Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus.

BACKGROUND: Although coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study. METHODS: We constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis. RESULTS: Based on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay. CONCLUSION: Our work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.

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.

In Silico Prediction and Bioactivity Evaluation of Chemical Ingredients Against Influenza A Virus From Isatis tinctoria L.

Influenza A virus (IAV) is one of the major causes of seasonal endemic diseases and unpredictable periodic pandemics. Due to the high mutation rate and drug resistance, it poses a persistent threat and challenge to public health. Isatis tinctoria L. (Banlangen, BLG), a traditional herbal medicine widely used in Asian countries, has been reported to possess strong efficacy on respiratory viruses, including IAV. However, its effective anti-IAV components and the mechanism of actions (MOAs) are not yet fully elucidated. In this study, we first summarized the chemical components and corresponding contents in BLG according to current available chemical analysis literature. We then presented a network-based in silico framework for identifying potential drug candidates against IAV from BLG. A total of 269 components in BLG were initially screened by drug-likeness and ADME (absorption, distribution, metabolism, and excretion) evaluation. Thereafter, network predictive models were built via the integration of compound-target networks and influenza virus-host proteins. We highlighted 23 compounds that possessed high potential as anti-influenza virus agents. Through experimental evaluation, six compounds, namely, eupatorin, dinatin, linarin, tryptanthrin, indirubin, and acacetin, exhibited good inhibitory activity against wild-type H1N1 and H3N2. Particularly, they also exerted significant effects on drug-resistant strains. Finally, we explored the anti-IAV MOAs of BLG and showcased the potential biological pathways by systems pharmacology analysis. In conclusion, this work provides important information on BLG regarding its use in the development of anti-IAV drugs, and the network-based prediction framework proposed here also offers a powerfulful strategy for the in silico identification of novel drug candidates from complex components of herbal medicine.

Effects of Naodesheng tablets on amyloid beta-induced dysfunction: A traditional Chinese herbal formula with novel therapeutic potential in Alzheimer's disease revealed by systems pharmacology.

Naodesheng (NDS) tablets have been widely used to treat ischemic stroke clinically. NDS relieves neurological function impairment and improve learning and memory in rats with focal cerebral ischemia, suggesting that NDS has potential for Alzheimer's disease (AD) treatment. However, there are no studies about its effective material basis and possible mechanisms. In this study, a systems pharmacology method was applied to reveal the potential molecular mechanism of NDS in the treatment of AD. First, we obtained 360 NDS candidate constituents through ADMET filter analysis. Then, 115 AD-related targets were uncovered by pharmacophore model prediction via mapping the predicted targets against AD-related proteins. In addition, compound-target and target-function networks were established to suggest potential synergistic effects among the candidate constituents. Furthermore, potential targets regulated by NDS were integrated into AD-related pathways to demonstrate the therapeutic mechanism of NDS in AD treatment. Subsequently, a validation experiment proved the therapeutic effect of NDS on cognitive dysfunction in rats with intracerebroventricular injection of Aß. We found that administration of NDS tablets regulates ß-amyloid metabolism, improves synaptic plasticity, inhibits neuroinflammation and improves learning and memory function. In conclusion, this is the first study to provide a comprehensive systems pharmacology approach to elucidate the potential therapeutic mechanism of NDS tablets for AD treatment. We suggest that the protective effects of NDS in neurodegenerative conditions could be partly attributed to its role in improving synaptic plasticity and inhibiting neuroinflammation via NF-κB signaling pathway inhibition and cAMP/PKA/CREB signaling pathway activation.

Discovery of Multitarget-Directed Ligands Against Influenza A Virus From Compound Yizhihao Through a Predictive System for Compound-Protein Interactions.

Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus-host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.

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.