taiwan c Graduate Institute of Toxicology, National Taiwan University College of Medicine, National Taiwan University, Taipei 100, Taiwan d Department of Materials Science and Engineering, National Taiwan University, Taipei 106, Taiwan e Department and Graduate Institute of Forensic Medicine, National Taiwan University, Taipei 100, Taiwan b
art ic l e i nf o
a b s t r a c t
Article history: Received 19 January 2015 Received in revised form 23 April 2015 Accepted 26 April 2015 Available online 9 May 2015
A fast and accurate drug screening to identify the possible presence of a wide variety of pharmaceutical and illicit drugs is increasingly requested in forensic and clinical toxicology. The current first-line screening relies on immunoassays. They determine only certain common drugs of which antibodies are commercially available. To address the issue, a rapid screening using secondary ion mass spectrometry (SIMS) has been developed. In the study, SIMS directly analyzed ketamine in whole blood without any pretreatment. While the untreated blood has a complicated composition, principal-components analysis (PCA) is used to detect unknown specimens by building up an analytical model from blank samples which were spiked with ketamine at 100 ng mL 1, to simulate the presence of ketamine. Each characteristics m/z is normalized and scaled by multiplying the root square of intensity and square of corresponding m/z, developed by National Institute of Standards and Technology (NIST). Using linear regression and the result of PCA, this study enables to correctly distinguish ketamine positive and negative groups in an unknown set of specimens. The quantity of ketamine in an unknown set was determined using gas chromatography–mass spectrometry (GC–MS) as the reference methodology. Instead limited by commercially available antibodies, SIMS detects target molecules straight despite the label-free detection capabilities of SIMS, additional data processing (here, PCA) can be used to fully analyse the produced data, which extends the range of analytes of interest on drug screening. Furthermore, extremely low sample volume, 5 mL, is required owing to the high spatial resolution of SIMS. In addition, while the whole blood is analyzed within 3 min, the whole analysis has been shortened significantly and high throughput can be achieved. & 2015 Elsevier B.V. All rights reserved.
Keywords: Ketamine Whole blood Drug screening Secondary ion mass spectrometry (SIMS) Principal-components analysis (PCA) Gas chromatography–mass spectrometry (GC–MS)
1. Introduction Fast screening for both legal and illegal drugs has historically been accomplished using immunoassays (e.g. enzyme-linked immuno sorbent assay (ELISA) and enzyme multiplied immunoassay technique (EMIT) [1–3]. They are commonly used as first line screening methods in urine, blood or other bio-fluids [4–7]. The screening is performed with rapid on-site devices based on an immunoenzymatic reaction. Although they are relatively fast and offer high throughput analytical applications, immunoassays are n Corresponding author at: Forensic and Clinical Toxicology Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei 100, Taiwan. Tel.: þ886 2 23123456x88879; fax: þ 886 2 23218438. E-mail address: [email protected] (P.-S. Chen). 1 Authors contributed equally to this study.
http://dx.doi.org/10.1016/j.talanta.2015.04.074 0039-9140/& 2015 Elsevier B.V. All rights reserved.
only able to analyze limited common medicinal drugs and drugs of abuse where their antibodies are commercially available [8–10]. Another main deficiency of immunoassays is the cross-reactivity. Compounds with structures similar to the target drug interfere with the results of immunoassays. The amounts of drugs cannot be accurately measured, which leads to prevalence false positive reports or, more importantly, false negative screening results [11– 13]. Therefore, a second analytical method, a chromatographic separation (gas chromatography (GC) or liquid chromatography (LC), typically)–mass spectrometry (MS), has been developed either as a complement to immunoassays in clinical testing or as the analytical method in forensic and doping control applications [14–19]. A single MS experiment coupled with GC or LC is able to analyze complex specimens, detect and characterize a large number of known compounds, as well as identify unknown substances. The
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techniques perform high sensitivity and provide well separation. However, majority specimens are subjected to different preparation methods prior to analysis [20–24]. It is usually time consuming and labor intensive. Important missions to be achieved are shortening pretreatment procedure and minimizing sample volume required in both approaches. Alternatively, secondary ion mass spectrometry (SIMS), which sputters a specimen surface by ion bombardment has been emerged to identify target analytes on the biological samples. SIMS is capable of providing lateral resolution and imaging sensitivity on the order of parts-per-million [25–32]. SIMS is regarded as one of the most important microanalytical tools in the semiconductor material industry for analyzing trace amount of dopants and organic impurities. It allows the detection of the elements and small molecular fragments as well as the separation of stable and radioactive isotopes on a surface of materials. Local concentration can also be measured by scanning the ion beam and collecting spectra pixel by pixel with a lateral resolution commonly from 50 nm to 200 nm [29,33,34]. To map a complete surface of complex bio-sample, such as tissues or cells, it takes longer time to obtain enough counts in each pixel. For a homogeneous sample such as blood, an analysis focuses on extracting target analyte ions from a sample rather than profiling its surface, which means only few minutes are required to collect enough data. With the high-energy ion bombardment, the molecular structure in the surface tends to be broken hence SIMS typically uses isotope or hetero-element labeling to aid the identification of molecules of interest. With the recent development of cluster primary ion such as C 60 þ , more surface localized interaction and higher sputter rate are obtained and it is possible to generate secondary molecular ions of high mass that identified molecular species directly [35–38]. In other words, label-free analysis of biological and organic surface can be realized. To further enhance the intensity of these molecular ions and hence the sensitivity to molecular information, methods like enhanced oxygen-uptake [39], ion cosputtering [33,40], and optimization of analytical parameters [41] are also being developed. In the study, C60 þ -based molecular SIMS has been developed to determine the administration of ketamine in whole blood. Ketamine, marketed as an anesthetic for human and veterinary, continues to become popular in drug abuse scene. As its self-administration behavior is similar to central nervous system (CNS) depressant drugs, ketamine abuse quickly spreads worldwide. It has been then replaced in the controlled substance in many countries. Recent reports have indicated that ketamine abusers appeared to have severe bladder and kidney damage, such as ulcerative cystitis, severe dysuria, or bladder dysfunction, which are related to the effect of ketamine dose [42–47]. Biological fluids such as urine [48,49] and blood [48,50,51] are commonly used for determining the administration of ketamine. In Taiwan, a cutoff level of 100 ng mL 1 is set in human urine. However, there is no criteria for whole blood analysis. Whole blood usually has complex interferences which cause many difficult analytical problems. A sample preparation is required before analysis. Therefore, it is important to develop a rapid and precise screening method. To test the novel technique the same cutoff of ketamine is applied to whole blood analysis here. In the study, extremely low blood volume, 5 mL, was used for detection of ketamine without sample pre-treatment. GC–MS was employed as the reference methodology, for the qualitative confirmation as well as for the quantitative determinations of ketamine. Principal-components analysis (PCA) was performed on characteristic mass fragment intensities from each specimen. With the assistance of PCA, SIMS enabled to characterize the most chemically unique mass fragments of
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ketamine in complex blood samples and remove much of the ambiguity of similar low mass species that arose from biological samples.
Fig. 1. Scaled spectrum for negative control and spiked sample group. The y-axis scaled by multiplying the root square of intensity and square of corresponding m/z to emphasize the high m/z region. The broken lines indicated the standard deviation.
Fig. 2. (a) Distribution of scores for each component (empty symbol) and its percentage in variation (filled symbol). The standard deviation in scores is shown as bars. (b) PCA scores calculated using negative control (square) and spiked sample (circle) projected on PC1 and PC2 space. The elliptical confidence level is calculated using robust method. The scores of unknown samples calculated using linear regression are overlaid as triangles.
