化合物毒性預(yù)測模型構(gòu)建及煙草煙氣化學(xué)成分毒副作用預(yù)測研究
發(fā)布時間：2018-07-22 14:45
【摘要】：化合物毒性是導(dǎo)致藥物研發(fā)失敗的主要原因之一。將藥物安全性評價提前到藥物研發(fā)早期階段有助于縮短藥物研發(fā)周期,降低開發(fā)成本。除了藥物之外,食品添加劑、化妝品等與生活相關(guān)的其他化學(xué)品的安全性評估也是十分必要的。常規(guī)的毒理學(xué)試驗方法需要耗費大量的時間和金錢,難以滿足現(xiàn)代藥物研發(fā)以及環(huán)境化合物風(fēng)險評估的要求。研究者迫切需要依據(jù)“3R”原則開發(fā)替代方法,即減少(Reduce)實驗動物數(shù)量、優(yōu)化(Refine)實驗程序、替代(Replace)實驗動物。使用計算機(jī)方法進(jìn)行化合物毒性預(yù)測,可以用較低的成本實現(xiàn)對大批量化合物進(jìn)行快速的安全性評價。計算機(jī)方法的另一個優(yōu)勢是對藥物發(fā)現(xiàn)早期階段的虛擬化合物,也可以進(jìn)行毒性預(yù)測。因此,計算毒理學(xué)方法已經(jīng)成為化合物安全性評價中不可或缺的工具。本論文的研究工作主要包括兩大部分：第一部分是化合物毒性預(yù)測模型構(gòu)建,在這一部分工作中,分別構(gòu)建了化合物急性毒性和致癌毒性的預(yù)測模型,第二部分是煙草與煙氣化學(xué)成分毒副作用研究,在這里構(gòu)建了煙草與煙氣中的化學(xué)成分?jǐn)?shù)據(jù)庫,并對其進(jìn)行了毒副作用預(yù)測研究,另外,使用系統(tǒng)評價方法分析了吸煙對于帕金森癥患病風(fēng)險的影響。論文的第一章,首先介紹了化合物毒性預(yù)測研究的相關(guān)背景和意義,以及目前計算機(jī)預(yù)測化合物毒性的主要方法和技術(shù),包括交叉參照、定量／定性構(gòu)-毒關(guān)系、警示子結(jié)構(gòu)等。然后,介紹了國內(nèi)外有關(guān)煙草與煙氣及其有害成分的研究進(jìn)展。接下來,介紹了帕金森癥以及吸煙對帕金森癥發(fā)病率的研究背景和系統(tǒng)評價研究進(jìn)展?fàn)顩r。另外,也對用于系統(tǒng)評價的(?)neta分析(Meta-analysis)方法進(jìn)行了介紹�；衔锏募毙远拘允窃谒幬镅邪l(fā)以及生態(tài)風(fēng)險評估過程中必須要考慮的一個重要毒性性質(zhì)。動物實驗方法測定化合物的急性毒性需要大量的時間和資金投入,計算機(jī)預(yù)測是一種很重要的替代方法。論文第二章中,我們收集整理了12204種化合物的結(jié)構(gòu)及其大鼠口服急性毒性實驗數(shù)據(jù)。按照US EPA的毒性分級標(biāo)準(zhǔn),將所有化合物劃分為劇毒、高毒、中毒和微毒四個等級。然后,使用MACCS與FP4兩種分子指紋表征分子結(jié)構(gòu),結(jié)合支持向量機(jī)、κ-最近鄰居法、C4.5決策樹、隨機(jī)森林和樸素貝葉斯等五種機(jī)器學(xué)習(xí)方法分別構(gòu)建了多分類模型。其中,支持向量機(jī)本身是一個兩類問題的判別方法,我們采用“一對一”與“二叉樹”兩種策略用于多分類問題的解決。采用兩個分別包含1678和375個化合物的外部驗證集對構(gòu)建的多分類模型進(jìn)行了驗證。模型MACCS-SVMOAo表現(xiàn)出了最強的預(yù)測能力,對兩個外部驗證集的總體預(yù)測準(zhǔn)確率分別達(dá)到83.0%和89.9%。另外,我們采用信息增益技術(shù)和子結(jié)構(gòu)頻率分析方法,篩選了可能導(dǎo)致化合物產(chǎn)生急性毒性的優(yōu)勢子結(jié)構(gòu)片段。致癌性是另一種受到廣泛關(guān)注的化合物毒性性質(zhì)。論文第三章,我們提取了CPDB數(shù)據(jù)庫中829種化合物的大鼠致癌性實驗數(shù)據(jù),使用五種機(jī)器學(xué)習(xí)方法結(jié)合六種分子指紋類型,建立了用于預(yù)測化合物致癌毒性的二分類和三分類預(yù)測模型。使用測試集對模型進(jìn)行初步驗證后,分別選取了8個表現(xiàn)最好的二分類模型和7個表現(xiàn)最好的三分類模型,使用外部驗證集進(jìn)行進(jìn)一步驗證。外部驗證集由ISSCAN數(shù)據(jù)庫中的87種化合物構(gòu)成。預(yù)測能力最強的二分類模型MACCS-kNN整體預(yù)測準(zhǔn)確率達(dá)到83.91%,預(yù)測能力最強的三分類模型MACCS-kNN整體預(yù)測準(zhǔn)確率也達(dá)到80.46%。我們篩選了五種在致癌化合物中出現(xiàn)頻率明顯高于非致癌物的結(jié)構(gòu)片段,為化合物的致癌性研究提供警示作用。煙草煙氣是嚴(yán)重危害人體健康的的成分十分復(fù)雜的混合物質(zhì)。對煙草與煙氣中的化學(xué)成分進(jìn)行匯總和分析,有助于香煙風(fēng)險評估研究以及監(jiān)管香煙生產(chǎn)過程中有害成分的檢測。論文第四章,我們構(gòu)建了一個煙草與煙氣化學(xué)成分?jǐn)?shù)據(jù)庫(The Chemical Components of Tobacco and Tobacco Smoke, CCTTS)。 CCTTS提供了煙草與煙氣中5983種化學(xué)成分的化學(xué)結(jié)構(gòu)、基本物理化學(xué)性質(zhì)、毒性信息以及使用admetSAR工具預(yù)測得到的ADMET(吸收,分配,代謝,排泄和毒性)性質(zhì)。在這些化學(xué)成分中,568種已有明確的實驗毒性數(shù)據(jù),另有145種化學(xué)成分被預(yù)測為具有急性毒性或者致癌性。本數(shù)據(jù)庫將可通過互聯(lián)網(wǎng)進(jìn)行瀏覽和檢索。吸煙有害人體健康,不過許多流行病學(xué)研究表明吸煙有可能會降低患帕金森癥的風(fēng)險。論文的第五章,我們通過meta分析方法系統(tǒng)評價了現(xiàn)有的吸煙與帕金森癥風(fēng)險的研究結(jié)果。我們?nèi)娴貦z索了1960年到2014年10月期間有關(guān)吸煙與帕金森癥風(fēng)險相關(guān)性研究的英文文獻(xiàn),從中篩選了61項回顧性的病例對照研究和9項前瞻性的隊列研究。與不吸煙者相比,吸煙者患帕金森癥的相對風(fēng)險值RR(95%置信區(qū)間)的合并值為0.59(95%置信區(qū)間,0.56-0.62),表示曾經(jīng)吸煙者患帕金森癥的風(fēng)險比不吸煙者低41%。我們進(jìn)一步按照不同的流行病學(xué)研究方法、受試者性別、對照組來源、吸煙量以及研究年代等進(jìn)行了亞組分析。各個亞組的meta分析,都表現(xiàn)出了吸煙在帕金森癥方面的保護(hù)作用,這種保護(hù)性作用十分明顯,并且吸煙量越大,這種保護(hù)作用越強。我們總結(jié)了目前有關(guān)這種保護(hù)作用生物機(jī)制的一些假說,建議研究者們深入了解這一保護(hù)作用的生物機(jī)制,進(jìn)而有助于從煙草和煙氣成分中發(fā)現(xiàn)潛在的延緩和治療帕金森癥的藥物。論文的第六章對全文的研究工作進(jìn)行了總結(jié),并突出了論文的創(chuàng)新點。
