Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation.

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.

Anti-influenza effect and action mechanisms of the chemical constituent gallocatechin-7-gallate from Pithecellobium clypearia Benth.

Host cdc2-like kinase 1 (CLK1) is responsible for the alternative splicing of the influenza virus M2 gene during influenza virus infection and replication that has been recognized as a potential anti-influenza virus target. In this study, we showed that gallocatechin-7-gallate (J10688), a novel CLK1 inhibitor isolated from Pithecellobium clypearia Benth, exerted potent anti-influenza virus activity in vivo and in vitro. ICR mice were intranasally infected with a lethal dose of H1N1. Administration of J10688 (30 mg·kg-1·d-1, iv, for 5 days) significantly increased the survival rate of the H1N1-infected mice to 91.67% and prolong their mean survival time from 5.83 ± 1.74 days to 13.66 ± 1.15 days. J10688 administration also slowed down body weight loss, significantly alleviated influenza-induced acute lung injury, reduced lung virus titer, elevated the spleen and thymus indexes, and enhanced the immunological function. We further explored its anti-influenza mechanisms in the H1N1-infected A549 cells: as a novel CLK1 inhibitor, J10688 (3, 10, 30 µmol/L) dose-dependently impaired synthesis of the viral proteins NP and M2, and significantly downregulated the phosphorylation of splicing factors SF2/ASF and SC35, which regulate virus M2 gene alternative splicing. As a novel CLK1 inhibitor with potent anti-influenza activity in vitro and in vivo, J10688 could be a promising antiviral drug for the therapy of influenza A virus infection.

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease.

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.