Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation / 中国天然药物

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes, and multi-target drugs provide a promising therapy idea for the treatment of cancer. Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs. In this paper, 50 tumor-related targets were collected by searching the Therapeutic Target Database and Thomson Reuters Integrity database, and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time. Through the multi-target anti-cancer prediction system, some dominant fragments that act on multiple tumor-related targets were analyzed, which could be helpful in designing multi-target anti-cancer drugs. Anti-cancer traditional Chinese medicine (TCM) and its natural products were collected to form a TCM formula-based natural products library, and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system. As a result, alkaloids, flavonoids and terpenoids were predicted to act on multiple tumor-related targets. The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments. In conclusion, the multi-target anti-cancer prediction system is very effective and reliable, and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs. The anti-cancer natural compounds found in this paper will lay important information for further study.

Progress on the application of artificial intelligence technology in ligand-based and receptor structure-based drug screening / 药学学报

Artificial intelligence technology is being widely applied in drug screening. This paper introduces the characteristics of artificial intelligence, and summarizes the application and progress of artificial intelligence technology especially deep learning in drug screening, from ligand-based and receptor structure-based aspects. This paper also introduces how to apply artificial intelligence to drug design from these two aspects. Finally, we discuss the main limitations, challenges, and prospects of artificial intelligence technology in the field of drug screening.

Energy metabolism disorder and diseases: from effects to potential targets / 药学学报

Cellular energy metabolism disorder caused by dysfunction of nutrient utilization and mitochondrial damage contributes to a variety of diseases, including neurodegenerative diseases, cancer, metabolic diseases, and cardiovascular diseases. Understanding the effects of energy metabolism on diseases will help to improve our knowledge about disease etiology and may serve to develop strategies to delay disease progress. There are many compounds developed for targeting energy metabolism disorders, such as small molecules targeting the 18 kDa transporter (TSPO) for treatment of Alzheimer's disease, glucagon-like peptide-1 analogues for treatment of Parkinson's disease, inhibitors of glucose transporter 1 (GLUT1) and lactate dehydrogenase A for treatment of tumors, the fibroblast growth factors based treatment for type 2 diabetes (T2D), selective ligands of peroxisome proliferator-activated receptor (PPAR)-β/δ for treatment of cardiovascular diseases. We review here the abnormal energy metabolism of common energy metabolism disorder-related diseases, summarize the potential targets that may be used for new drug discovery, and the strategies for alleviating the disease process by improving energy metabolism.

Repositioning drug discovery for Alzheimer's disease based on global marketed drug data / 药学学报

Alzheimer's disease (AD) is a neurodegenerative disease that seriously threatens the life of the elderly and there is no effective therapy to treat or delay the onset of this disease. Due to the multifactorial etiology of this disease, the multi-target-directed ligand (MTDL) approach is an innovative and promising method in search for new drugs against AD. In order to find potential multi-target anti-AD drugs through reposition of current drugs, the database of global drugs on market were mined by an anti-AD multi-target prediction platform established in our laboratory. As a result, inositol nicotinate, cyproheptadine, curcumin, rosiglitazone, demecarium, oxybenzone, agomelatine, codeine, imipramine, dyclonine, melatonin, perospirone, and bufexamac were predicted to act on at least one anti-AD drug target yet act against AD through various mechanisms. The compound-target network was built using the Cytoscape. The prediction was validated by molecular docking between agomelatine and its multiple targets, including ADORA2A, ACHE, BACE1, PTGS2, MAOB, SIGMAR1 and ESR1. Agomelatine was shown to be able to act on all the targets above. In conclusion, the potential drugs for anti-AD therapy in the database for global drugs on market was partially uncovered using machine learning, network pharmacology, and molecular docking methods. This study provides important information for drug reposition in anti-AD therapy.

