Potential Drug Candidates Research Articles

Mixture-of-Experts Based Dissociation Kinetic Model for De Novo Design of HSP90 Inhibitors with Prolonged Residence Time.

The dissociation rate constant (koff) significantly impacts the drug potency and dosing frequency. This work proposes a powerful optimization-based framework for de novo drug design guided by koff. First, a comprehensive database containing 2,773 unique koff values is created. Based on the database, a novel generic dissociation kinetic model is developed with a mixture-of-experts architecture, enabling high-throughput predictions of koff with high accuracy. The developed model is then integrated with an optimization-based mathematical programming approach to design drug candidates with low koff. Finally, the τ-RAMD method is utilized to rigorously verify the designed potential drug candidates. In a case study, the framework successfully identified numerous new potential HSP90 inhibitor candidates, achieving a maximum 45.7% improvement in residence time (τ = 1/koff) compared to that of a known exceptional HSP90 inhibitor. These findings demonstrate the feasibility and effectiveness of the kinetics-guided optimization-based de novo drug design framework in designing drug candidates with prolonged τ.

Exploring characteristic features for effective HCN1 channel inhibition using integrated analytical approaches: 3D QSAR, molecular docking, homology modelling, ADME and molecular dynamics.

Neuropathic pain (NP) is characterized by hyperalgesia, allodynia, and spontaneous pain. Hyperpolarization-activated cyclic nucleotide-gated (HCN) channel involved in neuronal hyperexcitability, has emerged as an important target for the drug development of NP. HCN channels exist in four different isoforms, where HCN1 is majorly expressed in dorsal root ganglion having an imperative role in NP pathophysiology. A specific HCN1 channel inhibitor will hold the better potential to treat NP without disturbing the physiological roles of other HCN isoforms. The main objective is to identify and analyze the chemical properties of scaffolds with higher HCN1 channel specificity. The 3D-QSAR studies highlight the hydrophobic & hydrogen bond donor groups enhance specificity towards the HCN1 channel. Further, the molecular interaction of the scaffolds with the HCN1 pore was studied by generating an open-pore model of the HCN1 channel using homology modelling and then docking the molecules with it. In addition, the important residues involved in the interaction between HCN1 pore and scaffolds were also identified. Moreover, ADME predictions revealed that compounds had good oral bioavailability and solubility characteristics. Subsequently, molecular dynamics simulation studies revealed the better stability of the lead molecules A7 and A9 during interactions and ascertained them as potential drug candidates. Cumulative studies provided the important structural features for enhancing HCN1 channel-specific inhibition, paving the way to design and develop novel specific HCN1 channel inhibitors.

Aniline Derivatives Containing 1-Substituted 1,2,3-Triazole System as Potential Drug Candidates: Pharmacokinetic Profile Prediction, Lipophilicity Analysis Using Experimental and In Silico Studies

Background: The triazole ring is an attractive structural unit in medicinal chemistry, and chemical compounds containing this type of system in their structure exhibit a wide spectrum of biological activity. They are used in the development of new pharmaceuticals. One of the basic parameters considered in the initial phase of designing potential drugs is lipophilicity, which affects the bioavailability and pharmacokinetics of drugs. Methods: The study aimed to assess the lipophilicity of fifteen new triazole derivatives of aniline using reversed phase thin layer chromatography (RP-TLC) and free web servers. Based on in silico methods, the drug similarity and pharmacokinetic profile (ADMET) of synthesized molecules were assessed. Results: A relationship was observed between the structure of the title compound, including the position of substitution in the aniline ring, and the experimental values of lipophilicity parameters (logPTLC). Most of the algorithms used to determine theoretical logP values showed less sensitivity to structural differences of the tested molecules. All obtained derivatives satisfy the drug similarity rules formulated by Lipinski, Ghose and Veber. Moreover, in silico analysis of the ADME profile showed favorable values of parameters related to absorption.

