本文選題：藥物毒性預測 + 定量構效關系�。� 參考：《浙江大學》2014年博士論文

【摘要】：新藥研發(fā)失敗約有30%是由于存在安全性問題而導致的。因此,研究建立高效準確的藥物毒性預測方法,對于提高新藥研發(fā)的成功率具有重要意義,并己成為當今毒理學、藥物分析學、計算化學和系統(tǒng)生物學等多個學科共同關注的前沿命題。 傳統(tǒng)的毒理學實驗方法由于存在周期長、花費高以及需要消耗大量動物等缺陷,正日益被基于化合物結構的毒性預測方法(如定量構效關系,QSAR)和基于系統(tǒng)生物學的毒性預測方法(如毒理基因組學)所替代。其中,QSAR方法不依賴于實驗,具有快速、經(jīng)濟等優(yōu)點,已被廣泛用于藥物研發(fā)初期進行化合物的毒性評價,但這類方法對化學結構多樣且毒性位點或致毒機理差別較大的化合物預測效果較一般；毒理基因組學方法的適用范圍較廣,所建的基于生物標記物的毒性預測模型有助于加深人們對致毒機理的理解,其缺點是實驗費用昂貴,且基于基因芯片的建模預測技術的可靠性尚存爭議。 鑒于當前毒性預測方法準確率普遍不高的現(xiàn)狀,并針對不同的毒性問題,本論文研究建立和改良了多種基于QSAR和毒理基因組學的毒性預測方法： 1、研究提出了一種可用于藥物毒性預測的改進決策森林算法(IDF)。通過使用2個高維的基因組數(shù)據(jù)集進行測試并與多種常用的預測方法進行比較,結果表明,IDF算法不僅獲得了比原始決策森林算法更優(yōu)的結果,而且在準確率和穩(wěn)定性上也優(yōu)于其他多種常用的預測方法,說明把IDF算法用于對高維數(shù)據(jù)進行建模預測具有一定的優(yōu)勢。 2、研究提出了基于SVM(支持向量機)、kNN(k最近鄰法)和NC (Nearest Centroids,最近質(zhì)心)等算法的集成毒性預測方法�；诙鄠�數(shù)據(jù)集的測試結果表明,集成算法能夠大大提高預測準確率,而且結果更穩(wěn)定,其中集成SVM的方法最優(yōu),預測準確率提升超過3%。 3、通過使用來自大鼠血液的基因組表達數(shù)據(jù)建模,研究提出了可以用于預測藥物肝毒性的跨組織預測方法。基于一批來自大鼠血液的基因組表達數(shù)據(jù),本研究建立了多個可用于預測藥物肝毒性的模型,并在3個獨立的肝毒性數(shù)據(jù)集上進行了驗證,其中最高預測準確率達92%。此外還發(fā)現(xiàn)了6個血液基因可以作為藥物肝毒性的生物標記物。 4、研究提出了基于SVM和微粒群算法的P-糖蛋白底物預測方法。預測一個化合物是否P-糖蛋白底物,對研究該化合物的ADME/T性質(zhì)具有重要意義。因此,本研究建立了一種基于SVM和微粒群算法的P-糖蛋白底物預測方法。與已有文獻結果相比,本方法獲得了更高的預測準確率(約90%),且所建模型有更好的化學或生物學意義,可解釋性強。
[Abstract]:About 30% of new drug R & D failures are caused by the existence of safety problems. Therefore, it is of great significance to study the efficient and accurate method of predicting drug toxicity, and is of great importance to the success rate of new drug research and development, and has become the frontier life of many subjects such as toxicology, drug analysis, computational chemistry and system biology. Question.
Traditional toxicology experimental methods are being replaced by toxicity prediction methods based on compound structure (such as quantitative structure-activity relationship, QSAR) and toxicity prediction based on System Biology (such as toxicological group). The QSAR method is not dependent on the experiment, because of the long period, high cost and the need to consume a large number of animals. With the advantages of rapid, economic, and so on, it has been widely used in the initial stage of drug development to evaluate the toxicity of compounds. However, this method has a more general prediction effect on chemical compounds with a wide variety of toxic sites or toxic mechanisms, and a wide range of application of toxicological genomics methods. The measurement model helps to deepen people's understanding of the toxic mechanism. The disadvantage is that the experimental cost is expensive, and the reliability of the modeling prediction technology based on the gene chip is still in dispute.
In view of the prevalence of current toxicity prediction methods, and in response to different toxicity problems, a variety of methods of toxicity prediction based on QSAR and toxicology genomics have been established and improved in this paper.
1, the study proposed an improved decision forest algorithm (IDF), which can be used to predict drug toxicity. By using 2 high dimensional genomic data sets to test and compare with a variety of common prediction methods, the results show that the IDF algorithm not only obtained better results than the original decision forest algorithm, but also in accuracy and stability. It is also superior to many other commonly used prediction methods, which shows that IDF algorithm has certain advantages in modeling and forecasting high-dimensional data.
2, the study proposes an integrated toxicity prediction method based on SVM (support vector machine), kNN (k nearest neighbor) and NC (Nearest Centroids, the nearest centroid). The test results based on multiple data sets show that the integrated algorithm can greatly improve the accuracy of prediction, and the result is more stable, in which the method of integrating SVM is optimal and the prediction accuracy is proposed. Rise more than 3%.
3, by modeling the genome expression data from the rat's blood, the study proposed a cross tissue prediction method that could be used to predict the toxicity of the drug. Based on the genome expression data from a group of rat blood, a number of models that could be used to predict the toxicity of drug hepatotoxicity were established and advanced in 3 independent hepatotoxicity data sets. The highest prediction accuracy was 92%., and 6 blood genes were identified as biomarkers of drug hepatotoxicity.
4, the study proposed a method of P- glycoprotein substrate prediction based on SVM and particle swarm optimization. Predicting whether a compound is P- glycoprotein substrate is of great significance to the study of the ADME/T properties of the compound. Therefore, a method for predicting the substrate of P- glycoprotein based on SVM and particle swarm optimization is established. The method has a higher prediction accuracy (about 90%), and the model has better chemical or biological meaning and is more explanatory.

【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R994.3

【參考文獻】

相關期刊論文 前1條

1 王先良;于云江;王紅梅;趙秀閣;;毒理學發(fā)展的新方向——系統(tǒng)毒理學[J];環(huán)境與健康雜志;2007年06期

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本文編號：1898039