Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Ganoderma lucidum, a popular medicinal fungus, has been utilized to treat a variety of diseases. It possesses a unique therapeutic and pharmacological reputation in suppressing cancer/tumor progression, especially breast cancer, due to its embedded rich bioactive chemical constituents, mainly triterpenoids (ganoderic acids). The most prevalent malignant tumor in women with a high mortality and morbidity rate is breast cancer. Ganoderic acids A, D, DM, F, and H are evidenced in previous research to have breast cancer-preventive properties by exhibiting autophagic and apoptosis, anti-proliferative, and anti-angiogenesis effects. However, the anti-breast cancer mechanism remains unclear. The putative targets of the ganoderic acids were further determined using bioinformatics techniques and molecular docking calculation. Finally, the key targets were verified in vitro. A total of 53 potential target proteins associated with 202 pathways were predicted to be related to breast cancer. The potential targets were narrowed down to six key targets (AKT1, PIK3CA, epidermal growth factor receptor [EGFR], STAT1, ESR1, and CTNNB1), using different algorithms of the CytoHubba plugin, which were further validated using molecular docking analysis. The ganoderic acid DM (GADM) and the targets (PIK3CA and EGFR) with the strongest interactions were validated via MDA-MB-231 and MCF7 cells. The expression level of PIK3CA in both MDA-MB-231 and MCF7 cells was dose-dependently suppressed by GADM, whereas EGFR expression was unexpectedly increased, which warrants further investigation. These data indicated that the network pharmacology-based prediction of GADM targets for treating human breast cancer could be reliable.

As a resource with a variety of medicinal and edible values, Zanthoxylum bungeanum Maxim has been found to improve high-fat diet-induced metabolic-associated fatty liver disease (MAFLD).

The aim of this study was to predict the main active metabolites in Z. bungeanum Maxim. Based on network analysis, and to explore and validate their potential mechanisms of action through lipidomics and transcriptomic techniques.

MAFLD mouse model and cell model were established to evaluate the effect of active components in Z. bungeanum Maxim. on MAFLD. Serum biochemical indexes, pathological staining observation, lipid group and transcriptome were used to verify the mechanism of action of active components in Z. bungeanum Maxim. on MAFLD.

Quercetin can regulate the liver lipid metabolites of MAFLD mice through the Glycerophospholipid metabolic pathway, thereby improving liver lipid accumulation and liver injury. At the same time, quercetin can also improve MAFLD by reducing oleic acid-induced lipid accumulation in HepG2 cells, and inhibit ferroptosis through the p38 MAPK/ERK signaling pathway, thereby alleviating the progression of MAFLD.

Quercetin isolated from Z. bungeanum Maxim. has ameliorative effects on MAFLD, probably mainly by affecting lipid metabolic pathways and MAPK signaling pathways.

In this editorial, we comment on the article by Micucci et al published in the recent issue. We focus on the heterogenous nature of gastric cancer (GC) and the potential benefits of integrating traditional Chinese medicine (TCM) with the modern technology of network pharmacology (NP) and omics sequencing. GC is a heterogenous disease, as it incorporates several biochemical pathways that contribute to pathogenesis. TCM acknowledges the multifactorial, heterogenous nature of disease and utilizes an integrative approach to medicine. NP, a modern philosophy within drug development, integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body. Omics technologies, which is at the core of precision medicine, has allowed for this newfound principle of drug development. Metabolic pathways are better distinguished, leading to more targeted drug development. However, the use of omics technology needs to be employed to better characterize the subtypes of GC. This will allow TCM's use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.

Caryota urens, also known as Shivjata, has been documented in ancient Indian texts for its therapeutic benefits, addressing conditions from seminal weakness to gastric ulcers. This study aims to investigate its contemporary medicinal potential in treating breast cancer.

The study focuses on exploring the therapeutic potential of Caryota urens fruit against breast cancer, specifically targeting cell cycle genes CDK1, CDC25A, and PLK1 through bioinformatics, network pharmacology, and in vitro validation.

Using mass spectrometry and nuclear magnetic resonance (NMR), 60 key phytoconstituents from Caryota urens fruit were identified. Bioinformatics analysis, integrating Gene Cards and GEO databases, 15,474 breast cancer-associated genes focusing on the HR+/HER2-subtype were identified. Molecular docking and qPCR validated the interactions of key phytoconstituents, particularly Episesamin, with CDK1, CDC25A, and PLK1. In vitro studies were conducted on the MCF7 cell line, supplemented by ROC and survival analyses to evaluate diagnostic and therapeutic potential.

The bioinformatics analysis identified CDK1, CDC25A, and PLK1 as pivotal genes regulating cell cycle progression and breast cancer tumorigenesis. Network pharmacology and in vitro studies indicated that phytoconstituents, especially Episesamin, downregulated these genes in breast cancer cells. Molecular docking and qPCR confirmed these interactions, and ROC and survival analyses underscored their diagnostic and therapeutic significance.

This study suggests that Caryota urens fruit extract, particularly Episesamin, may inhibit breast cancer metastasis by downregulating CDK1, CDC25A, and PLK1, offering promising new strategies for targeting the cell cycle in breast cancer and emphasizing the value of integrating bioinformatics with experimental methods in cancer research.

The study was aimed to investigate the clinical effect and mechanism of Shugan-Jieyu-Jianpi (SGJYJP) formula for the treatment of irritable bowel syndrome (IBS) combined with non-alcoholic fatty liver disease (NAFLD). The clinical efficacy of SGJYJP was evaluated in 54 patients with IBS-NAFLD. The potential molecular mechanism of SGJYJP formula was investigated by network pharmacology. Animal models were constructed to explore the related mechanism. From clinical studies, the total effective rate of patients in SGJYJP group was significantly higher than that in pinaverium group. The protein expression of TGFB1 was declined in IBS-NAFLD rats, together with the increased expression of PTGS2 and TNF, which was abolished by SGJYJP treatment. SGJYJP significantly reduced the expression of TNF signalling related molecules of TRAF2, caspase-8, and elevated the expression of Bcl-xl in IBS-NAFLD animal models. SGJYJP may exert therapeutic effect on IBS-NAFLD by targeting PTGS2, TGFB1, and TNF genes and TNF signalling.

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

Flavonoids including quercetin, kaempferol, myricetin, rutin etc. have always been a part of traditional Chinese medicine for the treatment of several ailments. Rutin (RT), also known as rutoside, sophorin is one of the flavanol glycoside having structure resemblance with quercetin. It is found to exhibit several biological activities viz. anti-inflammatory, anticancer, antioxidant, cardioprotective, antidepressant, neuroprotective etc. but the mechanisms by which it exhibits these effects is still under research.

The protective effects of rutin against drug induced nephropathy have already been discovered. Therefore, in this study, the main focus is to explore the mechanism by which rutin provides protection against drug-induced nephropathy using modern method like network pharmacology and molecular docking.

Genes linked to drug-induced nephropathy and targets connected with rutin were obtained by searching through a number of extensive databases, including David software, Venn plot database, Swiss target prediction database, String database, Gene card & OMIM database, and Pubchem. In order to locate mapping targets, the acquired targets were examined and intersected. A protein-protein interaction (PPI) network was then built to find potential targets.