In silico Evaluation of the Feasibility of Magnolia officinalis Electron-shuttling Compounds as Parkinson’s Disease Remedy

Background: Parkinson’s Disease is one of the leading neurodegenerative disorders in the world. Currently, there is still no treatment that could completely cure the disease. Traditional Chinese Medicine has been a source for drug candidates, and many studies have elucidated its pharmacokinetic capabilities. Previous studies showed that Magnolia officinalis has anti-inflammatory, antioxidant, and bioenergy generation activities. Furthermore, the electron-shuttling and bioenergy-stimulating capabilities of herbal and brain disorder medicines have been linked to their effectiveness as a remedy. Objective: This preliminary study aims to evaluate the electron-shuttling compounds of Magnolia officinalis (i.e., acteoside, isoquercitrin, magnatriol B, obovatol, quercitrin, randaiol, and rutin) as potential drug candidates for Parkinson’s Disease. Methods: The seven electron-shuttling compounds were individually docked to the five Parkinson’s Disease-related proteins, namely aromatic L-amino acid decarboxylase, α-synuclein, monoamine oxidase B, catechol-o-methyltransferase, and A2A adenosine receptor, using LibDock. ADMET predictions were also made to screen the compounds further. Results: Molecular docking results showed that all compounds have relatively high LibDock scores against the proteins, with acteoside, isoquercitrin, and rutin having the highest scores. However, considering the ADMET results, only magnatriol B, obovatol, and randaiol had optimal properties as candidates for neurodegenerative drugs. Conclusion: The electron-shuttling compounds of M. officinalis, magnatriol B, obovatol, and randaiol, have the potential to be a remedy for Parkinson’s Disease due to their high probability of binding to the proteins.

Integrated mRNA-seq and miRNA-seq analysis reveals key transcription factors of HNF4α and KLF4 in ADPKD

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic disorder affecting the kidneys. Understanding epigenetic regulatory mechanisms and the role of microRNAs (miRNAs) is crucial for developing therapeutic interventions. Two mRNA datasets (GSE7869 and GSE35831) and miRNA expression data (GSE133530) from ADPKD patients were used to find differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs), with a focus on genes regulated by hub transcription factors (TFs) and their target genes. The expression of hub TFs was validated in human kidneys and animal models through Western Blot (WB) and RT-PCR analysis. The location of the hub TF proteins in kidney cells was observed by a laser confocal microscope. A total of 2037 DEGs were identified. DEM analysis resulted in 59 up-regulated and 107 down-regulated miRNAs. Predicted target DEGs of DEMs indicated two top dysregulated TFs: hepatocyte nuclear factor 4 alpha (HNF4α) and Kruppel-like factor 4 (KLF4). RT-PCR, WB, and immunochemistry results showed that mRNA and protein levels of HNF4α were significantly decreased while KLF4 levels were significantly up-regulated in human ADPKD kidneys and Pkd1 conditional knockout mice compared with normal controls. Laser confocal microscopy revealed that KLF4 was mainly located in the cytoplasm while HNF4α was in the nucleus. Functional enrichment analysis indicated that genes regulated by HNF4α were mainly associated with metabolic pathways, while KLF4-regulated genes were linked to kidney development. Drug response prediction analysis revealed potential drug candidates for ADPKD treatment, including BI-2536, Sepantronium, and AZD5582. This integrated analysis provides new epigenetic insights into the complex miRNA-TF-mRNA network in ADPKD and identifies HNF4α and KLF4 as key TFs. These findings offer valuable resources for further research and potential drug development for ADPKD.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

Construction and Optimization of a Yeast Cell Factory for Producing Active Unnatural Ginsenoside 3β-O-Glc2-DM.

