| 研究生: |
呂湘婷 Lu, Hsiang-Ting |
|---|---|
| 論文名稱: |
建立非標的代謝體質譜分析程序鑑定新興精神活性物質之代謝物並以4-MeO-α-PVP為例進行概念驗證 Developing a general procedure for identifying metabolites of new psychoactive substances employing mass spectrometry-based untargeted metabolomics using 4-MeO-α-PVP as an example to demonstrate proof-of-concept |
| 指導教授: |
廖寶琦
Liao, Pao-Chi |
| 學位類別: |
碩士 Master |
| 系所名稱: |
醫學院 - 環境醫學研究所 Department of Environmental and Occupational Health |
| 論文出版年: | 2019 |
| 畢業學年度: | 107 |
| 語文別: | 英文 |
| 論文頁數: | 39 |
| 中文關鍵詞: | 新興精神活性物質 、非標的代謝體學 、超高效液相層析質譜儀 |
| 外文關鍵詞: | New psychoactive substance, untargeted metabolomics, ultra-performance liquid chromatography-mass spectrometry |
| 相關次數: | 點閱:133 下載:2 |
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近年來,新興精神活性物質(NPS)藉由結構上微小修飾的改變,造成法庭科學鑑定其代謝物的一大挑戰。由於NPS的快速發展,發展一套代謝物鑑定的策略是迫切需要的。此研究的目的即為以超高效液相層析質譜儀(UPLC-MS)為基礎發展一道非標的代謝體質譜分析程序鑑定新興精神活性物質之代謝物並以4-MeO-α-PVP為例進行概念驗證。取六個濃度(0, 1, 2, 5, 10, 20 uM)的4-MeO-α-PVP各五重複,利用人類肝臟酵素S9進行溫育產生代謝物以UPLC-MS進行質譜分析。共有48個候選代謝物的訊號經由正交最小平方區別分析法(OPLS-DA)及斯皮爾曼分析(Spearman analysis)從複雜的質譜訊號庫(質譜訊號數平均20934個)中被過濾出來,並且這些訊號的峰面積與4-MeO-α-PVP藥物濃度呈現高度正相關(r>0.9, Q<0.001)。在這些被過濾出來的訊號中,共有40個可能的代謝物在不同溫育時間的樣本中得到驗證,其訊號強度隨著溫育時間增加而隨之增加;其中有8個代謝物與文獻吻合,有11個未被報導過的代謝物經由串聯式質譜分析被鑑定出來。根據上述結果,這道所提出的程序應用在鑑定NPS的代謝物,並成功利用4-MeO-α-PVP為例進行此方法的概念驗證。
New psychoactive substances (NPS) are continuously developed with minor modification, posing a challenge to identify their metabolites in forensic science. Due to the rapid emergence of NPS, a strategy for fast metabolite identification is essential. The specific aim of this study is to develop a procedure to identify the metabolites of NPS employing ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based untargeted metabolomic data processing approach, using an emerging NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), as an example to demonstrate proof-of-concept and applicability. Six levels of 4-MeO-α-PVP (0, 1, 2, 5, 10 and 20 μM, n=5 for each concentration) were incubated with human liver enzyme S9 fraction to generate metabolites and the resulting samples were analyzed by UPLC-MS. There were 48 metabolite candidates filtered from the complex MS dataset (# of average MS peaks =20934) using orthogonal partial least squares-discriminant analysis (OPLS-DA) and Spearman analysis, demonstrating high positive spearman correlations (r>0.9, Q<0.001) between 4-MeO-α-PVP concentrations and peak abundances. Among these filtered signals, there were 40 candidates verified with time-dependence experiment, eight of which were consistent with previously reported; 11 were elucidated by tandem MS (MS/MS) and not identified before. The proposed procedure is successfully applied to identify an emerging NPS metabolites according to the 19 identified metabolites of 4-MeO-α-PVP mentioned above.