From the KEGG pathway, the target pathway responsible for drug-induced nephropathy were found to be XDH, HSD17B2, MET, PRKCB, CD38, ALDH2, CDK1, PTK2, CYP19A1, TNF, F2, PTGS2, ESR1, GSK3B, GLO1, ALOX12, MMP3, PRKCZ, CXCR1, CA4, EGFR, PDE5A, F10, AKR1B1, DRD4, TERT, CA3, PLG, TP53, PRKCH, PIK3R1, PRKACA, CYP1B1, ALOX5, PLK1, CHEK1, KCNH2, PRKCD, MAPT, MPO, NOX4, AVPR2, ACHE, MCL1, KDR, ABCG2, CCR1, PIK3CG, FLT3, ADORA1, IL2, SYK, IGF1R, CA2, SERPINE1, INSR, PRKCA, APP, MMP9. From these identified targets, the 14 selected pathways which have major role in providing protection in drug-induced nephropathy have been discussed.

As RT can inhibit various metabolic and proinflammatory pathways involved, it can help in prevention and treatment of drug-induced nephropathy.

The revelation of mode of action of bioactive constituent rutin against drug-induced nephropathy provides a theoretical basis for designing more promising compounds in future for treatment of nephropathy.

This network pharmacology study represents a significant step in understanding the potential of Resveratrol as an antidiabetic agent and its molecular targets. Targets for Type 2 diabetes were obtained from the MalaCards and DisGeNET databases, while targets for Resveratrol were sourced from the STP and CTD databases. Subsequently, we performed matching to identify common disease-compound targets. The identified genes were analyzed using the ShinGO-0.76.3 database for functional enrichment analysis and KEGG pathway mapping. A protein-protein interaction network was then constructed using Cytoscape software, and hub genes were identified. These hub genes were subjected to molecular docking and dynamic simulations using AutoDock Vina and Gromacs software. According to functional enrichment and KEGG pathway analysis, Resveratrol influences insulin receptors, endoplasmic reticulum functions, and oxidoreductase activity and is involved in the estrogen and HIF-1 pathways. Ten hub genes were identified, including ESR1, PTGS2, SRC, NOS3, MMP9, IGF1R, CYP19A1, MTOR, MMP2, and PIK3CA. The proteins associated with these genes exhibited high interaction with Resveratrol in the molecular docking analysis, and molecular dynamics showed a stable interaction of Resveratrol with ESR1, MMP9, PIK3CA, and PTGS2. In conclusion, our work enhances the understanding of the antidiabetic activity of Resveratrol, which future studies should experimentally corroborate.

Arthritis is the cause of morbidity associated with Chikungunya virus (CHIKV) infection. It persists even after the virus has been cleared from the body. MBZM-NIBT was earlier shown to inhibit (CHIKV) infection in vitro and in vivo.

The objective of this study is to determine the ability of MBZM-N-IBT to manage arthritis independent of CHIKV infection.

The acute toxicity of MBZM-N-IBT was determined to find a permissible oral dose. Effects against inflammation and arthritis were determined in relevant preclinical models. Network pharmacology was used to propose possible modes of action.

It showed no acute toxicity orally, with an estimated LD50 of more than 5000 mg/kg in rats. It significantly reduced inflammation. Its effect against Complete Freund's Adjuvant (CFA) induced arthritis was comparable to that of Diclofenac sodium. Network pharmacology analysis revealed that MBZM-N-IBT can potentially interfere with multiple targets and pathways. MMP12 and CTSD were found to be the most probable hub targets of MBZM-N-IBT for its effect against arthritis.

In conclusion, MBZM-N-IBT is safe at 50 mg/kg and can manage arthritis independent of CHIKV infection through modulation of multiple pathways and arthritis-associated targets.

The current study aimed to evaluate the anti-inflammatory, anti-oxidant, and pronounced gastro-protective activities of β- Citronellol using in vitro, in vivo assays and in silico approaches.

In vitro assays, denaturation of bovine serum albumin, egg protein, and human Red Blood Cells (RBCs) membrane stabilization were performed, using Piroxicam as standard. For in vivo assessment, Histamine (0.1 ml from 1% w/v) and Formaldehyde (0.1 ml from 2% v/v) were used to mediate inflammation. In silico molecular docking and network pharmacology were employed to probe the possible target genes mediating gastroprotective effect of β-Citronellol at 25, 50, and 100 mg/kg, using indomethacin-induced (25 mg/kg i.p) gastric ulcer in rats. Moreover, Gastric tissues were evaluated for morphological, histopathological, and bio-chemical analysis of PGE2, COX-I, COX-II, 5-LOX, eNOS, ICAM-1, oxygen-free radical scavengers (SOD, CAT), and oxidative stress marker (MDA).

β-Citronellol prevented denaturation of proteins and RBCs membrane stabilization with maximum effect observed at 6,400 µg/mL. Citronellol decreased rat's paw edema. Network pharmacology and docking studies revealed gastro-protective potential of Citronellol possibly mediated through arachidonic acid pathways by targeting COX-I, COX-II, PGE2, and 5-LOX. Citronellol reduced the ulcer indices, and histopathological changes. Further, β-Citronellol (50 and 100 mg/kg) increased gastric PGE2, COX-1, and eNOS; while suppressing COX-2, 5-LOX and ICAM-1. Citronellol markedly enhanced the oxidative balance in isolated rat stomach tissues.

The anti-inflammatory, anti-oxidant, and gastro-protective effects of β-Citronellol against indomethacin-induced gastric ulcer model in rats through mediating COX-I, COX-II, PGE2, 5-LOX, eNOS, and ICAM-1 inflammatory markers.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung condition, the cause of which remains unknown and for which no effective therapeutic treatment is currently available. Chlorogenic acid (CGA), a natural polyphenolic compound found in different plants and foods, has emerged as a promising agent due to its anti-inflammatory, antioxidant, and antifibrotic properties. However, the molecular mechanisms underlying the therapeutic effect of CGA in IPF remain unclear. The purpose of this study was to analyze the pharmacological impact and underlying mechanisms of CGA in IPF.

Using network pharmacology analysis, genes associated with IPF and potential molecular targets of CGA were identified through specialized databases, and a protein-protein interaction (PPI) network was constructed. Molecular docking was performed to accurately select potential therapeutic targets. To investigate the effects of CGA on lung histology and key gene expression, a murine model of bleomycin-induced lung fibrosis was used.

Network pharmacology analysis identified 384 were overlapped between CGA and IPF. Key targets including AKT1, TP53, JUN, CASP3, BCL2, MMP9, NFKB1, EGFR, HIF1A, and IL1B were identified. Pathway analysis suggested the involvement of cancer, atherosclerosis, and inflammatory processes. Molecular docking confirmed the stable binding between CGA and targets. CGA regulated the expression mRNA of EGFR, MMP9, AKT1, BCL2 and IL1B and attenuated pulmonary fibrosis in the mouse model.

CGA is a promising multi-target therapeutic agent for IPF, which is supported by its efficacy in reducing fibrosis through the modulation of key pathways. This evidence provides a basis to further investigate CGA as an IPF potential treatment.

Hepatocellular Carcinoma (HCC) is commonly treated with surgery, liver transplantation, and chemotherapy, but recurrence and metastasis remain challenges. Natural complementary therapies like propolis, known for its hepatoprotective properties, are gaining interest due to limited efficacy and toxicity of conventional chemotherapy. This study aims to identify core targets for HCC, assess the therapeutic potential of East Kalimantan propolis (EKP) from stingless bees, and analyze the molecular interactions.