Ginsenosides are major active components of Panax ginseng, which are generally glycosylated at C3-OH and/or C20-OH of protopanaxadiol (PPD) and C6-OH and/or C20-OH of protopanaxatriol. However, the glucosides of dammarenediol-II (DM), which is the direct precursor of PPD, have scarcely been separated from P. ginseng. Because different positions and numbers of the hydroxyl and glycosyl groups lead to a diversity of structure and function of the ginsenosides, it can be inferred that DM glucosides may have different pharmacological activities compared with natural ginsenosides. Herein, we first constructed the cell factory for de novo biosynthesis of 3-O-(β-D-glucopyranosyl-(1→2)-β-D-glucopyranosyl)-dammar-24-ene-3β,20S-diol (3β-O-Glc2-DM) by introducing the codon-optimized genes encoding dammarenediol-II synthase, two UDP-glycosyltransferases (UGTs) including UGT74AC1-M7 from Siraitia grosvenorii and UGTPg29 from P. ginseng in Saccharomyces cerevisiae via the CRISPR/Cas9 system. The titer of 3β-O-Glc2-DM was then increased from 18.9 to 148.0 mg/L by several metabolic engineering strategies including overexpressing the rate-limiting enzymes of triterpenoid biosynthesis, balancing carbon flux of biosynthetic pathways of triterpenoid and ergosterol, and engineering endoplasmic reticulum. Furthermore, the 3β-O-Glc2-DM titer of 766.3 mg/L was achieved through fed-batch fermentation in a 3-L bioreactor. Finally, in vitro assays demonstrated that 3β-O-Glc2-DM exhibited a protective effect on H/R-induced cardiomyocyte damage. This work provides a feasible approach for production of 3β-O-Glc2-DM as a potential cardioprotective drug candidate.

ROSHAMBO: Open-Source Molecular Alignment and 3D Similarity Scoring.

Efficient virtual screening techniques are critical in drug discovery for identifying potential drug candidates. We present an open-source package for molecular alignment and 3D similarity calculations optimized for large-scale virtual screening of small molecules. This work parallels widely used proprietary tools and offers an approach complementary to structure-based virtual screening. Our package employs the PAPER software for optimizing molecular alignments based on Gaussian volume overlaps. GPU acceleration is utilized to significantly reduce computational time and resource requirements. After obtaining the optimal alignments between the target and the query molecules, both shape and color (based on pharmacophore features) scores are computed to assess molecular similarity, with aligned molecules optionally being output in sdf format. The package was benchmarked using the DUDE-Z public data sets. Results demonstrated the package's near-state-of-the-art performance and robustness across multiple target classes, with speed that enables many routine ligand-based drug discovery workflows. As an open-source and freely available resource (github.com/molecularinformatics/roshambo) with both a convenient Python API and command line interface, our package also addresses the need for accessible and efficient virtual screening tools in drug discovery.

Innovative Approach of High-Throughput Screening in the Drug Discovery Quest for Chronic Bronchitis Treatment

Chronic bronchitis (CB) is a challenging condition that worsens over time. Patients continue to experience an abundance of symptoms despite their best attempts to manage them. The etiology of CB is goblet cell overproduction and mucus hypersecretion, which aggravates airflow obstruction by luminal blockage of small airways, epithelial remodeling and modification of airway surface tension that predisposes to collapse. Larger randomized controlled trials are required to validate the pilot data that are currently accessible and assess the treatment’s durability as therapies are still in the early stages of clinical development. To overcome the prevailing increase in CB with the rise in air pollution globally, this study proposes a de novo drug discovery candidate for the effective treatment of CB. With the aid of cutting-edge technology of high-throughput screening, a phytochemical, apigenin, has been administered to target the Beta-2 adrenergic receptor protein and utilized as an exemplary ligand to target the discovery of a suitable drug candidate for Beta-2 adrenergic receptor protein. The drug likeness scores, no evident toxicity and binding affinity –9.092 kcal/mol the “CHEMBL294878” proves to be a potential drug candidate discovered for CB. A therapeutic drug specifically meant to treat CB can be developed with the help of the computational data provided by the research presented in this study.

