本文選題：代謝產(chǎn)物預(yù)測 + 代謝網(wǎng)絡(luò)預(yù)測��； 參考：《北京中醫(yī)藥大學(xué)》2017年碩士論文

【摘要】：背景:目前,多數(shù)中藥化學(xué)成分體內(nèi)代謝過程尚不清楚,這在一定程度上阻礙了中藥直接作用物質(zhì)基礎(chǔ)和作用機(jī)制的闡明。中藥化學(xué)成分體內(nèi)代謝產(chǎn)物的研究有助于揭示中藥體內(nèi)變化過程,從而為中藥直接作用物質(zhì)基礎(chǔ)和作用機(jī)制的闡明提供線索與指導(dǎo)。利用傳統(tǒng)實(shí)驗(yàn)的方法進(jìn)行中藥化學(xué)成分代謝產(chǎn)物研究費(fèi)時(shí)而又耗力,同時(shí)高成本、高消耗。而通過計(jì)算機(jī)模擬構(gòu)建代謝產(chǎn)物預(yù)測模型的方法因其安全、高效、低成本、低消耗等特點(diǎn)有效彌補(bǔ)了傳統(tǒng)實(shí)驗(yàn)的不足,被認(rèn)為是輔助藥物代謝產(chǎn)物研究的另一有效手段。近年來,越來越多基于計(jì)算機(jī)模擬的藥物代謝預(yù)測研究先后被報(bào)道,但主要是圍繞著代謝位點(diǎn)的預(yù)測展開的,少數(shù)模型能夠給出具體的代謝產(chǎn)物卻又無法提供代謝酶的信息,同時(shí)模型的可靠性有待提高。因此建立具有較高可靠性的代謝產(chǎn)物預(yù)測模型是目前中藥代謝預(yù)測研究領(lǐng)域亟待解決的問題。目的:本論文旨在構(gòu)建快速、有效的中藥化學(xué)成分代謝產(chǎn)物預(yù)測模型,彌補(bǔ)當(dāng)前代謝產(chǎn)物預(yù)測模型對于代謝產(chǎn)物和代謝酶二者不可兼得的不足,并將其應(yīng)用于中藥代謝網(wǎng)絡(luò)的構(gòu)建。方法:(1)代謝反應(yīng)數(shù)據(jù)的考察。從數(shù)據(jù)的準(zhǔn)確性、完備性、適用性和可獲得性四個(gè)方面對目前已有代謝反應(yīng)數(shù)據(jù)進(jìn)行考察。準(zhǔn)確性主要考察數(shù)據(jù)的原始出處。完備性主要考察數(shù)據(jù)庫對于當(dāng)前已知代謝反應(yīng)數(shù)據(jù)的覆蓋度。適用性主要考察數(shù)據(jù)信息是否完善,考慮到本論文使用的代謝反應(yīng)數(shù)據(jù)需催化代謝反應(yīng)的代謝酶已知,因此對于催化酶未知的代謝反應(yīng)數(shù)據(jù)不予考慮�？色@得性主要考察數(shù)據(jù)獲得的難易程度。最終,基于準(zhǔn)確性高、完備性好、適用性高、易于獲得的原則確定本論文的最佳數(shù)據(jù)源。(2)中藥化學(xué)成分代謝產(chǎn)物預(yù)測模型構(gòu)建方法。立足于收集的代謝反應(yīng)數(shù)據(jù),根據(jù)每個(gè)代謝位點(diǎn)發(fā)生代謝反應(yīng)的實(shí)際情況,進(jìn)行陽性代謝位點(diǎn)和未標(biāo)記代謝位點(diǎn)的劃分。利用簡單投票的方法進(jìn)行陰性代謝位點(diǎn)的篩選。將參數(shù)化的陽性代謝位點(diǎn)和陰性代謝位點(diǎn)作為輸入,基于CHI、IG、GR、Relief四種特征屬性選擇方法進(jìn)行特征屬性的篩選。立足于篩選所得的特征屬性,分別通過 Bayes、LibSVM、KStar、IBK、AdaBoost、Boosting、J48、RandomForest八種分類建模方法進(jìn)行代謝產(chǎn)物預(yù)測模型的構(gòu)建,結(jié)果形成32種不同的建模組合。通過對模型預(yù)測性能的比較,獲得最優(yōu)代謝產(chǎn)物預(yù)測模型。最終,通過以下三個(gè)方面對模型的可靠性進(jìn)行考察:第一,利用獨(dú)立測試集,從代謝位點(diǎn)水平上考察模型的可靠性。第二,基于文獻(xiàn)報(bào)道的經(jīng)典外部測試集,從化合物分子水平上考察模型的可靠性,并通過與既往文獻(xiàn)報(bào)道的代謝產(chǎn)物預(yù)測模型進(jìn)行比較,考察模型的預(yù)測性能。第三,基于文獻(xiàn)報(bào)道的中藥化學(xué)成分代謝數(shù)據(jù),考察本論文所構(gòu)建的代謝產(chǎn)物預(yù)測模型在中藥化學(xué)成分代謝產(chǎn)物預(yù)測中的表現(xiàn)。(3)中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型構(gòu)建方法�；诒菊撐臉�(gòu)建的兩套代謝產(chǎn)物預(yù)測模型構(gòu)建中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型,并基于文獻(xiàn)報(bào)道的中藥化學(xué)成分代謝網(wǎng)絡(luò)數(shù)據(jù)和藥物代謝網(wǎng)絡(luò)數(shù)據(jù)考察所建中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型的可靠性。結(jié)果:(1)CYP450 3A4,2D6和2C9介導(dǎo)代謝反應(yīng)代謝產(chǎn)物預(yù)測模型的構(gòu)建�；谖墨I(xiàn)報(bào)道的CYP450酶相關(guān)代謝反應(yīng)數(shù)據(jù),構(gòu)建了 CYP450 3A4,2D6和2C9介導(dǎo)的5類代謝反應(yīng)代謝產(chǎn)物預(yù)測模型。涉及的代謝反應(yīng)類型包括N-脫烷基反應(yīng)、O-脫烷基反應(yīng)、脂肪族羥基化反應(yīng)、芳香族羥基化反應(yīng)和S-氧化反應(yīng)。所有模型的10折交叉驗(yàn)證測試的準(zhǔn)確性均介于0.940-0.987之間,靈敏度和特異性分別大于0.856和0.968,受試者工作特征曲線下面積均在0.953以上。所有模型獨(dú)立測試集測試的準(zhǔn)確性均介于0.947-0.994之間,靈敏度和特異性分別大于0.864和0.933,受試者工作特征曲線下面積均在0.977以上。對于文獻(xiàn)報(bào)道的外部測試集,本論文給出的靈敏度和特異性值分別為0.821和0.956,與前人的研究(靈敏度:0.701,特異性:0.963)相比,本論文所建模型識別真陽性樣本的能力較前人的研究提高了 12%,識別真陰性樣本的能力與前人的研究相比無顯著差異。從分子水平上來講,外部測試集的34個(gè)分子中,本論文所建模型完全預(yù)測正確17個(gè)分子,優(yōu)于SOMP 13個(gè)分子完全預(yù)測正確的結(jié)果�？偟膩碚f,本論文所建模型的預(yù)測性能要優(yōu)于或至少與前人的工作一樣好。(2)氧化還原酶介導(dǎo)代謝反應(yīng)代謝產(chǎn)物預(yù)測模型的構(gòu)建。通過對11個(gè)候選代謝反應(yīng)數(shù)據(jù)庫的考察,最終選定BKM數(shù)據(jù)庫為最佳數(shù)據(jù)源�；贐KM數(shù)據(jù)庫收錄的代謝反應(yīng)數(shù)據(jù),建立了氧化還原酶介導(dǎo)的7類氧化代謝反應(yīng)代謝產(chǎn)物預(yù)測模型。涉及的代謝反應(yīng)類型包括C=C的形成反應(yīng)、醇氧化成酮反應(yīng)、脂肪族羥基化反應(yīng)、醇氧化成醛反應(yīng)、芳香族羥基化反應(yīng)、氧化脫氨反應(yīng)和N-脫烷基反應(yīng),共涉及代謝酶655個(gè)。