EKP compounds were analyzed using target prediction tools related to HCC, alongside clinical data from the Gene Expression Omnibus (GEO) database, to identify overlapping genes with clinical relevance. The selected genes were then subjected to protein-protein interaction (PPI), GO and KEGG enrichment, immunohistochemical comparison and survival analysis to identify potential core targets and related pathways for HCC therapy. Furthermore, molecular docking and dynamics were conducted to verify the molecular interactions and stability of EKP compounds with targets.

108 genes have been selected as HCC potential targets, which mostly associated with MicroRNAs in cancer, chemical carcinogenesis, and viral carcinogenesis pathways. These targets were obtained by overlapping genes from GEO clinical databases and target predictors. PPI network analysis revealed 4 main targets of propolis in HCC. Furthermore, differential expression genes, survival analysis, and Immunohistochemical analysis from databases suggested that AKR1C3 and MAPK1 promote HCC progression and shorten survival rate of HCC patients. Molecular docking and dynamic studies confirmed strong binding affinity and stability of Baicalein, Chrysin, Quercetin, and Myricetin with receptor targets within simulation time.

This study provides insight into the mechanism of action of EKP on HCC and identifies AKR1C3 and MAPK1 as candidate target treatments for future drug development.

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based ligand screening. Additionally, toxicity prediction, molecular docking, and molecular dynamics (MD) simulations were conducted. NP study identified key genes related to HCC, particularly the enzymes AKT1 and GSK3β. Pathway analysis revealed that crucial pathways like PI3K-AKT and WNT signalling play pivotal roles in HCC progression. Using ML, potential inhibitors for AKT1 and GSK3β were identified, including CHEMBL2177361 and CHEMBL403354 for AKT1, and CHEMBL3652546 and CHEMBL4641631 for GSK3β. post-MD analyses, including RMSD, 2D-RMSD, RMSD cluster, RMSF, PCA, DCCM, residence time analysis, diffusion coefficient, autoencoder-based dimensionality reduction, FEL and MM/GBSA were performed to understand the protein-ligand interactions. The present study reveals the stable interactions of the inhibitors with AKT1 and GSK3β. The binding free energies of all the four complexes were -39.9, -46.8, -41.6, and -45.9 kcal/mol, respectively. This research provides novel insights into the genes and pathways involved in the progression and pathogenesis of HCC using bioinformatics tools. Furthermore, ML-based virtual screening identified potent inhibitors against the target proteins of HCC, such as AKT1 and GSK3β.

Manuka essential oil has long been used in traditional medicine, though the effects of the oil on cancer cells have limited studies. The goal of this project was to treat cancer cell lines with manuka essential oil at different concentrations and to ascertain the effects on the cell proliferation of normal fibroblast (CUA-4) and on fibrosarcoma (HT-1080) cells. Cell lines were grown on 24-well plates, and subconfluent cultures were treated with varying concentrations of manuka oil for 24 h. The effect of the oil on proliferation and viability was measured through direct cell counting using trypan blue dye exclusion and through the use of an MTT assay. As the concentration of oil increased, proliferation of all cell lines tested decreased with increasing dosage, concurrently with a decrease in MTT activity. To determine if the decrease in cell numbers observed from manuka oil treatment is the result of apoptosis, PARP cleavage assays were performed, confirming that the treatment caused apoptosis in both normal fibroblasts and fibrosarcoma cells. The stress-activated MAPK protein, JNK, was activated by manuka essential oil treatment, occurring synergistically with a decrease in MKP-1 expression.

Background/Objectives: Dyslipidemia is a significant risk factor for atherosclerotic cardiovascular disease (ASCVD), a leading cause of death worldwide. Salvia miltiorrhiza Burge is widely used in East Asia for cardiovascular health, showing potential benefits in lowering cholesterol and reducing inflammation. Methods: This study systematically reviewed and conducted a meta-analysis of randomized controlled trials (RCTs) to assess the clinical effectiveness of Salvia miltiorrhiza in treating dyslipidemia. Moreover, network pharmacology and molecular docking analyses were performed to explore the mechanisms underlying the effects of Salvia miltiorrhiza. Results: The meta-analysis revealed that when Salvia miltiorrhiza is combined with statin therapy, it significantly enhances lipid profiles, including reductions in total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides and improvements in high-density lipoprotein cholesterol (HDL-C), compared to statin therapy alone. The in silico analyses indicated that Salvia miltiorrhiza may influence key biological pathways, such as the PI3K/Akt, JAK/STAT, and HMGCR pathways, which are involved in inflammation, lipid metabolism, and the development of atherosclerosis. Conclusions:Salvia miltiorrhiza shows potential as a complementary therapy for dyslipidemia, offering additional lipid-lowering and anti-inflammatory benefits.

This study investigates the molecular targets and pathways affected by valencene in non-small cell lung cancer (NSCLC) through network pharmacology and in vitro assays. Valencene's chemical structure was sourced from PubChem, and target identification utilized the PharmMapper database, cross-referenced with UniProtKB for official gene symbols. NSCLC-associated targets were identified via GeneCards, followed by protein-protein interaction analysis using STRING. Molecular docking studies employed AutoDock Vina to assess binding interactions with key nuclear receptors (RXRA, RXRB, RARA, RARB, THRB). Molecular dynamics simulations were conducted in GROMACS over 200 ns, while ADME/T properties were evaluated using Protox. In vitro assays measured cell viability in A549 and HEL 299 cells via MTT assays, assessed apoptosis through Hoechst staining, and evaluated mitochondrial potential with JC-1. Molecular docking revealed strong binding affinities of valencene (below - 5 kcal/mol) to nuclear receptors, outperforming 5-fluorouracil (5-FU). Molecular dynamics simulations indicated robust structural stability of the THRB-valencene complex, with favorable interaction energies. Notably, valencene exhibited a selectivity index of 2.293, higher than 5-FU's 2.231, suggesting enhanced safety for normal cells (HEL 299). Fluorescence microscopy confirmed dose-dependent DNA fragmentation and decreased mitochondrial membrane potential. These findings underscore valencene's potential as an effective therapeutic agent for lung cancer, demonstrating an IC50 of 16.71 μg/ml in A549 cells compared to 5-FU's 12.7 μg/ml, warranting further investigation in preclinical models and eventual clinical trials.

Breast Cancer (BC) is the most common cause of cancer-associated deaths in females worldwide. Despite advancements in BC treatment driven by extensive characterization of its molecular hallmarks, challenges such as drug resistance, tumor relapse, and metastasis persist. Therefore, there is an urgent need for alternative treatment approaches with multi-modal efficacy to overcome these hurdles. In this context, natural bioactives are increasingly recognized for their pivotal role as anti-cancer compounds. This study focuses on predicting molecular targets for key herbal phytoconstituents-gallic acid, piperine, quercetin, resveratrol, and beta-sitosterol-present in the polyherbal formulation, Krishnadi Churna. Using an in-silico network pharmacology model, key genes were identified and docked against these marker compounds and controls. Mammary carcinoma emerged as the most significant phenotype of the putative targets. Analysis of an online database revealed that out of 135 predicted targets, 134 were mutated in breast cancer patients. Notably, ESR1, CYP19A1, and EGFR were identified as key genes which are known to regulate the BC progression. Docking studies demonstrated that the herbal phytoconstituents had similar or better docking scores than positive controls for these key genes, with convincing protein-ligand interactions confirmed by molecular dynamics simulations, MM/GBSA and free energy landscape (FEL) analysis. Overall, this study highlights the predictive potential of herbal phytoconstituents in targeting BC genes, suggesting their promise as a basis for developing new therapeutic formulations for BC.