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Fig. 3. PCA loadings at characteristic fragment of ketamine: (a) PC1, (b) PC2, (c) PC3 and (d) PC4. (m/z 125 and 127 [CH2ClC6H4] þ ; m/z 152 and 154 [M–CO–NHCH3–C2H3] þ ; m/z 165 and 167 [M–CO–NHCH3–CH2] þ ; m/z 179 and 181 [M–CO–NHCH3] þ ; m/z 207 and 209 [M–NHCH3] þ ; m/z 220 and 222 [M–OH] þ ; m/z 224 and 226 [M–CH2 þ H] þ ; m/z 238 and 240 [Mþ H] þ ; m/z 254 and 252 [Mþ CH3] þ ; m/z 260 and 262 [MþNa] þ .)
Table 1 Method validation for GC–MS confirmatory analysis. Compound Linearity (ng mL 1)a
Ketamine
a
20–20,000
r2
LOD (ng mL 1)
Spiked blood
0.9995 20
Concentration (ng mL 1)
RSD (%) intraday, n ¼5
RSD (%) interday, n¼ 15
Calculated concentration (ng mL 1)
Difference (%)
100 10,000
2.7 3.1
5.9 5.0
95.7 9908.3
4.3 0.9
Blood sample spiked with 20, 40, 100, 200, 500, 1000, 5000, 10,000 and 20,000 ng mL 1, n¼ 3.
2. Materials and methods Table 2 Ketamine analysis using SIMS and GC–MS.
2.1. Material and reagents
Sample no.
GC–MS Concentration (ng mL 1)
SIMS Screening (100 ng mL 1)
105 121 144 165 186 194 199 322 342 394
ND ND ND ND 404.2 109.4 263.5 ND 447.4 414.5
þ þ þ þ þ
ND: not detected. þ : ketamine positive. : ketamine negative.
Ketamine standard solution (1 mg mL 1 in methanol), deuterated internal standard (IS), and ketamine-d4 (100 μg mL 1 in methanol) were purchased from Cerilliant Corporation (Texas, USA). HPLC grade solvents, such as hexane and dichloromethane, were purchased from J. T. Baker (USA). Sodium carbonate (Na2CO3) was provided by Sigma-Aldrich (St. Louis, MO). Deionized water was purified in a Milli-Q RO Plus 60 filtration system (Millipore, Massachusetts, USA) before use for preparing aqueous solutions. 2.2. SIMS measurement The instrument was based on a PHI 5000 VersaProbe scanning X-ray microprobe (ULVAC-PHI, Chigasaki, Japan) [48]. A Wien-filtered C60 þ ion source (IOG C60-10, Ionoptika, Chandler's Ford, UK)
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was operated at 10 nA (measured with an Au target) and 10 kV and rastered onto a 2 mm 2 mm area at the eucentric position with an incident angle of 70°. The quadrupole mass analyzer (EQS1000, Hiden, Warrington, UK) was located at 147° from the C60 þ ion source with a 35° take-off angle and a working distance of 60 mm. The mass analyzer was optimized for a count rate of 12C þ and the m/z was calibrated with ionized C60 vapor at m/z 720. During acquisition, a flooding electron source biased at 1 V was used to compensate for the charge-up effect. The sample surface is sputter-cleaned for 5 min to reach steady state; then 4–6 spectra were acquired for each specimen. The typical sampling volume is in the order of nL. 2.3. Multivariate analysis Spectra acquired from negative control group and spiked sample group are analyzed by robust principal component analysis (robPCA) using ChemoSpec: Exploratory Chemometrics for Spectroscopy (version 1.51-2) and SparseM: Sparse Linear Algebra (version 0.96) package in R: A Language and Environment for Statistical Computing (version 2.15.2) software. The intensity at each m/z is first normalized by total intensity then scaled by multiplying the root square of intensity and square of corresponding m/z, according to the mass spectral search software developed by National Institute of Standards and Technology (NIST). For scaling the spectrum, because the ion yield decreased significantly with increasing m/z, the information at higher m/z region where the fragments of ketamine are present is emphasized in order to down-play the contribution of elemental information at low m/z. Unscaled covariance matrix is then used to digest the high-dimension spectra to principal components. For unknown samples, spectra from each specimen is averaged, normalized and scaled; then linear regression is used to calculate the scores of first 4 principal components. 2.4. GC–MS measurement Analysis of ketamine was performed on a Thermo Trace GC Ultra gas chromatograph instrument (Texas, USA) equipped with a split/splitless injector and coupled with a thermo ISQ mass spectrometer (Texas, USA). A 30 m DB-5MS fused silica capillary column (0.25 mm I.D., 0.25 mm film thickness) was used for separation of the analyte. Initially the column temperature was at 130 °C for 1 min, and then it was ramped to 170 °C at 25 °C min 1. It was further raised to 200 °C at 20 °C min 1 and kept for 4 min; it was then increased to 280 °C at 60 °C min 1 and held for 2 min. The purity of helium carrier gas was 99.9995%, and the flow rate was 1.0 mL min 1. The inlet was operated at 300 °C and was used in the pulsed splitless mode. Ionization was operated in the electron impact (EI) mode at 70 eV. The temperature of the ion source was 230 °C and the temperature of the quadrupole mass filter was 150 °C. The mass spectrometer (MS) was operated in the total ion chromatogram (TIC) mode and a mass range of m/z 50–250 was scanned to confirm the retention time of ketamine. The selected ion monitoring (SIM) mode was used for the determination of the target compound. Three ion transitions for ketamine were monitored as follows: 180 (quantification), m/z 182 and 209. 2.5. Standard and working solutions Ketamine was prepared in 10 mL of methanol to obtain a standard stock solution with a concentration of 100 mg mL 1, which was stored at 20 °C. A working solution of 10 mg mL 1 containing ketamine was prepared in methanol every week and stored at 4 °C. A series of working solutions were made by diluting the 10 mg mL 1 solution with methanol daily and stored at 4 °C.
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2.6. Sample screening using SIMS The samples were human whole blood from forensic cases. The samples were vortexed for 2 min. 5 μL of blood was spotted on a piece of 52 nm thick thermal oxide on Si wafer (1 cm 1 cm). The wet blood spots were then placed under a nitrogen flow to dryness for 30 min. During the initial evaluation of SIMS, a portion of 1 mL blank whole blood was spiked with 10 mL, 10 mg mL 1 of ketamine, which was considered as spiked sample group and followed the above procedure. 2.7. Sample preparation for GC–MS analysis To 0.5 mL of each whole blood sample, 20 mL of 10 mg mL 1 ketamine-d4 (internal standard, IS) was spiked. 1 mL of 0.1 M Na2CO3 (aq) and 1.5 mL of dichloromethane: hexane (1:3 v/v) were added to blood samples, which were then vortexed for 2 min. The mixture was centrifuged at 4000 rpm for 10 min. 500 μL of the organic phase was transferred into a 12 mm 75 mm glass tube and evaporated to dryness under nitrogen at 40 °C (approximately 8 min). The residue was reconstituted in 50 mL of ethyl acetate and 1.5 mL was injected into the GC–MS for further analysis. 2.8. Confirmatory analysis by GC–MS The samples found containing no measurable amount of ketamine using the GC–MS method were used as negative control and for the preparation of calibration curve. The concentrations used for calibrants were 20, 40, 100, 200, 500, 1000, 5000, 10,000 and 20,000 ng mL 1. Two controls spiked ketamine at 100 and 10,000 ng mL 1 were prepared in whole blood described above. For identification, ion ratios were calculated for ketamine in the samples using the ions m/z 180, 182, and 209, and compared with those from a QC containing 100 ng mL 1 of ketamine. The concentration of ketamine in each sample was interpolated from the calibration line constructed using a ratio of the peak area of ketamine (m/z 180) to that of ketamine-d4 (m/z 184). Linear regression lines were calculated and the correlation coefficients were better than 0.995 in all cases. Positive results were reported if the concentration of ketamine was greater than 100 ng mL 1. The criteria applied for acceptable GC–MS identification were those used routinely in our laboratory. The relative retention time of the substance to be identified should not differ by more than 2%. Moreover, the abundances of at least three diagnostic ions should not differ by more than 20% relative for ion abundances. The abundance of a diagnostic ion must be determined from integrated ion chromatograms, where the abundance of the least intense ion must have the signal to noise ratio greater than 3:1.
