Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Protein sequence analysis examines the order of amino acids within protein sequences to unlock diverse types of a wealth of knowledge about biological processes and genetic disorders. It helps in forecasting disease susceptibility by finding unique protein signatures, or biomarkers that are linked to particular disease states. Protein Sequence analysis through wet-lab experiments is expensive, time-consuming and error prone. To facilitate large-scale proteomics sequence analysis, the biological community is striving for utilizing AI competence for transitioning from wet-lab to computer aided applications. However, Proteomics and AI are two distinct fields and development of AI-driven protein sequence analysis applications requires knowledge of both domains. To bridge the gap between both fields, various review articles have been written. However, these articles focus revolves around few individual tasks or specific applications rather than providing a comprehensive overview about wide tasks and applications. Following the need of a comprehensive literature that presents a holistic view of wide array of tasks and applications, contributions of this manuscript are manifold: It bridges the gap between Proteomics and AI fields by presenting a comprehensive array of AI-driven applications for 63 distinct protein sequence analysis tasks. It equips AI researchers by facilitating biological foundations of 63 protein sequence analysis tasks. It enhances development of AI-driven protein sequence analysis applications by providing comprehensive details of 68 protein databases. It presents a rich data landscape, encompassing 627 benchmark datasets of 63 diverse protein sequence analysis tasks. It highlights the utilization of 25 unique word embedding methods and 13 language models in AI-driven protein sequence analysis applications. It accelerates the development of AI-driven applications by facilitating current state-of-the-art performances across 63 protein sequence analysis tasks.

Exploration of the mechanism of Polyphyllin I against hepatocellular carcinoma based on network pharmacology, molecular docking and experimental validation

PURPOSE

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Targeted therapies hold promise for HCC treatment, and understanding the molecular mechanisms of action is crucial for developing novel therapeutic strategies. Polyphyllin I, a natural compound with known antitumor activity, represents a potential therapeutic candidate.

METHODS

This study employed a network pharmacology approach to investigate the anti-HCC effects of Polyphyllin I and its underlying mechanisms. Drug and disease related targets were identified and intersected to construct Components-Gene Symbols-Disease and Protein-Protein Interaction networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Molecular docking simulations were conducted to explore the interactions between Polyphyllin I and key pathway proteins (VEGF-C and β-catenin). Finally, in vitro and in vivo experiments validated the anti-HCC effects and underlying mechanisms of Polyphyllin I.

RESULTS

Network pharmacology analysis revealed that Polyphyllin I targets multiple genes and pathways implicated in HCC development and progression. GO and KEGG analyses identified significant enrichment of pathways related to cell proliferation, apoptosis and angiogenesis, including VEGF and the Wnt/β-catenin signaling pathways. Molecular docking simulations demonstrated strong binding affinities between Polyphyllin I and VEGF-C and β-catenin. In vitro and in vivo experiments confirmed that Polyphyllin I effectively inhibits HCC cell proliferation, induces apoptosis, and suppresses angiogenesis, potentially by modulating the VEGF-C and Wnt/β-catenin signaling pathways.

CONCLUSIONS

The study provides compelling evidence for the antitumor activity of Polyphyllin I in HCC and elucidates its possible molecular mechanisms, suggesting that Polyphyllin I holds great potential as a therapeutic agent for HCC.

Mechanistic Insights Into Ganoderma Lucidum for Diabetes Treatment via Network Pharmacology and Validation

Purpose

There is an urgent need to develop antidiabetic medications with minimal side effects and low toxicity. Ganoderma lucidum, a food-medicine homologous in China, has been used to treat diabetes. This study was aimed to explore the active ingredients and mechanism of G. lucidum in the treatment of diabetes.

Materials and Methods

Relevant compounds and targets of Ganoderma were collected from the TCMSP database, BATMAN-TCM database, relevant literature and PubChem. A diabetes-related target database was constructed using TTD, BATMAN-TCM, and Uniprot. A PPI network and H-C-T-P network were constructed to analyze interactions among these targets. GO and KEGG enrichment analyses were performed using WebGestalt. Molecular docking of the core compounds and key targets was carried out using AutoDock Vina. The predicted key targets were verified via qRT-PCR in PA-induced HepG2 cells, using GLAE (ethanol extract of Ganoderma lucidum) as the treatment.

Results

A total of 58 compounds were screened out in G. lucidum, of which 17 had predicted targets. G. lucidum was involved in metabolic processes, such as lipid binding, insulin secretion, and other pathways. Molecular docking results showed that the core component β-sitosterol had strong binding activity with key targets CASP3, PRKACA, and PGR. Based on the results of network pharmacology, the top 10 targets related to glucose and lipid metabolism were selected for validation. The results indicated that in a high-fat environment, glucose and lipid metabolism in HepG2 cells was improved, with decreased mRNA expression of CASP3, PRKACA, CYP19A1, NR3C1, JUN, and increased expression of PGR and RXRA.

Conclusion

Glucose and lipid metabolism are important for the anti-diabetic activity of G. lucidum. A strong interaction of β-sitosterol with CASP3, PRKACA, and PGR, which may be related to cell apoptosis, gluconeogenesis and insulin secretion, etc. This study lays the foundational groundwork for future drug development and therapeutic optimization.

Mechanistic evaluation of Jiu Wei Qing Zhi Gao in non-alcoholic fatty liver disease: insights from network Pharmacology and experimental validation

CONTEXT

Jiu Wei Qing Zhi Gao (JWQZG), a traditional Chinese medicine (TCM) formulation, is widely utilized in China for managing non-alcoholic fatty liver disease (NAFLD).

OBJECTIVE

This study aimed to elucidate the therapeutic mechanisms of JWQZG in the management of NAFLD.

MATERIALS AND METHODS

Network pharmacology was employed to predict the potential mechanisms of JWQZG in NAFLD management. In vivo experiments were conducted using C57BL/6J mice fed a high-fat diet (HFD) for 16 weeks, followed by treatment with JWQZG at three dosages (1.85, 3.7, and 7.4 g/kg/day) or metformin (150 mg/kg/day) for 8 weeks. In vitro studies utilized HepG2 cells exposed to 0.5 mM palmitic acid (PA) for 24 h to establish an NAFLD model, followed by exposure to JWQZG-containing serum at three concentrations for an additional 24 h. Western blot analysis was used to analyze the expression levels of key signaling pathway components.

RESULTS

Results of network pharmacology analysis identified the insulin signaling pathway as a potential mediator of the protective effects of JWQZG in NAFLD. Treatment with JWQZG markedly reduced hepatic steatosis and improved insulin resistance. This was accompanied by enhanced expression of key components in the insulin signaling pathway, including insulin receptor substrate 1 (IRS1), phosphorylated PI3K (p-PI3K), phosphorylated AKT (p-AKT), and phosphorylated GSK3β (p-GSK3β), compared to the NAFLD model group.

CONCLUSIONS

These findings provide robust evidence supporting the therapeutic potential of JWQZG in NAFLD and its modulation of the insulin signaling pathway. Furthermore, the study offers valuable insights for the discovery of anti-NAFLD compounds derived from TCM formulations.

Evaluating the Mechanism Underlying Multi-Compound Synergy of Banxia Decoction in the Treatment of Hashimoto's Thyroiditis Based on Network Pharmacology and Molecular Docking

Objective

We aimed to utilize network pharmacological analysis and molecular docking to elucidate the potential mechanisms of Banxia Decoction (BD) action in the treatment of Hashimoto's thyroiditis (HT).

Materials and Methods

Active compounds and HT-related targets were predicted using databases and the intersection of the results was taken. STRING and DAVID 6.8 tools were used to obtain the protein-protein interaction (PPI) network and perform GO and KEGG evaluations, respectively. Discovery Studio 2017 R2 was utilized to perform molecular docking and RT-qPCR was conducted to confirm hub gene expressions in clinical samples.

