Despite significant advances in targeted therapies and immunotherapies,non-small cell lung cancer(NSCLC)continues to present a global health challenge,with a modest five-year survival rate of 28%,largely due to the em...Despite significant advances in targeted therapies and immunotherapies,non-small cell lung cancer(NSCLC)continues to present a global health challenge,with a modest five-year survival rate of 28%,largely due to the emergence of treatment-resistant and metastatic tumors.In response,we synthesized a novel bioactive compound,ethyl 6-chlorocoumarin-3-carboxylyl L-theanine(TClC),which significantly inhibited NSCLC growth,epithelial mesenchymal transition(EMT),migration,and invasion in vitro and tumor growth and metastasis in vivo without inducing toxicity.TClC disrupts autocrine loops that promote tumor progression,particularly in stem-like CD133-positive NSCLC(CD133+LC)cells,which are pivotal in tumor metastasis.Through targeted molecular assays,we identified direct binding targets of TClC,including Akt,NF-κB,β-catenin,EZH2,and PD-L1.This interaction not only suppresses the expression of oncogenic factors and cancer stem cell markers but also downregulates the expression of a multidrug resistance transporter,underscoring the compound’s poly-pharmacological potential.These results position TClC as a promising candidate for NSCLC treatment,signaling a new era in the development of cancer therapies that directly target multiple critical cancer pathways.展开更多
Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development,such as lead discovery,drug repurposing and elucidation of potential drug side ...Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development,such as lead discovery,drug repurposing and elucidation of potential drug side effects.Therefore,a variety of machine learning-based models have been developed to predict these interactions.In this study,a model called auxiliary multi-task graph isomorphism network with uncertainty weighting(AMGU)was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network(MT-GIN)with the auxiliary learning and uncertainty weighting strategy.The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks(GNN)models on the internal test set.Furthermore,it also exhibited much better performance on two external test sets,suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity.Then,a naÏve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms,and the consistency of the interpretability results for 5typical epidermal growth factor receptor(EGFR)inhibitors with their structure-activity relationships could be observed.Finally,a free online web server called KIP was developed to predict the kinomewide polypharmacology effects of small molecules(http://cadd.zju.edu.cn/kip).展开更多
Polypharmacology,which focuses on designing drugs to target multiple receptors,has emerged as a new paradigm in drug discovery.To rationally design multi-target drugs,it is fundamental to understand protein-ligand int...Polypharmacology,which focuses on designing drugs to target multiple receptors,has emerged as a new paradigm in drug discovery.To rationally design multi-target drugs,it is fundamental to understand protein-ligand interactions on a proteome scale.We have developed a Proteome-wide Off-target Pipeline （POP） that integrates ligand binding site analysis,protein-ligand docking,the statistical analysis of docking scores,and electrostatic potential calculations.The utility of POP is demonstrated by a case study,in which the molecular mechanism of anti-cancer effect of Nelfinavir is hypothesized.By combining structural proteome-wide off-target identification and systems biology,it is possible for us to correlate drug perturbations with clinical outcomes.展开更多
OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquire...OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquired by integrating conventional databases;Traditional Chinese Medicine Information Database(TCM-ID)and Traditional Chinese Medicine Integrated Database(TCMID).Therapeutic effect of each herb on the type 2 diabetes was examined by analyzing annotated function information with a text-mining method.The Apriori algorithm,which is a classical method for extracting associations between object in large-scale databases,was employed to infer association rules between compound combinations and therapeutic effect on the target disease.The chemical composition and therapeutic information of each herb was used as a transaction,which consists of the chemical compound combination as an antecedent item set and the therapeutic effect as a consequent item.The association rules with high support and confidence value were suggested as candidate multi-component drugs for the type 2 diabetes.RESULTS Totally 40 941 association rules were inferred with support lower bound 0.05% and maximum rule length 4.With respect to support and confidence,the top-ranked compound combination was puerarin and daidzin(support=0.15%,confidence=100%).In addition,the top 16 compound combinations were composed of 11 individual chemical compounds;puerarin,daidzin,abscisic acid,batatisine,dopamine,cholesterol,daidzein,gamma-aminobutyric acid,stigmasterol,campesteryl ferulate,and campesterol.To validate therapeutic effect of the proposed compound combinations,literature evidences of each individual compound were investigated.Among the 11 individual compounds,six compounds were reported to be effective for the treatment of the diabetes mellitus.CONCLUSION By analyzing natural product in formation with association rule mining,16 compound combinations are suggested as candidate multi-component drugs for the type 2 diabetes.These compound combinations are recommended for further investigation in the context of drug development.展开更多
