ER and PGR targeting ability of phytocompounds derived from Centella asiatica and Andrographis paniculata: An in-silico approach

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

A Network Pharmacology and Molecular Dynamics Simulation-Based Study of Qing Run Hua Jie Decoction in Interstitial Pneumonia Treatment

Objective

This study is dedicated to revealing the potential mechanism of Qin Run Hua Jie (QRHJ) decoction in Interstitial pneumonia (IP) treatment.

Methods

The TCMSP database predicted the chemical components and targets of QRHJ decoction, and the IP-related genes were from the Genecards database. Cytoscape software was used to establish the interaction network. R package clusterProfiler was utilized for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The molecular docking analysis of target proteins and the corresponding active pharmaceutical ingredients in the core position of the interaction network was conducted. Then, molecular dynamics (MD) simulations of a potential active substance and its key targets were performed. The binding efficiency of EGFR and luteolin, HIF1A and diosgenin was detected by cellular thermal shift assay (CETSA), and protein expression was measured by Western blot. CCK-8 was used to detect cell activity.

Results

A total of 153 active ingredients, 127 targets and 362 IP-related genes were obtained. KEGG enrichment analysis identified IP-related signaling pathways including HIF-1 signaling pathway and TNF signaling pathway. The two key components luteolin and diosgenin stably bound to the key targets EGFR and HIF1A. Cell experiments further showed that EGFR and luteolin, HIF1A and diosgenin bound to exert anti-fibrotic effects.

Conclusion

As an active ingredient of QRHJ decoction, luteolin and diosgenin may exert therapeutic effect on IP through binding to the key target EGFR and HIF1A. This work initially revealed the key molecular mechanism of QRHJ decoction in IP treatment and offered theoretical evidence.

Chemical composition and therapeutic mechanism of Xuanbai Chengqi Decoction in the treatment of COVID-19 by network pharmacology, molecular docking and molecular dynamic analysis

Xuanbai Chengqi Decoction (XBCQD), a classic traditional Chinese medicine, has been widely used to treat COVID-19 in China with remarkable curative effect. However, the chemical composition and potential therapeutic mechanism is still unknown. Here, we used multiple open-source databases and literature mining to select compounds and potential targets for XBCQD. The COVID-19 related targets were collected from GeneCards and NCBI gene databases. After identifying putative targets of XBCQD for the treatment of COVID-19, PPI network was constructed by STRING database. The hub targets were extracted by Cytoscape 3.7.2 and MCODE analysis was carried out to extract modules in the PPI network. R 3.6.3 was used for GO enrichment and KEGG pathway analysis. The effective compounds were obtained via network pharmacology and bioinformatics analysis. Drug-likeness analysis and ADMET assessments were performed to select core compounds. Moreover, interactions between core compounds and hub targets were investigated through molecular docking, molecular dynamic (MD) simulations and MM-PBSA calculations. As a result, we collected 638 targets from 61 compounds of XBCQD and 845 COVID-19 related targets, of which 79 were putative targets. Based on the bioinformatics analysis, 10 core compounds and 34 hub targets of XBCQD for the treatment of COVID-19 were successfully screened. The enrichment analysis of GO and KEGG indicated that XBCQD mainly exerted therapeutic effects on COVID-19 by regulating signal pathways related to viral infection and inflammatory response. Meanwhile, the results of molecular docking showed that there was a stable binding between the core compounds and hub targets. Moreover, MD simulations and MM-PBSA analyses revealed that these compounds exhibited stable conformations and interacted well with hub targets during the simulations. In conclusion, our research comprehensively explained the multi-component, multi-target, and multi-pathway intervention mechanism of XBCQD in the treatment of COVID-19, which provided evidence and new insights for further research.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.