Drug Screening

Using Spectra Databases in Routine Work

  • Figure 1: Extracted ion chromatograms of the identified JWH-250 metabolites in an authentic patient urine sample using the MWW screening procedure.Figure 1: Extracted ion chromatograms of the identified JWH-250 metabolites in an authentic patient urine sample using the MWW screening procedure.

Nowadays screening for drugs and poisons in different bio matrices is still a challenge in the field of clinical and forensic toxicology as well as in the field of doping control. Several immunoassays and hyphenated chromatographic methods are established either for targeted analysis, or for comprehensive screening approaches [1-3]. In contrast to targeted analysis, which covers single or a couple of analytes, comprehensive screening approaches are covering up to several thousand different compounds in one single analysis. Thus comprehensive screening approaches are one of the most important analytical techniques in the field of clinical and forensic toxicology being the backbone of systematic toxicological analysis (STA) [1-3]. With regard to different bio matrices such as saliva, plasma/serum and urine, the requirements for comprehensive screening approaches are different. While screening for drugs and poisons in plasma/serum needs high analytical sensitivity, screening in urine samples needs to cover also metabolites of drugs and poisons, as most of these compounds are excreted in urine as metabolites [4].

With the development of hyphenated mass spectrometry devices, such as gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS), analytical methods are available, which provide high analytical sensitivity on the one hand and high identification power for unambiguous detection by reference spectra libraries on the other [1-3,5].

As urine is the matrix of choice for detection of drugs and poisons, caused by the noninvasive availability and long detection windows, the authors’ group developed and optimized comprehensive metabolite-based screening approaches by GC-MS and LC-MS during the last decades/years [6-13].
In this article (I) the general procedure for generating metabolite reference spectra, (II) the GC-MS and LC-MS reference libraries, and (III) two exemplarily applications of both reference libraries are described.

(I) Generation of reference mass spectra

Different single drugs were administered to different male Wistar rats in a single 20-mg/kg body mass dose or a common users’ dose in aqueous suspension by gastric intubation for toxicological diagnostic reasons according to the corresponding German law.

Urine was collected separately from the feces over a 24-h period and stabilized with sodium fluoride. Blank rat urine samples were collected before drug administration to check for the presence of interfering compounds. Afterwards all urine samples were extracted by liquid-liquid extraction, solid phase extraction and protein precipitation before and/or without chemical and enzymatic cleavage of phase II metabolites. The resulting extracts were analyzed either directly or after derivatization (acetylation, methylation,trmimethylsilylation, trifluoroacetylation, pentafluoropropionylation and/or heptafluorobutyrylation) by GC-MS and LC-MS. Drug metabolites were detected and characterized by skilled users and corresponding reference spectra were created and stored in the GC-MS and LC-MS libraries [6-8,10-13].

(II) Reference Libraries

GC-MS

The GC-MS approach and reference library was initially described by Maurer et al. in the year 1985 [14]. In the meanwhile the reference library was updated and extended several times. The new 2016 version of the reference database (Mass Spectral Library of Drugs, Poisons, Pesticides, Pollutants and Their Metabolites; Maurer, Pfleger, Weber; “MPW library”) covers 10,320 spectra among which over 7,600 data sets are from metabolites, over 4,200 from acetylated, over 1,500 from methylated, over 1,500 from trimethylsilylated, over 1,000 from trifluoroacetylated, over 1,500 each from pentafluoro-propionylated or heptafluoro¬butyrylated compounds [6]. All data records include computer searchable chemical structures (connection table), electron impact spectrum and other compound specific properties such as the retention index.

LC-MSn

The metabolite screening LC-MS approach was firstly described by Wissenbach et al. in 2011 [7,15-17]. The current version [7] covers >10,000 reference spectra (MS2 and MS3) of 1,500 toxicologically relevant compounds, 3,000 corresponding metabolites and 50 endogenous molecules. Inter instrument transferability of MWW reference spectra was shown for different manufactures and devices [17,18] An updated and enlarged version of this reference library will appear in 2016 [9].

(III) Applications

GC-MS

In a study performed by Meyer et al. the application of an automated deconvolution software was tested to be suitable for unknown screening by comparison of manual and software assisted data evaluation. Afterwards the software parameters were optimized for usage of the MPW reference library in human urine. Aim of the study was to speed up the evaluation and library matching of the recorded full-scan GC-MS spectra [7,19].

Sample Preparation and Analysis:
One hundred eleven authentic patient urine samples were worked up by acidic hydrolysis, liquid-liquid extraction, and acetylation as described by Maurer et al. [20]. Analysis was performed on a single quadrupole GC-MS device in full-scan mode using -70 eV electron ionization and 20 min run time as described by Maurer et al. [20].

