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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 何?芳 | zh_TW |
dc.contributor.author | 郭益宏 | zh_TW |
dc.contributor.author | Yi-Hung Kuo | en |
dc.date.accessioned | 2021-06-17T02:11:21Z | - |
dc.date.available | 2023-11-30 | - |
dc.date.copyright | 2018-03-29 | - |
dc.date.issued | 2017 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | Aarons, L. (2015). Mixed Effects Models for the Population Approach: Models, Tasks, Methods, and Tools. CPT: Pharmacometrics & Systems Pharmacology,4(1), e00010.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68020 | - |
dc.description.abstract | 背景
血液科病人之侵入性黴菌感染有極高的發病率與死亡率。而Voriconazole是治療侵入性黴菌感染的第一線藥物,在人體內主要經肝臟CYP2C19、CYP3A4、CYP2C9代謝。因voriconazole的血中濃度有極大的個體間變異與個體內變異,臨床使用上充滿挑戰性。本研究旨在探討血液科病人voriconazole的藥動學參數並分析影響voriconazole藥動學參數之因子。 方法 本研究於醫學中心進行收案,為單中心,回溯性分析之研究。研究對象為2012年1月1日到2016年12月31日期間,使用voriconazole且有血液疾病的病人。研究資料之voriconazole血中濃度值來自於療劑監測、係波谷濃度,不足以描敘藥物的分佈體積與吸收速率,故採回顧文獻在一室分佈的情況下固定藥物的分佈體積與吸收速率,藥品的清除情形評估用一級線性和非線性排除,尋找出最適當的個體間與個體內的變異模型;分析上利用non-linear mixed effect model去估算藥動學參數,年齡、身高、體重、性別、BMI、ALT、ALP、AST、T-bil、CRP、CYP2C19基因型、有無異體幹細胞移植、適應症、感染的嚴重程度、併用藥品等,均做為變項納入分析。 結果 本研究共納入123位病人,固定型個體內誤差和指數型個體間誤差為最適當的變異數模型,群體動態學估算出的模型為 CLi = CLpop x[1+0.438(Allo-HSCT)] x[1- 0.00956(age - 53)]x[1- 0.051(CRP - 5.58)]x[1- 0.286(CYP2C19 genotype)] 群體的藥品清除率為5.52(L/h),年齡、CYP2C19 基因型、有無異體幹細胞移植、CRP值為顯著影響藥動參數的因子,納入因子之後個體間的誤差從80.9%下降到63.6%,代表此模型有較佳的預測效果。 結論 本研究發現年齡、CYP2C19 慢性代謝者、CRP值與voriconazole血中濃度呈正相關,而有無異體幹細胞移植則和voriconazole血中濃度呈負相關,此模型可供朝精準醫療邁進。 | zh_TW |
dc.description.abstract | Background
Invasive fungal infection (IFI) is a major cause of morbidity and mortality for patients with hematological patients. Voriconazole is first-line agent for the treatment of IFI and is metabolized by CYP2C19, CYP3A4, and CYP2C9. Given the high inter- and intra-subject variability in voriconazole serum concentrations, it is challenging for its routine use in clinical setting. The aim of this study was to characterize pharmacokinetics of voriconazole and to identify factors significantly associated with pharmacokinetic parameters in patients with hematological patients. Methods The data of adult patients diagnosed with hematological patients in NTUH were collected retrospectively from January 2012 to December 2016. In these patients, voriconazole was used for prophylaxis or the treatment of fungal infection. Serum samples were collected at steady-state during their clinical care. Non-linear mixed effect modeling using monolix was performed to estimate population pharmacokinetics. The data did not provide information about the rate of