The Use of Traditional Chinese Medicine in Relieving EGFR-TKI-Associated Diarrhea Based on Network Pharmacology and Data Mining

Strategies and Recent Advances on Improving Efficient Antitumor of Lenvatinib Based on Nanoparticle Delivery System

Lenvatinib (LVN) is a potentially effective multiple-targeted receptor tyrosine kinase inhibitor approved for treating hepatocellular carcinoma, metastatic renal cell carcinoma and thyroid cancer. Nonetheless, poor pharmacokinetic properties including poor water solubility and rapid metabolic, complex tumor microenvironment, and drug resistance have impeded its satisfactory therapeutic efficacy. This article comprehensively reviews the uses of nanotechnology in LVN to improve antitumor effects. With the characteristic of high modifiability and loading capacity of the nano-drug delivery system, an active targeting approach, controllable drug release, and biomimetic strategies have been devised to deliver LVN to target tumors in sequence, compensating for the lack of passive targeting. The existing applications and advances of LVN in improving therapeutic efficacy include improving longer-term efficiency, achieving higher efficiency, combination therapy, tracking and diagnosing application and reducing toxicity. Therefore, using multiple strategies combined with photothermal, photodynamic, and immunoregulatory therapies potentially overcomes multi-drug resistance, regulates unfavorable tumor microenvironment, and yields higher synergistic antitumor effects. In brief, the nano-LVN delivery system has brought light to the war against cancer while at the same time improving the antitumor effect. More intelligent and multifunctional nanoparticles should be investigated and further converted into clinical applications in the future.

Unraveling the Mechanism of Xiaochaihu Granules in Alleviating Yeast-Induced Fever Based on Network Analysis and Experimental Validation

Xiaochaihu granules (XCHG) are extensively used to treat fever. Nevertheless, the underlying mechanism remains elusive. This study aimed to explore the potential of XCHG in mitigating yeast-induced fever and the underlying metabolic pathways. The chemical composition of XCHG was ascertained using ultra-fast liquid chromatography/quadrupole-time-of-flight tandem mass spectrometry (UFLC-Q-TOF-MS/MS), followed by integrated network analysis to predict potential targets. We then conducted experimental validation using pharmacological assays and metabolomics analysis in a yeast-induced mouse fever model. The study identified 133 compounds in XCHG, resulting in the development of a comprehensive network of herb-compound-biological functional modules. Subsequently, molecular dynamic (MD) simulations confirmed the stability of the complexes, including γ-aminobutyric acid B receptor 2 (GABBR2)-saikosaponin C, prostaglandin endoperoxide synthases (PTGS2)-lobetyolin, and NF-κB inhibitor IκBα (NFKBIA)-glycyrrhizic acid. Animal experiments demonstrated that XCHG reduced yeast-induced elevation in NFKBIA's downstream regulators [interleukin (IL)-1β and IL-8], inhibited PTGS2 activity, and consequently decreased prostaglandin E2 (PGE2) levels. XCHG also downregulated the levels of 5-hydroxytryptamine (5-HT), γ-aminobutyric acid (GABA), corticotropin releasing hormone (CRH), and adrenocorticotrophin (ACTH). These corroborated the network analysis results indicating XCHG's effectiveness against fever in targeting NFKBIA, PTGS2, and GABBR2. The hypothalamus metabolomics analysis identified 14 distinct metabolites as potential antipyretic biomarkers of XCHG. In conclusion, our findings suggest that XCHG alleviates yeast-induced fever by regulating inflammation/immune responses, neuromodulation, and metabolism modules, providing a scientific basis for the anti-inflammatory and antipyretic properties of XCHG.

Research on Optimization of Process Parameters of Traditional Chinese Medicine Based on Data Mining Technology

Data mining technology and methods are used to effectively optimize manufacturing process parameters due to the complexity and uniqueness of the process parameters. The data-mining-based optimization method for traditional Chinese medicine (TCM) process parameters is presented, along with a list of process parameters that have shown to be effective in actual production. The influencing factors of process parameters are analyzed and modeled using an attribute weight analysis and classification analysis algorithm. The optimization scheme of process parameters that meet the requirements is selected, and an example is given for verification, by selecting data records that fall within a certain error range and incorporating the rules of association knowledge discovery. The support vector classification algorithm has a higher accuracy, despite the algorithm's results being understandable. The support vector regression algorithm developed a reliable process optimization model.

Network Pharmacology Analysis of the Mechanisms of Compound Herba Sarcandrae (Fufang Zhongjiefeng) Aerosol in Chronic Pharyngitis Treatment

Purpose

This study aimed to investigate the molecular mechanisms of compound herba Sarcandrae aerosol, also known as the Fufang Zhongjiefeng (FFZJF) aerosol, in treating chronic pharyngitis (CP) using network pharmacology and in vivo experimental approaches.

Methods

Active compounds and putative targets of five herbs in FFZJF were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Chemistry Database, and Swiss Target Prediction databases. The therapeutic targets of CP were obtained from OMIM, Durgbank, DisGeNT, and GAD databases. The active compounds-target networks were constructed using Cytoscape 3.6.1. The overlapping targets of FFZJF active compounds and CP targets were further analyzed using the String database to construct protein–protein interaction (PPI) network. KEGG pathway and Gene Ontology enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery. The predicted targets and pathways were validated in a group A β-hemolytic streptococcus-induced rat CP model.

Results

There were 45 active compounds identified from FFZJF and 11 potential protein targets identified for CP treatment. PPI network demonstrated that IL6, PTGS2, TLR-4, and TNF may serve as the key targets of FFZJF for the treatment of CP. The main functional pathways involving these key targets include cytokine secretion, inflammatory response, MyD88-dependent toll-like receptor signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. In a rat CP model, the elevation of serum TNF-α, IL1β, and IL6 levels, as well as the upregulation of TLR-4, MyD88, NF-κB P65 in the pharyngeal mucosal tissues could be effectively reduced by FFZJF treatment in a dose-dependent manner.

Conclusion

Through a network pharmacology approach and animal study, we predicted and validated the active compounds of FFZJF and their potential targets for CP treatment. The results suggest that FFZJF can markedly alleviate GAS-induced chronic pharyngitis by modulating the TLR-4/MyD88/NF-κB signaling pathways.