Data Evaluation:
The full-scan data files acquired by the GC-MS system were screened for the presence of peaks and mass spectra of (derivatized) drugs, metabolites, and artifacts by a skilled user. Therefore (I) manual screening of the total ion chromatogram (TIC) was followed by screening for the different drug classes by extracted ion chromatograms (EIC) as described by Maurer et al. . (II) The identical GC-MS files where then analyzed by the free available automatic mass deconvolution and identification system (AMDIS) [19] (http://www.amdis.net/). Library matches obtained by AMDIS were inspected and verified by an experienced toxicologist. In all cases, identification was achieved by computer-assisted comparison of the peak underlying mass spectra with those of the MPW reference library. The final decision concerning the declaration of a library match to be a “true hit” was always done by a toxicologist on the basis of m/z correlations and their respective abundance.

Comparison:
Comparison of the results obtained by manual data evaluation and optimized AMDIS settings showed high agreement for both evaluation methods. Only once manual evaluation revealed an additionally “true hit” (phenobarbital) in comparison to AMDIS results. On the other hand AMDIS found additional true targets in 15 samples. Although most of these results were related to minor (metabolite) peaks, and none of these findings was relevant from an emergency toxicology perspective, this comparison study showed that AMDIS is a valid complementary data evaluation method for GC-MS analysis. Especially, as the data evaluation process was accelerated by the factor of 2.5 using automated data evaluation.

Summary:
Meyer et al. showed that automated software deconvolution can be used for the MPW screening procedure e.g. in urine and suggested to use AMDIS in addition to manual spectra evaluation [19]. A similar study for blood GC-MS screening using AMDIS and MPW was described recently [21].

LC-MSn

In the studies by Wissenbach et al. [16], the MWW library screening procedure was characterized with regard to recovery (RE), matrix effects (ME), process efficiency (PE), and limits of detection (LOD) for selected drugs of abuse. In a second step, urine screening results obtained by the GC-MS MPW approach were compared to those obtained by the LC-MS MWW screening [7].

Sample Preparation and Analysis:
For characterization of the analytical parameters, urine samples were spiked with 16 different drugs of abuse (also including phase I and phase II metabolites) and RE, ME, PE, and LODs were determined according to guidelines.
For the comparison study 500 authentic patient urine samples, which were submitted for routine drug testing, were screened by GC-MS and LC-MS using MPW and MWW reference library respectively.
All urine samples were precipitated by acetonitrile, evaporated and suspended as described by Wissenbach et al. [7,15,16]. LC-MS analysis was performed on a linear ion trap using data dependent MS1 full-scan, MS2 and MS3 product ion acquisition in a 25 min chromatographic run as described for the MWW reference library [7]. LCQuan software was used for the determination of RE, ME, and PE for the characterization of analytical parameters, while ToxID software was used for library matching of all acquired LC-MS samples giving LOD and screening results [15].

Results:
With regard to RE, ME, PE, and LOD the MWW screening procedure sample preparation showed good results and was found to be suitable for STA [15]. As shown exemplarily in Table 1 various metabolites were detected for a wide range of retention time in an authentic urine sample. This provided unambiguous detection of a drug intake, even if ME are disturbing the detection in some chromatographic retention time regions.
By comparison of GC-MS MPW with LC-MS MWW screening results, it was shown that both screening approaches are complementary as both of them have pros and cons [15]. On the one hand LC-MS provided false negative results for some samples containing low and not relevant concentrations (from an emergency toxicology perspective) of diazepam, lorazepam, and/or their metabolites. This was caused by the sample preparation for the GC-MS approach, which included acid hydrolysis. Thus benzodiazepine like compounds and their metabolites were all transferred to the corresponding benzophenones, while for the LC-MS approach, they were split into several (smaller) peaks of various phase I and II metabolites according to the applied sample preparation [16].
On the other hand samples were screened false negative for synthetic cannabinoids by GC-MS, while the LC-MS screening approach was able to detect those compounds by the usage of metabolite reference spectra as exemplified in Figure 1.

Summary:
Wissenbach et al. showed in this and  similar studies [10-13,22-26] that the MWW screening approach was suitable for various drug classes and concluded that the MWW screening approach provides a useful complement to the established GC-MS approach [15-17].
Conclusion
Hyphenated mass spectrometry using metabolite-based reference libraries are the backbone of STA. The MPW GC-MS approach is widely used since several decades and well established in the field of clinical and forensic toxicology and doping control. Nowadays LC-MS analysis is more and more in focus: With the development of the MWW LC-MS library, the authors being opinion leaders in the field provided a complementary screening approach using state of the art LC-MS. According to that both screening approaches are nowadays established as part of the STA in several institutes in field of clinical and forensic toxicology [10-13,22-32]. Additionally the MWW screening approach was successfully applied in other fields such as pharmacoepidemiology [33].
 