3. Results and discussion The aim of this study was to screen ketamine samples in whole blood applying no sample preparation. Our approach used GC–MS to confirm the concentration of ketamine in all specimens. A group determined without the presence of ketamine was considered as blank and negative control group. Though SIMS does not allow the separation of targets and interferences, the use of instrument with automated fragmentation with PCA model enables the distinctions to be made. To optimize the parameters of SIMS and PCA model, blank whole blood was spiked with ketamine at 100 ng mL 1. The scaled spectra for ketamine spiked sample and negative control are shown in Fig. 1. Owing to the complex mixture of the whole blood, the pattern of ketamine was overlapped by strong backgrounds and it was not possible to distinguish the
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administration of ketamine. Using PCA, the screening plot (Fig. 2a) indicated that the first 4 PCs described more than 90% of variations. Hence 4 PCs were used for subsequent analysis. It is clear that while the PC1 described the most (59%) variation, the score cannot completely discriminate the spiked samples from negative controls. On the other hand, the PC2 clearly separated the different groups while described 23% of variance. By plotting the scores in PC1 and PC2 space, Fig. 2b reveals clear discrimination of sample groups where spiked samples are located at upper-left quadrant and negative controls are at lowerright quadrant. In other words, negative PC1 and positive PC2 suggest the presence of ketamine abuse. For easier comparison, the referencing spectrum of ketamine was scaled and overlaid on the loading and the loadings of characteristic ketamine fragments are listed in Fig. 3. It is clear that besides [M þH] þ of ketamine (with 37Cl at m/z 240), [M–CH2 þH] þ (at m/z 224 and with 37Cl at 226), [M–NHCH3] þ (with m/z 37Cl at 209) and peaks at lower m/z that may overlap with other species, negative PC1 loadings corresponding to the characteristic molecular fragments of ketamine are found. Therefore, lower PC1 score may suggest the presence of ketamine. However, owing to the overlapping of PC1 scores prevent a clear discrimination of spiked samples from negative controls (Fig. 2a), PC1 alone cannot be used to identify the administration of ketamine. For PC2, positive loadings corresponding to [MþNa] þ , [Mþ CH3] þ , [Mþ H] þ , [M–CH2 þ H] þ , [M–NHCH3] þ , [M–CO–NHCH3] þ , [M–CO–NHCH3–CH2] þ , [M–CO–NHCH3–C2H3] þ and [CH2ClC6H4] þ (with 37Cl at m/z 240) were identified. Therefore, the positive PC2 clearly indicated the presence of ketamine. While the higher background at high mass shows negative loading of PC1 and positive loading of PC2, it may be argued that the background dominated the separation of groups. However, since the averaged spectra showed comparable background (Fig. 2) and the analysis of variance (ANOVA) reveals no significant difference between the sample groups at the level of 0.05, the effect of the background was dismissed. Using the first 4 PCA loading determined with spiked sample and negative control, scores of spectra obtained from unknown samples were calculated using linear regression. The resulting scores (triangle) are overlay on the PC1 and PC2 space as shown in Fig. 2b. The analysis suggests that ketamine was present over the 100 ng mL 1 in samples 186, 194, 199, 342, and 394 while samples 105, 121, 144, 165 and 322 were negative. To validate the result, the presences of ketamine in these unknown samples were analyzed using GC–MS method. For confirmatory analysis, liquid–liquid extraction was applied prior to GC–MS analysis, not least to pre-concentrate ketamine from whole blood. The SIM mode also eliminated the contribution of interferences. Blank samples were spiked with ketamine for method validation. The estimated linearity is shown in Table 1. A calibration curve was constructed for different concentrations ranging from 20 to 20,000 ng mL 1 in whole blood. The coefficient of determination (r2) was 0.9995. The limit of detection (LOD) was 20 ng mL 1. Intraday relative standard deviations (RSDs) were 2.7% and 3.1% at concentrations of 100 and 10,000 ng mL 1, respectively. Interday RSDs were 5.9% and 5.0% at concentrations of 100 and 10,000 ng mL 1, respectively. Three diagnostic ions were used to identify ketamine in GC–MS (m/z 180, 182 and 209) corresponded to those observed in SIMS. Five of 10 unknown samples were confirmed the administration of ketamine, which were samples 186, 194, 199, 342, and 394. Ketamine was not detected in the other samples. Comparison of SIMS with the more established GC–MS showed good agreement between the methods (Table 2). It is worth noting that in Fig. 2b the negative unknown cases are located far from the overlay area
with the positive group. On the other hand, the positive samples 194 and 199, which confirmed relative lower ketamine concentrations (109.4 and 263.5 ng mL 1, respectively) are close to the border of the two groups. The other positive samples which contained ketamine more than 404 ng mL 1 spread away from the overlay area. This result indicates that the SIMS–PCA method is correlative to the regular GC–MS methods and could be used for rapid screening with extremely low sample consumption.
4. Conclusions The proposed label-free SIMS–PCA method offers a promising alternative to the conventional drug screening methods. The common immunoassay cannot provide an accurate measurement near cut-off values and generate false reports. Screening using SIMS with PCA has been developed a complement to immunoassays in clinical and forensic testing. It successfully distinguished the difference of positive and negative ketamine groups from complex blood samples. Although SIMS with PCA is not really suitable for quantification, it provides a quick pre-screening prior to routine GC–MS analysis. It requires extremely low volume (5 mL in this work and the actual analysis volume is mm mm nm hence only pL is required in principle) of whole blood and no sample preparation is necessary. In other words, it is ideal for trace analysis. In addition, SIMS is straight forward to automate the data acquisition and it only takes a few minutes for each sample and is suitable for rapid, high throughput analysis. The study provides a desirable method on drug screening in terms of accuracy, speed and sample volume.
