劉仁沛Liu, Jen-Pei臺灣大學:農藝學研究所游忠憲Yue, Chung-hsienChung-hsienYue2010-05-052018-07-112010-05-052018-07-112008U0001-2907200819111200http://ntur.lib.ntu.edu.tw//handle/246246/180078為了學名與原廠鼻腔噴霧器之生體相等性，本論文藉由與標準鼻腔噴霧器的噴霧效果來做比較是否為生體相等。在試驗過程中，因噴霧藥效為局部性的，並不會進入血液而影響其他身體器官，未避免人體實驗中包含的變異(如：力道、吸氣量…等)，所以以機器實驗來取代人體實驗。在分析過程中，我們關注的有噴霧樣本、小霧滴大小分佈及雲霧狀幾何學，而分析方法是採用非剖析分析(nonprofile analyses)。最後的判定準則是將我們的標準線性化後，去觀測其95%信賴區間的上界，依結果判定學名噴霧器是否與原廠噴霧器為生體相等性。In order to evaluate bioequivalence between the generic and innovator investigate the equivalence in characteristics between the generic and innovator nasal aerosols. Because the nasal aerosols only act locally and its active ingredient does not absorb into the body system, therefore in vivo bioequivalence studies are replaced by the in vitro bioequivalence studies using machinery experiment to avoid variability caused the factors much that volume of breath. The quality of nasal aerosols and sprays can be characterized by spray patterns, droplet size distribution and plume geometry. The 2003 US FDA draft guidance proposed the application of population bioequivalence to evaluate the in vitro bioequivalence between the generic and innovator nasal aerosols and sprays. Such analysis is referred to as the nonprofile analysis. We applied the statistical methods of in vitro bioequivalence in the US FDA guidance to a dataset of droplet size distribution. We also compare and discuss the results obtained under the 2-stage nested random-effects model and the model suggested in the 2003 US FDA draft guidance.中文摘要 Ibstract IIables 1hapter 1 Introduction 2hapter 2 Nasal Aerosols and Sprays and In Vitro Bioequivalence 4.1 Study Design 4.2 Data Collection 5.2.1 Emitted Dose Uniformity, Priming, Priming/Re-Priming, and Tail-Off Profile 5.2.2 Spray Pattern 6.2.3 Droplet Size Distribution 7.2.4 Plume Geometry 8hapter 3 Statistical Methods for Evaluation of In Vitro Bioequivalence 10.1 Descriptive Statistics 10.2 Non-Profile Analysis 11.3.1 Bioequivalence Criteria 11.3.2 Criterion 13.3.3 Hypothesis 15.3.4 95% Upper Confidence Bounds for Linearized Criteria 15.3 Two Methods to estimate and 16.3.1 Variance Components 16.3.2 MST 17hapter 4 Example 18.1 Study Design 18.2 Statistical Analyses 19.2.1 Estimated Linearized Criteria 19.2.2 95% Upper Confidence Bounds for Linearized Criteria 24.3 Results 28hapter 5 Discussion and Summary 30eferences 31application/pdf488128 bytesapplication/pdfen-US鼻腔噴霧器,小霧滴大小分佈,噴霧樣本,雲霧狀幾何學,線性標準Nasal Aerosols, Droplet Size Distribution, Spray Pattern, Plume Geometry, Linearized Criterianthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/180078/1/ntu-97-R95621208-1.pdf