A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through triple attention mechanisms to enhance machine understanding of molecular functionality and improve the accuracy of virus-drug association identification. SpHN-VDA outperforms leading models across three datasets, particularly excelling in out-of-distribution and cold-start scenarios. It also exhibits enhanced robustness against data perturbation, ranging from 20% to 40%. It accurately identifies critical motifs for binding sites, even without protein residue annotations. Leveraging reliability of SpHN-VDA, we have identified 25 potential candidate drugs through gene expression analysis and CMap. Molecular docking experiments with the SARS-CoV-2 spike protein further corroborate the predictions. This research highlights the broad potential of SpHN-VDA to enhance drug repositioning and identify effective treatments for various diseases.

Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention

Collagen VI-related dystrophies (COL6RD) are a group of rare muscle disorders caused by mutations in specific genes responsible for type VI collagen production. It affects muscles, joints, and connective tissues, leading to weakness, joint problems, and structural issues. Currently, there is no effective treatment for COL6RD; its management typically addresses symptoms and complications. Therefore, it is essential to decipher the disease’s molecular mechanisms, identify drug targets, and develop effective treatment strategies to treat COL6RD. In this study, we employed differential gene expression analysis, weighted gene co-expression network analysis, and genome-scale metabolic modeling to investigate gene expression patterns in COL6RD patients, uncovering key genes, significant metabolites, and disease-related pathophysiological pathways. First, we performed differential gene expression and weighted gene co-expression network analyses, which led to the identification of 12 genes (CHCHD10, MRPS24, TRIP10, RNF123, MRPS15, NDUFB4, COX10, FUNDC2, MDH2, RPL3L, NDUFB11, PARVB) as potential hub genes involved in the disease. Second, we utilized a drug repurposing strategy to identify pharmaceutical candidates that could potentially modulate these genes and be effective in the treatment. Next, we utilized context-specific genome-scale metabolic models to compare metabolic variations between healthy individuals and COL6RD patients. Finally, we conducted reporter metabolite analysis to identify reporter metabolites (e.g., phosphatidates, nicotinate ribonucleotide, ubiquinol, ferricytochrome C). In summary, our analysis revealed critical genes and pathways associated with COL6RD and identified potential targets, reporter metabolites, and candidate drugs for therapeutic interventions.

The Application and Challenges of Artificial Intelligence in G Protein-Coupled Receptor Drug Virtual Screening

G protein-coupled receptors play important roles in various diseases and are key targets for drug discovery. Virtual screening has traditionally been used to identify potential drug candidates from large compound libraries but faces challenges in computational resources and time constraints. The integration of artificial intelligence technologies has improved screening precision and reduced costs. This review discusses current developments, challenges, and future directions in leveraging artificial intelligence to advance GPCR drug screening.

Emerging Roles of ncRNAs in Type 2 Diabetes Mellitus: From Mechanisms to Drug Discovery

Type 2 diabetes mellitus (T2DM), a high-incidence chronic metabolic disorder, has emerged as a global health issue, where most patients need lifelong medication. Gaining insights into molecular mechanisms involved in T2DM development is expected to provide novel strategies for clinical prevention and treatment. Growing evidence validates that non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) function as crucial regulators in multiple biological processes of T2DM, inspiring various potential targets and drug candidates. In this review, we summarize the current understanding of ncRNA roles in T2DM and discuss the potential use of ncRNAs as targets and active molecules for drug discovery.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets.