Gastric cancer poses a significant global health challenge, necessitating innovative approaches for biomarker discovery and therapeutic intervention. This study employs a multifaceted strategy integrating network biology, drug repurposing, and virtual screening to elucidate and expand the molecular landscape of gastric cancer. We identified and prioritized key genes implicated in gastric cancer by utilizing data from diverse databases and text-mining techniques. Network analysis underscored intricate gene interactions, emphasizing potential therapeutic targets such as CTNNB1, BCL2, TP53, etc, and highlighted ACTB among the top hub genes crucial in disease progression. Drug repurposing on 626 FDA-approved drugs for digestive system-related cancers revealed Norgestimate and Nimesulide as likely top candidates for gastric cancer, validated by molecular docking and dynamics simulations. Further, combinatorial synthesis of scaffold libraries derived from known chemotypes generated 56,160 virtual compounds, of which 76 new compounds were prioritized based on promising binding affinities and interactions at critical residues. Hotspot residue analysis identified GLU 214 and others as essential for ligand binding stability, enhancing compound efficacy and specificity. These findings support the therapeutic potential of targeting beta-actin protein in gastric cancer treatment, suggesting a future for further experimental validation and clinical translation. In conclusion, this study highlights the potential of repurposable drugs and virtual screening which can be used in combination with existing anti-gastric cancer drugs for gastric cancer therapy, emphasizing the role of computational methodologies in drug discovery.

This study utilized network pharmacology and docking analyses to explore a groundbreaking therapeutic approach for managing the neuropathic pain and depressive disorder (NP/DD) comorbidity. Schisandra chinensis (SC), a common Chinese medicine, has demonstrated numerous beneficial effects in treating neuropsychological disorders. The main objective of this study was to identify potential bioactive components of SC and investigate their interactions with relevant target genes associated with NP/DD. To gain insights into the underlying molecular mechanisms, GO and KEGG analyses were conducted. Furthermore, molecular docking analysis was employed to validate the therapeutic relevance of SC's active ingredients. Seven bioactive components of SC, namely Longikaurin A, Deoxyharringtonine, Angeloylgomisin O, Schisandrin B, Gomisin A, Gomisin G, and Gomisin R, exhibited effectiveness in the treatment of NP/DD. From this list, the first five components were selected for further analysis. The analyses revealed a complex network of interactions between the targets of SC and NP/DD, providing valuable information about the molecular mechanisms involved in the treatment of NP/DD with SC. SC components demonstrated the ability to regulate pathways involving tumor necrosis factor (TNF), vascular endothelial growth factor (VEGF), and other growth hormones (GH). Overall, this study contributes to our understanding of the molecular mechanisms underlying the effects of SC in treating NP/DD. Further investigation is necessary to explore the therapeutic potential of SC as a viable strategy for NP/DD comorbidity. These findings lay a solid foundation for future research endeavors in this field, holding potential implications for the development of novel therapeutic interventions targeting NP/DD.

The pyGROMODS, an easy-to-use cross-platform python-based package, with a graphical user interface, for the generation of molecular dynamic (MD) input files and running MD simulation (MDS) of proteins, peptides, and protein-ligand complex using GROMACS, is here presented. Four routes, with underlining Python scripts, are implemented in pyGROMODS for the generation of MD input files. They are 'RLmulti' for processing multi-ligand protein complex, 'RLmany' for processing multiple ligands against a single protein target, 'RLsingle' for processing multiple pairs of proteins and ligands, and 'PPmore' for processing peptides or proteins without ligands or non-standard residues. In addition, using the package, the generated input files or appropriate input files from other sources can be uploaded to run MDS with GROMACS. The pyGROMODS is implemented with a unique ability to search the host machine systems for the installation of the required software, update and/or install required Python packages, allow the user to pre-define working directory, and generate unique workflow organization with well-defined folders and files in a well-organized manner. The pyGROMODS, which is released under the MIT License, is freely available for download via the GitHub (https://github.com/Dankem/pyGROMODS) and Zenodo (https://doi.org/10.5281/zenodo.7912747) repositories. The precompiled executables can also be downloaded from Zenodo (https://doi.org/10.5281/zenodo.8087090), and a video tutorial can be downloaded from https://youtu.be/I4OKc6uVx1M.Communicated by Ramaswamy H. Sarma.

Globally, the prevalence of breast cancer (BC) is increasing at an alarming level, despite early detection and technological improvements. Alkaloids are diverse chemical groups, and many within this class have been reported as potential anticancer compounds. Chabamide F (F) and chabamide G (G) are two dimeric amide alkaloids found in a traditional medicinal plant, Piper chaba, and possess significant cytotoxic effects. However, their scientific rationalization in BC remains unknown. Here, we aimed to investigate their potential and molecular mechanisms for BC through in silico approaches. From network pharmacology, we identified 64 BC-related genes as targets. GO and KEGG studies showed that they were involved in various biological processes and mostly expressed in BC-related pathways such as RAS, PI3K-AKT, estrogen, MAPK, and FoxO pathways. However, PPI analysis revealed SRC and AKT1 as hub genes, which play key roles in BC tumorigenesis and metastasis. Molecular docking revealed the strong binding affinity of F (- 10.7 kcal/mol) and G (- 9.4 and - 11.7 kcal/mol) for SRC and AKT1, respectively, as well as the acquisition of vital residues to inhibit them. Their long-term stability was evaluated using 200 ns molecular dynamics simulation. The RMSD, RMSF, Rg, and SASA analyses showed that the G-SRC and G-AKT1 complexes were excellently stable compared to the control, dasatinib, and capivasertib, respectively. Additionally, the PCA and DCCM analyses revealed a significant reduction in the residual correlation and motions. By contrast, the stability of the F-SRC complex was greater than that of the control, whereas it was moderately stable in complex with AKT1. The MMPBSA analysis demonstrated higher binding energies for both compounds than the controls. In particular, the binding energy of G for SRC and AKT1 was - 120.671 ± 16.997 and - 130.437 ± 19.111 kJ/mol, respectively, which was approximately twice as high as the control molecules. Van der Waal and polar solvation energies significantly contributed to this energy. Furthermore, both of them exhibited significant interactions with the binding site residues of both proteins. In summary, this study indicates that these two molecules could be a potential ATP-competitive inhibitor of SRC and an allosteric inhibitor of AKT1.

