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.

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 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 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.

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.

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>

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.

Identification of ligands for human LOX-1 through fluorescence polarization-based high throughput screening / 药学学报

<p><b>AIM</b>To develop a fluorescence polarization-based high throughput screening and identify ligands for human Lectin-like oxidized low-density lipoprotein receptor-1 (hLOX-1).</p><p><b>METHODS</b>Sequential ultracentrifugation at 4 degrees C from normolipidemic fasting volunteers to obtain low density lipoprotein (LDL), which was modified by CuSO4 (5 micromol x L(-1)) at 37 degrees C for 24 h. The assay was based on the interaction between receptor and ligand, and hLOX-1 was labeled by FITC and bound to its specific ligand, oxLDL. Different reaction time and DMSO concentration were optimized to determine the stability and tolerance of fluorescence polarization (FP) assay. 3 200 compounds were screened in black 384-well microplate by FP-based competitive displacement assay, at excitation filter of 485 nm and emission filter of 530 nm. Z' was used to assess the assay quality.</p><p><b>RESULTS</b>The FP-based HTS was formatted in a 384-well microplate with a Z' factor of 0. 75, and three active compounds for hLOX-1 were identified with IC50 below 40 micromol x L(-1) from total 3 200 compounds.</p><p><b>CONCLUSION</b>The results indicated that the fluorescence polarization assay is stable, sensitive, reproducible and well suited for high throughput screening efforts.</p>