absorption and distribution processes so that absorption rate (ka) and apparent volume of distribution (Vd) were fixed according to the values from literature. One compartment open model with linear or nonlinear elimination pathway was used to estimate the clearance values. . Suitable models for interindividual variability and intraindividual error models were explored. Age, weight, height , BMI , ALT ,T-bil , ALP, CRP , CYP2C19 genotype, indication , patients with and without allogeneic hematopoietic stem cell transplantation (Allo-HSCT), co-medication and infectious disease severity were included for covariate evaluation. Results A total of 123 patients were enrolled in our study. Residual and inter-individual variabilities were best described by constant and exponential error models, respectively.The population clearance of voriconazole was estimated to be 5.52 L/h. The model indicated age, CRP, CYP2C19 genotype, and Allo-HSCT are statistically significant covariates influencing voriconazole pharmacokinetics. The final model was estimated to be CLi = CLpop x[1+0.438(Allo-HSCT)] x[1- 0.00956(age-53)]x[1- 0.051(CRP-5.58)]x[1- 0.286(CYP2C19 genotype)] In addition, the standard deviation of CL has decreased from 0.809 to 0.636 as part of the inter-individual variability in CL explained by the covariate. Final model had better predicitive perferomance compared to base model. Conclusions Voriconazole concentrations are inversely associated with age, CRP, and CYP2C19 PM. Allo-HSCT are positively correlated with voriconazole exposure. The current model would provide a good way to optimize individual dosage for voriconazole. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:11:21Z (GMT). No. of bitstreams: 1 ntu-106-R04451009-1.pdf: 5720165 bytes, checksum: ed4299e27cabdc612318a64ca3cea4d7 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iv 目錄 vii 圖目錄 x 表目錄 xii 第一章 前言 1 第二章 文獻回顧 3 2.1群體藥動學簡介 3 2.1.1 群體藥動學應用和傳統藥動學的差異 3 2.1.2群體藥動學理論 3 2.1.3 群體藥動學模型的建立 5 2.2 Voriconazole綜述 7 2.2.1藥理機轉 7 2.2.2 藥品動態學 8 2.2.3 適應症與使用劑量 9 2.2.4 藥品不良反應 10 2.2.5療劑監測 11 2.2.6藥品的交互作用 12 2.2.7 CYP2C19和voriconazole血中濃度的相關性 12 2.2.8 發炎反應和voriconazole血中濃度的相關性 13 2.2.9 異體幹細胞移植和voriconazole血中濃度的關係 14 2.2.10 Voriconazole的群體藥動學研究 16 第三章 研究目的 31 第四章 研究方法 32 4.1研究對象、地點 32 4.2納入條件及排除條件 32 4.3 資料收集 33 4.3.1 病人基本資料 33 4.3.2 影響血中濃度的因子 33 4.3.3 併用藥物 34 4.4 實驗室分析 34 4.4.1 濃度測量方法 34 4.4.2 DNA抽取 35 4.4.3 CYP2C19基因型分析 35 4.5 群體藥動模型 36 4.5.1研究設計 36 4.5.2 藥動模型 36 4.5.3 模型的確認 38 第五章 研究結果 42 5.1收案情形 42 5.2 Voriconazole藥物動態資料與模型的建立 44 5.2.1基本模型的建立 44 5.2.2鑑別顯著影響Voriconazole藥動學的因子 44 5.2.3判讀模型建立後的圖型 46 5.2.4模型的驗證 46 第六章 研究討論 74 6.1群體藥動學模型 74 6.2 