臺灣博碩士論文加值系統

阿茲海默症又稱老年痴呆症，是一種常見的失智症，至今全世界約有4700萬人患有失智症，其中阿茲海默症就佔了六到七，患者會有大腦認知與記憶功能退化的症狀。乙醯膽鹼為神經系統中常見的神經傳導物質，有研究顯示乙醯膽鹼的增加有助於減緩患者的症狀，所以阿茲海默症藥物以乙醯膽鹼酯酶抑制劑為主。神經系統中發現了乙醯膽鹼兩個水解酶，分別為乙醯膽鹼酯酶與丁醯膽鹼酯酶，在乙醯膽鹼酯酶缺失的情況下，丁醯膽鹼酯酶能代替乙醯膽鹼酯酶執行乙醯膽鹼水解作用，因此，篩選能抑制兩個水解酶的化合物，為本研究的主要目的。利用虛擬篩選進行實驗，包含遺傳近似函數、多元迴歸分析、支持向量機器、分子對接與分子動力學，以中醫藥資料庫進行分子對接，建立預測活性模型和分子對接分數，再進行分子動力學，評估結合的穩定性，最後篩選出三個候選藥物，分別為Saussureamine B、Scoulerine和Anisodine，期望提供更具抑制活性的阿茲海默症藥物。

The most common type of dementia is Alzheimer's disease (AD), which makes up 60% to 70% of cases. Globally, dementia affects 47 million people. Patients will have cognitive and memory function of brain degeneration symptoms. Acetylcholine (ACh) is the neurotransmitter used at the Central Nervous System (CNS). Pharmacological treatment of AD is based on the use of acetylcholinesterase inhibitors, which have beneficial effects on cognitive, functional, and behavioural symptoms of the disease. Two types of ACh enzyme are found in the CNS, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). BuChE enzyme activity extended to all parts of the brain receiving cholinergic innervation and that it could hydrolyze the ACh surrogate AChE. Screening compounds that inhibit both hydrolases is the primary objective of this study. The experiment by virtual screening, including genetic function approximation (GFA), multiple regression analysis (MLR), support vector machines (SVM), molecular docking and molecular dynamics simulation (MD). The model was subsequently used as a molecular docking to identify potential hits form Traditional Chinese Medicine (TCM) database. The binding stabilities of these hits were further validated using molecular dynamics simulations. Finally, three candidate drugs were identified as Saussureamine B, Scoulerine and Anisodine. It is desirable to provide more inhibitory activity dual target inhibitor for AD.

摘要 I
ABSTRACT II
誌謝 III
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 阿茲海默症簡介 1
1.2 阿茲海默症成因 2
1.3 阿茲海默症藥物 3
1.4 乙醯膽鹼 4
1.5 乙醯膽鹼酯酶 5
1.6 丁醯膽鹼酯酶 5
1.7 電腦輔助藥物設計 (Computer-assisted drug design；CADD) 6
1.8 虛擬篩選（Virtual screening） 8
1.9 藥物動力學 (Pharmacokinetics) 9
1.10 TCM候選化合物 10
1.11 研究目的 11
第二章 材料與方法 12
2.1 方法流程 12
2.2 資料來源 12
2.3 遺傳函數近似Genetic Function Approximation (GFA) 13
2.4 多元迴歸分析Multiple Linear Regression (MLR) 13
2.5 支援向量機器 Support Vector Machine (SVM) 13
2.6 分子對接 (Molecular Docking) 14
2.7 分子動力學 (Molecular Dynamics Simulation) 14
2.8 藥物動力學 (ADMET) 15
第三章 結果與討論 16
3.1 遺傳函數近似、支持向量機器和多元迴歸分析 16
3.2 分子對接 17
3.3 分子動力學 19
3.4 藥物動力學 23
第四章 結論 24
參考文獻 26

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