The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multitargeted strategies. This contrasts to the majority o...The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multitargeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with "important" nodes, such as "hubs" or "bottlenecks", encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting "important" targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities("dirty drugs") or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical(graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.展开更多
With the goal of suggesting dual inhibitors of HIV reverse transcriptase (RT) and integrase (IN), herein we report the molecular docking of an initial set of 556 compounds related to the pyridinone class. Docking with...With the goal of suggesting dual inhibitors of HIV reverse transcriptase (RT) and integrase (IN), herein we report the molecular docking of an initial set of 556 compounds related to the pyridinone class. Docking with multiple crystallographic structures of HIV-1 RT led to 160 potential binders of RT interacting with key amino acid residues at the enzyme’s allosteric site. Compounds selected from the docking with RT were further docked with a crystallographic structure of HIV-1 IN. A total of 31 structures had the potential to make contacts with Mg2+ ions located in a small space between DNA and IN. Interactions with Mg2+ ions are relevant because they participate in the stabilization of the IN-DNA complex. In conclusion, 31 compounds synthetically accessible are proposed as dual inhibitors of RT and IN. It is hypothesized that the suggested compounds will inhibit RT by occupying the allosteric site for NNRTIs and will inhibit the catalytic activity of IN by destabilizing the IN-DNA complex. The main perspective of this work is the synthesis and biological testing of the candidate molecules.展开更多
Diabetes mellitus(DM)is a progressive metabolic disease characterized by chronic hyperglycemia and caused by different degree of pancreatic islet dysfunction and/or insulin resistance(IR).Long course DM can lead to a ...Diabetes mellitus(DM)is a progressive metabolic disease characterized by chronic hyperglycemia and caused by different degree of pancreatic islet dysfunction and/or insulin resistance(IR).Long course DM can lead to a variety of macrovascular and microvascular complications which involve artery vessels,heart,kidney,retina,nervous system,etc.In recent years,DM has attracted more and more attention due to its high morbidity and mortality.In addition to achieve effective glycemic control,prevention of complications has also been considered a priority for type 2 diabetes mellitus(T2DM)management.Herein,we provide a comprehensive overview on the pharmacotherapeutics for T2DM and perspectives on the future directions of basic and translational research on anti-diabetic therapy and pharmatheutical development of new drugs.展开更多
Despite significant advancements in kinase-targeted therapy,the emergence of acquired drug resistance to targets such as KRAS and MEK remains a challenge.Extracellular-regulated kinase 1/2(ERK1/2),positioned at the te...Despite significant advancements in kinase-targeted therapy,the emergence of acquired drug resistance to targets such as KRAS and MEK remains a challenge.Extracellular-regulated kinase 1/2(ERK1/2),positioned at the terminus of this pathway,is highly conserved and less susceptible to mutations,thereby garnering attention as a crucial therapeutical target.However,attempts to use monotherapies that target ERK1/2 have achieved only limited clinical success,mainly due to the issues of limited efficacy and the emergence of drug resistance.Herein,we present a proof of concept that extracellular-regulated kinase 5(ERK5)acts as a compensatory pathway after ERK1/2 inhibition in triple-negative breast cancer(TNBC).By utilizing the principle of polypharmacology,we computationally designed SKLB-D18,a first-in-class molecule that selectively targets ERK1/2 and ERK5,with nanomolar potency and high specificity for both targets.SKLB-D18 demonstrated excellent tolerability in mice and demonstrated superior in vivo anti-tumor efficacy,not only exceeding the existing clinical ERK1/2 inhibitor BVD-523,but also the combination regimen of BVD-523 and the ERK5 inhibitor XMD8-92.Mechanistically,we showed that SKLB-D18,as an autophagy agonist,played a role in mammalian target of rapamycin(mTOR)/70 ribosomal protein S6 kinase(p70S6K)and nuclear receptor coactivator 4(NCOA4)-mediated ferroptosis,which may mitigate multidrug resistance.展开更多
基金supported by the National Key Research and Development Program of China(2017YFB0702600,2017YFB0702602,2017YFB0702602-2)the Shandong Provincial Natural Science Foundation(ZR2019MH076,ZR2022MH291)+3 种基金the Ministry of Science and Technology of the People’s Republic of China(“863 grant”,2012AA020206)the Department of Science and Technology of Shan-dong Province(20092009GG10002087)the National Natural Science Foundation of China(81603024,30973553)the NIH grant R01 CA186100,Wenzhou Institute University of Chinese Academy of Sciences,and Corbett Estate Fund for the Cancer Research(62285-531021-41800,62285-531021-51800,62285-531021-61800,62285-531021-71800).