Autors: Hans H. Maurer1, Armin A. Weber1 and Dirk K. Wissenbach2
 

Affiliations
1Department of Experimental and Clinical Toxicology, Institute of Experimental and Clinical Pharmacology and Toxicology, Saarland University, Homburg/Saar, Germany
2Institute of Forensic Medicine, Jena University Hospital, Jena, Germany

Contact
Prof. Dr. Dr. h.c. (UGent) Hans H. Maurer

Department of Experimental
and Clinical Toxicology
Saarland University
Homburg/Saar, Germany
hans.maurer@uks.eu
 

Table 1: ToxID result file for analysis of an authentic patient urine sample using MWW reference library.

Actual RT

Compound Name

Compound Info

m/z

SI

RSI

0.7

MS2_Nicotine-M (3-HO-cotinine)_wideband_35

Metabolite

193

915

939

0.9

MS2_Morphine-M (3-glucuronide)_wideband_35

Metabolite

462

809

890

1.2

MS2_Morphine-M/artifact (448)_wideband_35

Metabolite

448

916

931

1.4

MS2_Nicotine-M (cotinine)_wideband_35

Metabolite

177

856

947

2.0

MS2_Morphine_wideband_35

Parent

286

794

879

3.5

MS2_Noscapine-M (420-glucuronide)_wideband_35

Metabolite

596

982

986

3.8

MS2_Codeine-M (glucuronide)_wideband_35

Metabolite

476

942

945

4.5

MS2_Codeine_wideband_35

Parent

300

786

830

4.7

MS2_Caffeine_wideband_35

Parent

195

879

954

5.0

MS2_Noscapine-M (402-glucuronide)_wideband_35

Metabolite

578

988

990

5.2

MS2_Ceftriaxone_wideband_35

Parent

555

908

955

5.4

MS2_Quinine-M (HO-) _wideband_35

Metabolite

341

958

965

5.4

MS2_Cocaine-M (cocaethylene-HO-glucuronide)_wideband_35

Metabolite

510

991

991

5.5

MS2_Papaverine-M (O-demethyl-glucuronide) isomer 1_wideband_35

Metabolite

502

987

998

5.9

MS2_Cocaine-M (benzoylecgonine)_wideband_35

Metabolite

290

933

933

5.9

MS2_Cocaine-M (nor-)_wideband_35

Metabolite

290

763

784

6.8

MS2_Protriptyline-M (HO-ring-glucuronide) isomer 2_wideband_35

Metabolite

456

655

840

6.9

MS2_Quinine-M (glucuronide)_wideband_35

Metabolite

501

848

925

7.0

MS2_Cocaine-M (di-HO-methoxy-HO-ring-glucuronide)_wideband_35

Metabolite

556

675

841

7.5

MS2_Quinine_wideband_35

Parent

325

942

943

7.6

MS2_Cocaine-M (cocaethylene-HO-)_wideband_35

Metabolite

334

975

998

8.3

MS2_Quinine-M (dihydro-)_wideband_35

Metabolite

327

786

899

8.5

MS2_Cocaine-M/artifact (benzoylecgonine-HO-ring) isomer 2 ME_wideband_35

Metabolite

316

976

978

8.6

MS2_Cocaine_wideband_35

Parent

304

992

997

9.4

MS2_Noscapine-M (432)_wideband_35

Metabolite

432

899

948

9.7

MS2_Papaverine_wideband_35

Parent

340

723

838

10.1

MS2_Dihydromorphine-M (glucuronide)_wideband_35

Metabolite

464

632

941

10.1

MS2_Noscapine_wideband_35

Parent

414

950

973

10.5

MS2_Cocaine-M (cocaethylene)_wideband_35

Metabolite

318

958

960

11.4

MS2_Noscapine-M (497)_wideband_35

Metabolite

497

895

929

11.8

MS2_Chavicine derivate (290)_wideband_35

Parent

290

822

993

13.2

MS2_Methadone-M (EDDP)_wideband_35

Metabolite

278

910

910

14.6

MS2_Dibenzepin-M (HO-) isomer 1_wideband_35

Metabolite

312

712

732

14.6

MS2_Methadone_wideband_35

Parent

310

899

899

14.7

MS2_Methadone-M (N-oxide)_wideband_35

Metabolite

326

999

999

References

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      [6]    Hans H. Maurer, Karl Pfleger and Armin A. Weber: Mass Spectral Library of Drugs, Poisons, Pesticides, Pollutants and their Metabolites, Wiley-VCH, Weinheim, 2016.