[Abstract]:The toxicity of compounds is one of the main causes of the failure of drug research and development. The early stage of drug safety assessment to the early stage of drug development can help to shorten the cycle of drug development and reduce the cost of development. Besides, it is also necessary to evaluate the safety of other chemicals, such as food additives, cosmetics and other related chemicals. Regulatory toxicology test methods need to spend a lot of time and money. It is difficult to meet the requirements of modern drug development and environmental compound risk assessment. Researchers urgently need to develop alternative methods based on the "3R" principle, that is, to reduce the number of experimental animals (Reduce), to optimize (Refine) experimental procedures, to replace (Replace) experimental animals. The computational method can be used to predict the toxicity of compounds, which can be used to evaluate the safety of large quantities of compounds at a lower cost. The other advantage of the computer method is to detect the virtual compounds at the early stage of the drug discovery and to predict the toxicity. Therefore, the method of computational toxicology has become an evaluation of the safety of compounds. The research work of this paper consists of two major parts: the first part is the construction of the compound toxicity prediction model. In this part, the prediction model of acute toxicity and carcinogenicity of compounds is constructed, the second part is the study of the side effects of tobacco and flue gas, and the tobacco is constructed here. The chemical composition database in the flue gas is used to predict the toxic side effects and the effects of smoking on the risk of Parkinson's disease. In the first chapter, the background and significance of the study of compound toxicity prediction are introduced, and the computer prediction of the toxicity of compounds at present is introduced. The main methods and techniques, including cross reference, quantitative / qualitative toxic relationship, warning sub structure, etc., then introduced the research progress on tobacco and smoke and its harmful components at home and abroad. Then, the research background and systematic evaluation of the incidence of Parkinson's disease and smoking on the incidence of Parkinson's disease are introduced. The (?) NETA analysis (Meta-analysis) method used for system evaluation is introduced. The acute toxicity of compounds is an important toxic nature to be considered in the process of drug development and ecological risk assessment. Animal experimental methods require a large amount of time and capital to determine the acute toxicity of compounds. Computer prediction is a kind of method. In the second chapter, we collected and collated the structure of 12204 compounds and their rat oral acute toxicity test data. According to the toxicity grading standard of US EPA, all the compounds were classified into four levels of toxic, high toxicity, poisoning and microtoxicity. Then, two molecular fingerprints of MACCS and FP4 were used to characterize molecular structures, Combined with support vector machines, kappa nearest neighbor method, C4.5 decision tree, random forest and naive Bayes, the multi classification model is constructed respectively. Among them, support vector machine itself is a discriminant method of two kinds of problems. We use the "one to one" and "two forked tree" two strategies to solve the multi classification problem. Using two external validation sets containing 1678 and 375 compounds, the multi classification model is validated. The model MACCS-SVMOAo shows the strongest prediction ability, and the overall prediction accuracy for two external validation sets is 83% and 89.9%. respectively. We use the information gain technology and the substructure frequency analysis method, The predominant substructure fragment which may lead to the acute toxicity of the compound is screened. Carcinogenicity is another kind of toxic properties which are widely concerned. In the third chapter, we extracted the experimental