References [1] S.H. Cosbey, K.L. Peters, A. Quinn, A. Bentley, J. Anal. Toxicol. 37 (2013) 74–82. [2] P. McLaughlin, D. Pounder, P. Maskell, D. Osselton, Forensic Toxicol. 31 (2013) 113–118. [3] M. Petrie, K.L. Lynch, S. Ekins, J.S. Chang, R.J. Goetz, A.H.B. Wu, M.D. Krasowski, Clin. Toxicol. 51 (2013) 83–91. [4] J. Rohrich, J. Becker, T. Kaufmann, S. Zorntlein, R. Urban, Forensic Sci. Int. 215 (2012) 3–7. [5] P. Fernandez, S. Seoane, C. Vazquez, A.M. Bermejo, A.M. Carro, R.A. Lorenzo, Anal. Bioanal. Chem. 401 (2011) 2177–2186. [6] K.M. Kirschbaum, F. Musshoff, R. Schmithausen, S. Stockhausen, B. Madea, Forensic Sci. Int. 212 (2011) 252–255. [7] C. Moore, Drug Test. Anal. 3 (2011) 166–168. [8] A. Arroyo, M. Sánchez, M. Palahí, M. Barbal, M.T. Marrón, A. Mora, Legal Med. 13 (2011) 240–244. [9] R.D. Johnson, S.R. Botch, J. Anal. Toxicol. 35 (2011) 65–74. [10] C.A. Mueller, W. Weinmann, S. Dresen, A. Schreiber, M. Gergov, Rapid Commun. Mass Spectrom. 19 (2005) 1332–1338. [11] S.W. Cotten, D.L. Duncan, E.A. Burch, Clin. Biochem. 45 (2012) 605–609. [12] Y.C. Kaplan, A. Erol, B. Karadas, Ther. Drug Monitor. 34 (2012) 493–495. [13] B.T. Ly, S.L. Thornton, C. Buono, J.A. Stone, A.H.B. Wu, Ann. Emerg. Med. 59 (2012) 545–547. [14] R. Diaz, M. Ibanez, J.V. Sancho, F. Hernandez, J. Chromatogr. A 1276 (2013) 47–57. [15] X. Ding, H. Ghobarah, X.L. Zhang, A. Jaochico, X.R. Liu, G. Deshmukh, B.M. Liederer, C. Hop, B. Dean, Rapid Commun. Mass Spectrom. 27 (2013) 401–408. [16] C. Gomez-Canela, N. Cortes-Francisco, F. Ventura, J. Caixach, S. Lacorte, J. Chromatogr. A 1276 (2013) 78–94. [17] J.C. Dominguez-Romero, J.F. Garcia-Reyes, R. Martinez-Romero, P. Berton, E. Martinez-Lara, M.L. Del Moral-Leal, A. Molina-Diaz, Anal. Chim. Acta 761 (2013) 1–10. [18] A. Masia, M. Ibanez, C. Blasco, J.V. Sancho, Y. Pico, F. Hernandez, Anal. Chim. Acta 761 (2013) 117–127. [19] R.C. Yue, L. Zhao, Y.H. Hu, P. Jiang, S.P. Wang, L. Xiang, W.C. Liu, W.D. Zhang, R.H. Liu, J. Ethnopharmacol. 145 (2013) 465–475. [20] R. Hoesli, A. Tobler, S. Koenig, S. Muehlebach, J. Anal. Toxicol. 37 (2013) 102–109. [21] A.F. Felicioni Oliveira, E.C. de Figueiredo, A.J. dos Santos-Neto, J. Pharm. Biomed. Anal. 73 (2013) 53–58. [22] D.R. Bunch, S.H. Wang, Anal. Bioanal. Chem. 405 (2013) 3021–3033. [23] S. Tulipani, R. Llorach, M. Urpi-Sarda, C. Andres-Lacueva, Anal. Chem. 85 (2013) 341–348.
H.-Y. Liao et al. / Talanta 143 (2015) 50–55
[24] F.S. Mirnaghi, J. Pawliszyn, Anal. Chem. 84 (2012) 8301–8309. [25] J.S. Fletcher, X.A. Conlan, N.P. Lockyer, J.C. Vickerman, Appl. Surf. Sci. 252 (2006) 6513–6516. [26] E.A. Jones, N.P. Lockyer, J.C. Vickerman, Int. J. Mass Spectrom. 260 (2007) 146–157. [27] S.F. Boulyga, Int. J. Mass Spectrom. 307 (2011) 200–210. [28] A. Delcorte, P. Bertrand, B.J. Garrison, K. Hamraoui, T. Mouhib, O.A. Restrepo, C.N. Santos, S. Yunus, Surf. Interface Anal. 42 (2010) 1380–1386. [29] J.S. Fletcher, A. Henderson, G.X. Biddulph, S. Vaidyanathan, N.P. Lockyer, J. C. Vickerman, Appl. Surf. Sci. 255 (2008) 1264–1270. [30] A. Piwowar, J. Fletcher, N. Lockyer, J. Vickerman, Surf. Interface Anal. 43 (2011) 207–210. [31] C.J. Chang, H.Y. Chang, Y.W. You, H.Y. Liao, Y.T. Kuo, W.L. Kao, G.J. Yen, M.H. Tsai, J.J. Shyue, Anal. Chim. Acta 718 (2012) 64–69. [32] E.J. Lanni, S.S. Rubakhin, J.V. Sweedler, J. Proteomics 75 (2012) 5036–5051. [33] H.-Y. Liao, K.-Y. Lin, W.-L. Kao, H.-Y. Chang, C.-C. Huang, J.-J. Shyue, Anal. Chem. 85 (2013) 3781–3788. [34] R. Levisetti, Annu. Rev. Biophys. Biophys. Chem. 17 (1988) 325–347. [35] N. Winograd, B.J. Garrison, Annu. Rev. Phys. Chem. 61 (2010) 305–322. [36] S. Muramoto, D.J. Graham, M.S. Wagner, T.G. Lee, D.W. Moon, D.G. Castner, J. Phys. Chem. C 115 (2011) 24247–24255. [37] J.C. Vickerman, Surf. Sci. 603 (2009) 1926–1936. [38] C.M. Mahoney, Mass Spectrom. Rev. 29 (2010) 247–293. [39] H.Y. Liao, M.H. Tsai, Y.W. You, H.Y. Chang, C.C. Huang, J.J. Shyue, Anal. Chem. 84 (2012) 3355–3361.
55
[40] Y.W. You, H.Y. Chang, W.C. Lin, C.H. Kuo, S.H. Lee, W.L. Kao, G.J. Yen, C.J. Chang, C.P. Liu, C.C. Huang, H.Y. Liao, J.J. Shyue, Rapid Commun. Mass Spectrom. 25 (2011) 2897–2904. [41] H.Y. Liao, M.H. Tsai, H.Y. Chang, Y.W. You, C.C. Huang, J.J. Shyue, Anal. Chem. 84 (2012) 9318–9323. [42] P.S. Chu, S.C. Kwok, K.M. Lam, T.Y. Chu, S.W. Chan, C.W. Man, W.K. Ma, K.L. Chui, M.K. Yiu, Y.C. Chan, M.L. Tse, F.L. Lau, Hong Kong Med. J. 13 (2007) 311–313. [43] R. Shahani, C. Streutker, B. Dickson, R.J. Stewart, Urology 69 (2007) 810–812. [44] Y.-W. Chiew, C.-S. Yang, Kidney Int. 76 (2009) 123–124. [45] T.H. Tsai, T.L. Cha, C.M. Lin, C.W. Tsao, S.H. Tang, F.P. Chuang, S.T. Wu, G.H. Sun, D.S. Yu, S.Y. Chang, Int. J. Urol. 16 (2009) 826–829. [46] C.H. Chen, M.H. Lee, Y.C. Chen, M.F. Lin, J. Formos. Med. Assoc. 110 (2011) 787–791. [47] S. Tan, W.M. Chan, M.S. Wai, L.K. Hui, V.W. Hui, A.E. James, L.Y. Yeung, D.T. Yew, Microsc. Res. Tech. 74 (2011) 1192–1198. [48] L. Zhang, Z.-H. Wang, H. Li, Y. Liu, M. Zhao, Y. Jiang, W.-S. Zhao, J. Chromatogr. B 955–956 (2014) 10–19. [49] Y. Yamini, S. Seidi, R. Feizbakhsh, T. Baheri, M. Rezazadeh, J. Sep. Sci. 37 (2014) 2364–2371. [50] S. Odoardi, L. Anzillotti, S. Strano-Rossi, Forensic Sci. Int. 243 (2014) 61–67. [51] N.S. Nosseir, G. Michels, P. Binder, M.H.J. Wiesen, C. Muller, J. Chromatogr. B 973 (2014) 133–141.