Results

A total of 136 active compounds in BD were screened, and 74 potential targets related to HT were identified in BD. Further, 17 key targets in the PPI network were identified and HIF1A, EP300, PRKCA, and TERT were included for subnet analysis. Next, a network of "Chinese medicine-active compound-potential target-signal pathway" was obtained and the HIF-1 signaling pathway was identified as the key pathway. Finally, 8 active compounds and their stable binding to target proteins were confirmed by molecular docking; MAPK3, SRC, TERT, and HIF1A were upregulated in HT relative to the goiter samples.

Conclusion

The integration of network pharmacology and molecular docking provides a systematic framework for exploring the multi-component and multi-target characteristics of BD in HT, underscores the therapeutic potential of BD in HT by targeting genes and pathways involved in immune regulation and oxidative stress. These findings not only enhance our understanding of BD's pharmacological mechanisms but also lay the groundwork for the development of novel therapeutic strategies for HT.

Exploring the active ingredients and potential mechanisms of Pingchan granules in Parkinson's disease treatment through network pharmacology and transcriptomics

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, poses significant challenges to single-target therapeutic strategies due to its complex etiology. This has driven interest in multi-target approaches, particularly those leveraging natural compounds. Pingchan granules (PCG), a traditional Chinese medicine composed of plant- and animal-derived compounds, have shown efficacy in alleviating PD symptoms. Here, we identify 96 PCG-associated anti-PD targets, enriched in neuronal synaptic signaling and G protein-coupled receptor pathways. Through protein-protein interaction network analysis of anti-PD targets and random forest modeling of substantia nigra transcriptomic data from PD patients, SLC6A3 and SRC emerged as central hub targets, with Mendelian randomization further validating SRC as a potential therapeutic target. Molecular docking and single-cell sequencing reveal that dauricine, PCG's principal active compound, binds strongly to SLC6A3 and SRC, modulating glucose metabolism pathways in dopaminergic neurons. These findings illuminate the molecular basis of PCG's therapeutic effects, offer a foundation for future drug development, and underscore the potential of dauricine as a targeted treatment for PD.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Exploring the molecular mechanism of icariin improving spinal cord injury through network pharmacology combined with experimental verification

This study aimed to investigate the potential pharmacological effects of icariin (ICA) in the treatment of spinal cord injury (SCI). Network pharmacology was used to focus on the potential targets and biological processes of ICA in SCI. Molecular docking was used to verify the ability of ICA to bind to its core targets. Finally, valuate the efficacy and potential mechanisms of ICA in treating spinal cord injury through in vitro and in vivo experiments. A total of 37 targets were screened out, and core genes were screened out from the protein‒protein interaction network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these targets are enriched mainly in response to hypoxia, regulation of the cellular response to stress, and the TGF-beta signaling pathway. Molecular docking analysis showed that ICA has good docking ability with core targets. In animal experiments, Basso, Beattie and Bresnahan scores, catwalk gait analysis, hematoxylin and eosin staining, and RT-qPCR showed that ICA can inhibit spinal cord inflammation and effectively improve the behavioral and histological recovery after SCI rats. Western blot and immunofluorescence showed that ICA can reduce astrocyte activation and downregulate the TGF-beta signaling pathway after SCI. In addition, ICA can promote axonal nerve elongation and promotes angiogenesis after spinal cord injury in rats. In vitro experiments revealed that ICA can inhibit TGFβ1-induced activation of the TGF-beta signaling pathway and astrocyte activation. ICA treats SCI through multiple targets and pathways. ICA plays a major role in protecting nerves, promoting angiogenesis, and inhibiting reactive astrocyte activation in the treatment of SCI.

Exploring the bioactive properties and mechanism of Aegle marmelos in the treatment of inflammatory bowel disease through network pharmacology and a molecular docking approach

BACKGROUND

Inflammatory bowel diseases (IBD) are recurrent inflammatory conditions that occur in the gastrointestinal tract, for which current treatment does not have satisfactory results, thus we require new therapies to combat the complex pathogenesis of IBD. Herbal medicines have been used for years to cure IBD. One of the plants from Ayurveda, Aegle marmelos (AM), commonly known as Bael, which belongs to the family Rutaceae, has ethnomedicinal properties in treating IBD due to its various phytochemicals. However, the mechanisms underlying the effect of AM remain to be elucidated.

METHODS

In this study, an in silico approach, molecular docking, and enrichment analysis were implemented to uncover the potential multicomponent synergistic effect and its molecular mechanism in treating IBD. Putative targets of IBD were obtained through OMIM, GeneCards, and DisGeNET databases. Compounds of AM were screened for their targets using a Swiss target prediction database and Super-PRED database. The common targets amongst AM and IBD were analyzed and the network was constructed using Cytoscape (3.10.0). Protein-protein interactions of target genes of the compounds was carried out through a STRING database. Then, the INPUT database was used to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Molecular docking of top 6 compounds with hub targets was carried out using Autodock vina.

RESULTS

In the study, 46 effective compounds and 358 targets of AM were identified and further analyzed, 80 hub targets depending on the degree were considered effective against IBD. Through CytoHubba we identified AKT1, SRC, MAPK3, MAPK1, EGFR, IL6, TNF, HSP90AA1 and CASP3 as the top 10 hub targets that may contribute to the mechanistic role of AM in treating IBD. Aegeline, auraptene, bergapten, imperatorin, marmesin, and nodakenin were the most potent compounds of AM and those that possess a higher binding affinity to PI3K, AKT, and EGFR. PI3-AKT signaling pathway, EGFR tyrosine kinase inhibitor, and MAP Kinase signaling pathway are the major pathways having a correlation with AM.

CONCLUSION

The study unveils the mechanism of AM in alleviating IBD through the EGFR-mediated PI3K/AKT pathway, stating its multi-component, multi-targeted therapeutic efficacy through multiple pathways.

Network Pharmacology Analysis and Biological Validation Systemically Identified the Active Ingredients and Molecular Targets of Kudzu Root on Osteoporosis

As a traditional medicinal food, Kudzu root (KR) has been proven to be an effective medicine for treating osteoporosis (OP). However, its precise targets and underlying integrated pharmacological mechanisms on OP have not yet been systematically investigated. The aim of the present study was to systemically explore the active ingredients, molecular targets, and ingredient-target network of KR against OP by the methods of network pharmacology followed by biological validation in a glucocorticoid-induced bone loss model of zebrafish. Our results identified a total of 15 active compounds with good pharmacokinetic properties in KR and 119 targets related to OP from correspondent databases, forming an ingredient-target network. Additionally, the protein-protein interaction (PPI) network further identified 39 core targets. Enrichment analyses with functional annotation revealed that the TNF signaling pathway and osteoclast differentiation process were significantly enriched by multi-targets including AKT1, P65, MAPK14, JUN, TNF-α, MMP9, IL6, and IL1B, etc., and served as the critical targets for molecular docking, molecular dynamics simulation, and in vivo experiment validation. These critical targets performed effectively in molecular docking and molecular dynamics, with AKT1, MMP9, and TNF-α exhibiting more prominent binding energy with Coumestrol, Genistein, and Genistein 7-glucoside, respectively. Further experimental validation in a zebrafish model indicated that KR could regulate the expressions of critical targets (AKT1, P65, MAPK14, JUN, TNF-α, and MMP9). This study provides a systemic perspective of the relationships between the active ingredients of KR and their multi-targets in OP, thereby constructing a pharmacological network to clarify the mechanisms by which KR ameliorates OP.