Couto RAS, Goncalves LM, Carvalho F, Rodrigues JA, Rodrigues CMP, Quinaz MB. 2018. The analytical challenge in the determination of cathinones, key-players in the worldwide phenomenon of novel psychoactive substances. Critical Reviews in Analytical Chemistry 48:372-390.
Diao XX, Huestis MA. 2019. New synthetic cannabinoids metabolism and strategies to best identify optimal marker metabolites. Frontiers in Chemistry 7.
Dolengevich-Segal H, Rodriguez-Salgado B, Gomez-Arnau J, Sanchez-Mateos D. 2017. An approach to the new psychoactive drugs phenomenon. Salud Mental 40:71-82.
E. CK, Maree T, C. NN. 2016. Patterns and correlates of new psychoactive substance use in a sample of australian high school students. Drug and Alcohol Review 35:338-344.
Ellefsen KN, Wohlfarth A, Swortwood MJ, Diao X, Concheiro M, Huestis MA. 2016. 4-methoxy-alpha-pvp: In silico prediction, metabolic stability, and metabolite identification by human hepatocyte incubation and high-resolution mass spectrometry. Forensic Toxicol 34:61-75.
EMCDDA. 2019. Europe drug report. European Union, Lisbon.
Hsu JF, Tien CP, Shih CL, Liao PM, Wong HI, Liao PC. 2019. Using a high-resolution mass spectrometry-based metabolomics strategy for comprehensively screening and identifying biomarkers of phthalate exposure: Method development and application. Environment international 128:261-270.
Kolesnikova TO, Khatsko SL, Demin KA, Shevyrin VA, Kalueff AV. 2019. Dark classics in chemical neuroscience: Alpha-pyrrolidinovalerophenone ("flakka"). ACS Chem Neurosci 10:168-174.
LaPointe J, Musselman B, O'Neill T, Shepard JR. 2015. Detection of "bath salt" synthetic cathinones and metabolites in urine via dart-ms and solid phase microextraction. Journal of the American Society for Mass Spectrometry 26:159-165.
Liu H, Garrett TJ, Su Z, Khoo C, Gu L. 2017. Uhplc-q-orbitrap-hrms-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption. The Journal of Nutritional Biochemistry 45:67-76.
Patti GJ, Yanes O, Siuzdak G. 2012. Innovation: Metabolomics: The apogee of the omics trilogy. Nature reviews Molecular cell biology 13:263-269.
Paul G, Michael EB, Roumen S. 2013. Getting up to speed with the public health and regulatory challenges posed by new psychoactive substances in the information age. Addiction 108:1700-1703.
SAMHSA. 2017. Mandatory guidelines for federal workplace drug testing programs https://www.samhsa.gov/sites/default/files/workplace/frn_vol_82_7920_.pdf.
Shen C, Sun Z, Chen D, Su X, Jiang J, Li G, et al. 2015. Developing urinary metabolomic signatures as early bladder cancer diagnostic markers. Omics : a journal of integrative biology 19:1-11.
Tyrkko E, Pelander A, Ketola RA, Ojanpera I. 2013. In silico and in vitro metabolism studies support identification of designer drugs in human urine by liquid chromatography/quadrupole-time-of-flight mass spectrometry. Analytical and bioanalytical chemistry 405:6697-6709.
UNODC. 2013. The challenge of new psychoactive substances. United Nations, Vienna.
UNODC. 2017. World drug report 2017-pre-briefing to the member states. United Nations, Vienna.
Wold S, Antti H, Lindgren F, Öhman J. 1998. Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 44:175-185.
Worley B, Powers R. 2013. Multivariate analysis in metabolomics. Curr Metabolomics 1:92-107.
Zhang J, Huang Z, Chen M, Xia Y, Martin FL, Hang W, et al. 2014. Urinary metabolome identifies signatures of oligozoospermic infertile men. Fertility and sterility 102:44-53.e12.
校內:2022-12-31公開