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease / 中国天然药物

Naodesheng (NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer's disease (AD) only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharmacophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease / 中国天然药物

Naodesheng (NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, is widely applied for the treatment of cardio/cerebrovascular ischemic diseases, ischemic stroke, and sequelae of cerebral hemorrhage, etc. At present, the studies on NDS formula for Alzheimer's disease (AD) only focus on single component of this prescription, and there is no report about the synergistic mechanism of the constituents in NDS formula for the potential treatment of dementia. Therefore, the present study aimed to predict the potential targets and uncover the mechanisms of NDS formula for the treatment of AD. Firstly, we collected the constituents in NDS formula and key targets toward AD. Then, drug-likeness, oral bioavailability, and blood-brain barrier permeability were evaluated to find drug-like and lead-like constituents for treatment of central nervous system diseases. By combining the advantages of machine learning, molecular docking, and pharmacophore mapping, we attempted to predict the targets of constituents and find potential multi-target compounds from NDS formula. Finally, we built constituent-target network, constituent-target-target network and target-biological pathway network to study the network pharmacology of the constituents in NDS formula. To the best of our knowledge, this represented the first to study the mechanism of NDS formula for potential efficacy for AD treatment by means of the virtual screening and network pharmacology methods.

Network mechanism of effective constituents from the compound Yizhihao against influenza / 中国药理学与毒理学杂志

Influenza caused by influenza virus,seriously threaten human life and health.Drug treatment is one of the effective measurement. However, there are only two classes of drugs, one class is M2 blockers and another is neuraminidase (NA)inhibitors. The recent antiviral surveillance studies reported a global significant increase in M2 blocker resistance among influenza viruses, and the resistant virus strains against NA inhibitor are also reported in clinical treatment.Therefore thediscovery of new medicines with low resistance has become very urgent.As all known,traditional medicines with multi-target features and network mechanism often possess low resistance. Compound Yizhihao, which consists of radix isatidis,folium isatidis,Artemisia rupestris,is one of the famous traditional medicine for influenza treatment in China, however its mechanism of action against influenza is unclear. In this study, the multiple targets related with influenza disease and the known chemical constituents from Compound Yizhihao were collected, and multi-target QSAR (mt-QSAR) classification models were developed by Na?ve Bayesian algorithm and verified by various datasets. Then the classification models were applied to predict the effective constituents and their drug targets.Finally,the constituent-target-pathway network was constructed,which revealed the effective constituents and their network mechanism in Compound Yizhihao. This study will lay important basis for the clinical uses for influenza treatment and for the further research and development of the effective constituents.

Network mechanism on effective constituents from Polygala Tenuifolia for anti-Alzheimer Disease / 中国药理学与毒理学杂志

OBJECTIVE To clarify out the network pharmacology mechanism of Polygala tenuifolia against Alzheimer disease(AD).METHODS Firstly,we collected the chemical constituents from Polyg-ala tenuifolia and key targets toward AD.Machine learning algorithms were applied to construct classifi-ers for predicting the effective constituents. Secondly, docking models were utilized for further evalua-tion.Finally,we built constituent-target,target-target network and target-biology pathway network.RE-SULTS 104 chemical constituents Polygala tenuifolia from were collected.Through prediction of blood-brain penetration and validation,36 chemical constituents were selected among 100 chemical constitu-ents,their action targets mainly focused on AChE,COX-2,TNF-α,insulin-degrading enzyme and APP. Their main structure types include Polygala saponins, Polygala glycosides, Polygala shrubby ketones, polygala xanthones and sterols,which acted on AchE,APP,M-TAU,GSK3β and 5HT1A with high fre-quency.Gene-Ontology and KEGG enrichment analysis showed that the main pathways of these con-stituents involve in neurotransmitter release,synaptic conduction and synaptic plasticity,apoptosis reg-ulation,phosphorylation pathway,Ca2+signaling pathway,and so on.CONCLUSION This study uncov-ered a network mechanism of Polygala tenuifolia against Alzheimer disease,which may provide impor-tant information for the further study and new drug development.

Network pharmacology study of the effective constituents in the Compound Yizhihao against influenza disease / 药学学报

Compound Yizhihao, consists of Radix isatidis, Folium isatidis, Artemisia rupestris, has a significant therapeutic effect on the treatment of influenza and fever. However, the mechanism of its action is still unclear. In this investigation, we collected the key target molecule of influenza disease and the chemical constituents of Compound Yizhihao, and developed Naïve Bayesian classification models based on the input molecular fingerprints and molecule descriptors. The built models were further applied to construct classifiers for predicting the effective constituents. We used the professional network-building software to build the constituent-target network and target-pathway network, which revealed the network pharmacology of the effective constituents in Compound Yizhihao. It will contribute to the further research of mechanism of Compound Yizhihao.