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Talanta journal homepage: www.elsevier.com/locate/talanta
Rapid label-free determination of ketamine in whole blood using secondary ion mass spectrometry Hua-Yang Liao a, Jung-Hsuan Chen b,c, Jing-Jong Shyue a,d, Chia-Tung Shun b,e, Huei-Wen Chen c, Su-Wei Liao e,1, Chih-Kang Hong e,1, Pai-Shan Chen b,e,n a
Research Center for Applied Sciences, Academia Sinica, Taipei 115, Taiwan Forensic and Clinical Toxicology Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei 100, Taiwan c Graduate Institute of Toxicology, National Taiwan University College of Medicine, National Taiwan University, Taipei 100, Taiwan d Department of Materials Science and Engineering, National Taiwan University, Taipei 106, Taiwan e Department and Graduate Institute of Forensic Medicine, National Taiwan University, Taipei 100, Taiwan b
art ic l e i nf o
a b s t r a c t
Article history: Received 19 January 2015 Received in revised form 23 April 2015 Accepted 26 April 2015 Available online 9 May 2015
A fast and accurate drug screening to identify the possible presence of a wide variety of pharmaceutical and illicit drugs is increasingly requested in forensic and clinical toxicology. The current first-line screening relies on immunoassays. They determine only certain common drugs of which antibodies are commercially available. To address the issue, a rapid screening using secondary ion mass spectrometry (SIMS) has been developed. In the study, SIMS directly analyzed ketamine in whole blood without any pretreatment. While the untreated blood has a complicated composition, principal-components analysis (PCA) is used to detect unknown specimens by building up an analytical model from blank samples which were spiked with ketamine at 100 ng mL 1, to simulate the presence of ketamine. Each characteristics m/z is normalized and scaled by multiplying the root square of intensity and square of corresponding m/z, developed by National Institute of Standards and Technology (NIST). Using linear regression and the result of PCA, this study enables to correctly distinguish ketamine positive and negative groups in an unknown set of specimens. The quantity of ketamine in an unknown set was determined using gas chromatography–mass spectrometry (GC–MS) as the reference methodology. Instead limited by commercially available antibodies, SIMS detects target molecules straight despite the label-free detection capabilities of SIMS, additional data processing (here, PCA) can be used to fully analyse the produced data, which extends the range of analytes of interest on drug screening. Furthermore, extremely low sample volume, 5 mL, is required owing to the high spatial resolution of SIMS. In addition, while the whole blood is analyzed within 3 min, the whole analysis has been shortened significantly and high throughput can be achieved. & 2015 Elsevier B.V. All rights reserved.
Keywords: Ketamine Whole blood Drug screening Secondary ion mass spectrometry (SIMS) Principal-components analysis (PCA) Gas chromatography–mass spectrometry (GC–MS)
1. Introduction Fast screening for both legal and illegal drugs has historically been accomplished using immunoassays (e.g. enzyme-linked immuno sorbent assay (ELISA) and enzyme multiplied immunoassay technique (EMIT) [1–3]. They are commonly used as first line screening methods in urine, blood or other bio-fluids [4–7]. The screening is performed with rapid on-site devices based on an immunoenzymatic reaction. Although they are relatively fast and offer high throughput analytical applications, immunoassays are n Corresponding author at: Forensic and Clinical Toxicology Center, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei 100, Taiwan. Tel.: þ886 2 23123456x88879; fax: þ 886 2 23218438. E-mail address: [email protected] (P.-S. Chen). 1 Authors contributed equally to this study.
http://dx.doi.org/10.1016/j.talanta.2015.04.074 0039-9140/& 2015 Elsevier B.V. All rights reserved.
only able to analyze limited common medicinal drugs and drugs of abuse where their antibodies are commercially available [8–10]. Another main deficiency of immunoassays is the cross-reactivity. Compounds with structures similar to the target drug interfere with the results of immunoassays. The amounts of drugs cannot be accurately measured, which leads to prevalence false positive reports or, more importantly, false negative screening results [11– 13]. Therefore, a second analytical method, a chromatographic separation (gas chromatography (GC) or liquid chromatography (LC), typically)–mass spectrometry (MS), has been developed either as a complement to immunoassays in clinical testing or as the analytical method in forensic and doping control applications [14–19]. A single MS experiment coupled with GC or LC is able to analyze complex specimens, detect and characterize a large number of known compounds, as well as identify unknown substances. The
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techniques perform high sensitivity and provide well separation. However, majority specimens are subjected to different preparation methods prior to analysis [20–24]. It is usually time consuming and labor intensive. Important missions to be achieved are shortening pretreatment procedure and minimizing sample volume required in both approaches. Alternatively, secondary ion mass spectrometry (SIMS), which sputters a specimen surface by ion bombardment has been emerged to identify target analytes on the biological samples. SIMS is capable of providing lateral resolution and imaging sensitivity on the order of parts-per-million [25–32]. SIMS is regarded as one of the most important microanalytical tools in the semiconductor material industry for analyzing trace amount of dopants and organic impurities. It allows the detection of the elements and small molecular fragments as well as the separation of stable and radioactive isotopes on a surface of materials. Local concentration can also be measured by scanning the ion beam and collecting spectra pixel by pixel with a lateral resolution commonly from 50 nm to 200 nm [29,33,34]. To map a complete surface of complex bio-sample, such as tissues or cells, it takes longer time to obtain enough counts in each pixel. For a homogeneous sample such as blood, an analysis focuses on extracting target analyte ions from a sample rather than profiling its surface, which means only few minutes are required to collect enough data. With the high-energy ion bombardment, the molecular structure in the surface tends to be broken hence SIMS typically uses isotope or hetero-element labeling to aid the identification of molecules of interest. With the recent development of cluster primary ion such as C 60 þ , more surface localized interaction and higher sputter rate are obtained and it is possible to generate secondary molecular ions of high mass that identified molecular species directly [35–38]. In other words, label-free analysis of biological and organic surface can be realized. To further enhance the intensity of these molecular ions and hence the sensitivity to molecular information, methods like enhanced oxygen-uptake [39], ion cosputtering [33,40], and optimization of analytical parameters [41] are also being developed. In the study, C60 þ -based molecular SIMS has been developed to determine the administration of ketamine in whole blood. Ketamine, marketed as an anesthetic for human and veterinary, continues to become popular in drug abuse scene. As its self-administration behavior is similar to central nervous system (CNS) depressant drugs, ketamine abuse quickly spreads worldwide. It has been then replaced in the controlled substance in many countries. Recent reports have indicated that ketamine abusers appeared to have severe bladder and kidney damage, such as ulcerative cystitis, severe dysuria, or bladder dysfunction, which are related to the effect of ketamine dose [42–47]. Biological fluids such as urine [48,49] and blood [48,50,51] are commonly used for determining the administration of ketamine. In Taiwan, a cutoff level of 100 ng mL 1 is set in human urine. However, there is no criteria for whole blood analysis. Whole blood usually has complex interferences which cause many difficult analytical problems. A sample preparation is required before analysis. Therefore, it is important to develop a rapid and precise screening method. To test the novel technique the same cutoff of ketamine is applied to whole blood analysis here. In the study, extremely low blood volume, 5 mL, was used for detection of ketamine without sample pre-treatment. GC–MS was employed as the reference methodology, for the qualitative confirmation as well as for the quantitative determinations of ketamine. Principal-components analysis (PCA) was performed on characteristic mass fragment intensities from each specimen. With the assistance of PCA, SIMS enabled to characterize the most chemically unique mass fragments of
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ketamine in complex blood samples and remove much of the ambiguity of similar low mass species that arose from biological samples.
Fig. 1. Scaled spectrum for negative control and spiked sample group. The y-axis scaled by multiplying the root square of intensity and square of corresponding m/z to emphasize the high m/z region. The broken lines indicated the standard deviation.
