滲透率(Peff)對於口服藥物是很重要的因素，藥物在腸道的吸收跟滲透率有直接的影響。一般測量口服藥物在腸道的滲透率，主要是用活體內(in vivo)和試管內(in vitro)的方式來測量。活體內的測量方法，優點是實驗數據比較接近真實的情況，但缺點是花費的時間跟資源較多。試管內的測量方法，優點是效率比前者好，測量方式相對的較容易，但缺點是測量的情況跟實際上是有所差別的，導致可能有較大的誤差。因此，本實驗根據藥物在空腸(jejunum)的滲透率(Peff)進行定量分析，根據從1997年到2017年的文獻中收集大鼠的活體內測量實驗數據，透過使用回歸支持向量機產生電腦計算(in silico)模型預測口服藥物在腸道中的滲透率。本實驗所建立的HSVR模型對於藥物的預測和實驗上得到的數據有很好的表現。在training set中(n = 53, r2 = 0.93, q2cv = 0.84, RMSE = 0.17, s = 0.07)，在test set中(n = 13, q2 = 0.75–0.85, RMSE = 0.26, s = 0.14)，在outlier 中(n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09)。根據這個HSVR模型，可以藉由收集數據，快速又準確的預測藥物的滲透率。而在生物藥物處置分類系統(BDDCS)中，使用分配係數與滲透率相近於原本用溶解度與代謝率的表現進而去幫助藥物設計。

Permeability is an important parameter for oral drugs. Intestinal absorption is Closely related to permeability in drugs. Generally speaking, oral drug intestinal permeability measured with two method, in vivo method and in vitro method. Pros of in vivo method are experiment data closer to the reality. Cons are need a lot of time and money. In contrast, Pros of in vitro method are efficient than the first one. Cons are large measurement deviation with unreal situation. An in silico model was produced to quantitatively the oral drug permeability using the hierarchical support vector regression (HSVR) scheme based on the in vivo rat experimental data collected from the literatures(1997-2017). The predictions by HSVR are in good performance with the experiment for those drugs in the training set (n = 53, r2 = 0.93, q2cv = 0.84, RMSE = 0.17, s = 0.07) and test set (n = 13, q2 = 0.75–0.85, RMSE = 0.26, s = 0.14) and outlier (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). Thus, it can be asserted that HSVR can quickly and accurately predict the drug permeability. In addition, the derived model can be adopted as the preliminary metric to carry out biopharmaceutics drug disposition classification system (BDDCS).

TABLE OF CONTENTS………………………………….………………………………..….i
摘要……………………………………………………………………………………………ii
ABSTRACT…………………………………………………………………………………..iii
KEYWORDS…………………………………………………………………………………iii
TABLE………………………………………………………………………………………..iv
FUGURE…………………………………………………………………….………………..v
ABBREVIATIONS…………………………………………………………………………...vi
1. Introduction……………………….……………………………………………..………..1
2. Materials and methods………………………………….…………………………….…...3
2.1 Data compilation………………………………………………………….…..… 3
2.2 Data partition………………………………………………………………….…3
2.3 Structure and optimization………………………………………………………3
2.4 Hierarchical support vector regression…………………………………………..4
2.5 Predictive evaluations…………………………………………………………...5
3. Results………………………………………………………………………….…………9
3.1 Data partition……………………………………….………………….…………9
3.2 SVR………………………………………………………………………………9
3.3 HSVR…………………………………………………………………………...10
3.4 Predictive evaluations…………………………………………………………..11
3.5 Mock test………………………………………………………………………..12
4. Discussion……………………………………………………………………………….13
5. Conclusion……………………………………………………………………………….15
6. Reference…………………………………………………………………………...........17
7. Figure……………………………………………………………………………………23
8. Table……………………………………………………………………………………..51

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