Network pharmacology study of effective constituents of traditional Chinese medicine for Alzheimer's disease treatment / 药学学报

This study aims to investigate the network pharmacology of Chinese medicinal formulae for treatment of Alzheimer's disease. Machine learning algorithms were applied to construct classifiers in predicting the active molecules against 25 key targets toward Alzheimer's disease (AD). By extensive data profiling, we compiled 13 classical traditional Chinese medicine (TCM) formulas with clinical efficacy for AD. There were 7 Chinese herbs with a frequency of 5 or higher in our study. Based on the predicted results, we built constituent-target, and further construct target-target interaction network by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) and target-disease network by DAVID (Database for Annotation, Visualization and Integrated Discovery) and gene disease database to study the synergistic mechanism of the herbal constituents in the Chinese traditional patent medicine. By prediction of blood-brain penetration and validation by TCMsp (traditional Chinese medicine systems pharmacology) and Drugbank, we found 7 typical multi-target constituents which have diverse structure. The mechanism uncovered by this study may offer a deep insight into the action mechanism of TCMs for AD. The predicted inhibitors for the AD-related targets may provide a good source of new lead constituents against AD.

Expression of relaxin family peptide receptor 1 in the lung tissue of silicosis rats / 中国职业医学

OBJECTIVE: To investigate the potential effect of relaxin family peptide receptor 1( RXFP1) in the process of silica-induced silicosis. METHODS: Sixty-four specific pathogen free male Wistar rats were randomly divided into control group and experimental group. By one time intratracheal infusion,rats in experimental group were treated with 0. 1 m L 500 g / L silica dust suspension while the control group was treated with 0. 1 m L sodium chloride physiological solution. Eight rats from each group were sacrificed on day 1,7,14 and 28 after exposure. Histopathologic changes of the lung tissue were performed with hematoxylin-eosin staining. The expressions of Rxfp1 mRNA and RXFP1 protein in rat lungs were detected by real-time polymerase chain reaction and immunohistochemical staining,respectively. RESULTS: After 28 days of exposure,the grey nodules were observed by naked eye in the lung of the experimental group. The fracture and silicotic nodules could be seen in alveolar interval with light microscope. Compared with the control group,the Rxfp1 mRNA relative expression level in the lungs of experimental group was increased to 145% after 1 day of exposure( P < 0. 01),followed by a decrease on day 7 and 14 and reached similar level of control group( P > 0. 05). By day 28,it dropped to45% of control group( P < 0. 01). The RXFP1 protein relative expression in experimental group was significantly up-regulated since the 7th day compared to that of the control group( P < 0. 01). And it reached to the highest level on the28 th day( P < 0. 01). CONCLUSION: The RXFP1 might play an important role in inhibiting silicosis.

Neonatal complications and birth defects in infants conceived by in vitro fertilization / 中国当代儿科杂志

<p><b>OBJECTIVE</b>To investigate the survival quality of infants conceived by in vitro fertilization (IVF) and to identify the factors that cause birth defects and neonatal complications in IVF infants.</p><p><b>METHODS</b>The study included 150 IVF infants (IVF group) and 200 naturally conceived infants (control group). Indicators such as birth situation, gestational disease, birth defects, and neonatal complications were compared between groups. The influencing factors for birth defects and neonatal complications were analyzed by non-conditional logistic regression analysis.</p><p><b>RESULTS</b>Compared with the control group, the IVF group had increased incidences of twin pregnancy and low birth weight (P<0.01) but decreased average birth weight (P<0.05). In the IVF group, the mother's age was elder, with higher incidence of cesarean section, premature rupture of membranes, and pregnancy complications, as compared with the control group (P<0.05). There was no significant difference in the incidence of birth defects between the two groups (P>0.05). The IVF group had higher incidence rates of low birth weight and neonatal scleroderma (P<0.05), with a longer hospital stay (P<0.01), as compared with the control group. The non-conditional logistic regression analysis indicated that IVF, prematurity, twin pregnancy, and pregnancy complications were risk factors for low birth weight (P<0.05).</p><p><b>CONCLUSIONS</b>There is no significant difference in the incidence of birth defects between IVF and naturally conceived infants. However, IVF infants have higher incidences of twin pregnancy and low birth weight, with a longer hospital stay, as compared with naturally conceived infants. Natural conceiving, avoiding prematurity, twin pregnancy, and pregnancy complications will reduce the incidence of low birth weight.</p>