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    [10]    Achim T. Caspar, Andreas G. Helfer, Julian A. Michely, Volker Auwaerter, Simon D. Brandt, Markus R. Meyer and Hans H. Maurer: Studies on the Metabolism and Toxicological Detection of the New Psychoactive Designer Drug 2-(4-iodo-2,5-Dimethoxyphenyl)-N-[(2-methoxyphenyl)methyl]ethanamine (25I-NBOMe) in Human and Rat Urine Using GC-MS, LC-MSn, and LC-HR-MS/MS, Analytical and Bioanalytical Chemistry 407 (2015) 6697, DOI: 10.1007/s00216-015-8828-6.

    [11]    Andreas G. Helfer, Alain Turcant, David Boels, Séverine Ferec, Bénédicte Lelievre, Jessica Welter, Markus R. Meyer and Hans H. Maurer:  Elucidation of the Metabolites of the Novel Psychoactive Substance 4-methyl-N-ethyl-cathinone (4-MEC) in Human Urine and Pooled Liver Microsomes by GC-MS and LC-HR-MS/MS Techniques and of its Detectability by GC-MS or LC-MSn Standard Screening Approaches, Drug Testing  and Analysis 7 (2015) 368, DOI: 10.1002/dta.1682.

    [12]    Golo M.J. Meyer, Carina S.D. Wink, Josef Zapp and Hans H. Maurer: GC-MS, LC-MSn, LC-High Resolution MSn, and NMR Studies on the Metabolism and Toxicological Detection of Mesembrine and Mesembrenone, the Main Alkaloids of the Legal High "Kanna" Isolation from Sceletium Tortuosum, Analytical and Bioanalytical Chemistry 407 (2015) 761.

    [13]    Jessica Welter, Pierce Kavanagh, Markus R. Meyer and Hans H. Maurer: Benzofuran Analogues of Amphetamine and Methamphetamine: Studies on the Metabolism and Toxicological Analysis of 5-APB and 5-MAPB in Urine and Plasma Using GC-MS and LC-(HR)-MSn Techniques, Analytical and Bioanalytical Chemistry 407 (2015) 1371.

    [14]    Karl Pfleger, Hans Maurer and Armin Weber: Mass Spectral and GC Data of Drugs, Poisons, and their Metabolites, VCH publisher, Weinheim, 1985.

    [15]    Dirk K. Wissenbach, Markus R. Meyer, Daniela Remane, Armin A. Weber and Hans H. Maurer: Development of the First Metabolite-Based LC-MSn Urine Drug Screening Procedure-Exemplified for Antidepressants, Analytical and Bioanalytical Chemistry 400 (2011) 79.

    [16]    Dirk K. Wissenbach, Markus R. Meyer, Daniela Remane, Anika A. Philipp, Armin A. Weber and Hans H. Maurer: Drugs of Abuse Screening in Urine as Part of a Metabolite-Based LC-MSn Screening Concept, Analytical and Bioanalytical Chemistry 400 (2011) 3481.

    [17]    Dirk K. Wissenbach, Markus R. Meyer, Armin A. Weber, Daniela Remane, Andreas H. Ewald, Frank T. Peters and Hans H. Maurer: Towards a Universal LC-MS Screening Procedure -- can an LIT LC-MSn Screening Approach and Reference Library Be Used On A Quadrupole-LIT Hybrid Instrument?, Journal of Mass Spectrometry 47 (2012) 66, DOI: 10.1002/jms.2027.

    [18]    B. Schneider, M. Meyer, A. Kiehne and C. Baessmann, Abstract book to the 52nd Annual Meeting of TIAFT, Buenos Aires, Argentina, November 9-13 (2014) 128.

    [19]    Markus R. Meyer, Frank T. Peters and Hans H. Maurer: Automated Mass Spectral Deconvolution and Identification System for GC-MS Screening for Drugs, Poisons, and Metabolites in Urine, Clinical Chemistry 56 (2010) 575.

    [20]    Hans H. Maurer, Karl Pfleger and Armin A. Weber: Mass Spectral and GC Data of Drugs, Poisons, Pesticides, Pollutants and their Metabolites, Wiley-VCH, Weinheim (Germany), 2011.