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

Single-cell transcriptomics reveal diverging pathobiology and opportunities for precision targeting in scleroderma-associated versus idiopathic pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) involves progressive cellular and molecular change within the pulmonary vasculature, leading to increased vascular resistance. Current therapies targeting nitric oxide (NO), endothelin, and prostacyclin pathways yield variable treatment responses. Patients with systemic sclerosis-associated PAH (SSc-PAH) often experience worse outcomes than those with idiopathic PAH (IPAH). Lung tissue samples from four SSc-PAH, four IPAH, and four failed donor specimens were obtained from the Pulmonary Hypertension Breakthrough Initiative (PHBI) lung tissue bank. Single-cell RNA sequencing (scRNAseq) was performed using the 10X Genomics Chromium Flex platform. Data normalization, clustering, and differential expression analysis were conducted using Seurat. Additional analyses included gene set enrichment analysis (GSEA), transcription factor activity analysis, and ligand-receptor signaling. Pharmacotranscriptomic screening was performed using the Connectivity Map. SSc-PAH samples showed a higher proportion of fibroblasts and dendritic cells/macrophages compared to IPAH and donor samples. GSEA revealed enriched pathways related to epithelial-to-mesenchymal transition (EMT), apoptosis, and vascular remodeling in SSc-PAH samples. There was pronounced differential gene expression across diverse pulmonary vascular cell types and in various epithelial cell types in both IPAH and SSc-PAH, with epithelial to endothelial cell signaling observed. Macrophage to endothelial cell signaling was particularly pronounced in SSc-PAH. Pharmacotranscriptomic screening identified TIE2, GSK-3, and PKC inhibitors, among other compounds, as potential drug candidates for reversing SSc-PAH gene expression signatures. Overlapping and distinct gene expression patterns exist in SSc-PAH versus IPAH, with significant molecular differences suggesting unique pathogenic mechanisms in SSc-PAH. These findings highlight the potential for precision-targeted therapies to improve SSc-PAH patient outcomes. Future studies should validate these targets clinically and explore their therapeutic efficacy.

Phytoconstituents of Withania somnifera (L.) Dunal (Ashwagandha) unveiled potential cerebroside sulfotransferase inhibitors: insight through virtual screening, molecular dynamics, toxicity, and reverse pharmacophore analysis

Cerebroside sulfotransferase (CST) is considered as therapeutic target for substrate reduction therapy (SRT) for metachromatic leukodystrophy (MLD). The present study evaluates the therapeutic potential of 57 phytoconstituents of Withania somnifera against CST. Using binding score cutoff ≤-7.0 kcal/mol, top 10 compounds were screened and after ADME and toxicity-based screening, Withasomidienone, 2,4-methylene-cholesterol, and 2,3-Didehydrosomnifericin were identified as safe and potent drug candidates for CST inhibition. Key substrate binding site residues involved in interaction were LYS82, LYS85, SER89, TYR176, PHE170, PHE177. Four steroidal Lactone-based withanolide backbone of these compounds played a critical role in stabilizing their position in the active site pocket. 100 ns molecular dynamics simulation and subsequent trajectory analysis through structural deviation and compactness, principal components, free energy landscape and correlation matrix confirmed the stability of CST-2,3-Didehydrosomnifericin complex throughout the simulation and therefore is considered as the most potent drug candidate for CST inhibition and Withasomidienone as the second most potent drug candidate. The reverse pharmacophore analysis further confirmed the specificity of these two compounds towards CST as no major cross targets were identified. Thus, identified compounds in this study strongly present their candidature for oral drug and provide route for further development of more specific CST inhibitors.

A new N-acylhydrazone oxadiazole derivative with activity against mycobacteria.

Aim: To evaluate the anti-Mycobacterium tuberculosis (Mtb) potential of the hybrid oxadiazol-4-methoxynaphthalene (6n) derived from N-acylhydrazone (4k).Materials & methods: The study determined the minimal inhibitory concentration of (6n) against Mtb H37Rv and Mtb clinical isolates, potential combination of (6n) with anti-tuberculosis drugs and carried out time kill curve assay of Mtb H37Rv. Additional contribution for the analysis of (6n) was explored by in silico pharmacokinetics, and in vitro and in vivo cytotoxicity determinations.Results: The newly synthesized molecule (6n) demonstrated anti-Mtb activity, low cytotoxicity and selectivity for Mtb.Conclusion: The derivative (6n) emerges as a potential anti-TB drug candidate.