XenoMet: A Corpus of Texts to Extract Data on Metabolites of Xenobiotics

Understanding the biotransformation of xenobiotics in the human body is critical for a comprehensive assessment of drug effects since pharmacologically active drug metabolites may exhibit a range of biological effects that often differ from those of the original pharmaceutical agent. Studies of the biotransformation mechanisms of xenobiotics have resulted in numerous publications. Extracting information about the parent compounds (substrates) and their metabolites from the texts allows retrieval of information on their biological activities, molecular mechanisms of action, and toxicity. Manual curation of the names of xenobiotics, their metabolites, and biotransformation reactions in the text is a challenging task due to the large number of publications related to studies of pharmaceutical agents metabolism. Our aim is to create an annotated corpus of texts that can be used for automated extraction of the names of xenobiotics, including pharmaceutical agents that undergo biotransformation and their metabolites. Prior to manual annotation of the corpus, semiautomatic annotation was carried out based on the earlier developed rule-based method for parent compounds and their metabolites extraction. To create XenoMet, we automatically extracted relevant texts from PubMed using a query based on MeSH terms. The names of biotransformation reactions were recognized by using an in-house-developed dictionary. Then, we manually verified the extracted data by correcting errors in the named entity annotation and identified the associations between substrates and metabolites. We tested the applicability of XenoMet for the reconstruction of a metabolic tree and for the automated extraction of the chemical names of substrates, metabolites, and reactions of biotransformation. Classification of the named entities of metabolites, substrates, and biotransformation reactions by a conditional random fields approach using XenoMet as the training set provides an F1-score of 0.79.

Network Pharmacology and Bioinformatics Study of Six Medicinal Food Homologous Plants Against Colorectal Cancer

Integrating network pharmacological analysis and bioinformatic techniques, this study systematically investigated the molecular mechanisms of six medicinal food homologous plants (Astragalus membranaceus, Ganoderma lucidum, Dioscorea opposite, Curcuma longa, Glycyrrhiza uralensis, and Pueraria lobata) against colorectal cancer. Through screening the TCMSP database, 303 active compounds and 453 drug targets were identified. By integrating differential expression gene analysis with WGCNA on the GSE41258 dataset from the GEO database, 49 potential therapeutic targets were identified. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in drug response, fatty acid metabolism, and key cancer-related pathways. Cross-validation using three machine learning algorithms-LASSO regression, SVM-RFE, and Random Forest-pinpointed four critical target genes: CA1, CCND1, CXCL2, and EIF6. Further, CIBERSORT immune infiltration analysis revealed strong associations between these core genes and the tumor immune microenvironment in colorectal cancer patients, notably in modulating M0 macrophage infiltration and mast cell activity. Molecular docking analyses confirmed robust binding interactions between active compounds and core target proteins. This study systematically elucidated the molecular mechanisms of six medicinal food homologous plants against colorectal cancer, providing scientific evidence for their rational clinical application.

Patent research in academic literature. Landscape and trends with a focus on patent analytics

Patent analytics is crucial for understanding innovation dynamics and technological trends. However, a comprehensive overview of this rapidly evolving field is lacking. This study presents a data-driven analysis of patent research, employing citation network analysis to categorize and examine research clusters. Here, we show that patent research is characterized by interconnected themes spanning fundamental patent systems, indicator development, methodological advancements, intellectual property management practices, and diverse applications. We reveal central research areas in patent strategies, technological impact, and patent citation research while identifying emerging focuses on environmental sustainability and corporate innovation. The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.

Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity

BACKGROUND

Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.

METHODS

We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.

RESULTS

We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.

CONCLUSION

Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: a docking, ADMET and PBPK assessment study

The oral pathogen Fusobacterium nucleatum has recently been associated with an elevated risk of colorectal cancer (CRC), endometrial metastasis, chemoresistance, inflammation, metastasis, and DNA damage, along with several other diseases. This study aimed to explore the disruption of protein machinery of F. nucleatum via inhibition of elongation factor thermo unstable (Ef-Tu) protein, through natural products. No study on Ef-Tu inhibition by natural products or in Fusobacterium spp. exists till todate. Ef-Tu is an abundant specialized drug target in bacteria that varies from human Ef-Tu. Elfamycins target Ef-Tu and hence, Enacyloxin IIa was used to generate pharmacophore for virtual screening of three natural product libraries, Natural Product Activity and Species Source (NPASS) (n = 30000 molecules), Tibetan medicinal plant database (n = 54 molecules) and African medicinal plant database (n > 6000 molecules). Peptaibol Septocylindrin B (NPC141050), Hirtusneanoside, and ZINC95486259 were prioritized from these libraries as potential therapeutic candidates. ADMET profiling was done for safety assessment, physiological-based pharmacokinetic modeling in human and mouse for getting insight into drug interaction with body tissues and molecular dynamics was used to assess stability of the best hit NPC141050 (Septocylindrin B). Based on the promising results, we propose further in vitro, in vivo and pharmacokinetic testing on the lead Septocylindrin B, for possible translation into therapeutic interventions.

Reveal the potent antidepressant effects of Zhi-Zi-Hou-Pu Decoction based on integrated network pharmacology and DDI analysis by deep learning

Background and objective

The multi-targets and multi-components of Traditional Chinese medicine (TCM) coincide with the complex pathogenesis of depression. Zhi-Zi-Hou-Pu Decoction (ZZHPD) has been approved in clinical medication with good antidepression effects for centuries, while the mechanisms under the iceberg haven't been addressed systematically. This study explored its inner active ingredients - potent pharmacological mechanism - DDI to explore more comprehensively and deeply understanding of the complicated TCM in treatment.

Methods

This research utilized network pharmacology combined with molecular docking to identify pharmacological targets and molecular interactions between ZZHPD and depression. Verification of major active compounds was conducted through UPLC-Q-TOF-MS/MS and assays on LPS-induced neuroblastoma cells. Additionally, the DDIMDL model, a deep learning-based approach, was used to predict DDIs, focusing on serum concentration, metabolism, effectiveness, and adverse reactions.

Results

The antidepressant mechanisms of ZZHPD involve the serotonergic synapse, neuroactive ligand-receptor interaction, and dopaminergic synapse signaling pathways. Eighteen active compounds were identified, with honokiol and eriocitrin significantly modulating neuronal inflammation and promoting differentiation of neuroimmune cells through genes like COMT, PI3KCA, PTPN11, and MAPK1. DDI predictions indicated that eriocitrin's serum concentration increases when combined with hesperidin, while hesperetin's metabolism decreases with certain flavonoids. These findings provide crucial insights into the nervous system's effectiveness and potential cardiovascular or nervous system adverse reactions from core compound combinations.

Conclusions

This study provides insights into the TCM interpretation, drug compatibility or combined medication for further clinical application or potential drug pairs with a cost-effective method of integrated network pharmacology and deep learning.

Network-based hub biomarker discovery for glaucoma

OBJECTIVE

Glaucoma is an optic neuropathy and the leading cause of irreversible blindness worldwide. However, the early detection of glaucoma remains challenging, as chronic forms of glaucoma remain largely asymptomatic until considerable irreversible visual field deficits have ensued. Thus, biomarkers that facilitate early diagnosis and treatment for glaucoma patients with a high risk of progression are pressing.

METHODS AND ANALYSIS

Human disease-biomarker interactions network and human disease-target-drug interactions network were first constructed based on multiomics data. The greedy search algorithm was used to search for the hub biomarkers and drug targets for glaucoma. Genome-wide association studies and epidemiological data from the UK Biobank were used to verify our results. Biological network and functional analysis was conducted to find common network features and pathways.

RESULTS

We identified 10 hub biomarkers/drug targets for the diagnosis, treatment and prognosis for glaucoma. These results were verified by text mining and genomic/epidemiology data. We also predicted the new application of BMP1 and MMP9 to diagnose glaucoma and confirm the theory of hub biomarkers with multiple clinical applications. Further, relevant pivotal pathways for these hub biomolecules were discovered, which may serve as foundations for future biomarker and drug target prediction for glaucoma.

