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系統識別號 U0026-2710201315185000
論文名稱(中文) 詳盡鑑定鄰苯二甲酸二異壬酯體外與體內代謝物做為人類毒物暴露指標
論文名稱(英文) Comprehensive identification of in vitro and in vivo metabolites of di-isononyl phthalates for discovering toxicant exposure markers in human
校院名稱 成功大學
系所名稱(中) 環境醫學研究所
系所名稱(英) Institute of Environmental and Occupational Health
學年度 102
學期 1
出版年 102
研究生(中文) 徐繹昇
研究生(英文) Yi-Sheng Hsu
學號 S76004060
學位類別 碩士
語文別 中文
論文頁數 87頁
口試委員 指導教授-廖寶琦
口試委員-李茂榮
口試委員-陳逸然
口試委員-許菁芳
中文關鍵字 鄰苯二甲酸二異壬酯  代謝物  詳盡  鑑定  同位素  質量虧損  驗證  暴露指標 
英文關鍵字 di-isononyl phthalate  metabolites  comprehensive  identification  isotope  mass defect  validation  exposure marker 
學科別分類
中文摘要 鄰苯二甲酸二異壬酯 (DINP) 是由一群支鏈上含有九個碳的同分異構物所組成的物質,DINP 的使用範圍相當廣泛,常被使用在工業製程中做為塑化劑與添加劑使用。過去動物實驗結果指出 DINP 會造成肝臟與腎臟的毒性效應,並干擾內分泌系統,因此 DINP 也可能會造成人類健康上的危害。根據先前研究,二十位受試者在口服同位素標定的 DINP 後,尿液中四個 DINP 代謝物的含量僅占32.9%,者結果也暗示著仍有其他 DINP 代謝物仍未被發現,因此本研究的目的為利用體外代謝的方式鑑定出 DINP 可能的代謝物訊號並於人類尿液中來做驗證,在研究中我們使用同位素追蹤法 (Signal Mining Algorithm with Isotope Tracing, SMAIT) 以及質量虧損過濾法 (Mass Defect Filter, MDF) 兩種方法鑑定 DINP 可能的代謝物訊號。由於 DINP 是由多種同分異構物所組成的,如果以 DINP 作為前驅物質會造成分析上的複雜度,因此本研究選擇單一支鏈的鄰苯二甲酸酯 (mono-methyl octyl phthalate) 作為體外代謝的前驅物質。在 SMAIT 方法當中,將未標定與經同位素標定的前驅物質依不同比例混合 (3:7, 4:6, 5:5, 6:4, 7:3),經人類肝臟酵素代謝後產生 DINP 代謝物,隨後利用固相萃取進行樣本的淨化與濃縮,使用高解析度的液相層析質譜儀 (LC-MS) 分析,將所得的 LC-MS 數據透過 SMAIT 軟體篩選可能的 DINP 代謝物訊號,再利用高解析度的 LC-MS/MS 針對可能的 DINP 代謝物訊號進行碎片離子的分析,最後獲得十四個可能的 DINP 代謝物訊號。在 MDF 方法當中,單獨將未經標定的前驅物質經人類肝臟酵素代謝後產生 DINP 代謝物,隨後利用固相萃取進行樣本的淨化與濃縮,使用高解析度的 LC-MS 分析,將所得的 LC-MS 數據透過 MDF 軟體篩選並排除與 DINP 非相關的離子資訊,再利用高解析度的 LC-MS/MS 針對可能的 DINP 代謝物訊號進行碎片離子的分析,最後獲得十個可能的 DINP 代謝物訊號。而這十個可能的 DINP 代謝物訊號都包括在 SMAIT 方法所找到的代謝物中,因此透過這兩種方法總計找到十四個可能的 DINP 代謝物訊號。我們收集並利用 LC-MS/MS 分析在工作中暴露到 DINP 的橡膠工人尿液,在尿液中可檢測出九個可能的 DINP 代謝物。最後總結我們在複雜的 LC-MS 數據中,透過 SMAIT 以及 MDF總計有十四個可能的 DINP 代謝物被找到,其中有九個訊號 m/z 293.139, 305.139, 307.155, 319.155, 321.170, 323.150, 361.127, 373.126, 375.142 可在人類尿液中測得,未來將建議將這九個訊號做更深入的探討並做為 DINP 暴露指標的評估。
英文摘要 Di-isononyl phthalates (DINP), isomers containing isomeric nine-carbon branch chain dialkyl phthalates, are widely used in industry as plasticizers and additives. DINP have been reported to have toxic effects on liver and kidney; they could interfere with the endocrine system in experimental animals. DINP thus may potentially have adverse health effects on humans. In a previous study, the four metabolites of DINP had 32.9% fractional excretion (on a molar basis) in the urine of 20 volunteers after a single oral dose of deuterated di-isononyl phthalates (D4-DINP). The low percentage of these four DINP metabolites seems to suggest that there may be other DINP metabolites that are yet to be identified. The purpose of this study is to use in vitro metabolism for identifying potential DINP metabolites and validating them in human urine. We identified potential DINP metabolite signals by two methods; Signal Mining Algorithm with Isotope Tracing (SMAIT) and Mass Defect Filter (MDF). We chose mono side-chain phthalate, mono-methyl octyl phthalate, as a precursor in this study because the presence of many isomers in DINP may cause problems during analysis. In the method of SMAIT, five isotope concentration ratios of native to isotope labeled precursors (3:7, 4:6, 5:5, 6:4, 7:3) were incubated with human liver enzymes