所有模型10折交叉驗(yàn)證的準(zhǔn)確性均介于0.901-0.995之間,除醇氧化成酮反應(yīng)和芳香族羥基化反應(yīng)的靈敏度分別為0.777和0.765之外,其他5類代謝反應(yīng)的靈敏度均在0.875-0.988之間,所有模型的特異性和受試者工作特征曲線下面積分別大于0.944和0.915。對于獨(dú)立測試集而言,所有模型的準(zhǔn)確性均介于0.885-0.969之間,除芳香族羥基化反應(yīng)之外,其他6類代謝反應(yīng)的靈敏度均大于0.777,所有模型的特異性和受試者工作特征曲線下面積分別大于0.963和0.916。訓(xùn)練集和獨(dú)立測試集的平衡精度分別大于0.858和0.813。對于文獻(xiàn)報(bào)道的外部測試集,從代謝位點(diǎn)水平上來講,34個(gè)陽性代謝位點(diǎn)中31個(gè)代謝位點(diǎn)完全預(yù)測正確,靈敏度為0.912。從分子水平上來講,外部測試集31個(gè)化合物分子中23個(gè)分子完全預(yù)測正確。結(jié)果表明,當(dāng)涉及的代謝酶更多、數(shù)據(jù)量更大、數(shù)據(jù)結(jié)構(gòu)更加復(fù)雜時(shí),筆者提出的代謝產(chǎn)物預(yù)測模型構(gòu)建方法依然合理有效。(3)中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型的構(gòu)建�；�(1)和(2)所建的兩套代謝產(chǎn)物預(yù)測模型初步建立了中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型。該中藥化學(xué)成成分代謝網(wǎng)絡(luò)預(yù)測模型的預(yù)測范圍如下:CYP450 3A4,2D6和2C9介導(dǎo)的N-脫烷基反應(yīng)、O-脫烷基反應(yīng)、脂肪族羥基化反應(yīng)、芳香族羥基化反應(yīng),S-氧化反應(yīng)等5類代謝反應(yīng)和氧化還原酶介導(dǎo)的C=C的形成反應(yīng)、醇氧化成酮反應(yīng)、脂肪族羥基化反應(yīng)、醇氧化成醛反應(yīng)、芳香族羥基化反應(yīng)、氧化脫氨反應(yīng)、N-脫烷基反應(yīng)等7類代謝反應(yīng)。最后,基于文獻(xiàn)報(bào)道的西酞普蘭代謝網(wǎng)絡(luò)和烏頭堿代謝網(wǎng)絡(luò)考察了所建代謝網(wǎng)絡(luò)預(yù)測模型的可靠性。結(jié)果發(fā)現(xiàn),文獻(xiàn)報(bào)道的西酞普蘭代謝過程和烏頭堿代謝過程在本論文預(yù)測的結(jié)果中均有一定程度的體現(xiàn)。結(jié)論:本論文首次提出了基于化學(xué)鍵的代謝產(chǎn)物預(yù)測模型構(gòu)建方法。并成功構(gòu)建了CYP450 3A4,2D6和2C9介導(dǎo)的5類代謝反應(yīng)代謝產(chǎn)物預(yù)測模型和氧化還原酶介導(dǎo)的7類代謝反應(yīng)代謝產(chǎn)物預(yù)測模型。測試結(jié)果表明本論文構(gòu)建的代謝產(chǎn)物預(yù)測模型真實(shí)、可靠,其預(yù)測性能優(yōu)于或至少與既往文獻(xiàn)報(bào)道的代謝產(chǎn)物預(yù)測模型一樣好。最終,在以上兩套代謝產(chǎn)物預(yù)測模型的基礎(chǔ)上,初步構(gòu)建了中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型。測試結(jié)果表明本論文構(gòu)建的代謝網(wǎng)絡(luò)預(yù)測模型具有一定的可靠性,在一定程度上可以反應(yīng)中藥化學(xué)成分的體內(nèi)代謝過程�？偟膩碚f,本論文所建的中藥化學(xué)成分代謝產(chǎn)物預(yù)測模型和中藥化學(xué)成分代謝網(wǎng)絡(luò)預(yù)測模型將為中藥直接作用物質(zhì)基礎(chǔ)的發(fā)現(xiàn)和中藥作用機(jī)制的闡明提供線索,也將為新藥開發(fā)過程中先導(dǎo)化合物的優(yōu)化提供策略。
[Abstract]:Background: at present, the metabolic process of most Chinese medicine chemical components is not clear, which hinders the clarification of the material basis and mechanism of the direct action of traditional Chinese medicine. The study of the metabolites in the body of Chinese medicine is helpful to reveal the process of the change in the body of Chinese medicine, and to explain the direct effect of the material basis and mechanism of action for the Chinese traditional medicine. It provides clues and guidance. Using traditional experimental methods to study the metabolites of chemical components of traditional Chinese medicine is time-consuming, consuming, high cost and high consumption, and the method of building the prediction model of metabolites by computer simulation has effectively made up the shortage of traditional experiments because of its safety, efficiency, low cost and low consumption. In recent years, more and more studies have been reported on the prediction of drug metabolism based on computer simulation, but mainly around the prediction of metabolic sites. A few models can give specific metabolites but can not provide the information of metabolic enzymes, and the reliability of the model is also available. Therefore, the establishment of a high reliability metabolic product prediction model is an urgent problem in the field of Chinese medicine metabolism prediction