Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”co...Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.展开更多
Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro...Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro-extraction gas chromatography/mass spectrometry(HS-SPME-GC/MS),was elaborated and further employed to holistically compare the compositional difference of the volatile components simultaneously from 12 Panax herbal medicines,which included P.ginseng(PG),P.quinquefolius(PQ),P.notoginseng(PN),red ginseng(PGR),P.ginseng leaf(PGL),P.quinquefolius leaf(PQL),P.notoginseng leaf(PNL),P.ginseng flower(PGF),P.quinquefolius flower(PQF),P.notoginseng flower(PNF),P.japonicus(PJ),and P.japonicus var.major(PJvm).Chromatographic separation was performed on an HP-5MS elastic quartz capillary column using helium as the carrier gas,enabling good resolution within 1 h.We were able to characterize totally 259 volatile compounds,including 82 terpenes(T),46 alcohols(Alc),29 ketones(K),25 aldehydes(Ald),21 esters(E),and the others.By analyzing 90 batches of ginseng samples based on the untargeted metabolomics workflows,236 differential ions were unveiled,and accordingly 36 differential volatile components were discovered.It is the first report that simultaneously compares the compositional difference of volatile components among 12 Panax herbal medicines,and useful information is provided for the quality control of ginseng aside from the well-known ginsenosides.展开更多
Plant-derived nanovesicles have gained attention given their similarity to mammalian exosomes and advantages such as low cost,sustainability,and tissue targeting.Thus,they hold promise for disease treatment and drug d...Plant-derived nanovesicles have gained attention given their similarity to mammalian exosomes and advantages such as low cost,sustainability,and tissue targeting.Thus,they hold promise for disease treatment and drug delivery.In this study,we proposed a time-efficient method,PEG 8000 combined with sucrose density gradient centrifugation to prepare ginger-derived nanovesicles(GDNVs).Subsequently,curcumin(CUR)was loaded onto GDNV by ultrasonic incubation.The optimum conditions for ginger-derived nanovesicles loaded with curcumin(CG)were ultrasound time of 3 min,a carrier-to-drug ratio(GDNV:CUR)of 1:1.The study achieved a high loading capacity(94.027%±0.094%)and encapsulation efficiency(89.300%±0.344%).Finally,the drugs'in vivo distribution and anti-colitis activity were investigated in mice.CG was primarily distributed in the colon after oral administration.Compared to CUR and GDNV,CG was superior in improving disease activity,colon length,liver and spleen coefficients,myeloperoxidase activity,and biochemical factor levels in ulcerative colitis(UC)mice.In addition,CG plays a protective role against UC by modulating serum metabolite levels and gut flora.In summary,our study demonstrated that GDNV can be used for CUR delivery with enhanced therapeutic potential.展开更多
Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we pr...Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support.We developed four deep neural network(DNN)models for AD drugs screening at the disease and target levels.The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level,while the acetylcholinesterase(AChE),monoamine oxidase-A(MAO-A),and 5-hydroxytryptamine 6(5-HT6)models were trained for specific AD targets.All four models performed excellently and were used to identify potential AD agents in the Kaixinsan(KXS)formula.High-scoring compounds underwent experimental validation at the enzyme,cellular,and animal levels.Compounds like 2,4-di-tert-butylphenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A.Additionally,13 compounds,includingα-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglialβ-amyloid phagocytosis,aiding in clearing AD pathological substances.Our results demonstrate the effectiveness of deep learning models in developing AD therapies and provide a strong platform for AD drug discovery.展开更多
基金supported by the National Key Research and Development Program of China(No.2024YFC3506900)Science and Technology Program of Tianjin(No.24ZXZSSS00460)Special Project for Technological Innovation in New Productive Forces of Modern Chinese Medicines(No.24ZXZKSY00010)。
文摘Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.
基金National Natural Science Foundation of China(Grant No.81872996)Natural Science Foundation of Tianjin of China(Grant No.20JCYBJC00060).