to generate DINP metabolites. The samples were cleaned-up and concentrated by solid phase extraction (SPE). Then, high resolution liquid chromatography-mass spectrometry (LC-MS) analyses were performed. LC-MS data were processed by in-house software, SMAIT, which efficiently filtered probable DINP metabolite signals. Subsequently, high resolution LC-MS/MS was performed to obtain fragment ion data. Fourteen potential metabolites were identified which were related to the fragment ions of the precursor. In the MDF analysis, the native precursor was incubated with human liver enzymes to generate DINP metabolites. The samples were cleaned-up and concentrated by SPE. Subsequently, high resolution LC-MS analyses were performed. LC-MS data were processed by MDF to remove the non-DINP-related ions. This was followed by high resolution LC-MS/MS to obtain fragment ions. Ten potential metabolites were related to the fragment ions of the precursor. The ten potential metabolites identified by MDF were also found on SMAIT analysis; thus, a total of fourteen potential metabolites were identified by SMAIT and MDF analysis. We collected urine samples from rubber workers (who had occupational exposure to DINP) and analyzed them by LC-MS/MS. We detected nine potential DINP metabolites in these urine samples. In conclusion, a total of fourteen potential DINP metabolites were identified using in-house software, SMAIT, and MDF from complex LC-MS data. Our data indicates that DINP metabolites of m/z 293.139, 305.139, 307.155, 319.155, 321.170, 323.150, 361.127, 373.126 and 375.142 are potential exposure markers.
論文目次 摘要 ii
Abstract iv
致謝 vi
Table list ix
Figure list x
Abbreviations xiii
Chapter 1 Research background 1
1-1 Phthalate esters 1
1-1-1 Usage 1
1-1-2 Classification of phthalate esters 1
1-1-3 The use of phthalate esters 4
1-1-4 Routes of exposure 6
1-2 Di-iso-nonyl phthalate 6
1-2-1 Physical and chemical properties 6
1-2-2 Usage 7
1-2-3 DINP toxicity 8
1-2-4 DINP metabolism 8
1-3 Techniques for metabolite detection and identification 10
Chapter 2 Research objectives 13
Chapter 3 Material and methods 14
3-1 Research scheme 14
3-2 Research materials and methods 16
3-2-1 MINP precursor selection 16
3-2-2 Mono-4-methyl octyl phthalate and D4-mono-4-methyl octyl phthalate selection 16
3-2-3 MINP in vitro metabolism 17
3-2-4 High resolution LC-MS analysis 19
3-2-5 Method of SMAIT to trace probable DINP metabolite signals 20
3-2-6 Method of MDF to pick out probable DINP metabolites signals 23
3-2-7 Identification of possible DINP metabolite signals by high resolution LC-MS/MS 24
3-2-8 Human urine sample collection and preparation 25
Chapter 4 Results and discussion 27
4-1 Probable DINP metabolites from SMAIT 27
4-1-1 High resolution LC-MS results traced from SMAIT 27
4-1-2 High resolution LC-MS/MS results for probable DINP metabolites 38
4-2 Probable DINP metabolite from MDF 59
4-2-1 High resolution LC-MS results filtered by MDF 59
4-2-2 High resolution LC-MS/MS results for probable DINP metabolites 67
4-3 Compare LC-MS/MS results of SMAIT and MDF 76
4-4 Human urine results 78
4-5 Summaries of in vitro metabolism and human urine results 80
Chapter 5 Conclusions 81
References 82
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