Fig. 2. (a) Distribution of scores for each component (empty symbol) and its percentage in variation (filled symbol). The standard deviation in scores is shown as bars. (b) PCA scores calculated using negative control (square) and spiked sample (circle) projected on PC1 and PC2 space. The elliptical confidence level is calculated using robust method. The scores of unknown samples calculated using linear regression are overlaid as triangles.
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Fig. 3. PCA loadings at characteristic fragment of ketamine: (a) PC1, (b) PC2, (c) PC3 and (d) PC4. (m/z 125 and 127 [CH2ClC6H4] þ ; m/z 152 and 154 [M–CO–NHCH3–C2H3] þ ; m/z 165 and 167 [M–CO–NHCH3–CH2] þ ; m/z 179 and 181 [M–CO–NHCH3] þ ; m/z 207 and 209 [M–NHCH3] þ ; m/z 220 and 222 [M–OH] þ ; m/z 224 and 226 [M–CH2 þ H] þ ; m/z 238 and 240 [Mþ H] þ ; m/z 254 and 252 [Mþ CH3] þ ; m/z 260 and 262 [MþNa] þ .)
Table 1 Method validation for GC–MS confirmatory analysis. Compound Linearity (ng mL 1)a
Ketamine
a
20–20,000
r2
LOD (ng mL 1)
Spiked blood
0.9995 20
Concentration (ng mL 1)
RSD (%) intraday, n ¼5
RSD (%) interday, n¼ 15
Calculated concentration (ng mL 1)
Difference (%)
100 10,000
2.7 3.1
5.9 5.0
95.7 9908.3
4.3 0.9
Blood sample spiked with 20, 40, 100, 200, 500, 1000, 5000, 10,000 and 20,000 ng mL 1, n¼ 3.
2. Materials and methods Table 2 Ketamine analysis using SIMS and GC–MS.
2.1. Material and reagents
Sample no.
GC–MS Concentration (ng mL 1)
SIMS Screening (100 ng mL 1)
105 121 144 165 186 194 199 322 342 394
ND ND ND ND 404.2 109.4 263.5 ND 447.4 414.5
þ þ þ þ þ
ND: not detected. þ : ketamine positive. : ketamine negative.
Ketamine standard solution (1 mg mL 1 in methanol), deuterated internal standard (IS), and ketamine-d4 (100 μg mL 1 in methanol) were purchased from Cerilliant Corporation (Texas, USA). HPLC grade solvents, such as hexane and dichloromethane, were purchased from J. T. Baker (USA). Sodium carbonate (Na2CO3) was provided by Sigma-Aldrich (St. Louis, MO). Deionized water was purified in a Milli-Q RO Plus 60 filtration system (Millipore, Massachusetts, USA) before use for preparing aqueous solutions. 2.2. SIMS measurement The instrument was based on a PHI 5000 VersaProbe scanning X-ray microprobe (ULVAC-PHI, Chigasaki, Japan) [48]. A Wien-filtered C60 þ ion source (IOG C60-10, Ionoptika, Chandler's Ford, UK)
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was operated at 10 nA (measured with an Au target) and 10 kV and rastered onto a 2 mm 2 mm area at the eucentric position with an incident angle of 70°. The quadrupole mass analyzer (EQS1000, Hiden, Warrington, UK) was located at 147° from the C60 þ ion source with a 35° take-off angle and a working distance of 60 mm. The mass analyzer was optimized for a count rate of 12C þ and the m/z was calibrated with ionized C60 vapor at m/z 720. During acquisition, a flooding electron source biased at 1 V was used to compensate for the charge-up effect. The sample surface is sputter-cleaned for 5 min to reach steady state; then 4–6 spectra were acquired for each specimen. The typical sampling volume is in the order of nL. 2.3. Multivariate analysis Spectra acquired from negative control group and spiked sample group are analyzed by robust principal component analysis (robPCA) using ChemoSpec: Exploratory Chemometrics for Spectroscopy (version 1.51-2) and SparseM: Sparse Linear Algebra (version 0.96) package in R: A Language and Environment for Statistical Computing (version 2.15.2) software. The intensity at each m/z is first normalized by total intensity then scaled by multiplying the root square of intensity and square of corresponding m/z, according to the mass spectral search software developed by National Institute of Standards and Technology (NIST). For scaling the spectrum, because the ion yield decreased significantly with increasing m/z, the information at higher m/z region where the fragments of ketamine are present is emphasized in order to down-play the contribution of elemental information at low m/z. Unscaled covariance matrix is then used to digest the high-dimension spectra to principal components. For unknown samples, spectra from each specimen is averaged, normalized and scaled; then linear regression is used to calculate the scores of first 4 principal components. 2.4. GC–MS measurement Analysis of ketamine was performed on a Thermo Trace GC Ultra gas chromatograph instrument (Texas, USA) equipped with a split/splitless injector and coupled with a thermo ISQ mass spectrometer (Texas, USA). A 30 m DB-5MS fused silica capillary column (0.25 mm I.D., 0.25 mm film thickness) was used for separation of the analyte. Initially the column temperature was at 130 °C for 1 min, and then it was ramped to 170 °C at 25 °C min 1. It was further raised to 200 °C at 20 °C min 1 and kept for 4 min; it was then increased to 280 °C at 60 °C min 1 and held for 2 min. The purity of helium carrier gas was 99.9995%, and the flow rate was 1.0 mL min 1. The inlet was operated at 300 °C and was used in the pulsed splitless mode. Ionization was operated in the electron impact (EI) mode at 70 eV. The temperature of the ion source was 230 °C and the temperature of the quadrupole mass filter was 150 °C. The mass spectrometer (MS) was operated in the total ion chromatogram (TIC) mode and a mass range of m/z 50–250 was scanned to confirm the retention time of ketamine. The selected ion monitoring (SIM) mode was used for the determination of the target compound. Three ion transitions for ketamine were monitored as follows: 180 (quantification), m/z 182 and 209. 2.5. Standard and working solutions Ketamine was prepared in 10 mL of methanol to obtain a standard stock solution with a concentration of 100 mg mL 1, which was stored at 20 °C. A working solution of 10 mg mL 1 containing ketamine was prepared in methanol every week and stored at 4 °C. A series of working solutions were made by diluting the 10 mg mL 1 solution with methanol daily and stored at 4 °C.
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2.6. Sample screening using SIMS The samples were human whole blood from forensic cases. The samples were vortexed for 2 min. 5 μL of blood was spotted on a piece of 52 nm thick thermal oxide on Si wafer (1 cm 1 cm). The wet blood spots were then placed under a nitrogen flow to dryness for 30 min. During the initial evaluation of SIMS, a portion of 1 mL blank whole blood was spiked with 10 mL, 10 mg mL 1 of ketamine, which was considered as spiked sample group and followed the above procedure. 2.7. Sample preparation for GC–MS analysis To 0.5 mL of each whole blood sample, 20 mL of 10 mg mL 1 ketamine-d4 (internal standard, IS) was spiked. 1 mL of 0.1 M Na2CO3 (aq) and 1.5 mL of dichloromethane: hexane (1:3 v/v) were added to blood samples, which were then vortexed for 2 min. The mixture was centrifuged at 4000 rpm for 10 min. 500 μL of the organic phase was transferred into a 12 mm 75 mm glass tube and evaporated to dryness under nitrogen at 40 °C (approximately 8 min). The residue was reconstituted in 50 mL of ethyl acetate and 1.5 mL was injected into the GC–MS for further analysis. 2.8. Confirmatory analysis by GC–MS The samples found containing no measurable amount of ketamine using the GC–MS method were used as negative control and for the preparation of calibration curve. The concentrations used for calibrants were 20, 40, 100, 200, 500, 1000, 5000, 10,000 and 20,000 ng mL 1. Two controls spiked ketamine at 100 and 10,000 ng mL 1 were prepared in whole blood described above. For identification, ion ratios were calculated for ketamine in the samples using the ions m/z 180, 182, and 209, and compared with those from a QC containing 100 ng mL 1 of ketamine. The concentration of ketamine in each sample was interpolated from the calibration line constructed using a ratio of the peak area of ketamine (m/z 180) to that of ketamine-d4 (m/z 184). Linear regression lines were calculated and the correlation coefficients were better than 0.995 in all cases. Positive results were reported if the concentration of ketamine was greater than 100 ng mL 1. The criteria applied for acceptable GC–MS identification were those used routinely in our laboratory. The relative retention time of the substance to be identified should not differ by more than 2%. Moreover, the abundances of at least three diagnostic ions should not differ by more than 20% relative for ion abundances. The abundance of a diagnostic ion must be determined from integrated ion chromatograms, where the abundance of the least intense ion must have the signal to noise ratio greater than 3:1.
