ABSTRACT
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.
Subject(s)
Humans , Antineoplastic Agents/pharmacology , Bayes Theorem , Drugs, Chinese Herbal/chemistry , Medicine, Chinese Traditional , Neoplasms/drug therapyABSTRACT
Xiaoshuan Tongluo formula is effective in treating mental retardation and speech astringency caused by cerebral thrombosis, but its mechanism remains unclear. In this investigation, by collecting the chemical constituents from Xiaoshuan Tongluo formula and the targets related to stroke, we obtained 1 251 constituents from the formula and 10 drug targets related with stroke. We established 18 prediction models of compound-target interaction for 10 stroke-related targets, using molecular docking method and machine learning methods includes Naive Bayesian and recursive partitioning based on the input of molecular fingerprints and molecular descriptors. Using these models, we predicted the active chemical constituents from Xiaoshuan Tongluo formula and their drug targets, 153 potential active constituents were discovered, 22 among them could interact with at least two drug targets related with stroke. On this basis, the chemical constituent-target network was constructed using network construction software, and then the important metabolic pathways of the targets were identified by using Gene-Ontology (GO) enrichment analysis, such as blood coagulation, positive regulation of angiogenesis, positive regulation of ion transport and so on. On this basis, a target-pathway network was constructed. In conclusion, using machine learning, molecular docking, virtual screening, data mining and network construction, this study explored and partially revealed the active chemical constituents and chemical constituent-target-pathway network action mechanism of Xiaoshuan Tongluo formula against stroke, which will provide important information for its further study.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.