CYP2C19基因與voriconazole之PPK 74 6.3 發炎情形與voriconazole之關係探討 75 6.4年齡與voriconazole藥動學影響 76 6.5異體幹細胞移植後與voriconazole血中濃度的影響 77 6.6研究限制與未來展望 77 第七章 研究結論 79 第八章 參考文獻 80 第九章 附錄 89 9.1 Linear elimination equations 89 9.2 Non-linear elimination equations 90 9.3 Shinymlx function equations 91 圖目錄 圖2-1群體藥動學模型建立流程圖 22 圖2-2藥血中濃度和時間關係 23 圖2-3個體的重量和個體藥動參數的關係圖 23 圖2-4清除率和η作圖 24 圖2-5 Scatter plot 25 圖2-6 Individually weighted residuals versus time 25 圖2-7 Voriconazole和Fluconazole的化學結構 26 圖2-8 Voriconazole作用的位置 26 圖2-9 Voriconazole代謝路徑 27 圖2-10參與Voriconazole代謝路徑的肝臟酵素 28 圖2-11 C-reactive protein在體內生成過程 29 圖5-1收案過程 53 圖5-2觀察值與IPRED之散佈圖在一級線性排除固定ka=1.1( h-1), Vd=101(L) 54 圖5-3觀察值與IPRED之散佈圖在一級線性排除固定ka=1.1( h-1), Vd=200(L) 55 圖5-4觀察值與IPRED之散佈圖在非線性排除固定ka=12.65( h-1), Vd=185.88(L) 56 圖5-5 Base model residual plots 57 圖5-6藥品清除率與CYP2C19基因型分組情形 58 圖5-7藥品清除率分組情形 59 圖5-8受試者基本資料與藥品清除率關係 60 圖5-9受試者檢驗數據與藥品清除率關係 61 圖5-10類別性資料與藥品清除率關係-1 62 圖5-11類別性資料與藥品清除率關係-2 63 圖5-12受試者年齡與藥品清除率關係式 64 圖5-13 CRP與藥品清除率關係式 65 圖5-14基本模型預測值與觀測值散佈圖 66 圖5-15最終模型預測值與觀測值散佈圖 67 圖5-16 Final model residual plots 68 圖5-17 Model validation 69 圖5-18標準劑量濃度預測圖。 70 圖5-19當個體清除率> 9.70(L/h),調整劑量後濃度預測圖 71 圖5-20當個體清除率< 2.45(L/h),調整劑量後濃度預測圖 72 表目錄 表2-1 Residual error function 28 表2-2 Interindividual error function 28 表2-3 Equations for a simple population pharmacokinetic model 29 表2-4 Voriconazole對各種黴菌的最小抑菌濃度 30 表2-5 Voriconazole藥動參數 30 表2-6 影響Voriconazole血中濃度的藥物整理表 31 表2-7 Voriconazole群體動態學研究整理表 32 表4-1 CYP2C19基因型的檢測 50 表4-2 Voriconazole群體藥動學文獻回顧 51 表5-1病人基本資料 59 表5-2病人疾病診斷表 60 表5-3收集群體之臨床狀態及檢驗數據 61 表5-4 Forward addition of covariate 62 表5-5 Backward elimination of covariate 63 表5-6本研究群體之藥動學參數 64 | - |
dc.language.iso | zh_TW | - |
dc.title | Voriconazole在血液科病人之群體藥動學研究 | zh_TW |
dc.title | Population Pharmacokinetics of Voriconazole in Hematological Patients | en |
dc.type | Thesis | - |
dc.date.schoolyear | 106-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 林君榮 | zh_TW |
dc.contributor.coadvisor | ; | en |
dc.contributor.oralexamcommittee | 李勇進;林芳如 | zh_TW |
dc.contributor.oralexamcommittee | ;; | en |
dc.subject.keyword | voriconazole,CYP2C19基因型,C-reactive protein,療劑監測,異體幹細胞移植,群體動態學模型, | zh_TW |
dc.subject.keyword | voriconazole,CYP2C19 genotype,C-reactive protein,therapeutic drug monitoring,allogeneic hematopoietic stem cell transplantation,population pharmacokinetics, | en |
dc.relation.page | 92 | - |
dc.identifier.doi | 10.6342/NTU201800050 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2018-01-17 | - |
dc.contributor.author-college | 醫學院 | - |
dc.contributor.author-dept | 臨床藥學研究所 | - |
顯示於系所單位: | 臨床藥學研究所 |
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