Development of HTS model on SERT inhibitors combined biological screening model with HTVS / 药学学报

In order to improve the efficiency of drug screening on serotonin transporter (SERT) inhibitors, a high-throughput screening (HTS) model is established in RBL-2H3 cells. The RBL-2H3 cells are very similar to the serotonin genetic neuro, in modulation of post-receptor mechanisms and transduction pathway of SERT reactivated. Depending on a fluorescence substrate ASP+ used in detection method of inhibitor rates, it's convenient, quick, accurate and effective, not making the environmental biohazard compared with radioactive experiments. Furthermore, biological screening model combined with computer aided virtual screening technique describing high-throughput virtual screening (HTVS). Bayesian classification method and molecular fingerprint similarity were applied to virtual screening technique, for screening compounds in compound library. Some compounds have been found, and then validated further by biological screening model. Combination of HTS and HTVS improves the efficiency of screening SERT inhibitors.

Research advance in the drug target prediction based on chemoinformatics / 药学学报

The emerging of network pharmacology and polypharmacology forces the scientists to recognize and explore new mechanisms of existing drugs. The drug target prediction can play a key significance on the elucidation of the molecular mechanism of drugs and drug reposition. In this paper, we systematically review the existing approaches to the prediction of biological targets of small molecule based on chemoinformatics, including ligand-based prediction, receptor-based prediction and data mining-based prediction. We also depict the strength of these methods as well as their applications, and put forward their developing direction.

Inhibition of acetylcholinesterase by two genistein derivatives: kinetic analysis, molecular docking and molecular dynamics simulation

In this study two genistein derivatives (G1 and G2) are reported as inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), and differences in the inhibition of AChE are described. Although they differ in structure by a single methyl group, the inhibitory effect of G1 (IC50=264 nmol/L) on AChE was 80 times stronger than that of G2 (IC50=21,210 nmol/L). Enzyme-kinetic analysis, molecular docking and molecular dynamics (MD) simulations were conducted to better understand the molecular basis for this difference. The results obtained by kinetic analysis demonstrated that G1 can interact with both the catalytic active site and peripheral anionic site of AChE. The predicted binding free energies of two complexes calculated by the molecular mechanics/generalized born surface area (MM/GBSA) method were consistent with the experimental data. The analysis of the individual energy terms suggested that a difference between the net electrostatic contributions (ΔE ele+ΔG GB) was responsible for the binding affinities of these two inhibitors. Additionally, analysis of the molecular mechanics and MM/GBSA free energy decomposition revealed that the difference between G1 and G2 originated from interactions with Tyr124, Glu292, Val294 and Phe338 of AChE. In conclusion, the results reveal significant differences at the molecular level in the mechanism of inhibition of AChE by these structurally related compounds.

Active neuraminidase constituents of Polygonum cuspidatum against influenza A(H1N1) influenza virus / 中国中药杂志

<p><b>OBJECTIVE</b>To isolate and identify active neuraminidase constituents of Polygonum cuspidatum against influenza A (H1N1) influenza virus.</p><p><b>METHOD</b>On the basis of the bioassay-guided fractionation,such chromatographic methods as silica gel, sephadex LH-20 and HPLC were adopted to isolate active constituents of extracts from Polygonum cuspidatum, and their molecular structures were identifiied on the basis of their spectral data such as NMR and MS and physico-chemical properties.</p><p><b>RESULT</b>Seven compounds were isolated from the ethyl acetate extract of P. cuspidatum and identified as 2-methoxystypandrone (1), emodin (2), resveratrol (3), polydatin (4), emodin-8-O-beta-D-glucopyranoside (5), (E)-3, 5, 12-trihydroxystilbene-3-O-beta-D-glucopyranoside-2'-(3", 4", 5"-trihydroxybenzoate) (6) and catechin-3-O-gallate (7), respectively. Among them, the NA test showed that compounds 3, 6 and 7 had inhibitory effect against NAs activity, with IC50 values of 129.8, 44.8 and 21.3 micromol x L(-1), respectively. Moreover, the further CPE test showed compounds 6 and 7 had significant inhibitory effect against H1N influenza virus (EC50 = 5.9, 0.9 micromol x L(-1), respectively), with very low cytotoxicity to the host cells, their therapeutic selective index(SI) in MDCK cells ranged from 56 to 269.</p><p><b>CONCLUSION</b>The neuraminidase inhibitors against H1N1 anti-influenza virus isolated from extracts of P. cuspidatum on the basis of the bioassay-guided fractionation are significant in specifying their therapeutic material basis and drug R&D against influenza.</p>