research. Objective: the purpose of this paper is to construct a fast and effective model for predicting the metabolites of the chemical composition of Chinese medicine, and make up for the current metabolic product prediction model for the metabolic products and the metabolic enzyme two. It is applied to the construction of the metabolic network of traditional Chinese medicine. Methods: (1) investigation of metabolic response data. From the four aspects of data accuracy, completeness, applicability and availability, the existing metabolic response data are investigated. The accuracy mainly examines the original source of the data. The coverage of the metabolic response data. Applicability mainly to investigate whether the data information is perfect. Considering that metabolic enzymes used in this paper need to be known as metabolic enzymes in the catalytic metabolic reaction, the metabolic response data of the unknown catalytic enzyme are not considered. High quality, good completeness, high applicability and easy access to the best data sources of this paper. (2) the construction method of the prediction model of the metabolites of chemical components of Chinese medicine. Based on the collected metabolic reaction data, the positive metabolic sites and unlabeled metabolic sites are divided according to the actual situation of metabolic reactions at each metabolic site. The screening of negative metabolic sites was carried out by using a simple voting method. The parameterized positive metabolic sites and negative metabolic sites were used as input to select four characteristic attribute selection methods based on CHI, IG, GR and Relief. Based on the selected characteristics, Bayes, LibSVM, KStar, IBK, AdaBoost, Boosting, respectively, were selected. J48, RandomForest eight classification modeling methods for the construction of metabolic product prediction model, resulting in the formation of 32 different modeling combinations. Through the comparison of the prediction performance of the model, the optimal metabolic product prediction model was obtained. Finally, the reliability of the model was investigated through the following three aspects: first, the use of independent test set, from the metabolic site. The reliability of the model was examined at point level. Second, based on the classical external test set, the reliability of the model was investigated from the molecular level of the compound, and the predictive performance of the model was compared with the previously reported metabolic product prediction model. Third, based on the literature report of the metabolic data of the chemical composition of Chinese medicine, The performance of metabolic product prediction model in this paper is observed in the prediction of metabolic products of Chinese medicine chemical components. (3) construction method of metabolic network prediction model of Chinese medicine chemical composition. Based on the two sets of metabolic product prediction models constructed in this paper, the pretest model of metabolic network of Chinese Medicine chemical composition is constructed, and based on the literature report of Chinese Medicine Chemistry The reliability of the prediction model of the chemical composition metabolic network of Chinese medicine was constructed by the composition