Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain m...Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain multiple related targets and an appropriate combination can maximize benefits while minimizing adverse reactions.As such,it is especially important to derive mathematical models for their quantitative calculation.In this paper,we introduce mathematical rules for the synergistic,additive,and antagonistic effects of multi-drug combinations developed in our laboratory.We have established a“onebelt,one-line”model and provide examples of the quantitative calculation of the synergistic,additive,and antagonistic effects of a combination of multiple components.We also explain how to scientifically and precisely determine the intensity of these synergies,additions and antagonisms,as well as their corresponding dose ranges,thereby laying a solid theoretical foundation for market listing combinatorial drugs and foods for special medical purposes.展开更多
Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentat...Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.展开更多
目的·以氯氮平-氟伏沙明合用为例,通过构建针对中国群体的生理药物代谢动力学(physiologically based pharmacokinetic,PBPK)模型,预测氯氮平联合用药的药物相互作用(drug-drug interaction,DDI)并对氯氮平进行剂量优化。方法·...目的·以氯氮平-氟伏沙明合用为例,通过构建针对中国群体的生理药物代谢动力学(physiologically based pharmacokinetic,PBPK)模型,预测氯氮平联合用药的药物相互作用(drug-drug interaction,DDI)并对氯氮平进行剂量优化。方法·通过文献及药理学相关数据库获取氯氮平及氟伏沙明的基本理化性质参数,药物吸收、分布、代谢及排泄(absorption,distribution,metabolism and excretion,ADME)相关参数及中国群体的生理解剖相关参数,利用PK-Sim®软件构建2种药物的PBPK模型。以平均百分比误差(mean percentage error,MPE)和平均绝对百分比误差(mean absolute percentage error,MAPE),或者预测药时曲线下面积(area under the curve,AUC)或峰浓度(peak concentration,Cmax)与实测AUC或Cmax的比值为判断指标,并通过真实世界血药浓度数据进行模型验证。在此基础上结合氟伏沙明对氯氮平的抑制作用参数构建氯氮平-氟伏沙明联合用药的PBPK模型,预测氯氮平的药物代谢动力学变化。以药时曲线下面积比值(area under the curve ratio,AUCR)或峰浓度比值(peak concentration ratio,CmaxR)的90%置信区间为评价指标判断是否存在临床显著的DDI(无效应边界为80%~125%)。根据PBPK模型量化氯氮平-氟伏沙明联合用药后氯氮平的药物代谢动力学变化,并制定氯氮平的剂量优化方案。结果·构建的氯氮平、氟伏沙明模型验证的MPE绝对值≤10%且MAPE<25%,说明预测的药时曲线是准确的。氯氮平-氟伏沙明合用的PBPK模型的AUC预测值与实测值的比值在1.25以内,可准确地预测药物代谢动力学参数。氯氮平-氟伏沙明联用模型的预测结果提示,氯氮平-氟伏沙明联合用药的AUCR和CmaxR的90%置信区间均不完全位于无效应边界内,说明两药合用会发生临床显著性的DDI。此外,PBPK模型的剂量优化结果提示:受试者联合服用氯氮平及氟伏沙明时,氯氮平的剂量减少至原本剂量的50%,可使氯氮平的暴露水平与单药治疗时保持一致。结论·研究建立的PBPK模型可以较好模拟联合用药对氯氮平药物代谢动力学的影响,对于预测药物可能的相互作用及剂量优化方案有参考意义。如果治疗过程中需要合用氯氮平和氟伏沙明,须警惕临床显著的DDI,并应优化氯氮平的剂量。展开更多
文摘Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain multiple related targets and an appropriate combination can maximize benefits while minimizing adverse reactions.As such,it is especially important to derive mathematical models for their quantitative calculation.In this paper,we introduce mathematical rules for the synergistic,additive,and antagonistic effects of multi-drug combinations developed in our laboratory.We have established a“onebelt,one-line”model and provide examples of the quantitative calculation of the synergistic,additive,and antagonistic effects of a combination of multiple components.We also explain how to scientifically and precisely determine the intensity of these synergies,additions and antagonisms,as well as their corresponding dose ranges,thereby laying a solid theoretical foundation for market listing combinatorial drugs and foods for special medical purposes.
文摘Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.
文摘目的·以氯氮平-氟伏沙明合用为例,通过构建针对中国群体的生理药物代谢动力学(physiologically based pharmacokinetic,PBPK)模型,预测氯氮平联合用药的药物相互作用(drug-drug interaction,DDI)并对氯氮平进行剂量优化。方法·通过文献及药理学相关数据库获取氯氮平及氟伏沙明的基本理化性质参数,药物吸收、分布、代谢及排泄(absorption,distribution,metabolism and excretion,ADME)相关参数及中国群体的生理解剖相关参数,利用PK-Sim®软件构建2种药物的PBPK模型。以平均百分比误差(mean percentage error,MPE)和平均绝对百分比误差(mean absolute percentage error,MAPE),或者预测药时曲线下面积(area under the curve,AUC)或峰浓度(peak concentration,Cmax)与实测AUC或Cmax的比值为判断指标,并通过真实世界血药浓度数据进行模型验证。在此基础上结合氟伏沙明对氯氮平的抑制作用参数构建氯氮平-氟伏沙明联合用药的PBPK模型,预测氯氮平的药物代谢动力学变化。以药时曲线下面积比值(area under the curve ratio,AUCR)或峰浓度比值(peak concentration ratio,CmaxR)的90%置信区间为评价指标判断是否存在临床显著的DDI(无效应边界为80%~125%)。根据PBPK模型量化氯氮平-氟伏沙明联合用药后氯氮平的药物代谢动力学变化,并制定氯氮平的剂量优化方案。结果·构建的氯氮平、氟伏沙明模型验证的MPE绝对值≤10%且MAPE<25%,说明预测的药时曲线是准确的。氯氮平-氟伏沙明合用的PBPK模型的AUC预测值与实测值的比值在1.25以内,可准确地预测药物代谢动力学参数。氯氮平-氟伏沙明联用模型的预测结果提示,氯氮平-氟伏沙明联合用药的AUCR和CmaxR的90%置信区间均不完全位于无效应边界内,说明两药合用会发生临床显著性的DDI。此外,PBPK模型的剂量优化结果提示:受试者联合服用氯氮平及氟伏沙明时,氯氮平的剂量减少至原本剂量的50%,可使氯氮平的暴露水平与单药治疗时保持一致。结论·研究建立的PBPK模型可以较好模拟联合用药对氯氮平药物代谢动力学的影响,对于预测药物可能的相互作用及剂量优化方案有参考意义。如果治疗过程中需要合用氯氮平和氟伏沙明,须警惕临床显著的DDI,并应优化氯氮平的剂量。