data of the rat carcinogenicity of 829 compounds in the CPDB database, combined with five kinds of machine learning methods to combine six types of molecular fingerprints. Two classification and three classification prediction models used to predict the carcinogenicity of compounds. After using the test set to verify the model, 8 two classification models with the best performance and 7 best three classification models are selected, and the external validation sets are used for further verification. The external validation set is composed of 87 kinds of combination in the ISSCAN database. The two classification model with the strongest prediction ability, the overall prediction accuracy of MACCS-kNN, is 83.91%, and the overall prediction accuracy of the three classification model, MACCS-kNN, which is the most powerful, has also reached 80.46%.. We screened five types of carcinogenic compounds in the carcinogenic compounds. For warning. Tobacco smoke is a complex compound that seriously endangering human health. The summary and analysis of chemical components in tobacco and smoke contribute to the study of cigarette risk assessment and the detection of harmful components in the process of cigarette production. In the fourth chapter, we build a tobacco and flue gas chemistry. The component database (The Chemical Components of Tobacco and Tobacco Smoke, CCTTS). CCTTS provides the chemical structure of 5983 chemical components in tobacco and flue gas, basic physicochemical properties, toxicity information, and ADMET (absorption, distribution, metabolism, excretion and toxicity) predicted by the use of admetSAR tools. In these chemical components, 5 68 kinds of clear experimental toxicity data, and another 145 chemical components are predicted to be acute or carcinogenic. This database will be accessible and retrieved through the Internet. Smoking is harmful to human health, but many epidemiological studies suggest that smoking may reduce the risk of Parkinson's disease. The fifth chapter of the paper, we The current study of smoking and Parkinson's risk was systematically evaluated by the meta analysis. We retrieved the English literature on the risk of smoking and Parkinson's disease during the period from 1960 to October 2014, and screened 61 retrospective case control studies and 9 prospective cohort studies. Compared to smokers, the relative risk value of Parkinson's disease RR (95% confidence interval) was 0.59 (95% confidence interval, 0.56-0.62), indicating that smokers had a risk of suffering from Parkinson's disease than non smokers and 41%. we further followed different epidemiological methods, subjects sex, control sources, smoking, and age of study. Meta analysis in each subgroup showed the protective effect of smoking on Parkinson's disease. This protective effect was obvious, and the greater the amount of smoking, the stronger the protective effect. We summarized some hypotheses about the biological mechanism of this protective effect, and suggest that researchers understand this one in depth. The biological mechanism of protection helps to discover potential drugs for delay and treatment of Parkinson's disease from tobacco and smoke components. The sixth chapter of the paper summarizes the research work of the full text, and highlights the innovation of the paper.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：R99
本文編號：2137802