文摘Despite significant advances in targeted therapies and immunotherapies,non-small cell lung cancer(NSCLC)continues to present a global health challenge,with a modest five-year survival rate of 28%,largely due to the emergence of treatment-resistant and metastatic tumors.In response,we synthesized a novel bioactive compound,ethyl 6-chlorocoumarin-3-carboxylyl L-theanine(TClC),which significantly inhibited NSCLC growth,epithelial mesenchymal transition(EMT),migration,and invasion in vitro and tumor growth and metastasis in vivo without inducing toxicity.TClC disrupts autocrine loops that promote tumor progression,particularly in stem-like CD133-positive NSCLC(CD133+LC)cells,which are pivotal in tumor metastasis.Through targeted molecular assays,we identified direct binding targets of TClC,including Akt,NF-κB,β-catenin,EZH2,and PD-L1.This interaction not only suppresses the expression of oncogenic factors and cancer stem cell markers but also downregulates the expression of a multidrug resistance transporter,underscoring the compound’s poly-pharmacological potential.These results position TClC as a promising candidate for NSCLC treatment,signaling a new era in the development of cancer therapies that directly target multiple critical cancer pathways.
基金financially supported by National Key Research and Development Program of China(2021YFF1201400)National Natural Science Foundation of China(21575128,81773632,22173118)+1 种基金Natural Science Foundation of Zhejiang Province(LZ19H300001,China)Science and Technology Innovation Program of Hunan Province(2021RC4011,China)。
文摘Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development,such as lead discovery,drug repurposing and elucidation of potential drug side effects.Therefore,a variety of machine learning-based models have been developed to predict these interactions.In this study,a model called auxiliary multi-task graph isomorphism network with uncertainty weighting(AMGU)was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network(MT-GIN)with the auxiliary learning and uncertainty weighting strategy.The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks(GNN)models on the internal test set.Furthermore,it also exhibited much better performance on two external test sets,suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity.Then,a naÏve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms,and the consistency of the interpretability results for 5typical epidermal growth factor receptor(EGFR)inhibitors with their structure-activity relationships could be observed.Finally,a free online web server called KIP was developed to predict the kinomewide polypharmacology effects of small molecules(http://cadd.zju.edu.cn/kip).
基金supported by the National Institutes of Health GM078596High Performance Computing Center at The City University of New York
文摘Polypharmacology,which focuses on designing drugs to target multiple receptors,has emerged as a new paradigm in drug discovery.To rationally design multi-target drugs,it is fundamental to understand protein-ligand interactions on a proteome scale.We have developed a Proteome-wide Off-target Pipeline （POP） that integrates ligand binding site analysis,protein-ligand docking,the statistical analysis of docking scores,and electrostatic potential calculations.The utility of POP is demonstrated by a case study,in which the molecular mechanism of anti-cancer effect of Nelfinavir is hypothesized.By combining structural proteome-wide off-target identification and systems biology,it is possible for us to correlate drug perturbations with clinical outcomes.
基金The project supported by the Bio-Synergy Research Project(NRF-2012M3A9C4048758)of the Ministry of Science,ICT and Future Planning through the National Research Foundation
文摘OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquired by integrating conventional databases;Traditional Chinese Medicine Information Database(TCM-ID)and Traditional Chinese Medicine Integrated Database(TCMID).Therapeutic effect of each herb on the type 2 diabetes was examined by analyzing annotated function information with a text-mining method.The Apriori algorithm,which is a classical method for extracting associations between object in large-scale databases,was employed to infer association rules between compound combinations and therapeutic effect on the target disease.The chemical composition and therapeutic information of each herb was used as a transaction,which consists of the chemical compound combination as an antecedent item set and the therapeutic effect as a consequent item.The association rules with high support and confidence value were suggested as candidate multi-component drugs for the type 2 diabetes.RESULTS Totally 40 941 association rules were inferred with support lower bound 0.05% and maximum rule length 4.With respect to support and confidence,the top-ranked compound combination was puerarin and daidzin(support=0.15%,confidence=100%).In addition,the top 16 compound combinations were composed of 11 individual chemical compounds;puerarin,daidzin,abscisic acid,batatisine,dopamine,cholesterol,daidzein,gamma-aminobutyric acid,stigmasterol,campesteryl ferulate,and campesterol.To validate therapeutic effect of the proposed compound combinations,literature evidences of each individual compound were investigated.Among the 11 individual compounds,six compounds were reported to be effective for the treatment of the diabetes mellitus.CONCLUSION By analyzing natural product in formation with association rule mining,16 compound combinations are suggested as candidate multi-component drugs for the type 2 diabetes.These compound combinations are recommended for further investigation in the context of drug development.