3. Results and discussion The aim of this study was to screen ketamine samples in whole blood applying no sample preparation. Our approach used GC–MS to confirm the concentration of ketamine in all specimens. A group determined without the presence of ketamine was considered as blank and negative control group. Though SIMS does not allow the separation of targets and interferences, the use of instrument with automated fragmentation with PCA model enables the distinctions to be made. To optimize the parameters of SIMS and PCA model, blank whole blood was spiked with ketamine at 100 ng mL 1. The scaled spectra for ketamine spiked sample and negative control are shown in Fig. 1. Owing to the complex mixture of the whole blood, the pattern of ketamine was overlapped by strong backgrounds and it was not possible to distinguish the
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administration of ketamine. Using PCA, the screening plot (Fig. 2a) indicated that the first 4 PCs described more than 90% of variations. Hence 4 PCs were used for subsequent analysis. It is clear that while the PC1 described the most (59%) variation, the score cannot completely discriminate the spiked samples from negative controls. On the other hand, the PC2 clearly separated the different groups while described 23% of variance. By plotting the scores in PC1 and PC2 space, Fig. 2b reveals clear discrimination of sample groups where spiked samples are located at upper-left quadrant and negative controls are at lowerright quadrant. In other words, negative PC1 and positive PC2 suggest the presence of ketamine abuse. For easier comparison, the referencing spectrum of ketamine was scaled and overlaid on the loading and the loadings of characteristic ketamine fragments are listed in Fig. 3. It is clear that besides [M þH] þ of ketamine (with 37Cl at m/z 240), [M–CH2 þH] þ (at m/z 224 and with 37Cl at 226), [M–NHCH3] þ (with m/z 37Cl at 209) and peaks at lower m/z that may overlap with other species, negative PC1 loadings corresponding to the characteristic molecular fragments of ketamine are found. Therefore, lower PC1 score may suggest the presence of ketamine. However, owing to the overlapping of PC1 scores prevent a clear discrimination of spiked samples from negative controls (Fig. 2a), PC1 alone cannot be used to identify the administration of ketamine. For PC2, positive loadings corresponding to [MþNa] þ , [Mþ CH3] þ , [Mþ H] þ , [M–CH2 þ H] þ , [M–NHCH3] þ , [M–CO–NHCH3] þ , [M–CO–NHCH3–CH2] þ , [M–CO–NHCH3–C2H3] þ and [CH2ClC6H4] þ (with 37Cl at m/z 240) were identified. Therefore, the positive PC2 clearly indicated the presence of ketamine. While the higher background at high mass shows negative loading of PC1 and positive loading of PC2, it may be argued that the background dominated the separation of groups. However, since the averaged spectra showed comparable background (Fig. 2) and the analysis of variance (ANOVA) reveals no significant difference between the sample groups at the level of 0.05, the effect of the background was dismissed. Using the first 4 PCA loading determined with spiked sample and negative control, scores of spectra obtained from unknown samples were calculated using linear regression. The resulting scores (triangle) are overlay on the PC1 and PC2 space as shown in Fig. 2b. The analysis suggests that ketamine was present over the 100 ng mL 1 in samples 186, 194, 199, 342, and 394 while samples 105, 121, 144, 165 and 322 were negative. To validate the result, the presences of ketamine in these unknown samples were analyzed using GC–MS method. For confirmatory analysis, liquid–liquid extraction was applied prior to GC–MS analysis, not least to pre-concentrate ketamine from whole blood. The SIM mode also eliminated the contribution of interferences. Blank samples were spiked with ketamine for method validation. The estimated linearity is shown in Table 1. A calibration curve was constructed for different concentrations ranging from 20 to 20,000 ng mL 1 in whole blood. The coefficient of determination (r2) was 0.9995. The limit of detection (LOD) was 20 ng mL 1. Intraday relative standard deviations (RSDs) were 2.7% and 3.1% at concentrations of 100 and 10,000 ng mL 1, respectively. Interday RSDs were 5.9% and 5.0% at concentrations of 100 and 10,000 ng mL 1, respectively. Three diagnostic ions were used to identify ketamine in GC–MS (m/z 180, 182 and 209) corresponded to those observed in SIMS. Five of 10 unknown samples were confirmed the administration of ketamine, which were samples 186, 194, 199, 342, and 394. Ketamine was not detected in the other samples. Comparison of SIMS with the more established GC–MS showed good agreement between the methods (Table 2). It is worth noting that in Fig. 2b the negative unknown cases are located far from the overlay area
with the positive group. On the other hand, the positive samples 194 and 199, which confirmed relative lower ketamine concentrations (109.4 and 263.5 ng mL 1, respectively) are close to the border of the two groups. The other positive samples which contained ketamine more than 404 ng mL 1 spread away from the overlay area. This result indicates that the SIMS–PCA method is correlative to the regular GC–MS methods and could be used for rapid screening with extremely low sample consumption.
4. Conclusions The proposed label-free SIMS–PCA method offers a promising alternative to the conventional drug screening methods. The common immunoassay cannot provide an accurate measurement near cut-off values and generate false reports. Screening using SIMS with PCA has been developed a complement to immunoassays in clinical and forensic testing. It successfully distinguished the difference of positive and negative ketamine groups from complex blood samples. Although SIMS with PCA is not really suitable for quantification, it provides a quick pre-screening prior to routine GC–MS analysis. It requires extremely low volume (5 mL in this work and the actual analysis volume is mm mm nm hence only pL is required in principle) of whole blood and no sample preparation is necessary. In other words, it is ideal for trace analysis. In addition, SIMS is straight forward to automate the data acquisition and it only takes a few minutes for each sample and is suitable for rapid, high throughput analysis. The study provides a desirable method on drug screening in terms of accuracy, speed and sample volume.