CONCLUSION

We have used a network-based approach to identify hub diagnostic and therapeutic biomarkers for glaucoma and detected relationships between glaucoma and associated diseases. Several hub biomarkers were identified and verified, which may play more important roles in the diagnosis and treatment of glaucoma.

Human brain proteome-wide association study provides insights into the genetic components of protein abundance in obesity

BACKGROUNDS

Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances.

METHODS

We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level.

RESULTS

By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes (N ranged from 325,153 to 806,834), we identified 51 genes whose cis-regulated brain protein abundance was associated with obesity. These 51 genes were enriched in 11 metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 51 genes had high drug repurposing value. Ten of the 51 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes.

CONCLUSIONS

Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.

Network pharmacology and experimental study of Angelica sinensis and Astragalus membranaceus capsules in treating heart failure

Objective

This study explores the mechanism of AAC in intervening heart failure (HF) using network pharmacology, molecular docking, and in vitro experimental validation.

Methods

The "active component-target" network and the "drug-disease target" protein interaction network were constructed using Cytoscape 3.9.0 and STRING Database. GO and KEGG enrichment analysis was performed using DAVID database. Then, the molecular docking of major compounds and target proteins was carried out using Autodock 1.5.7, and visualized with PyMOL 2.4.0 software. Finally, in vitro experimental validation was performed to explore the potential targets of AAC in treating HF.

Results

The study revealed significant targets implicated in a variety of GO bioprocess programs and KEGG signaling networks. The primary chemicals to have strong binding ability with target proteins in molecular docking, with quercetin having the best binding energy with MAPK at -6.72 Kcal/Mol.Validation of cellular experiments showed that AAC might reduce the apoptosis that doxorubicin causes in AC16 cells by controlling the levels of PIK3CA, AKT1, and MAPK1.

Conclusion

This study preliminarily reveals that AAC can treat HF through multiple components and multiple targets by using network pharmacology, molecular docking, and experimental validation.

Mechanistic exploration of bioactive constituents in Gnetum gnemon for GPCR-related cancer treatment through network pharmacology and molecular docking

G Protein-Coupled Receptors (GPCRs) are integral membrane proteins that have gained considerable attention as drug targets, particularly in cancer treatment. In this study, we explored the capacity of bioactive compounds derived from Gnetum gnemon (GG) for the development of of pharmaceuticals targeting GPCRs within the context of cancer therapy. Integrated approach combined network pharmacology and molecular docking to identify and validate the underlying pharmacological mechanisms. We retrieved targets for GG-derived compounds and GPCRs-related cancer from databases. Subsequently, we established a protein-protein interaction (PPI) network by mapping the shared targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were employed to predict the mechanism of action of these targets. Molecular docking was conducted to validate our findings. We identified a total of 265 targets associated with GG-derived bioactive compounds for the treatment of GPCRs-related cancer. Functional enrichment analysis revealed the promising therapeutic effects of these targets on GPCRs-related cancer pathways. The PPI network analysis identified hub targets, including MAPK3, SRC, EGFR, STAT3, ESR1, MTOR, CCND1, and PPARG, which demonstrate as treatment targets for GPCRs-related cancer using GG-derived compounds. Additionally, molecular docking experiments demonstrated the strong binding affinity of gnetin A, gnetin C, (-)-viniferin, and resveratrol dimer, thus inhibiting MAPK3, SRC, EGFR, and MTOR. Survival analysis established the clinical prognostic relevance of identified hub genes in cancer. This study presents a novel approach for comprehending the therapeutic mechanisms of GG-derived active compounds and thereby paving the way for their prospective clinical applications in the field of cancer treatment.

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and preprocessing of data on ingredients and targets, we employ an innovative autoencoder framework to fuse multiple drug (target) topological similarity matrices. Additionally, we employ denoising blocks, sparse blocks, and stacked blocks to extract crucial features from the similarity matrix, reducing noise and enhancing accuracy across diverse datasets. Our results indicate that the CWI-DTI model shows improved performance compared to several existing state-of-the-art methods on the datasets tested in both Western and Chinese medicine databases. The findings of this study hold immense promise for advancing DTI prediction in Chinese and Western medicine, thus fostering more efficient drug discovery and repurposing endeavors. Our model is available at https://github.com/WANG-BIN-LAB/CWIDTI .

Pharmacogenomics and Big Data in medical oncology: developments and challenges

Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology.

Systems Biology Methods via Genome-Wide RNA Sequences to Investigate Pathogenic Mechanisms for Identifying Biomarkers and Constructing a DNN-Based Drug-Target Interaction Model to Predict Potential Molecular Drugs for Treating Atopic Dermatitis

This study aimed to construct genome-wide genetic and epigenetic networks (GWGENs) of atopic dermatitis (AD) and healthy controls through systems biology methods based on genome-wide microarray data. Subsequently, the core GWGENs of AD and healthy controls were extracted from their real GWGENs by the principal network projection (PNP) method for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Then, we identified the abnormal signaling pathways by comparing the core signaling pathways of AD and healthy controls to investigate the pathogenesis of AD. Then, IL-1β, GATA3, Akt, and NF-κB were selected as biomarkers for their important roles in the abnormal regulation of downstream genes, leading to cellular dysfunctions in AD patients. Next, a deep neural network (DNN)-based drug-target interaction (DTI) model was pre-trained on DTI databases to predict molecular drugs that interact with these biomarkers. Finally, we screened the candidate molecular drugs based on drug toxicity, sensitivity, and regulatory ability as drug design specifications to select potential molecular drugs for these biomarkers to treat AD, including metformin, allantoin, and U-0126, which have shown potential for therapeutic treatment by regulating abnormal immune responses and restoring the pathogenic signaling pathways of AD.

Prediction of molecular targets for antidepressant potential of hydroalcoholic extract of Tamarindus indica using network pharmacology approach and evaluating its efficacy in Chronic Unpredictable Mild Stress model in mice

The prevalence of psychological disorders has surged since the 1990s, posing a significant global health burden with depressed individuals averaging six lost hours per week and contributing to over 20% of all missed workdays. Current antidepressants, while effective for some, have limited efficacy, dietary restrictions, and adverse effects, including liver damage and hypertension. Natural remedies offer promising therapeutic potential with minimal side effects. Tamarindus indica (TI) is a plant that grows in the shape of a tree. Network pharmacology of TI revealed the key targets MAPK, D1-6, 5HT, DAT, MAO, COMT, PKA, PKC, AKT, and VMAT, which are linked to prominent key pathways such as dopaminergic and serotonergic. The cell viability assays on SH-Sy5y cells indicated a favourable safety profile with an IC50 of 573.99 µg/ml and further, the in vivo efficacy was observed through Chronic Unpredictable Mild Stress (CUMS) model in mice. The hydroalcoholic extract of TI demonstrated antidepressant effects, significantly reducing immobility time in the Tail Suspension Test (TST) and Forced Swim Test (FST). Additionally, locomotor activity, assessed via the Open Field Test (OFT), was significantly increased in the treatment group compared to CUMS mice. Biochemical analyses revealed elevated Brain Derived Neurotropic Factor (BDNF), decreased cortisol levels, and reduced catechol-O-methyltransferase (COMT) concentration in TI-treated (50 mg/kg) groups. These findings underscore the potential of TI as a natural antidepressant, offering a promising avenue for further therapeutic development in depression management. The current study did not evaluate the level of neurotransmitters in the brain, which will be evaluated in future studies.

Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology

Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

The role of circular RNA during the urological cancer metastasis: exploring regulatory mechanisms and potential therapeutic targets

Metastasis is a major contributor to treatment failure and death in urological cancers, representing an important biomedical challenge at present. Metastases form as a result of cancer cells leaving the primary site, entering the vasculature and lymphatic vessels, and colonizing clones elsewhere in the body. However, the specific regulatory mechanisms of action underlying the metastatic process of urological cancers remain incompletely elucidated. With the deepening of research, circular RNAs (circRNAs) have been found to not only play a significant role in tumor progression and prognosis but also show aberrant expression in various tumor metastases, consequently impacting tumor metastasis through multiple pathways. Therefore, circRNAs are emerging as potential tumor markers and treatment targets. This review summarizes the research progress on elucidating how circRNAs regulate the urological cancer invasion-metastasis cascade response and related processes, as well as their role in immune microenvironment remodeling and circRNA vaccines. This body of work highlights circRNA regulation as an emerging therapeutic target for urological cancers, which should motivate further specific research in this regard.

Drug to genome to drug: a computational large-scale chemogenomics screening for novel drug candidates against sporotrichosis

Sporotrichosis is recognized as the predominant subcutaneous mycosis in South America, attributed to pathogenic species within the Sporothrix genus. Notably, in Brazil, Sporothrix brasiliensis emerges as the principal species, exhibiting significant sapronotic, zoonotic and enzootic epidemic potential. Consequently, the discovery of novel therapeutic agents for the treatment of sporotrichosis is imperative. The present study is dedicated to the repositioning of pharmaceuticals for sporotrichosis therapy. To achieve this goal, we designed a pipeline with the following steps: (a) compilation and preparation of Sporothrix genome data; (b) identification of orthologous proteins among the species; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Sporothrix essential targets using validated genes from Saccharomyces cerevisiae; (e) molecular modeling studies; and (f) experimental validation of selected candidates. Based on this approach, we were able to prioritize eight drugs for in vitro experimental validation. Among the evaluated compounds, everolimus and bifonazole demonstrated minimum inhibitory concentration (MIC) values of 0.5 µg/mL and 4.0 µg/mL, respectively. Subsequently, molecular docking studies suggest that bifonazole and everolimus may target specific proteins within S. brasiliensis- namely, sterol 14-α-demethylase and serine/threonine-protein kinase TOR, respectively. These findings shed light on the potential binding affinities and binding modes of bifonazole and everolimus with their probable targets, providing a preliminary understanding of the antifungal mechanism of action of these compounds. In conclusion, our research advances the understanding of the therapeutic potential of bifonazole and everolimus, supporting their further investigation as antifungal agents for sporotrichosis in prospective hit-to-lead and preclinical investigations.

Kurarinone, a flavonoid from Radix Sophorae Flavescentis, inhibits RANKL-induced osteoclastogenesis in mouse bone marrow-derived monocyte/macrophages

Inflammation-induced osteoclast proliferation is a crucial contributor to impaired bone metabolism. Kurarinone (KR), a flavonoid extracted from the Radix Sophorae Flavescentis, exhibits notable anti-inflammatory properties. Nevertheless, the precise influence of KR on osteoclast formation remains unclear. This study's objective was to assess the impact of KR on osteoclast activity in vitro and unravel its underlying mechanism. Initially, a target network for KR-osteoclastogenesis-osteoporosis was constructed using network pharmacology. Subsequently, the intersecting targets were identified through the Venny platform and a PPI network was created using Cytoscape 3.9.1. Key targets within the network were identified employing topological algorithms. GO enrichment and KEGG pathway analysis were then performed on these targets to explore their specific functions and pathways. Additionally, molecular docking of potential core targets of KR was conducted, and the results were validated through cell experiments. A total of 83 target genes overlapped between KR and osteoclastogenesis-osteoporosis targets. Enrichment analysis revealed their role in inflammatory response, protein tyrosine kinase activity, osteoclast differentiation, and MAPK and NF-κB signaling pathways. PPI analysis and molecular docking demonstrate that key targets MAPK14 and MAPK8 exhibit more stable binding with KR compared to other proteins. In vitro experiments demonstrate that KR effectively inhibits osteoclast differentiation and bone resorption without cellular toxicity. It suppresses key osteoclast genes (NFATc1, c-Fos, TRAP, MMP9, Ctsk, Atp6v2), hinders IκB-α degradation, and inhibits ERK and JNK phosphorylation, while not affecting p38 phosphorylation. The results indicate that KR may inhibit osteoclast maturation and bone resorption by blocking NF-κB and MAPK signaling pathways, suggesting its potential as a natural therapeutic agent for osteoporosis.

The trajectory of vesicular proteomic signatures from HBV-HCC by chitosan-magnetic bead-based separation and DIA-proteomic analysis

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer often associated with chronic hepatitis B virus infection (CHB) and liver cirrhosis (LC), underscoring the critical need for biomarker discovery to improve patient outcomes. Emerging as a promising avenue for biomarker development, proteomic technology leveraging liquid biopsy from small extracellular vesicles (sEV) offers new insights. Here, we evaluated various methods for sEV isolation and identified polysaccharide chitosan (CS) as an optimal approach. Subsequently, we employed optimized CS-based magnetic beads (Mag-CS) for sEV separation from serum samples of healthy controls, CHB, LC, and HBV-HCC patients. Leveraging data-independent acquisition mass spectrometry coupled with machine learning, we uncovered potential vesicular protein biomarker signatures (KNG1, F11, KLKB1, CAPNS1, CDH1, CPN2, NME2) capable of distinguishing HBV-HCC from CHB, LC, and non-HCC conditions. Collectively, our findings highlight the utility of Mag-CS-based sEV isolation for identifying early detection biomarkers in HBV-HCC.

Shensong yangxin, a multi-functional traditional Chinese medicine for arrhythmia: A review of components, pharmacological mechanisms, and clinical applications

As a common cardiovascular disease (CVD), Arrhythmia refers to any abnormality in the origin, frequency, rhythm, conduction velocity, timing, pathway, sequence, or other aspect of cardiac impulses, and it is one of the common cardiovascular diseases in clinical practice. At present, various ion channel blockers are used for treatment of arrhythmia that include Na+ ion channel blockers, K+ ion channel blockers and Ca2+ ion channel blockers. While these drugs offer benefits, they have led to a gradual increase in drug-related adverse reactions across various systems. As a result, the quest for safe and effective antiarrhythmic drugs is pressing. Recent years have seen some advancements in the treatment of ventricular arrhythmias using traditional Chinese medicine(TCM). The theory of Luobing in TCM has proposed a new drug intervention strategy of "fast and slow treatment, integrated regulation" leading to a shift in mindset from "antiarrhythmic" to "rhythm-regulating". Guided by this theory, the development of Shen Song Yang Xin Capsules (SSYX) has involved various Chinese medicinal ingredients that comprehensively regulate the myocardial electrophysiological mechanism, exerting antiarrhythmic effects on multiple ion channels and non-ion channels. Similarly, in clinical studies, evidence-based research has confirmed that SSYX combined with conventional antiarrhythmic drugs can more effectively reduce the occurrence of arrhythmias. Therefore, this article provides a comprehensive review of the composition and mechanisms of action, pharmacological components, network pharmacology analysis, and clinical applications of SSYX guided by the theory of Luobing, aiming to offer valuable insights for improved clinical management of arrhythmias and related research.

Curcumin for gastric cancer: Mechanism prediction via network pharmacology, docking, and in vitro experiments

BACKGROUND

Curcumin originates from the natural herb turmeric, and its antitumor effects have been known about for a long time. However, the mechanism by which curcumin affects gastric cancer (GC) has not been elucidated.

AIM

To elucidate the potential mechanisms of curcumin in the treatment of GC.

METHODS

Network pharmacological approaches were used to perform network analysis of Curcumin. We first analyzed Lipinski's Rule of Five for the use of Curcumin. Curcumin latent targets were predicted using the PharmMapper, SwissTargetPrediction and DrugBank network databases. GC disease targets were mined through the GeneCard, OMIM, DrugBank and TTD network databases. Then, GO enrichment, KEGG enrichment, protein-protein interaction (PPI), and overall survival analyses were performed. The results were further verified through molecular docking, differential expression analysis and cell experiments.

