分會

第三十四分會：環(huán)境化學(xué)

摘要

EDC-Predictor:一種整合藥理學(xué)和毒性學(xué)譜預(yù)測內(nèi)分泌干擾物的策略及其應(yīng)用 俞卓杭，吳曾睿，李衛(wèi)華，劉桂霞，唐赟* 華東理工大學(xué)藥學(xué)院，上海市梅隴路130號，200237 *Email: ytang234@ecust.edu.cn 內(nèi)分泌干擾物（EDC）被定義為在機體的生長、發(fā)育、生殖過程中，干擾體內(nèi)血源性激素的正常生理過程而導(dǎo)致內(nèi)分泌系統(tǒng)功能紊亂的外源性化合物。已有報道表明內(nèi)分泌干擾不僅與這幾個核受體（NR）相關(guān)，還可能與其它靶標(biāo)蛋白相關(guān)，例如與激素合成相關(guān)的酶等。因此從系統(tǒng)藥理學(xué)和計算毒理學(xué)兩種角度來發(fā)現(xiàn)潛在的EDC可能是行之有效的策略。 在這個研究中，我們收集了1334個EDCs和1474個Non-EDCs。隨后我們?yōu)檫@些化合物生成了四種類型特征：（1）基于化學(xué)結(jié)構(gòu)信息計算分子指紋；（2）基于藥物靶標(biāo)相互作用構(gòu)建基于網(wǎng)絡(luò)的靶標(biāo)譜；（3）基于大規(guī)模的毒理學(xué)數(shù)據(jù)構(gòu)建基于機器學(xué)習(xí)的靶標(biāo)譜；（4）整合兩種靶標(biāo)譜構(gòu)建了組合靶標(biāo)譜（CTP）。三種靶標(biāo)譜的分析結(jié)果表明其能夠區(qū)分NR相關(guān)的EDC、其他EDC、Non-EDC?；谶@些分子特征使用了機器學(xué)習(xí)方法構(gòu)建了計算預(yù)測模型，模型結(jié)果表明使用CTP構(gòu)建的最優(yōu)模型無論在五折交叉驗證和外部驗證上均表現(xiàn)最優(yōu)。結(jié)合統(tǒng)計檢驗和通路富集分析，我們發(fā)現(xiàn)識別得到的關(guān)鍵靶標(biāo)在內(nèi)分泌干擾中發(fā)揮著重要作用。為了展示EDC-Predictor的實用價值，我們將其應(yīng)用于兩個案例研究，并將其與以前的預(yù)測工具進行了比較。實驗結(jié)果表明EDC Predictor不僅識別更多機制的EDC，涵蓋了NR和其他機制，同時在準(zhǔn)確性上整體也表現(xiàn)更優(yōu)。 綜上所述，整合藥理學(xué)譜和毒性學(xué)譜的新策略不僅預(yù)測NR相關(guān)的EDC，也能嘗試從其他機制來發(fā)現(xiàn)EDC。同時本研究提出的策略也可以拓展到其他研究領(lǐng)域，如化合物的毒性效應(yīng)預(yù)測等。 Fig. 1 Workflow of EDC Table1. The evaluation indicators of EDC prediction models using three features in test set validation. Feature Method Accuracy Precision Recall F1 score MCC AUC FP RF 0.824 0.833 0.779 0.806 0.646 0.900 NBTP XGB 0.851 0.865 0.806 0.835 0.700 0.909 CTP XGB 0.835 0.854 0.779 0.815 0.668 0.907 關(guān)鍵詞：內(nèi)分泌干擾物；網(wǎng)絡(luò)推理；毒理學(xué)譜；藥理學(xué)譜；計算預(yù)測； 參考文獻(xiàn) [1] Yu, Z.; Wu, Z.; M. Zhou.; Tang, Y. Environ. Sci. Technol. 2023, just accepted. [2] Wu, Z.; Lu, W.; Wu, D.; Tang, Y., Br. J. Pharmacol. 2016, 173, 3372. [3] Sun, L.; Yang, H.; Cai, Y.; Tang, Y. J. Chem. Inf. Model. 2019, 59, 973. EDC-Predictor: A novel strategy for prediction of endocrine-disrupting chemicals by integrating pharmacological and toxicological profiles Zhuohang Yu, Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang* School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 Endocrine-disrupting chemicals (EDCs) are defined as exogenous compounds that interfere with normal physiological processes of natural hormones during the growth, development and reproduction of the body, resulting in dysfunction of the endocrine system. Previous studies demonstrated that endocrine disruption is not only related to these nuclear receptors (NRs), but also related to other target proteins, such as enzymes related to hormone synthesis. Therefore, it may be an effective strategy to identify potential EDC from the perspectives of systems pharmacology and computational toxicology. In this study, we collected 1334 EDCs and 1474 Non-EDCs. Subsequently, we generated four features for these compounds: (1) calculating molecular fingerprints based on chemical structural information; (2) constructing a network-based target profiles based on drug-target interactions; (3) constructing machine learning-based target profiles based on large-scale toxicology data; (4) constructing combined target profiles (CTP) integrating two target profiles. The analysis results of three target profiles indicate that they can distinguish between NR-related EDCs, other EDCs, and Non-EDCs. Based on these features, prediction models were constructed using machine learning methods. The prediction results of models showed that the optimal model constructed by CTP performed best on the 5-fold cross validation and external validation. Combining statistical testing and pathway enrichment analysis, we found that identified key targets play an important role in endocrine disruption. To demonstrate the practical value of EDC-Predictor, we applied it in two case studies and compared it with previous prediction tools. The results indicate that the EDC-Predictor not only assist in identification of EDCs with more types of mechanisms, but also performs better in accuracy. In summary, the novel strategy of integrating pharmacological and toxicological profiles not only predicts NR-related EDCs, but also identifies EDCs from other mechanisms. Meanwhile, this strategy can be easily extended to other research fields, such as toxic effect prediction of compounds.

關(guān)鍵詞

內(nèi)分泌干擾;網(wǎng)絡(luò)推理;毒理學(xué)譜;藥理學(xué)譜;計算預(yù)測

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