From seeds to survival rates: investigating Linum usitatissimum's potential against ovarian cancer through network pharmacology

Advancing COVID-19 Treatment: The Role of Non-covalent Inhibitors Unveiled by Integrated Machine Learning and Network Pharmacology

INTRODUCTION

The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR).

METHODS

Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features. Molecular docking and molecular dynamics simulations, conducted over a 100 nanosecond period, enhanced the ML-based screening by providing insights into the binding affinities and interaction dynamics with BCL2 and EGFR. Network pharmacology analysis identified these proteins as hub targets within the COVID-19 protein-protein interaction network, highlighting their roles in apoptosis regulation and cellular signaling.

RESULTS

The identified inhibitors exhibited strong binding affinities, suggesting potential efficacy in disrupting viral life cycles and impeding disease progression. Comparative analysis with existing literature affirmed the relevance of BCL2 and EGFR in COVID-19 therapy and underscored the novelty of integrating network pharmacology with ML. This multidisciplinary approach establishes a framework for emerging pathogen treatments and advocates for subsequent in vitro and in vivo validation, emphasizing a multi-targeted drug design strategy against viral adaptability.

CONCLUSION

This study's findings are crucial for the ongoing development of therapeutic agents against COVID-19, leveraging computational and network-based strategies.

An Integrative Network Pharmacology and Bioinformatics Approach for Deciphering the Multi-target Effect of Nyctanthes arbortristis L. against COVID-19

INTRODUCTION

The COVID-19 pandemic represents a significant challenge across scientific, medical, and societal dimensions. The unpredictability of the disease progression, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), underscores the urgent need for identifying compounds that target multiple aspects of the virus to ensure swift and effective treatment. Nyctanthes arbortristis L., a delicate, perennial, deciduous shrub found across various Asian regions, has been recognized for its wide range of pharmacological benefits, including hepatoprotective, antimalarial, antibacterial, anti-inflammatory, antioxidant, and antiviral properties.

METHODS

Various in vitro studies revealed the therapeutic significance of Nyctanthes arbortristis against COVID-19. However, the exact molecular mechanism remains unclarified. In the present study, a network pharmacology approach was employed to uncover the active ingredients, their potential targets, and signaling pathways in Nyctanthes arbortristis for the treatment of COVID-19. In the framework of this study, we explored the active ingredient-target-pathway network and figured out that naringetol, ursolic acid, betasitosterol, and daucosterol decisively contributed to the development of COVID-19 by affecting IL6, MAPK3, and MDM2 genes.

RESULTS

The results of molecular docking analysis indicated that Nyctanthes arbortristis exerted effective binding capacity in COVID-19. Further, we disclosed the targets, biological functions, and signaling pathways of Nyctanthes arbortristis in COVID-19. The analysis indicated that Nyctanthes arbortristis could help treat COVID-19 through the enhancement of immunologic functions, inhibition of inflammatory reactions and regulation of the cellular microenvironment. In short, the current study used a series of network pharmacologybased and computational analyses to understand and characterize the binding capacity, biological functions, pharmacological targets and therapeutic mechanisms of Nyctanthes arbortristis in COVID-19.

CONCLUSION

However, the findings were not validated in actual COVID-19 patients, so further investigation is needed to confirm the potential use of Nyctanthes arbortristis for treating COVID-19.

Mechanism of Sophorae Flavescentis Radix against ovarian cancer via new pharmacology, molecular docking, and experimental verification

The study aims to elucidate the pharmacological mechanisms of Sophorae Flavescentis Radix (SFR, Kushen) against ovarian cancer (OV) by employing an integrated approach that encompasses network pharmacology, molecular docking, and experimental validation. The effective components and potential targets of SFR were identified through screening the Traditional Chinese Medicine Systems Pharmacology (TSMSP) public database using network pharmacology. Core anti-OV targets were pinpointed using protein-protein interaction (PPI) networks. Datasets from The Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) were used to investigate the mRNA and protein expressions of critical target genes in both normal and cancerous ovarian tissues, alongside their relationship to overall ovarian survival. Functional and pathway enrichment assessments of putative targets were carried out with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The assessment of stable binding effects was conducted through molecular docking with quercetin, luteolin, and formononetin, and validated by anti-OV cell activity. The investigation identified 22 active SFR components yielding 152 potential targets following the intersection with known OV targets. Analysis of PPI network highlighted 13 crucial target genes, including tumor necrosis factor (TNF) and interleukin-1A (IL-1A). GO enrichment analysis covered 703 biological activities, 72 cellular components, and 144 chemical functions. The KEGG enrichment analysis suggested that anti-cancer effects of SFR are mediated by the TNF, interleukin-17 (IL-17), and AGE-RAGE signaling pathways. Molecular docking demonstrated that TNF and IL-1A were stable and strong binding to quercetin, luteolin, and formononetin, indicating that these stable structures significantly inhibited A2780 OV cell viability. This study demonstrated the ability of TNF and IL-1A combined with quercetin, luteolin, and formononetin to decrease the activity of OV cells, suggesting potential therapeutic effect against OV.

Novel drug discovery: Advancing Alzheimer's therapy through machine learning and network pharmacology

Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline. Despite extensive research, its complex etiology remains elusive, necessitating new treatments. This study utilized machine learning (ML) to analyze compounds with neuroprotective potential. This approach exposed the disease's complexity and identified important proteins, namely MTOR and BCL2, as central to the pathogenic network of AD. MTOR regulates neuronal autophagy and survival, whereas BCL2 regulates apoptosis, both of which are disrupted in AD. The identified compounds, including Armepavine, Oprea1_264702,1-cyclopropyl-7-fluoro-8-methoxy-4-oxoquinoline-3-carboxylic acid,(2S)-4'-Hydroxy-5,7,3'-trimethoxyflavan,Oprea1_130514,Sativanone,5-hydroxy-7,8-dimethoxyflavanone,7,4'-Dihydroxy-8,3'-dimethoxyflavanone,N,1-dicyclopropyl-6,Difluoro-Methoxy-Gatifloxacin,6,8-difluoro-1-(2-fluoroethyl),1-ethyl-6-fluoro-7-(4-methylpiperidin-1-yl),Avicenol C, demonstrated potential modulatory effects on these proteins. The potential for synergistic effects of these drugs in treating AD has been revealed via network pharmacology. By targeting numerous proteins at once, these chemicals may provide a more comprehensive therapeutic approach, addressing many aspects of AD's complex pathophysiology. A Molecular docking, dynamic simulation, and Principle Component Analysis have confirmed these drugs' efficacy by establishing substantial binding affinities and interactions with important proteins such as MTOR and BCL2. This evidence implies that various compounds may interact within the AD pathological framework, providing a sophisticated and multifaceted therapy strategy. In conclusion, our study establishes a solid foundation for the use of these drugs in AD therapy. Thus current study highlights the possibility of multi-targeted, synergistic therapeutic approaches in addressing the complex pathophysiology of AD by integrating machine learning, network pharmacology, and molecular docking simulations. This holistic technique not only advances drug development but also opens up new avenues for developing more effective treatments for this difficult and widespread disease.