[Abstract]:The toxicity of compounds is one of the main causes of the failure of drug research and development. The early stage of drug safety assessment to the early stage of drug development can help to shorten the cycle of drug development and reduce the cost of development. Besides, it is also necessary to evaluate the safety of other chemicals, such as food additives, cosmetics and other related chemicals. Regulatory toxicology test methods need to spend a lot of time and money. It is difficult to meet the requirements of modern drug development and environmental compound risk assessment. Researchers urgently need to develop alternative methods based on the "3R" principle, that is, to reduce the number of experimental animals (Reduce), to optimize (Refine) experimental procedures, to replace (Replace) experimental animals. The computational method can be used to predict the toxicity of compounds, which can be used to evaluate the safety of large quantities of compounds at a lower cost. The other advantage of the computer method is to detect the virtual compounds at the early stage of the drug discovery and to predict the toxicity. Therefore, the method of computational toxicology has become an evaluation of the safety of compounds. The research work of this paper consists of two major parts: the first part is the construction of the compound toxicity prediction model. In this part, the prediction model of acute toxicity and carcinogenicity of compounds is constructed, the second part is the study of the side effects of tobacco and flue gas, and the tobacco is constructed here. The chemical composition database in the flue gas is used to predict the toxic side effects and the effects of smoking on the risk of Parkinson's disease. In the first chapter, the background and significance of the study of compound toxicity prediction are introduced, and the computer prediction of the toxicity of compounds at present is introduced. The main methods and techniques, including cross reference, quantitative / qualitative toxic relationship, warning sub structure, etc., then introduced the research progress on tobacco and smoke and its harmful components at home and abroad. Then, the research background and systematic evaluation of the incidence of Parkinson's disease and smoking on the incidence of Parkinson's disease are introduced. The (?) NETA analysis (Meta-analysis) method used for system evaluation is introduced. The acute toxicity of compounds is an important toxic nature to be considered in the process of drug development and ecological risk assessment. Animal experimental methods require a large amount of time and capital to determine the acute toxicity of compounds. Computer prediction is a kind of method. In the second chapter, we collected and collated the structure of 12204 compounds and their rat oral acute toxicity test data. According to the toxicity grading standard of US EPA, all the compounds were classified into four levels of toxic, high toxicity, poisoning and microtoxicity. Then, two molecular fingerprints of MACCS and FP4 were used to characterize molecular structures, Combined with support vector machines, kappa nearest neighbor method, C4.5 decision tree, random forest and naive Bayes, the multi classification model is constructed respectively. Among them, support vector machine itself is a discriminant method of two kinds of problems. We use the "one to one" and "two forked tree" two strategies to solve the multi classification problem. Using two external validation sets containing 1678 and 375 compounds, the multi classification model is validated. The model MACCS-SVMOAo shows the strongest prediction ability, and the overall prediction accuracy for two external validation sets is 83% and 89.9%. respectively. We use the information gain technology and the substructure frequency analysis method, The predominant substructure fragment which may lead to the acute toxicity of the compound is