metabolic network data and the drug metabolic network data. Results: (1) the construction of the prediction model of metabolic reaction products mediated by CYP450 3A4,2D6 and 2C9. Based on the reported data of the related metabolic reaction of CYP450 enzymes, the 5 types of CYP450 3A4,2D6 and 2C9 are constructed. Metabolic response metabolite prediction models. The types of metabolic reactions involved include N- dealkylation, O- dealkylation, aliphatic hydroxylation, aromatic hydroxylation and S- oxidation. The accuracy of all 90% off cross validation tests of all models is between 0.940-0.987, sensitivity and specificity of more than 0.856 and 0.968, respectively. The area under the working characteristic curve of the subjects is above 0.953. The accuracy of all model independent test sets is between 0.947-0.994, sensitivity and specificity are more than 0.864 and 0.933 respectively. The area under the working characteristic curve of the subjects is more than 0.977. For the external test set reported in the literature, the sensitivity and specificity of this paper are given. The heterosexual values are 0.821 and 0.956 respectively. Compared with previous studies (sensitivity: 0.701, specificity: 0.963), the ability of the model to identify the true positive samples is 12% higher than that of previous studies. The ability to identify the true negative samples is not significantly different from those of previous studies. From the molecular level, the 34 molecules of the external test set are on the molecular level. In this paper, the model is completely predicted by 17 molecules, which is better than SOMP 13 molecules to fully predict the correct results. In general, the prediction performance of the model in this paper is better than or at least as good as the previous work. (2) the construction of the oxidoreductase mediated metabolic product prediction model of the oxidoreductase mediated by 11 candidate metabolites. According to the investigation of the database, the BKM database is selected as the best data source. Based on the metabolic response data of the BKM database, a predictive model of 7 kinds of oxidoreductase mediated metabolic reaction products is established. The metabolic reaction types include the formation reaction of C=C, the alkyl ketone reaction, the aliphatic hydroxylation reaction, and the alcohol oxidation. Aldehyde reaction, aromatic hydroxylation, oxidative deamination and N- dealkylation involved 655 metabolic enzymes. The accuracy of all 90% off cross validation of all models was between 0.901-0.995, and the sensitivity of alcohol oxide to ketone reaction and aromatic hydroxylation was 0.777 and 0.765 respectively, and the sensitivity of the other 5 types of metabolic reactions were all In 0.875-0.988, the specificity of all models and the area under the working characteristic curve of the subjects were greater than 0.944 and 0.915. respectively. For the independent test set, the accuracy of all models was between 0.885-0.969. In addition to the aromatic hydroxylation, the sensitivity of the other 6 types of metabolic reactions was greater than 0.777, and the specificity of all models and the specificity of all models were more than 0.777. The area under the working characteristic curve of the subjects was greater than 0.963 and the 0.916. training set and the independent test set were more than 0.858 and 0.813. for the external test set. From the level of metabolic sites, 31 metabolic sites in 34 positive metabolic sites were completely pretested and the sensitivity was 0.912. from the molecular level. 