Down Syndrome (DS), characterized by trisomy of chromosome 21, leads to the overexpression of several genes contributing to various pathologies, including cognitive deficits and early-onset Alzheimer's disease. This study aimed to identify the intersection genes of two polyphenolic compounds, apigenin and naringenin, and their potential therapeutic targets in DS using network pharmacology. Key proteins implicated in DS, comprising DYRK1A, APP, CBS, and ETS2, were selected for molecular docking and dynamics simulations to assess the binding affinities and stability of the protein-ligand interactions. Molecular docking revealed that naringenin exhibited the highest binding affinity to DYRK1A with a score of -9.3 kcal/mol, followed by CBS, APP, and ETS2. Moreover, molecular docking studies included positive control drugs, such as lamellarin D, valiltramiprosate, benserazide, and TK216, which exhibited binding affinities ranging from -5.5 to -8.9 kcal/mol. Apigenin showed strong binding to APP with a score of -8.8 kcal/mol, suggesting its potential in modulating amyloid-beta levels. These interactions were further validated through molecular dynamics simulations, demonstrating stable binding throughout the 100 ns simulation period. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses indicated minimal fluctuations, confirming the stability of the complexes. The findings suggest that apigenin and naringenin could serve as effective therapeutic agents for DS by targeting key proteins involved in its pathology. Future studies should focus on in vivo validation, clinical trials, and exploring combination therapies to fully harness the therapeutic potential of these compounds for managing DS. This study underscores the promising role of network pharmacology in identifying novel therapeutic targets and agents for complex disorders like DS.

The cysteine residues of Keap1 such as C151, C273, and C288 are critical for its repressor activity on Nrf2. However, to date, no molecules have been identified to covalently modify all three cysteine residues for Nrf2 activation. Hence, in this study, our goal is to discover new Keap1 covalent inhibitors that can undergo a Michael addition with all three cysteine residues. The Keap1's intervening region was modeled using Modeller v10.4. Covalent docking and binding free energy were calculated using CovDock. Molecular dynamics (MD) was performed using Desmond. Various in-vitro assays were carried out to confirm the neuroprotective effects of the hit molecule in 6-OHDA-treated SH-SY5Y cells. Further, the best hit was evaluated in vivo for its ability to improve rotenone-induced postural instability and cognitive impairment in male rats. Finally, network pharmacology was used to summarize the complete molecular mechanism of the hit molecule. Chalcone and plumbagin were found to form the necessary covalent bonds with all three cysteine residues. However, MD analysis indicated that the binding of plumbagin is more stable than chalcone. Plumbagin displayed neuroprotective effects in 6-OHDA-treated SH-SY5Y cells at concentrations 0.01 and 0.1 μM. Plumbagin at 0.1 µM had positive effects on reactive oxygen species formation and glutathione levels. Plumbagin also improved postural instability and cognitive impairment in rotenone-treated male rats. Our network analysis indicated that plumbagin could also improve dopamine signaling. Additionally, plumbagin could exhibit anti-oxidant and anti-inflammatory activity through the activation of Nrf2. Cumulatively, our study suggests that plumbagin is a novel Keap1 covalent inhibitor for Nrf2-mediated neuroprotection in PD.

Triple-negative breast cancer (TNBC) is one of the most common types of serious breast cancer. Due to the absence of therapeutic hormone receptors, TNBC treatment generally involves chemotherapy which results in various side effects and resistance development. Herbal compounds, including α-mangostin, have shown potential anticancer effects against TNBC. However, rigorous screening is needed to uncover its mechanisms and characteristics. The aim of this study was to understand the molecular mechanism of α-mangostin against TNBC and its possible limitations. The study design used is an in si lico study. The study involved database mining and compound characteristic analysis. Network pharmacology and molecular docking were also done to explore potential target and molecular mechanisms against TNBC. There was no statistical analysis conducted as this study relies on predefined algorithms and simulation models. Instead, a parameter threshold was used for each analysis to ensure its reliability. Prediction of Activity Spectra for Substances prediction and Gene Ontology-Kyoto Encyclopedia of Genes and Genomes enrichment predicted potential anticancer effects of α-mangostin through the regulation of enzyme activity and apoptotic pathway. Compound property predictions showed α-mangostin to have promising drug-likeness with sufficient bioavailability and low biodegradability. However, α-mangostin still has some potential limitations in water solubility and toxicity risks. Through network pharmacology, 75 potential target proteins of α-mangostin in TNBC cases were found. The top three most significant of which (AKT1, CTNNB1, and HSPAA91) were proven to bind with α-mangostin through molecular docking. Study results suggested α-mangostin to have a promising anticancer and chemopreventive activity with great drug-likeness and pharmacokinetic properties. It was revealed that α-mangostin can bind to key components in TNBC-related pathways, including AKT1, CTNNB1, and HSP90AA1 proteins. However, further experimental studies may be needed to verify its effectiveness as well as possible solubility and toxicity limitations.

Using network pharmacology and experimental validation to explore the therapeutic efficacy and mechanism of curcumin (Cur) in periodontitis treatment.

Network pharmacology was utilized to predict target gene interactions of Cur-Periodontitis. Molecular docking was used to investigate the binding affinity of Cur for the predicted targets. A mouse model with ligature-induced periodontitis (LIP) was used to verify the therapeutic effect of Cur. Microcomputed tomography (micro-CT) was used to evaluate alveolar bone resorption, while western blotting, haematoxylin-eosin staining and immunohistochemistry were used to analyse the change in immunopathology. SYTOX Green staining was used to assess the in vitro effect of Cur in a mouse bone marrow-isolated neutrophil model exposed to lipopolysaccharide.

Network pharmacology identified 114 potential target genes. Enrichment analysis showed that Cur can modulate the production of neutrophil extracellular traps (NETs). Molecular docking experiments suggested that Cur effectively binds to neutrophil elastase (ELANE), peptidylarginine deiminase 4 (PAD4) and cathepsin G, three enzymes involved in NETs. In LIP mice, Cur alleviated alveolar bone resorption and reduced the expression of ELANE and PAD4 in a time-dependent but dose-independent manner. Cur can directly inhibit NET formation in the cell model.

Our research suggested that Cur may alleviate experimental periodontitis by inhibiting NET formation.

Drug research is increasingly using Network Pharmacology (NP) to tackle complex conditions like Metabolic Syndrome (MetS), which is characterized by obesity, hyperglycemia, and dyslipidemia. Single-action drugs are inadequate to treat MetS, which is marked by a range of complications including glucose intolerance, hyperlipidemia, mitochondrial dysfunction, and inflammation.

To analyze Chandraprabha vati using Network Pharmacology to assess its potential in alleviating MetS-related complications.

The genes related to MetS, inflammation, and the target genes of the CPV components were identified using network pharmacology tools like DisgNET and BindingDB. Followed by mapping of the CPV target genes with the genes implicated in MetS and inflammation to identify putative potential targets. Gene ontology, pathway enrichment analysis, and STRING database were employed for further exploration. Furthermore, drug-target-protein interactions network were visualized using Cytoscape 3.9.1.

The results showed that out of the 225 target genes of the CPV components, 33 overlapping and 19 non-overlapping genes could be potential targets for MetS. Similarly, 14 overlapping and 7 non-overlapping genes could be potential targets for inflammation. The CPV bioactives target genes were found to be involved in lipid and insulin homeostasis via several pathways revealed by the pathway analysis. The importance of CPV in treating MetS was supported by GO enrichment data; this could be due to its potential to influence pathways linked to metabolism, ER stress, mitochondrial dysfunction, oxidative stress, and inflammation.

These results offer a promising approach to developing treatment and repurposing CPV for complex conditions such as MetS.

