中医药在新型冠状病毒疫情防控中的贡献彰显了其在维护人民生命健康中不可或缺的地位.“有毒”中药占常用中药材和饮片的13.47%,市场规模超过168亿元,临床一定范围内具有确切疗效及重要价值[1,2].然而,随着中医药疗效在世界范围内被逐...中医药在新型冠状病毒疫情防控中的贡献彰显了其在维护人民生命健康中不可或缺的地位.“有毒”中药占常用中药材和饮片的13.47%,市场规模超过168亿元,临床一定范围内具有确切疗效及重要价值[1,2].然而,随着中医药疗效在世界范围内被逐渐认可,中药不良反应事件的数量及关注度近年均有所上升,例如,2017年Nature发表的“China rolls back regulations for traditional medicine despite safety concerns”指出,中药的安全隐患是其临床应用时需要解决的一个至关重要的问题[1].同年,Science Translational Medicine发表的封面论文[2]指出,马兜铃酸与肝癌的发生发展显著相关,导致含有马兜铃酸的植物被世界卫生组织国际癌症研究机构列入一类致癌物清单.随着国际上对中药安全使用的需求日趋严格,要求“说清楚、讲明白”的呼声日益增高,符合中医药特点的中药安全性评价体系的不完善不仅会导致“有毒”中药国际化进程陷入困境,也将严重影响“有毒”中药及其上市品种在临床更广泛的应用,制约中医药现代化、国际化进程.大数据科技创新为中医药行业发展带来了新的机遇与挑战.2020年,《中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见》首次将“数据”列为一种新型生产要素,明确了数据资源的战略意义,为中医药科研界及产业界如何促进数据开发利用、实现数据赋能带来了重要命题;依托北斗地基增强系统、高精度位置服务终端、时空智能解决方案,数据学和数据科学领域的蓬勃发展将为解决“有毒”中药安全使用的临床需求及成果转化过程中存在的瓶颈问题带来新的契机.展开更多
Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, wh...Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan(CPT-11),and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually,two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine.展开更多
Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Ab...Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.展开更多
文摘中医药在新型冠状病毒疫情防控中的贡献彰显了其在维护人民生命健康中不可或缺的地位.“有毒”中药占常用中药材和饮片的13.47%,市场规模超过168亿元,临床一定范围内具有确切疗效及重要价值[1,2].然而,随着中医药疗效在世界范围内被逐渐认可,中药不良反应事件的数量及关注度近年均有所上升,例如,2017年Nature发表的“China rolls back regulations for traditional medicine despite safety concerns”指出,中药的安全隐患是其临床应用时需要解决的一个至关重要的问题[1].同年,Science Translational Medicine发表的封面论文[2]指出,马兜铃酸与肝癌的发生发展显著相关,导致含有马兜铃酸的植物被世界卫生组织国际癌症研究机构列入一类致癌物清单.随着国际上对中药安全使用的需求日趋严格,要求“说清楚、讲明白”的呼声日益增高,符合中医药特点的中药安全性评价体系的不完善不仅会导致“有毒”中药国际化进程陷入困境,也将严重影响“有毒”中药及其上市品种在临床更广泛的应用,制约中医药现代化、国际化进程.大数据科技创新为中医药行业发展带来了新的机遇与挑战.2020年,《中共中央国务院关于构建更加完善的要素市场化配置体制机制的意见》首次将“数据”列为一种新型生产要素,明确了数据资源的战略意义,为中医药科研界及产业界如何促进数据开发利用、实现数据赋能带来了重要命题;依托北斗地基增强系统、高精度位置服务终端、时空智能解决方案,数据学和数据科学领域的蓬勃发展将为解决“有毒”中药安全使用的临床需求及成果转化过程中存在的瓶颈问题带来新的契机.
基金financially supported by the NSFC(Nos.81773861,81773682,81573385 and 81302733,China)Macao Science and Technology Development Fund(FDCT,No.006/2015/A1,China)+2 种基金Jiangsu Six Talent Peaks Program(YY-046,China)the Program for Jiangsu Province Innovative Research(KYCX17_0681,China)funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD,China)
文摘Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan(CPT-11),and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually,two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0082)
文摘Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.