References [1] S.H. Cosbey, K.L. Peters, A. Quinn, A. Bentley, J. Anal. Toxicol. 37 (2013) 74–82. [2] P. McLaughlin, D. Pounder, P. Maskell, D. Osselton, Forensic Toxicol. 31 (2013) 113–118. [3] M. Petrie, K.L. Lynch, S. Ekins, J.S. Chang, R.J. Goetz, A.H.B. Wu, M.D. Krasowski, Clin. Toxicol. 51 (2013) 83–91. [4] J. Rohrich, J. Becker, T. Kaufmann, S. Zorntlein, R. Urban, Forensic Sci. Int. 215 (2012) 3–7. [5] P. Fernandez, S. Seoane, C. Vazquez, A.M. Bermejo, A.M. Carro, R.A. Lorenzo, Anal. Bioanal. Chem. 401 (2011) 2177–2186. [6] K.M. Kirschbaum, F. Musshoff, R. Schmithausen, S. Stockhausen, B. Madea, Forensic Sci. Int. 212 (2011) 252–255. [7] C. Moore, Drug Test. Anal. 3 (2011) 166–168. [8] A. Arroyo, M. Sánchez, M. Palahí, M. Barbal, M.T. Marrón, A. Mora, Legal Med. 13 (2011) 240–244. [9] R.D. Johnson, S.R. Botch, J. Anal. Toxicol. 35 (2011) 65–74. [10] C.A. Mueller, W. Weinmann, S. Dresen, A. Schreiber, M. Gergov, Rapid Commun. Mass Spectrom. 19 (2005) 1332–1338. [11] S.W. Cotten, D.L. Duncan, E.A. Burch, Clin. Biochem. 45 (2012) 605–609. [12] Y.C. Kaplan, A. Erol, B. Karadas, Ther. Drug Monitor. 34 (2012) 493–495. [13] B.T. Ly, S.L. Thornton, C. Buono, J.A. Stone, A.H.B. Wu, Ann. Emerg. Med. 59 (2012) 545–547. [14] R. Diaz, M. Ibanez, J.V. Sancho, F. Hernandez, J. Chromatogr. A 1276 (2013) 47–57. [15] X. Ding, H. Ghobarah, X.L. Zhang, A. Jaochico, X.R. Liu, G. Deshmukh, B.M. Liederer, C. Hop, B. Dean, Rapid Commun. Mass Spectrom. 27 (2013) 401–408. [16] C. Gomez-Canela, N. Cortes-Francisco, F. Ventura, J. Caixach, S. Lacorte, J. Chromatogr. A 1276 (2013) 78–94. [17] J.C. Dominguez-Romero, J.F. Garcia-Reyes, R. Martinez-Romero, P. Berton, E. Martinez-Lara, M.L. Del Moral-Leal, A. Molina-Diaz, Anal. Chim. Acta 761 (2013) 1–10. [18] A. Masia, M. Ibanez, C. Blasco, J.V. Sancho, Y. Pico, F. Hernandez, Anal. Chim. Acta 761 (2013) 117–127. [19] R.C. Yue, L. Zhao, Y.H. Hu, P. Jiang, S.P. Wang, L. Xiang, W.C. Liu, W.D. Zhang, R.H. Liu, J. Ethnopharmacol. 145 (2013) 465–475. [20] R. Hoesli, A. Tobler, S. Koenig, S. Muehlebach, J. Anal. Toxicol. 37 (2013) 102–109. [21] A.F. Felicioni Oliveira, E.C. de Figueiredo, A.J. dos Santos-Neto, J. Pharm. Biomed. Anal. 73 (2013) 53–58. [22] D.R. Bunch, S.H. Wang, Anal. Bioanal. Chem. 405 (2013) 3021–3033. [23] S. Tulipani, R. Llorach, M. Urpi-Sarda, C. Andres-Lacueva, Anal. Chem. 85 (2013) 341–348.
H.-Y. Liao et al. / Talanta 143 (2015) 50–55
[24] F.S. Mirnaghi, J. Pawliszyn, Anal. Chem. 84 (2012) 8301–8309. [25] J.S. Fletcher, X.A. Conlan, N.P. Lockyer, J.C. Vickerman, Appl. Surf. Sci. 252 (2006) 6513–6516. [26] E.A. Jones, N.P. Lockyer, J.C. Vickerman, Int. J. Mass Spectrom. 260 (2007) 146–157. [27] S.F. Boulyga, Int. J. Mass Spectrom. 307 (2011) 200–210. [28] A. Delcorte, P. Bertrand, B.J. Garrison, K. Hamraoui, T. Mouhib, O.A. Restrepo, C.N. Santos, S. Yunus, Surf. Interface Anal. 42 (2010) 1380–1386. [29] J.S. Fletcher, A. Henderson, G.X. Biddulph, S. Vaidyanathan, N.P. Lockyer, J. C. Vickerman, Appl. Surf. Sci. 255 (2008) 1264–1270. [30] A. Piwowar, J. Fletcher, N. Lockyer, J. Vickerman, Surf. Interface Anal. 43 (2011) 207–210. [31] C.J. Chang, H.Y. Chang, Y.W. You, H.Y. Liao, Y.T. Kuo, W.L. Kao, G.J. Yen, M.H. Tsai, J.J. Shyue, Anal. Chim. Acta 718 (2012) 64–69. [32] E.J. Lanni, S.S. Rubakhin, J.V. Sweedler, J. Proteomics 75 (2012) 5036–5051. [33] H.-Y. Liao, K.-Y. Lin, W.-L. Kao, H.-Y. Chang, C.-C. Huang, J.-J. Shyue, Anal. Chem. 85 (2013) 3781–3788. [34] R. Levisetti, Annu. Rev. Biophys. Biophys. Chem. 17 (1988) 325–347. [35] N. Winograd, B.J. Garrison, Annu. Rev. Phys. Chem. 61 (2010) 305–322. [36] S. Muramoto, D.J. Graham, M.S. Wagner, T.G. Lee, D.W. Moon, D.G. Castner, J. Phys. Chem. C 115 (2011) 24247–24255. [37] J.C. Vickerman, Surf. Sci. 603 (2009) 1926–1936. [38] C.M. Mahoney, Mass Spectrom. Rev. 29 (2010) 247–293. [39] H.Y. Liao, M.H. Tsai, Y.W. You, H.Y. Chang, C.C. Huang, J.J. Shyue, Anal. Chem. 84 (2012) 3355–3361.
55
[40] Y.W. You, H.Y. Chang, W.C. Lin, C.H. Kuo, S.H. Lee, W.L. Kao, G.J. Yen, C.J. Chang, C.P. Liu, C.C. Huang, H.Y. Liao, J.J. Shyue, Rapid Commun. Mass Spectrom. 25 (2011) 2897–2904. [41] H.Y. Liao, M.H. Tsai, H.Y. Chang, Y.W. You, C.C. Huang, J.J. Shyue, Anal. Chem. 84 (2012) 9318–9323. [42] P.S. Chu, S.C. Kwok, K.M. Lam, T.Y. Chu, S.W. Chan, C.W. Man, W.K. Ma, K.L. Chui, M.K. Yiu, Y.C. Chan, M.L. Tse, F.L. Lau, Hong Kong Med. J. 13 (2007) 311–313. [43] R. Shahani, C. Streutker, B. Dickson, R.J. Stewart, Urology 69 (2007) 810–812. [44] Y.-W. Chiew, C.-S. Yang, Kidney Int. 76 (2009) 123–124. [45] T.H. Tsai, T.L. Cha, C.M. Lin, C.W. Tsao, S.H. Tang, F.P. Chuang, S.T. Wu, G.H. Sun, D.S. Yu, S.Y. Chang, Int. J. Urol. 16 (2009) 826–829. [46] C.H. Chen, M.H. Lee, Y.C. Chen, M.F. Lin, J. Formos. Med. Assoc. 110 (2011) 787–791. [47] S. Tan, W.M. Chan, M.S. Wai, L.K. Hui, V.W. Hui, A.E. James, L.Y. Yeung, D.T. Yew, Microsc. Res. Tech. 74 (2011) 1192–1198. [48] L. Zhang, Z.-H. Wang, H. Li, Y. Liu, M. Zhao, Y. Jiang, W.-S. Zhao, J. Chromatogr. B 955–956 (2014) 10–19. [49] Y. Yamini, S. Seidi, R. Feizbakhsh, T. Baheri, M. Rezazadeh, J. Sep. Sci. 37 (2014) 2364–2371. [50] S. Odoardi, L. Anzillotti, S. Strano-Rossi, Forensic Sci. Int. 243 (2014) 61–67. [51] N.S. Nosseir, G. Michels, P. Binder, M.H.J. Wiesen, C. Muller, J. Chromatogr. B 973 (2014) 133–141.