Epilepsy originates from unusual electrical rhythm within brain cells, causes seizures. Calotropis species have been utilized to treat a wide spectrum of ailments since antiquity. Despite chemical and biological investigations, there have been minimal studies on their anticonvulsant activity, and the molecular targets of this plant constituents are unexplored. This study aimed to investigate the plausible epileptic targets of Calotropis phytoconstituents through network pharmacology, and to evaluate their binding strength and stability with the identified targets. In detail, 125 phytoconstituents of the Calotropis plant (C. procera and C. gigantea) were assessed for their drug-likeness (DL), blood-brain-barrier (BBB) permeability and oral bioavailability (OB). Network analysis revealed that targets PTGS2 and PPAR-γ were ranked first and fourth, respectively, among the top ten hub genes significantly linked with antiepileptic drug targets. Additionally, docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) were employed to validate the compound-gene interactions. Docking studies suggested ergost-5-en-3-ol, stigmasterol and β-sitosterol exhibit stronger binding affinity and favorable interactions than co-crystallized ligands with both the targets. Furthermore, both MD simulations and MM-PBSA calculations substantiated the docking results. Combined data revealed that Calotropis phytoconstituents ergost-5-en-3-ol, stigmasterol, and β-sitosterol might be the best inhibitors of both PTGS2 and PPAR-γ.

Celery (Apium graveolens L.) has long been considered as a potential herbal medicine for the prevention and treatment of gout. However, the relationship between the chemical constituents and pharmacological activities of this medicinal plant has not been fully investigated yet. Therefore, this study aims to apply network pharmacology, molecular docking and molecular dynamics to explore the relationship between the chemical constituents of celery seed and its biological effects in the treatment of gout. Network pharmacology was built and analyzed based on the data collected from GeneCards, OMIM databases and SwissTargetPrediction web server using Cytoscape 3.9.0 software. The GO and KEGG pathway analysis of the potential targets of celery seed related to gout disease was performed using the ShinyGO v0.75 app. Molecular docking and molecular dynamics were carried out using Autodock vina and NAMD 2.14 software, respectively. The network analysis identified 16 active compounds and thirteen key targets of celery seed in the treatment of gout. The GO analysis and the KEGG pathway enrichment analysis suggested that the mechanism of action of the chemical constituents of celery seed might be involved in several pathways, notably the PI3K-Akt signaling pathway, Ras signaling pathway, and HIF-1 signaling pathway, respectively. Molecular docking and molecular dynamics revealed that apiumetin might be an important chemical that plays a key role in the pharmacological effect of celery seed. These results might be useful to select the Q-markers to control the quality of the products from celery seeds.Communicated by Ramaswamy H. Sarma.

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Allium cepa, commonly known as onion, is a widely consumed spice that possesses numerous pharmacological properties. A. cepa bioactive components are often explored in the treatment of inflammation-related complications. However, the molecular mechanism via which they exert their anti-inflammatory effects remains unknown. Therefore, this study aimed to elucidate the anti-inflammatory mechanism of A. cepa bioactive components. Consequently, the bioactive compounds of A. cepa were obtained from a database, while the potential targets of the sixty-nine compounds with desirable pharmacokinetic properties were predicted. Subsequently, the targets of inflammation were acquired from the GeneCards database. The protein-protein interaction (PPI) between the sixty-six shared targets of the bioactive compounds and inflammation was retrieved from the String database and visualized using Cytoscape v3.9.1 software. Gene Ontology (GO) analysis of the ten core targets from the PPI network revealed that A. cepa bioactive compounds could be involved in regulating biological processes such as response to oxygen-containing compounds and response to inflammation while Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis revealed that A. cepa compounds might modulate pathways including AGE-RAGE signaling pathway, interleukin (IL)-17 signalling pathway, and tumor necrosis factor signaling pathway. Molecular docking analysis showed that 1-O-(4-Coumaroyl)-beta-D-glucose, stigmasterol, campesterol, and diosgenin have high binding affinities for core targets including EGFR, ALB, MMP9, CASP3, and CCL5. This study successfully elucidated the potential anti-inflammatory mechanism of A. cepa bioactive compounds, hence, providing new insights into the development of alternative anti-inflammatory drugs.

Background and purpose: The Bushenyiqi decoction (BYD), a contemporary prescription of traditional Chinese medicine (TCM), has been observed to significantly ameliorate asthma symptoms in patients based on clinical observations. Although multi-component and multi-target characteristics are important attributes of BYD treatment, its pharmacological effect on asthma and the underlying mechanism of action remain unclear. Method: Network pharmacology: the asthma-related genes were retrieved from the GeneCards and OMIM database. The active constituents of BYD and their corresponding target genes were collected from the TCMSP database. The underlying pathways associated with overlapping targets between BYD and asthma were identified through GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. Experimental validation: pulmonary function tests, enzyme-linked immunosorbent assay (ELISA), Hematoxylin and eosin (HE), periodic acid-Schiff (PAS), and Masson's trichrome stainings were conducted to validate the efficacy of BYD in ameliorating airway inflammation in allergic asthma mice. Western blot (WB) and molecular docking were performed to confirm the involvement of the underlying pathway in BYD treatment of asthma. Results: The results of animal experiments demonstrated that BYD may improve airway responsiveness and suppress airway inflammation in allergic asthma mice. The network pharmacological analysis revealed the involvement of 11 potentially key active components, 9 potential key targets, and the phosphatidylinositol3 kinase-RAC-α serine/threonine-protein kinase (PI3K/AKT) signaling pathway in the mechanism of action of BYD for asthma treatment. Our findings have confirmed that BYD effectively alleviated airway inflammation by targeting interleukin 6 (IL-6), epidermal growth factor receptor (EGFR), and hypoxia inducible factor 1 alpha (HIF1A), with quercetin, kaempferol, and luteolin performing as the pivotal active constituents. BYD may potentially reduce inflammatory cell infiltration in lung tissues by regulating the PI3K/AKT signaling pathway. Conclusion: In conclusion, the integration of network pharmacology and biological experiments has demonstrated that key constituents of BYD, such as quercetin, kaempferol, and luteolin, exhibit targeted effects on IL-6, EGFR, and HIF1A in combating asthma-related inflammation through inhibition of the PI3K/AKT signaling pathway. The findings of this investigation provide evidence supporting the effectiveness of TCM's "bushenyiqi" therapy in asthma management, as corroborated by contemporary medical technology.

Bell's palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell's palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell's palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell's palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell's palsy. STRING database was used for generating the protein-protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein-protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell's palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1-cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex's dynamic finesse. This study evaluated compound-target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell's palsy, as well as helping in lowering down the blood pressure.Communicated by Ramaswamy H. Sarma.

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

In the current study, Neosetophomone B (NSP-B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues' flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of -36.85 kcal/mol for the FGF6-NSP-B complex, -43.87 kcal/mol for the FGF20-NSP-B complex, and -37.42 kcal/mol for the FGF22-NSP-B complex, and -41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of -30.05 kcal/mol for the FGF6-NSP-B complex, -39.62 kcal/mol for the FGF20-NSP-B complex, -34.89 kcal/mol for the FGF22-NSP-B complex, and -37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy.

Natural lactones have been used in traditional and folklore medicine for centuries owing to their anti-inflammatory properties. The study uses a multifaceted approach to identify lead anti-inflammatory lactones from the SISTEMATX natural products database. The study analyzed the natural lactone database, revealing 18 lactones linked to inflammation targets. The primary targets were PTGES, PTGS1, COX-2, ALOX5 and IL1B. STX 12273 was the best hit, with the lowest binding energy and potential for inhibiting the COX-2 enzyme. The study suggested natural lactone, STX 12273, from the SISTEMATX database with anti-inflammatory potential and postulated its use for inflammation treatment or prevention.

