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標題: 以模糊理論建立預測經皮滲透係數的最佳化模型
A Predictive Model for Skin Permeability Using Fuzzy Theory and Its Optimization
作者: 楊浚與
Yang, Chun-Yu
關鍵字: 經皮;transdermal;滲透係數;模糊理論;預測模型;最佳化;permeability;fuzzy theory;predictive model;optimization
出版社: 化學工程學系所
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本研究目的針對模糊推論系統能預測化學促滲劑對滲透係數的影響作效能上的評估,目前建立出2套模糊推論模型(FIS和ANFIS)可預估化合物的經皮滲透性,該推論系統的模型是由許多資料所構成的,包括實驗室數據,已發表的數據資料、模型,以及專家的意見。模型輸入含有化合物資訊(分子量和分配係數)以及化學促滲劑比例(w/w);其中,化合物分子量分佈範圍介於18-5808 Da,分配係數(log Kow)範圍在-1.85至59.2之間,化學促滲劑分配係數落在-1.7到9.21間,使用比例為0.1%-100%,預估滲透係數(log P)分佈在-6.0到-0.19之間。此模型是模糊規則(fuzzy rule)的集合,能表示出輸入值和藥物經皮滲透係數間的關係,而模型品質藉由模糊分類與模型輸出的明確數值(crisp)和文獻數據相比可得知。

Mathematical models of skin permeability play an important role in various fields including prediction of transdermal drug delivery and assessment of dermal exposure to chemicals. A number of algorithms to predict skin permeability coefficients have been developed. However, assumptions of the system were often oversimplified. Predicting skin permeability was thus deemed an ambiguous endeavor. Fuzzy modeling provides a mean to account for this ambiguity.
The objective of this study is to assess the performance of a fuzzy inference system for predicting skin permeability coefficients with chemical penetration enhancers (CPEs). Two fuzzy inference models (FIS and ANFIS) were developed to predict the permeability of compounds permeating through skin. These models were derived from multiple data sources including laboratory data, published data bases, published models and expert opinion. Inputs of the model include compound properties (molecular weight, ranging from 18 to 5808 Da, and octanol-H2O partition coefficient, ranging from -1.85 to 59.2), octanol-H2O partition coefficient for CPEs (ranging from -1.7 to 9.21) and the weight ratio of CPEs, ranging from 0.1% to 100%. The predicted logarithm values of permeability coefficient ranged from -6.0 to -0.19. Each model was a collection of rules that correlate input to the skin permeability of a combination of the compounds and CPEs. The model was then evaluated by comparing predicted and actual fuzzy classification, and defuzzification of the predicted outputs to get crisp values for correlating estimates with published values.
The better means chosen was the Adaptive Neural Fuzzy Inference System (ANFIS) implemented in the MATLAB Fuzzy Toolbox because refinement of the numeric parameters is useful to enhance the model fit. Error back propagation (BP) and hybrid were used as the optimal training. The role of permeation enhancers is unveiled explicitly and quantitatively in this fuzzy model for the first time.
其他識別: U0005-1608201217434800
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