RESULTS

We identified a total of 48 curcumin-related genes with 31 overlapping GC-related targets. The intersection targets between curcumin and GC have been enriched in 81 GO biological processes and 22 significant pathways. Following PPI analysis, 6 hub targets were identified, namely, estrogen receptor 1 (ESR1), epidermal growth factor receptor (EGFR), cytochrome P450 family 3 subfamily A member 4 (CYP3A4), mitogen-activated protein kinase 14 (MAPK14), cytochrome P450 family 1 subfamily A member 2 (CYP1A2), and cytochrome p450 family 2 subfamily B member 6 (CYP2B6). These factors are correlated with decreased survival rates among patients diagnosed with GC. Molecular docking analysis further substantiated the strong binding interactions between Curcumin and the hub target genes. The experimental findings demonstrated that curcumin not only effectively inhibits the growth of BGC-823 cells but also suppresses their proliferation. mRNA levels of hub targets CYP3A4, MAPK14, CYP1A2, and CYP2B6 in BGC-823 cells were significantly increased in each dose group.

CONCLUSION

Curcumin can play an anti-GC role through a variety of targets, pathways and biological processes.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways

Rationale

Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation.

Methods

In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines.

Results

Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R.

Conclusion

These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

Screening study on significant Chinese herb for anti-idiopathic pulmonary fibrosis by combining clinical experience prescriptions and molecular dynamics simulation technologies

Various techniques such as data mining, network pharmacology, molecular docking and molecular dynamics simulation were used in this study to screen and validate effective herbal medicines for the treatment of idiopathic pulmonary fibrosis (IPF) and to reveal their mechanisms of action at the molecular level. The use of this approach will provide new tools and ideas for future drug screening, especially for the application of herbal medicines in the treatment of complex diseases. Among them, the five identified core targets, including IL6, TP53, AKT1, VEGFA, and TNF, as well as a series of major active compounds, will be important references for future anti-IPF drug development. This information will accelerate the discovery and development of relevant drugs. Meanwhile, this study further confirmed the potential value of four Chinese herbal medicines, including Gancao, Danshen, Huangqin, and Sanqi, in the treatment of IPF. This will promote more clinical trials and practices to confirm and optimise the application of these herbs. Finally, this study is an important theoretical guide to enhance the advantages of Chinese herbal medicines in the prevention and treatment of major and difficult diseases, as well as to understand and utilise the potential efficacy of Chinese herbal medicines. This will further promote the scientific research and clinical application of herbal medicines and provide more possibilities for future disease treatmentCommunicated by Ramaswamy H. Sarma.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Tongxinluo capsule as a multi-functional traditional Chinese medicine in treating cardiovascular disease: A review of components, pharmacological mechanisms, and clinical applications

Cardiovascular diseases (CVDs) are one of the most significant diseases that pose a threat to human health. The innovative traditional Chinese medicine Tongxinluo Capsule, developed under the guidance of the theory of traditional Chinese medicine, has good clinical efficacy in various cardiovascular diseases, this medicine has effects such as blood protection, vascular protection, myocardial protection, stabilizing vulnerable plaques, and vasodilation. However, CVDs are a multifactorial disease, and their underlying mechanisms are not fully understood. Therefore, exploring the mechanism of action and clinical application of Tongxinluo Capsule in the treatment of various cardiovascular diseases is beneficial for exerting its therapeutic effect from multiple components, targets, and pathways. At the same time, it provides broader treatment ideas for other difficult to treat diseases in the cardiovascular event chain, and has significant theoretical and clinical significance for improving the treatment of cardiovascular diseases with traditional Chinese medicine.

Exploring the therapeutic effect of core components in Xuanshen Yishen mixture on hypertension through network pharmacology

OBJECTIVE

This study aims to elucidate the mechanism of action and impact of the "Xuanshen Yishen Mixture" (XYM) on hypertension.

METHODS

Active components were identified and potential targets were predicted using the Traditional Chinese Medicine Systems Pharmacology database. Hypertension-related targets were collected from GeneCards, DRUGBANK, OMIM, TTD, and PharmaGKB databases. Intersections of disease and drug targets were visualized using the R package "VennDiagram". A protein-protein interaction network was established via the STRING database. GO function enrichment and KEGG pathway analyses were conducted using "clusterProfiler", while "Cytoscape" was used to construct a "drug-component-target" network. Additionally, data from 60 patients with essential hypertension from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine were retrospectively analyzed. Patients were divided into a control group (n = 30) and an XYM group (n = 30) based on treatment regimen.

RESULTS

Sixty active ingredients and 98 related targets were identified from Uncaria, Radix Scrophulariae, and Epimedium in hypertension treatment. Key active components such as quercetin, kaempferol, yohimbine, and beta-sitosterol were pinpointed, with PTGS2, PTGS1, AR, DPP4, and F2 as crucial targets. KEGG pathway analysis highlighted significant pathways including IL-17 signaling, TNF signaling, Relaxin signaling, and HIF-1 signaling. Clinical data indicated that XYM's therapeutic effects are comparable to those of valsartan, which significantly reduced diastolic and systolic blood pressure and demonstrated good biosafety.

CONCLUSIONS

Uncaria, Radix Scrophulariae, and Epimedium effectively mitigate hypertension through multiple components, targets, and pathways. Additionally, DPP4, IL-17, and TNF-α are identified as potential therapeutic targets for traditional Chinese medicine preparations in hypertension treatment. This study provides a foundation for further investigation into XYM's mechanisms in hypertension management.

Luobitong Potentiates MTX's Anti-Rheumatoid Arthritis Activity via Targeting Multiple Inflammatory Pathways

Background

The LuoBiTong (LBT) capsule, a novel traditional Chinese medicine formulation, is currently in Phase III clinical trials. Preliminary preclinical and Phase II clinical studies suggest its efficacy and safety in treating rheumatoid arthritis (RA). However, the underlying mechanisms of its action remain to be elucidated.This research aims to explore the effects and mechanisms of LBT in conjunction with a maintenance dose of methotrexate (M-MTX) on RA.

Methods

A Collagen-Induced Arthritis (CIA) mouse model was used to evaluate the anti-RA effects of LBT combined with M-MTX. Assessments included foot swelling, arthritis scoring, serum inflammatory factor analysis, and histopathological examination of the foot. These effects were compared with those of high-dose MTX (H-MTX). Network pharmacology was employed to construct a compound-target network for RA, based on drug composition, to predict its potential mechanism of action. Flow cytometry, Western Blot, and immunohistochemical analyses in animal models identified multiple inflammatory pathways targeted by LBT to augment the anti-RA effects of MTX.

Results

The study revealed that LBT combined with M-MTX significantly alleviated CIA-induced arthritis without adverse effects. The combination of LBT and M-MTX showed similar or superior efficacy in regulating macrophage polarization, NF-κB, MAPK signaling pathways, and in the suppression of TH-17 expression in proinflammatory cells. These findings suggest that LBT may exert a multi-pathway therapeutic effect in RA treatment. The predicted pharmacological targets and mechanisms align well with this hypothesis.

Conclusion

LBT, when combined with MTX, enhances the anti-RA effect by targeting multiple inflammatory pathways, demonstrating significant therapeutic potential.

Experimental verification about treatment of Bu-Shen-Yi-Jing-Fang in Alzheimer's disease by the analysis of the feasible signaling pathway of network pharmacology

CONTEXT

Bu-shen-yi-jing-fang (BSYJF) has been reported to reduce amyloid-β (Aβ)1-42 deposition in the brain of APP/PS1 mice and ameliorate cognitive function. However, its neuroprotective mechanism remains unclear.

OBJECTIVE

This study aims to investigate whether BSYJF exerts a protective effect on Aβ1-42-induced oxidative stress injury and explore its possible mechanism.