Network pharmacology approach has been observed a powerful tool to predict underlying complex pharmacological mechanism of herbs. Asparagus racemosus has been reported to show ameliorative effects in treating epilepsy and comorbid memory dysfunction but mechanism of this amelioration is elusive. Hence a network pharmacology approach was employed to investigate the plausible mechanism of A. recemosus.

: Bioactive compounds of A. racemosus were extracted based on the TCMSP, PCIDB, and BATMAN-TCM database. The potential targets of bioactive compounds were collected using target fishing. Epilepsy and comorbid dementia genes were collected from DISGENET. A PPI network among these targets was constructed using the intersecting key targets between herb targets and disease targets. Besides, DAVID bioinformatics resource was utilized for the pathway enrichment analysis on GO and KEGG. Ultimately, phytochemical compound-target genes-Pathways network has been assembled utilizing Cytoscape to decipher the mechanism of the herb.

The network analysis revealed that 5 targets (CASP3, TNF, VEGFA, PTGS2 and CNR1) might be the key therapeutic targets of asparagus on Epilepsy comorbid Alzheimer's disease. Based on high connectivity, four hub compounds with the highest connectivity were noted and it includes Shatavarin V, Sarsasapogenin, Shatavarin IX, and Shatavarin VI. A total of 19 KEGG terms were enriched as the potential pathways of A. racemosus in Epilepsy comorbid Alzheimer's disease.

This study envisaged the pharmacological and molecular mechanism of A. racemosus against epilepsy comorbid Alzheimer's disease and put forward a strategy to uncover the mechanisms of Traditional Indian Medicine based on network pharmacology.

The online version contains supplementary material available at 10.1007/s40203-023-00169-x.

Alzheimer's disease (AD) is a deteriorating neural disorder, and currently, available drugs are ineffective in its treatment. Emblica officinalis (Amla) is widely recognised in the Indian medicinal system for ameliorative effects in managing diabetes, hyperlipidaemia and neurological diseases. Thus, we aimed to identify the active phytoconstituents of E. officinalis and their role in inhibiting the potential targets for the possible treatment of AD. The network pharmacology approach, gene ontology, molecular docking and molecular dynamics simulation (MDS) studies were performed. A total of 36 bioactive components in E. officinalis, 95 predicted anti-AD targets, and 3398 AD-related targets were identified from different databases. The network analysis showed that BACE1, ABCB1 and AChE, CA2 are the most potential AD targets. Based on gene ontology and topology analysis results, BACE1 was a significant target related to AD pathways, and quercetin, kaempferol and myricetin showed the highest interaction with target genes. The molecular docking results found that rutin and quercetin displayed better binding affinities -7.5, -5.67 kcal/mol than the BACE1 bound internal ligand. Furthermore, MDS results suggested that quercetin and rutin could be potential inhibitors against BACE-1 protein and may have therapeutic effects in treating AD. Such promising results could be further helpful in new drug discovery against AD.Communicated by Ramaswamy H. Sarma.

Maternal obesity, characterized by an elevated body mass index (BMI) during pregnancy, is known to have adverse effects on the offspring. However, a recent study suggests that Elateriospermum tapos (E. tapos) yogurt may hold potential in mitigating excessive weight retention post-pregnancy. Thus, this study aims to employ network pharmacology to explore the pharmacological effects of the bioactive compounds present in E. tapos yogurt against maternal obesity. Initially, a screening process is conducted to identify the bioactive compounds in E. tapos yogurt, followed by the prediction of potential gene targets for these compounds using Swiss Target Prediction and the SuperPred databases. Maternal obesity-associated genes are sourced from the OMIM, DisGeNet, and GeneCards databases. The interaction between the identified compounds and maternal obesity genes is established via protein-protein interaction analysis, gene ontology examination, and KEGG pathway analysis. To validate the results, molecular docking studies are conducted using AutoDock Tools software. The findings reveal that out of the 64 compounds analyzed, three meet the screening criteria, resulting in a total of 380 potential gene targets. Among these targets, 240 are shared with maternal obesity-related genes. Further analysis demonstrates the favorable affinity of these active compounds with key targets, linking them to biological processes involving protein phosphorylation, inflammation, as well as the pathways related to lipid metabolism, atherosclerosis, and the other signaling pathways. In conclusion, this study provides valuable insights into the potential pharmacological effects of the bioactive compounds found in E. tapos yogurt against maternal obesity. These findings open avenues for further exploration and potential therapeutic interventions targeting maternal obesity.

Globally, lung cancer remains one of the leading causes of cancer-related mortality, warranting the exploration of novel and effective therapeutic approaches. Soy-fermented food products have long been associated with potential health benefits, including anticancer properties. There is still a lack of understanding of the active components of these drugs as well as their underlying mechanistic pathways responsible for their anti-lung cancer effects. In this study, we have undertaken an integrated approach combining network pharmacology and molecular docking to elucidate the mechanism of action of soy-fermented food products against lung cancer through simulation and in vitro validation. Using network pharmacology, we constructed a comprehensive network of interactions between the identified isoflavones in soy-fermented food products and lung cancer-associated targets. Molecular docking was performed to predict the binding affinities of these compounds with key lung cancer-related proteins. Additionally, molecular simulation was utilized to investigate the stability of the compound-target complexes over time, providing insights into their dynamic interactions. Our results identified daidzein as a potential active component in soy-fermented food products with high binding affinities towards critical lung cancer targets. Molecular dynamic simulations confirmed the stability of the daidzein-MMP9 and daidzein-HSP90AA1 complexes, suggesting their potential as effective inhibitors. Additionally, in vitro validation experiments demonstrated that treatment with daidzein significantly inhibited cancer cell proliferation and suppressed cancer cell migration and the invasion of A549 lung cancer cells. Consequently, the estrogen signaling pathway was recognized as the pathway modulated by daidzein against lung cancer. Overall, the findings of the present study highlight the therapeutic potential of soy-fermented food products in lung cancer treatment and provide valuable insights for the development of targeted therapies using the identified bioactive compounds. Further investigation and clinical studies are warranted to validate these findings and translate them into clinical applications for improved lung cancer management.

Epilepsy is a chronic neurological disorder marked by recurrent seizures, significantly affecting the population in Saudi Arabia across all age demographics. The global prevalence of active epilepsy is around 6.38/1,000 persons and in the Arabian region, the median prevalence of active epilepsy is 4.4/1,000 persons. However, over 75% of individuals are untreated. Consequently, the development of therapeutic strategies with increased efficacy and safety profiles is essential to improve the survival rate among epilepsy patients. The current study integrates network pharmacology along with Bioinformatics approaches to explore the potential molecular mechanisms of local flora of Saudi Arabia including Solanum incanum, Abrus precatorius, Withania somnifera, and Azadirachta indica in epilepsy treatment. In the preliminary phase, data related to the bioactive components of the local plants and the associated target genes of both these plants and epilepsy were gathered from scientific literature and open-source databases. This data was then analyzed to identify common targets between the plants and ovarian cancer. Based on these common targets, a protein-protein interaction (PPI) network was constructed utilizing the STRING database, which was subsequently incorporated into the Cytoscape software for identification of hub genes based on their degree of connectivity. Lastly, an interplay network depicting the associations between the compounds and the overlapping genes was formulated via Cytoscape, to study the potential network pharmacology implications of these active compounds in relation to ovarian cancer. Following that, a compound-target protein-pathway network was constructed which uncovered that namely abrectorin, genistin, (+)-catechin, precatorine, (+)-ascorbic acid, licoflavanone, skrofulein, stigmasterone, 5,7-Dihydroxy-4'-methoxy-8,3'-di-C-prenylflavanone could potentially be used as antagonists for the therapeutic management of epilepsy by targeting TNF and TP53 proteins. Furthermore, the implementation of molecular docking reinforces the binding affinity of the compound, indicating a robust stability of the forecasted compounds at the docked site. This research lays both a theoretical and experimental groundwork for more profound investigations and establishes a practical method for the strategic employment of active compounds in the development of anti-epileptic therapeutics.