基金support of the Faculty of Medicine,University of Chile
文摘The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multitargeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with "important" nodes, such as "hubs" or "bottlenecks", encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting "important" targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities("dirty drugs") or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical(graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders.
文摘With the goal of suggesting dual inhibitors of HIV reverse transcriptase (RT) and integrase (IN), herein we report the molecular docking of an initial set of 556 compounds related to the pyridinone class. Docking with multiple crystallographic structures of HIV-1 RT led to 160 potential binders of RT interacting with key amino acid residues at the enzyme’s allosteric site. Compounds selected from the docking with RT were further docked with a crystallographic structure of HIV-1 IN. A total of 31 structures had the potential to make contacts with Mg2+ ions located in a small space between DNA and IN. Interactions with Mg2+ ions are relevant because they participate in the stabilization of the IN-DNA complex. In conclusion, 31 compounds synthetically accessible are proposed as dual inhibitors of RT and IN. It is hypothesized that the suggested compounds will inhibit RT by occupying the allosteric site for NNRTIs and will inhibit the catalytic activity of IN by destabilizing the IN-DNA complex. The main perspective of this work is the synthesis and biological testing of the candidate molecules.
基金supported by the National Natural Science Foundation of China(NSFC)(No.81770809).
文摘Diabetes mellitus(DM)is a progressive metabolic disease characterized by chronic hyperglycemia and caused by different degree of pancreatic islet dysfunction and/or insulin resistance(IR).Long course DM can lead to a variety of macrovascular and microvascular complications which involve artery vessels,heart,kidney,retina,nervous system,etc.In recent years,DM has attracted more and more attention due to its high morbidity and mortality.In addition to achieve effective glycemic control,prevention of complications has also been considered a priority for type 2 diabetes mellitus(T2DM)management.Herein,we provide a comprehensive overview on the pharmacotherapeutics for T2DM and perspectives on the future directions of basic and translational research on anti-diabetic therapy and pharmatheutical development of new drugs.
基金supported by the National Natural Science Foundation of China(22377085,82273770,22177083,22477089,82404407)Natural Science Foundation of Sichuan Province(2023NSFSC1839,2025ZNSFSC1721)+1 种基金Foundation for Innovative Research Groups of the Natural Science Foundation of Sichuan Province(2024NSFTD0026)Postdoctor Research Fund of West China Hospital,Sichuan University(2024HXBH014).
文摘Despite significant advancements in kinase-targeted therapy,the emergence of acquired drug resistance to targets such as KRAS and MEK remains a challenge.Extracellular-regulated kinase 1/2(ERK1/2),positioned at the terminus of this pathway,is highly conserved and less susceptible to mutations,thereby garnering attention as a crucial therapeutical target.However,attempts to use monotherapies that target ERK1/2 have achieved only limited clinical success,mainly due to the issues of limited efficacy and the emergence of drug resistance.Herein,we present a proof of concept that extracellular-regulated kinase 5(ERK5)acts as a compensatory pathway after ERK1/2 inhibition in triple-negative breast cancer(TNBC).By utilizing the principle of polypharmacology,we computationally designed SKLB-D18,a first-in-class molecule that selectively targets ERK1/2 and ERK5,with nanomolar potency and high specificity for both targets.SKLB-D18 demonstrated excellent tolerability in mice and demonstrated superior in vivo anti-tumor efficacy,not only exceeding the existing clinical ERK1/2 inhibitor BVD-523,but also the combination regimen of BVD-523 and the ERK5 inhibitor XMD8-92.Mechanistically,we showed that SKLB-D18,as an autophagy agonist,played a role in mammalian target of rapamycin(mTOR)/70 ribosomal protein S6 kinase(p70S6K)and nuclear receptor coactivator 4(NCOA4)-mediated ferroptosis,which may mitigate multidrug resistance.