screened. Carcinogenicity is another kind of toxic properties which are widely concerned. In the third chapter, we extracted the experimental data of the rat carcinogenicity of 829 compounds in the CPDB database, combined with five kinds of machine learning methods to combine six types of molecular fingerprints. Two classification and three classification prediction models used to predict the carcinogenicity of compounds. After using the test set to verify the model, 8 two classification models with the best performance and 7 best three classification models are selected, and the external validation sets are used for further verification. The external validation set is composed of 87 kinds of combination in the ISSCAN database. The two classification model with the strongest prediction ability, the overall prediction accuracy of MACCS-kNN, is 83.91%, and the overall prediction accuracy of the three classification model, MACCS-kNN, which is the most powerful, has also reached 80.46%.. We screened five types of carcinogenic compounds in the carcinogenic compounds. For warning. Tobacco smoke is a complex compound that seriously endangering human health. The summary and analysis of chemical components in tobacco and smoke contribute to the study of cigarette risk assessment and the detection of harmful components in the process of cigarette production. In the fourth chapter, we build a tobacco and flue gas chemistry. The component database (The Chemical Components of Tobacco and Tobacco Smoke, CCTTS). CCTTS provides the chemical structure of 5983 chemical components in tobacco and flue gas, basic physicochemical properties, toxicity information, and ADMET (absorption, distribution, metabolism, excretion and toxicity) predicted by the use of admetSAR tools. In these chemical components, 5 68 kinds of clear experimental toxicity data, and another 145 chemical components are predicted to be acute or carcinogenic. This database will be accessible and retrieved through the Internet. Smoking is harmful to human health, but many epidemiological studies suggest that smoking may reduce the risk of Parkinson's disease. The fifth chapter of the paper, we The current study of smoking and Parkinson's risk was systematically evaluated by the meta analysis. We retrieved the English literature on the risk of smoking and Parkinson's disease during the period from 1960 to October 2014, and screened 61 retrospective case control studies and 9 prospective cohort studies. Compared to smokers, the relative risk value of Parkinson's disease RR (95% confidence interval) was 0.59 (95% confidence interval, 0.56-0.62), indicating that smokers had a risk of suffering from Parkinson's disease than non smokers and 41%. we further followed different epidemiological methods, subjects sex, control sources, smoking, and age of study. Meta analysis in each subgroup showed the protective effect of smoking on Parkinson's disease. This protective effect was obvious, and the greater the amount of smoking, the stronger the protective effect. We summarized some hypotheses about the biological mechanism of this protective effect, and suggest that researchers understand this one in depth. The biological mechanism of protection helps to discover potential drugs for delay and treatment of Parkinson's disease from tobacco and smoke components. The sixth chapter of the paper summarizes the research work of the full text, and highlights the innovation of the paper.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：R99
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1 秦愛平;Z24系列化合物毒性的優(yōu)化篩選[D];中國人民解放軍軍事醫(yī)學(xué)科學(xué)院;2003年
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