A study on the standard of influenza neuraminidase inhibition assay / 药学学报

In present study, standard method and standard operation practice for measuring the activities of influenza neuraminidase and its inhibitors have been established. The accuracy and stability of the method has been evaluated. Standard operation is as following: 10 microL sample, 30 microL neuraminidase and 60 microL substrate are added to one well of a 96-well plate, and then incubated at 37 degrees C for 1 h. The reaction was stopped with NaOH before fluorescence intensity determination. One unit of neuraminidase is defined as the amount of enzyme that produces 1 nmol 4-MU in 1 h under above conditions. The inhibition accuracy is indicated by an uncertainty measurement of 6.51 x 10(-12), and its stability was reaffirmed by determination of oseltamivir acid. In this study, systematic assessment of neuraminidase inhibitory assay not only provided theoretical basis of its application in drug discovery, but also made preliminary attempt to use uncertainty measurement as a parameter in biological measurement.

Network pharmacology: new guidelines for drug discovery / 药学学报

The development of new drug is not only the main driving force for the development of pharmaceutical industry, but also plays a very important role in the social development. However, with the increasing demands, new drug development is facing great difficulties in recent years. The hypothesis of highly selective single-target is meeting the challenges because of its limitations. Network pharmacology has been one of the new strategies for new drug discovery based on single-target drug research in recent years. This paper focused on the basis of network pharmacology and its research progress, discussed its development direction and application prospects, and analyzed its limitations and problems as well. The application of network pharmacology in new drug development is discussed by comparing its guidelines with those of traditional Chinese medicine theory and Effective Components Group hypothesis of Chinese medicines.

Evaluation of Chinese traditional patent medicines against influenza virus in vitro / 药学学报

To study in vitro anti-influenza viral activities of Chinese traditional patent medicines for influenza prevention and treatment, neuraminidase (NA) activity assay was used to examine NA inhibitory activity of 33 Chinese traditional patent medicines through fluorimetric assay, and influenza virus induced cytopathic effect (CPE) inhibition assay was used to verify their anti-influenza viral activities in vitro. The assay results showed that most liquid preparations displayed relatively high NA inhibitory activities, such as Shuanghuanglian oral liquid, Qingkailing oral liquid, Qingre Jiedu oral liquid, and Reduning injection. Among liquid preparations, Shuanghuanglian oral liquid not only displayed the highest NA inhibitory effect, but also exhibited obvious in vitro anti-viral activity in CPE experiment. Among solid preparations, Shuanghuanglian powder for injection showed the highest activity on NA inhibition, and Fufang Yuxingcao tablet showed relatively strong anti-influenza viral activity in CPE cells. From the results, it can be concluded that most Chinese traditional patent medicines possessed NA inhibitory activity, but only a few of them displayed significant in vitro anti-influenza viral activities. These results will provide important information for the isolation of active constituents, and for the clinical uses of Chinese traditional patent medicines for influenza treatment and prevention.

Research progress of virtual screening aided drug discovery / 药学学报

In the process of new drug discovery, the application of virtual screening can enrich active compounds, reduce the cost of drug screening, and increase the feasibility of drug screening. Therefore virtual screening technology has become an important approach for new drug discovery. As virtual screening and bioactivity screening possess different advantages, their combination can effectively promote new drug discovery. In the present paper, the application and the trend of removal of non-drug compounds, removal of false positive compounds, pharmacophore searching, molecular docking, and molecular similarity in the process of drug discovery are introduced in order to obtain more benefit from virtual screening strategy for new drug discovery.