23 of the 31 compounds in the external test set are correctly predicted. The results show that when the metabolic enzymes involved are more, the amount of data is larger, and the data structure is more complex, the method of building the metabolic product prediction model proposed by the author is still reasonable and effective. (3) the construction of the prediction model of the metabolic network of Chinese medicine. Based on (1) and (2) The two set of metabolic product prediction models initially established the prediction model of the chemical composition metabolic network of traditional Chinese medicine. The prediction range of the chemical composition metabolic network model of the traditional Chinese medicine is as follows: CYP450 3A4,2D6 and 2C9 mediated N- dealkylation, O- dealkylation, aliphatic hydroxylation, aromatic hydroxylation, S- oxidation, etc. 5 kinds of metabolic reactions and oxidoreductase mediated C=C formation reaction, alcohol oxidation to ketone reaction, aliphatic hydroxylation, alcohol oxidation to aldehyde reaction, aromatic hydroxylation, oxidation deamination, N- dealkylation, etc. Finally, based on the literature report of citalopram metabolic network and aconitine metabolic network The results showed that the metabolic processes of citalopram and the metabolic process of aconitine were reflected to some extent in the results of this paper. Conclusion: This paper first proposed a method to construct the prediction model of metabolites based on chemical bonds, and successfully constructed CYP450 3A4,2D6 and 2. The C9 mediated metabolic product prediction model and the oxidoreductase mediated metabolic product prediction model of the 7 metabolic reaction mediated by the oxidoreductase. The results show that the predictive model of the metabolites constructed in this paper is true and reliable, and its predictive performance is as good as or at least as good as that of the previously reported metabolite prediction model. On the basis of two sets of metabolic product prediction models, a preliminary construction of the prediction model of the chemical composition metabolic network of Chinese medicine has been built. The results show that the metabolic network prediction model constructed in this paper is reliable and can reflect the metabolic process in the body of the chemical composition of Chinese medicine to a certain extent. In general, the Chinese medicine chemistry built in this paper The prediction model of composition metabolites and the prediction model of chemical composition metabolic network of Chinese medicine will provide clues for the discovery of the material basis of the direct action of traditional Chinese medicine and the clarifying of the mechanism of action of traditional Chinese medicine, and will also provide a strategy for the optimization of the pilot compounds in the process of the development of new drugs.

【學(xué)位授予單位】：北京中醫(yī)藥大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R284

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