Breast cancer represents the leading cause of mortality among women worldwide. Since the complexity of breast cancer as a disease resides in its heterogeneity as it consists of several subtypes such as hormone receptor-positive subtypes: Luminal A, Luminal B, Her2- overexpressed, basal-like and hormone receptor-negative subtype: TNBC. Among all the subtypes, triple negative breast cancer (TNBC) is the most lethal and complex subtype. Moreover, the available treatment options like surgery, radiation therapy, and chemotherapy are not sufficient because of the associated side effects and drug resistance development. Therefore, discovery of new effective natural compounds with anti-tumor activity is required. In this pursuit, marine organisms provide a plentiful supply of such chemicals compounds. A marine compound Brugine found in the bark and stem of mangrove species Bruguiera sexangula is a potential anti-cancer compound. It has shown its cytotoxic activity against sarcoma 180 and lewis lung cancer. The molecular processes, however, are currently unknown. So, in order to research the molecular pathways this compound utilizes, we sought to apply a network pharmacology approach. The network pharmacology strategy we used in this investigation to identify and evaluate possible molecular pathways involved in the treatment of breast cancer with brugine was supported by simulation and molecular docking experiments. The study was conducted using various databases such as the cancer genome atlas (TCGA) for the genetic profile study of breast cancer, Swiss ADME for studying the pharmacodynamic study of brugine, Gene cards for collection of information of genes, STRING was used to study the interaction among proteins, AutoDock vina was to study the binding efficacy of brugine with the best fit protein. The results showed that the compound and breast cancer target network shared 90 common targets. According to the functional enrichment analysis brugine exhibited its effects in breast cancer via modulating certain pathways such as cAMP signaling pathway, JAK/STAT pathway, HIF-1 signaling pathway PI3K-Akt pathway, calcium signaling pathway, and Necroptosis. Molecular docking investigations demonstrated that the investigated marine compound has a high affinity for the key target, protein kinase A (PKA). A stable protein-ligand combination was created by the best hit molecule, according to molecular dynamics modeling. The purpose of this research was to examine the importance of brugine as a potentially effective treatment for breast cancer and to obtain knowledge of the molecular mechanism used by this substance in breast cancer.

The incidence of Hepatocellular Carcinoma (HCC) in Saudi Arabia is not surprising given the relatively high prevalence of hepatitis C virus (HCV) infection. Hepatitis C is also common in Saudi Arabia with a prevalence rate of 1% to 3% of the population, which further increases the risk of HCC. The incidence of HCC has been increasing in recent years, with HCV-related HCC accounting for a significant proportion of cases. Traditional medicine has long been a part of Saudi Arabian culture, and many medicinal plants have been used for centuries to treat various ailments, including cancer. Following that, this study combines network pharmacology with bioinformatics approaches to potentially revolutionize HCV-related HCC treatment by identifying effective phytochemicals of indigenous plants of Medina valley. Eight indigenous plants including Rumex vesicarius, Withania somnifera, Rhazya stricta, Heliotropium arbainense, Asphodelus fistulosus, Pulicaria incise, Commicarpus grandiflorus, and Senna alexandrina, were selected for the initial screening of potential drug-like compounds. At first, the information related to active compounds of eight indigenous plants was retrieved from public databases and through literature review which was later combined with differentially expressed genes (DEGs) obtained through microarray datasets. Later, a compound-target genes-disease network was constructed which uncovered that kaempferol, rhazimol, beta-sitosterol, 12-Hydroxy-3-keto-bisnor-4-cholenic acid, 5-O-caffeoylquinic acid, 24-Methyldesmosterol, stigmasterone, fucosterol, and withanolide_J decisively contributed to the cell growth and proliferation by affecting ALB and PTGS2 proteins. Moreover, the molecular docking and Molecular Dynamic (MD) simulation of 20 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. But the findings were not validated in actual patients, so further investigation is needed to confirm the potential use of selected medicinal plants towards HCV-related HC.

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

The number of preclinical and clinical studies evaluating natural products-based management of dementia has gradually increased, with an exponential rise in 2020 and 2021. Keeping this in mind, we examined current trends from 2016 to 2021 in order to assess the growth potential of natural products in the treatment of dementia. Publicly available literature was collected from various databases like PubMed and Google Scholar. Oxidative stress-related targets, NF-κB pathway, anti-tau aggregation, anti-AChE, and A-β aggregation were found to be common targets and pathways. A retrospective analysis of 33 antidementia natural compounds identified 125 sustainable resources distributed among 65 families, 39 orders, and 7 classes. We found that families such as Berberidaceae, Zingiberaceae, and Fabaceae, as well as orders such as Lamiales, Sapindales, and Myrtales, appear to be important and should be researched further for antidementia compounds. Moreover, some natural products, such as quercetin, curcumin, icariside II, berberine, and resveratrol, have a wide range of applications. Clinical studies and patents support the importance of dietary supplements and natural products, which we will also discuss. Finally, we conclude with the broad scope, future challenges, and opportunities for field researchers.

In view of the ethno medicinal use of Enhydra fluctuans for the treatment of kidney stones; the present study aimed to elucidate the molecular mechanisms involved in the amelioration of nephrolithiasis through a network pharmacology approach. The phytoconstituents were queried in DIGEP-Pred to identify the regulated proteins. The modulated proteins were then enriched in the STRING to predict the protein-protein interactions and the probably regulated pathways were traced in the Kyoto Encyclopedia of Genes and Genomes. Further, the network was constructed using Cytoscape ver 3.5.1. Results showed that β-carotene was found to be regulating maximum targets i.e. 26. In addition, 63 proteins were triggered by the components in which the vitamin D receptor was targeted by the maximum phytoconstituents i.e. 16. The enrichment analysis identified the regulation of 67 pathways in which fluid shear stress and atherosclerosis-associated pathways (KEGG entry hsa05418) regulated ten genes. Further, protein kinase C-α was traced in 23 different pathways. In addition, the majority of the regulated genes were identified from the extracellular space via the modulation of 43 genes. Also, nuclear receptor activity had the maximum molecular function via the regulation of 7 genes. Likewise, the response to organic substance was predicted to trigger the top genes i.e. 43. In contrast, Stigmasterol, Baicalein-7-o-glucoside, and Kauran-16-ol were found to have a high affinity to bind with the VDR receptor confirmed by the molecular modelling and the dynamics. Hence, the study elucidated the probable molecular mechanisms of E. fluctuans in managing nephrolithiasis and identified the lead molecules, their targets, and possible pathways.Communicated by Ramaswamy H. Sarma.