文摘Quality control of ginseng currently is mainly based on ginsenoside analysis,but rarely focuses on the volatile organic components.In the current work,an untargeted metabolomics approach,by headspace solid-phase micro-extraction gas chromatography/mass spectrometry(HS-SPME-GC/MS),was elaborated and further employed to holistically compare the compositional difference of the volatile components simultaneously from 12 Panax herbal medicines,which included P.ginseng(PG),P.quinquefolius(PQ),P.notoginseng(PN),red ginseng(PGR),P.ginseng leaf(PGL),P.quinquefolius leaf(PQL),P.notoginseng leaf(PNL),P.ginseng flower(PGF),P.quinquefolius flower(PQF),P.notoginseng flower(PNF),P.japonicus(PJ),and P.japonicus var.major(PJvm).Chromatographic separation was performed on an HP-5MS elastic quartz capillary column using helium as the carrier gas,enabling good resolution within 1 h.We were able to characterize totally 259 volatile compounds,including 82 terpenes(T),46 alcohols(Alc),29 ketones(K),25 aldehydes(Ald),21 esters(E),and the others.By analyzing 90 batches of ginseng samples based on the untargeted metabolomics workflows,236 differential ions were unveiled,and accordingly 36 differential volatile components were discovered.It is the first report that simultaneously compares the compositional difference of volatile components among 12 Panax herbal medicines,and useful information is provided for the quality control of ginseng aside from the well-known ginsenosides.
基金supported by the Science and Technology Program of Tianjin in China(Grant No.:23ZYJDSS00030).
文摘Plant-derived nanovesicles have gained attention given their similarity to mammalian exosomes and advantages such as low cost,sustainability,and tissue targeting.Thus,they hold promise for disease treatment and drug delivery.In this study,we proposed a time-efficient method,PEG 8000 combined with sucrose density gradient centrifugation to prepare ginger-derived nanovesicles(GDNVs).Subsequently,curcumin(CUR)was loaded onto GDNV by ultrasonic incubation.The optimum conditions for ginger-derived nanovesicles loaded with curcumin(CG)were ultrasound time of 3 min,a carrier-to-drug ratio(GDNV:CUR)of 1:1.The study achieved a high loading capacity(94.027%±0.094%)and encapsulation efficiency(89.300%±0.344%).Finally,the drugs'in vivo distribution and anti-colitis activity were investigated in mice.CG was primarily distributed in the colon after oral administration.Compared to CUR and GDNV,CG was superior in improving disease activity,colon length,liver and spleen coefficients,myeloperoxidase activity,and biochemical factor levels in ulcerative colitis(UC)mice.In addition,CG plays a protective role against UC by modulating serum metabolite levels and gut flora.In summary,our study demonstrated that GDNV can be used for CUR delivery with enhanced therapeutic potential.
基金This work was supported by the Science and Technology Project of Haihe Laboratory of Modern Chinese Medicine,China(Grant No.:22HHZYSS00003)the Science and Technology Program of Tianjin,China(Grant No.:22ZYJDSS00100)The authors would like to thank the support from Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine,China(Grant No:ZYYCXTD-D-202002).
文摘Alzheimer's disease(AD)is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success.Faced with this challenge,we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support.We developed four deep neural network(DNN)models for AD drugs screening at the disease and target levels.The AD model was trained with compounds labeled for AD activity to predict active compounds at the disease level,while the acetylcholinesterase(AChE),monoamine oxidase-A(MAO-A),and 5-hydroxytryptamine 6(5-HT6)models were trained for specific AD targets.All four models performed excellently and were used to identify potential AD agents in the Kaixinsan(KXS)formula.High-scoring compounds underwent experimental validation at the enzyme,cellular,and animal levels.Compounds like 2,4-di-tert-butylphenol and elemicin showed significant binding and inhibitory effects on AChE and MAO-A.Additionally,13 compounds,includingα-asarone,penetrated the blood-brain barrier(BBB),indicating potential brain target binding,and eight compounds enhanced microglialβ-amyloid phagocytosis,aiding in clearing AD pathological substances.Our results demonstrate the effectiveness of deep learning models in developing AD therapies and provide a strong platform for AD drug discovery.