MATERIALS AND METHODS

The platform databases TCMSP, Swiss, TTD, DrugBank, and GeneCards were used to mine the targets of Alzheimer's disease (AD) and BSYJF. The platform databases STRING and Metascape were used to build the interaction network of the target protein, and Cytoscape software was used to analyze this network and screen out the key pathways. Aβ1-42-treated SKNMC cells were established to verify the mechanism of BSYJF and the key proteins. The downstream proteins and antioxidants as well as apoptosis and ferroptosis of the PI3K/AKT/Nrf2 signaling pathway were validated using an in vitro SKNMC cell model experiment. The expression levels of related proteins were detected using Western blotting. Flow cytometry and immunofluorescence staining were used to analyze apoptosis and ferroptosis.

RESULTS

Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis considered the key signal pathways, mainly involving the PI3K/AKT signaling pathway. Experimental validation demonstrated that BSYJF treatment markedly increased the activity of the PI3K/AKT pathway, which could exert anti-AD effects.

CONCLUSIONS

Our data provided compelling evidence that the protective effects of BSYJF might be associated with their regulation of the PI3K/AKT/Nrf2 signaling pathway. These studies offered a potential therapy for natural herbal medicine treatment of AD.

Computer-aided discovery of novel aryl hydrocarbon receptor ligands to regulate CYP1A1 expression in inflammatory macrophages

The environmental factor aryl hydrocarbon receptor (AhR), a key protein connecting the external environmental signals (e.g., environmental endocrine disruptor TCDD) to internal cellular processes, is involved in the activation of peripheral macrophages and inflammatory response in human body. Thus, there is widespread interest in finding compounds to anti-inflammatory response in macrophages by targeting human AhR. Here, ensemble docking based-virtual screening was first used to screen a library (~200,000 compounds) against human AhR ligand binding domain (LBD) and 25 compounds were identified as potential inhibitors. Then, 9 out of the 25 ligands were found to down-regulate the mRNA expression of CYP1A1 (a downstream gene of AhR signaling) in AhR overexpressing macrophages. The most potent compound AE-411/41415610 was selected for further study and found to reduce both mRNA and protein expressions level of CYP1A1 in mouse peritoneal macrophage. Moreover, protein chip signal pathway analysis indicated that AE-411/41415610 play a role in regulating JAK-STAT and AKT-mTOR pathways. In sum, the discovered hits with novel scaffolds provided a starting point for future design of more effective AhR-targeted lead compounds to regulate CYP1A1 expression of inflammatory peritoneal macrophages.

Investigation of the active ingredients and mechanism of Shuangling extract in dextran sulfate sodium salt induced ulcerative colitis mice based on network pharmacology and experimental verification

OBJECTIVE

To explore the pharmacodynamic effects and potential mechanisms of Shuangling extract against ulcerative colitis (UC).

METHODS

The bioinformatics method was used to predict the active ingredients and action targets of Shuangling extract against UC in mice. And the biological experiments such as serum biochemical indexes and histopathological staining were used to verify the pharmacological effect and mechanism of Shuangling extract against UC in mice.

RESULTS

The Shuangling extract reduced the levels of seruminterleukin-1β (IL-1β), tumor necrosis factor-α (TNF-N), interleukin-6 (IL-6) and other inflammatory factors in UC mice and inhibited the inflammatory response. AKT Serine/threonine Kinase 1 and IL-6 may be the main targets of the anti-UC action of Shuangling extract, and the TNF signaling pathway, Forkhead box O signaling pathway and T-cell receptor signaling pathway may be the main signaling pathways.

CONCLUSION

The Shuangling extract could inhibit the inflammatory response induced by UC and regulate intestinal immune function through multiple targets and multiple channels, which provided a new option and theoretical basis for anti-UC.

The landscape of small-molecule prodrugs

Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs encompass a wide range of different chemical structures, therapeutic indications and properties. Here we provide the first holistic analysis of the current landscape of approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug development. We highlight rationales that underlie prodrug design, their indications, mechanisms of API release, the chemistry of promoieties added to APIs to form prodrugs and the market impact of prodrugs. On the basis of this analysis, we discuss strengths and limitations of current prodrug approaches and suggest areas for future development.

The Efficacy & Molecular Mechanisms of a Terpenoid Compound Ganoderic Acid C1 on Corticosteroid-Resistant Neutrophilic Airway Inflammation: In vivo and in vitro Validation

Introduction

Neutrophil predominant airway inflammation is associated with severe and steroid-resistant asthma clusters. Previously, we reported efficacy of ASHMI, a three-herb TCM asthma formula in a steroid-resistant neutrophil-dominant murine asthma model and further identified Ganoderic Acid C1 (GAC1) as a key ASHMI active compound in vitro. The objective of this study is to investigate GAC1 effect on neutrophil-dominant, steroid-resistant asthma in a murine model.

Methods

In this study, Balb/c mice were systematically sensitized with ragweed (RW) and alum and intranasally challenged with ragweed. Unsensitized/PBS challenged mice served as normal controls. Post sensitization, mice were given 4 weeks of oral treatment with GAC1 or acute dexamethasone (Dex) treatment at 48 hours prior to challenge. Pulmonary cytokines were measured by ELISA, and lung sections were processed for histology by H&E staining. Furthermore, GAC1 effect on MUC5AC expression and on reactive oxygen species (ROS) production in human lung epithelial cell line (NCI-H292) was determined by qRT-PCR and ROS assay kit, respectively. Computational analysis was applied to select potential targets of GAC1 in steroid-resistant neutrophil-dominant asthma. Molecular docking was performed to predict binding modes between GAC1 and Dex with TNF-α.

Results

The result of the study showed that chronic GAC1 treatment, significantly reduced pulmonary inflammation (P < 0.01-0.001 vs Sham) and airway neutrophilia (P < 0.01 vs Sham), inhibited TNF-α, IL-4 and IL-5 levels (P < 0.05-0.001 vs Sham). Acute Dex treatment reduced eosinophilic inflammation and IL-4, IL-5 levels, but had no effect on neutrophilia and TNF-α production. GAC1 treated H292 cells showed decreased MUC5AC gene expression and production of ROS (P < 0.001 vs stimulated/untreated cells). Molecular docking results showed binding energy of complex GAC1-TNF was -10.8 kcal/mol.

Discussion

GAC1 may be a promising anti-asthma botanical drug for treatment of steroid-resistant asthma.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Chinese medicine in the treatment of non-alcoholic fatty liver disease based on network pharmacology: a review

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by abnormalities in hepatic fat deposition, the incidence of which has been increasing year by year in recent years. It has become the largest chronic liver disease globally and one of the important causes of cirrhosis and even primary liver cancer formation. The pathogenesis of NAFLD has not yet been fully clarified. Modern medicine lacks targeted clinical treatment protocols for NAFLD, and most drugs lack efficacy and have high side effects. In contrast, Traditional Chinese Medicine (TCM) has significant advantages in the treatment and prevention of NAFLD, which have been widely recognized by scholars around the world. In recent years, through the establishment of a "medicine-disease-target-pathway" network relationship, network pharmacology can explore the molecular basis of the role of medicines in disease prevention and treatment from various perspectives, predicting the pharmacological mechanism of the corresponding medicines. This approach is compatible with the holistic view and treatment based on pattern differentiation of TCM and has been widely used in TCM research. In this paper, by searching relevant databases such as PubMed, Web of Science, and Embase, we reviewed and analyzed the relevant signaling pathways and specific mechanisms of action of single Chinese medicine, Chinese medicine combinations, and Chinese patent medicine for the treatment of NAFLD in recent years. These related studies fully demonstrated the therapeutic characteristics of TCM with multi-components, multi-targets, and multi-pathways, which provided strong support for the exact efficacy of TCM exerted in the clinic. In conclusion, we believe that network pharmacology is more in line with the TCM mindset of treating diseases, but with some limitations. In the future, we should eliminate the potential risks of false positives and false negatives, clarify the interconnectivity between components, targets, and diseases, and conduct deeper clinical or experimental studies.