    [21]    M. Grapp, H.H. Maurer and H. Desel, Drug Testing and Analysis (2015)

    [22]    Markus R. Meyer, Anna Holderbaum, Pierce Kavanagh and Hans H. Maurer: Low Resolution and High Resolution MS for Studies on the Metabolism and Toxicological Detection of the New Psychoactive Substance Methoxypiperamide (MeOP), Journal of Mass Spectrometry (2015), DOI: 10.1002/jms.3635

    [23]    Julian A. Michely, Andreas G. Helfer, Simon D. Brandt, Markus R. Meyer and Hans H. Maurer: Metabolism of the New Psychoactive Substances N,N-diallyltryptamine (DALT) and 5-methoxy-DALT and their Detectability in Urine by GC-MS, LC-MSn, and LC-HR-MS-MS, Analytical and Bioanalytical Chemistry 407 (2015) 7831.

    [24]    Jessica Welter, Simon D. Brandt, Pierce Kavanagh, Markus R. Meyer and Hans H. Maurer: Metabolic Fate, Mass Spectral Fragmentation, Detectability, and Differentiation in Urine of the Bonzufuran Designer Drugs 6-APB and 6-MAPB in Comparison to Their 5-Isomers Using GC-MS and LC-(HR)-MSn Techniques, Analytical and Bioanalytical Chemistry 407 (2015) 3457.

    [25]    Carina S.D. Wink, Golo M.J. Meyer, Josef Zapp and Hans H. Maurer: Lefetamine, a Controlled Drug and Pharmaceutical Lead of New Designer Drugs: Synthesis, Metabolism, and Detectability in Urine and Human Liver Preparations Using GC-MS, LC-MSn, and LC-high Resolution-MS/MS, Analytical and Bioanalytical Chemistry 407 (2015) 1545.

    [26]    Carina S.D. Wink, Markus R. Meyer, Tina Braun, Alain Turcant and Hans H. Maurer: Biotransformation and Detectability of the Designer Drug 2,5-dimethoxy-4-propylphenethylamine (2C-P) Studied in Urine by GC-MS, LC-MSn, and LC-high-resolution-MSn, Analytical and Bioanalytical Chemistry 407 (2015) 831.

    [27]    J. Welter-Luedeke and H.H. Maurer, Ther. Drug Monit. (2015)

    [28]    Markus R. Meyer, Carina Lindauer and Hans H. Maurer: Dimethocaine, a Synthetic Cocaine Derivative: Studies on its in vitro Metabolism Catalyzed by P450s and NAT2, Toxicology Letters 225 (2014) 139, DOI: 10.1016/j.toxlet.2013.11.033.

    [29]    Markus R. Meyer, Sandra Mauer, Golo M.J. Meyer, Julia Dinger, Birgit Klein, Folker Westphal and Hans H. Maurer: The in vivo and in vitro Metabolism and the Detectability in Urine of 3’,4’-methylenedioxy-alpha-pyrrolidinobutyrophenone (MDPBP), a new pyrrolidinophenone-type Designer Drug, Studied by GC-MS and LC-MSn, Drug Testing and Analysis 6 (2014) 746, DOI: 10.1002/dta.1559.

    [30]    Jessica Welter, Markus R. Meyer, Pierce Kavanagh and Hans H. Maurer: Studies on the Metabolism and the Detectability of 4-methyl-amphetamine and its isomers 2-methyl-amphetamine and 3-methyl-amphetamine in Rat Urine Using GC-MS and LC-(high-resolution)-MSn, Analytical and Bioanalytical Chemistry 406 (2014) 1957.

    [31]    Jessica Welter, Pierce Kavanagh and Hans H. Maurer: GC-MS and LC-(high-resolution)-MSn Studies on the Metabolic Fate and Detectability of Camfetamine in Rat Urine, Analytical and Bioanalytical Chemistry 406 (2014) 3815.

    [32]    Carina S.D. Wink, Golo M.J. Meyer, Dirk K. Wissenbach, Andrea Jacobsen-Bauer, Markus R. Meyer and Hans H. Maurer: Lefetamine-derived Designer Drugs N-ethyl-1,2-diphenylethylamine (NEDPA) and N-iso-propyl-1,2-diphenylethylamine (NPDPA): Metabolism and Detectability in Rat Urine Using GC-MS, LC-MSn and LC-HR-MS/MS, Drug Testing and Analysis 6 (2014) 1038, DOI: 10.1002/dta.1621.

    [33]    H. Hoeke, S. Roeder, T. Bertsche, I. Lehmann, M. Borte, B.M. von and D.K. Wissenbach, Drug Test. Anal. (2014)

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