Antioxidant activity, metabolic profiling, in-silico molecular docking and ADMET analysis of nano selenium treated sesame seed bioactive compounds as potential novel drug targets against cardiovascular disease related receptors

Sesame (Sesamum indicum) is abundant in a diverse range of lignans, including sesamin, and γ-tocopherol, constituting a cluster of bioactive phenolic compound used for food and medicinal purposes. Cardiovascular diseases remain a leading global health challenge, demanding vigilant prevention and innovative treatments. This study was carried out to evaluate the effect of plant mediated SeNPs on sesame metabolic profile and to screen and check the effect bioactive compounds against CVD via molecular drug docking technique. Three sesame germplasms TS-5, TH-6 and Till-18 were treated with varying concentrations (10, 20, 30, 40 and 50 ppm) of plant-mediated selenium nanoparticles (SeNPs). There were three groups of treatments group-1 got only seed pretreatments of SeNPs, Group-2 with only foliar applications of SeNPs and Group-3 with both seed pretreatments and foliar applications of SeNPs. It was found that plants treated with 40 ppm of SeNPS in group 3 exhibited the highest total phenolic and flavonoid content. Total phenolic content at T4 was highest for TS-5 (134%), TH-6 (132%), and Till-18 (112%). LCMS analysis revealed a total of 276 metabolites, with phenolics, flavonoids, and free fatty acids being most abundant. KEGG analysis indicated enrichment in free fatty acid and phenylalanine tryptophan pathways. ADMET analysis and virtual screening resulted in total of five metabolic compounds as a potential ligand against Hemoglobin beta subunit. Lowest binding energy was achieved by Delta-Tocopherol (-6.98) followed by Lactoflavin (-6.20) and Sesamin (-5.00). Lipinski rule of five revealed that all the compounds completely safe to be used as drug against CVD and specifically for HBB. It was concluded that bioactive compounds from sesame could be an alternative source of drug for CVD related problems and especially for HBB.

Mechanism of Bazi Bushen capsule in delaying the senescence of mesenchymal stem cells based on network pharmacology and experimental validation

Ageing is becoming an increasingly serious problem; therefore, there is an urgent need to find safe and effective anti-ageing drugs.

Aims

To investigate the effects of Bazi Bushen capsule (BZBS) on the senescence of mesenchymal stem cells (MSCs) and explore its mechanism of action.

Methods

Network pharmacology was used to predict the targets of BZBS in delaying senescence in MSCs. For in vitro studies, MSCs were treated with D-gal, BZBS, and NMN, and cell viability, cell senescence, stemness-related genes, and cell cycle were studied using cell counting kit-8 (CCK-8) assay, SA-β-galactosidase (SA-β-gal) staining, Quantitative Real-Time PCR (qPCR) and flow cytometry (FCM), respectively. Alkaline phosphatase (ALP), alizarin red, and oil red staining were used to determine the osteogenic and lipid differentiation abilities of MSCs. Finally, the expression of senescence-related genes and cyclin-related factors was detected by qPCR and western blotting.

Results

Network pharmacological analysis suggested that BZBS delayed cell senescence by interfering in the cell cycle. Our in vitro studies suggested that BZBS could significantly increase cell viability (P < 0.01), decrease the quantity of β-galactosidase+ cells (P < 0.01), downregulate p16 and p21 (P < 0.05, P < 0.01), improve adipogenic and osteogenic differentiation, and upregulate Nanog, OCT4 and SOX2 genes (P < 0.05, P < 0.01) in senescent MSCs. Moreover, BZBS significantly reduced the proportion of senescent MSCs in the G0/G1 phase (P < 0.01) and enhanced the expression of CDK4, Cyclin D1, and E2F1 (P < 0.05, P < 0.01, respectively). Upon treatment with HY-50767A, a CDK4 inhibitor, the upregulation of E2F1 was no longer observed in the BZBS group.

Conclusions

BZBS can protect MSCs against D-gal-induced senescence, which may be associated with cell cycle regulation via the Cyclin D1/CDK4/E2F1 signalling pathway.

Mechanisms of action of Shizhenqing granules for eczema treatment: Network pharmacology analysis and experimental validation

Background

Jiuwan decoction has been used to treat chronic eczema since the Qing Dynasty. According to clinical experience, Shizhenqing granules (SZQG), derived from the Jiuwan decoction, exert beneficial clinical effects on acute eczema and reduce recurrence. Therefore, we elucidated the underlying mechanisms of SZQG through network pharmacology, molecular docking, and experimental validation.

Methods

The main chemical components of SZQG were identified by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). And the targets of SZQG against eczema were screened out through online databases. Then, the regulatory network map of the "herbal compound-potential target" and the target protein-protein interaction (PPI) network was constructed. The Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using by R language. Additionally, the interaction between the active compounds and the targets was verified by molecular docking technology. Finally, an experiment in vivo was used to verify the effect and mechanism of SZQG on eczema.

Results

Using UHPLC-MS/MS, 158 main chemical compounds of SZQG were identified, and 72 compounds were selected according to the criteria for further analysis. All 237 potential targets of SZQG in eczema were explored using multiple online databases. The network with 14 core targets was screened out, including STAT3, RELA, TNF, JUN, MAPK3, IL-6, PIK3CA, STAT1, MAPK14, MAPK1, IL-4, NFKBIA, IL1B, and MYC. KEGG analyses indicated that the therapeutic effects of SZQG on eczema were predominantly associated with cytokine-cytokine receptor interaction, TNF, MAPK, NF-κB, toll-like receptor, T cell receptor, and Th1 and Th2 cell differentiation signaling pathways. Furthermore, the good affinity between the core compounds and core targets was verified by molecular docking technology, particularly for RELA and MAPK. Animal experiments revealed that SZQG downregulated MAPK14, RELA, T-bet, and GATA3 mRNA expression, reduced immunoglobulin E (IgE) and interleukin-4 (IL-4) serum concentrations, and improved eczema-like lesions in model rats.

Conclusion

This study identified potential targets and signaling pathways of SZQG in the treatment of eczema, whereby RELA and MAPK14 may constitute the main therapeutic targets of SZQG in cytokine regulation and reduction of inflammatory responses.

Antiplasmodial potential of phytochemicals from Citrus aurantifolia peels: a comprehensive in vitro and in silico study

Phytochemical investigation of Key lime (Citrus aurantifolia L., F. Rutaceae) peels afforded six metabolites, known as methyl isolimonate acetate (1), limonin (2), luteolin (3), 3`-hydroxygenkwanin (4), myricetin (5), and europetin (6). The structures of the isolated compounds were assigned by 1D NMR. In the case of limonin (2), further 1- and 2D NMR experiments were done to further confirm the structure of this most active metabolite. The antiplasmodial properties of the obtained compounds against the pathogenic NF54 strain of Plasmodium falciparum were assessed in vitro. According to antiplasmodial screening, only limonin (2), luteolin (3), and myricetin (5) were effective (IC50 values of 0.2, 3.4, and 5.9 µM, respectively). We explored the antiplasmodial potential of phytochemicals from C. aurantifolia peels using a stepwise in silico-based analysis. We first identified the unique proteins of P. falciparum that have no homolog in the human proteome, and then performed inverse docking, ΔGBinding calculation, and molecular dynamics simulation to predict the binding affinity and stability of the isolated compounds with these proteins. We found that limonin (2), luteolin (3), and myricetin (5) could interact with 20S a proteasome, choline kinase, and phosphocholine cytidylyltransferase, respectively, which are important enzymes for the survival and growth of the parasite. According to our findings, phytochemicals from C. aurantifolia peels can be considered as potential leads for the development of new safe and effective antiplasmodial agents.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.