Development of tripeptide-cyclotriphosphazene derivatives: In vitro cytotoxicity, genotoxicity studies and molecular docking analysis within ovarian and prostate cancer cell line receptors

Peptide-phosphazene compounds are important compounds of growing interest in biomedical research and have potential therapeutic effects. The tripeptide-cyclotriphosphazenes conjugates were synthesized and analyzed for their molecular docking analysis, visualizing their binding profiles within various cancer cell line receptors and tested in vitro cytotoxic and genotoxic properties. Determining in vitro cytotoxic studies of obtained compounds displayed cytotoxic effect against two selected human cancer cell lines, including ovarian (A2780) and prostate (PC-3), cancer cells. The compound DTAP demonstrated significantly higher efficacy at 100 µM in the PC-3 cancer cell line compared to the reference drug docetaxel at 50 µM. Among the tripeptide-phosphazene conjugtates, DTGG demonstrates the most promising anticancer activity with a logIC50 of 1.23 µM, forming five hydrogen bonds and a favorable salt bridge interaction, along with several hydrophobic interactions, thereby stabilizing its binding within the human ovarian tumor domain based on molecular docking analysis. The derivative DTGP emerges as the most potent among the DTG derivatives, achieving a ΔG model value of −108 kcal/mol, primarily due to a π-cationic interaction with the LYS204 amino acid in chain C, which significantly enhances its binding affinity. Additionally, DNA damage studies on human ovarian and prostate cancer cell lines determined that cell death due to DNA damage was the basis of the decrease in cell viability. These results support the evaluation of the compounds as potential drug candidates.

The active ingredient of Evodia rutaecarpa reduces inflammation in knee osteoarthritis rats through blocking calcium influx and NF-κB pathway.

Chronic inflammation significantly contributes to the progression of osteoarthritis (OA), and an anti-inflammatory small molecule derived from medicinal herbs could be a potential drug candidate for OA. Herein, we investigated the function and mechanism of Evodiamine (EAE), the active ingredient from Evodia rutaecarpa, in chondrocytes and macrophages in vitro and in vivo. The cytotoxicity of EAE was determined using an MTT assay. And the anti-inflammatory and anti-extracellular matrix (ECM) degradation effects of EAE were investigated using qRT-PCR, western blot (WB), immunofluorescence (IF). Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), Fluo-4AM, IF and AutoDock were used to elucidate the molecular mechanisms and signalling pathways of the reducing-inflammatory properties of EAE on chondrocytes in vitro. Moreover, the effect of EAE on macrophage polarization was detected by IF and flow cytometry (FC). Ultimately, we explored the in vivo therapeutic efficacy of EAE in an anterior cruciate ligament transection (ACLT)-induced OA model. The finding demonstrated that EAE blocked the phosphorylation of IKBα and Ca2+ influx, thereby curbing inflammation and ECM degradation. Additionally, EAE can prevent the polarization towards the M1 phenotype. Thus, our findings suggest that EAE has great potential as a therapeutic drug for the treatment of OA.

Exploring marine natural products for identifying putative candidates as EBNA1 inhibitors: An insight from molecular docking, molecular dynamics, and DFT computations

Epstein-Barr virus (EBV), namely a DNA neoplasm virus, is liable for over 1 % of malignant neoplasms involving Hodgkin's and Burkitt's lymphoma as well as ventral cancer. Despite the crucial role of EBV in carcinoma evolution, no treatment has been discovered yet against EBV. Epstein-Barr nuclear antigen 1 (EBNA1), the EBV-encoded latent protein, is produced in all EBV-linked neoplasms and is the only latent protein in these cancer types. EBNA1 protein has multiple roles in the upkeep, reproduction, and EBV genome separation and can thus act as an attractive therapeutic target for treating EBV-related malignancies. In the past few decades, attempts have been made to develop specialized EBNA1 inhibitors to reduce EBNA1 expression or obstruct EBNA1-relied processes, but none has been approved yet. Marine natural products (MNPs) have garnered significant interest as potential sources of antiviral drug candidates. In seeking potent drug candidates to inhibit EBV reproduction, an MNP database containing >14,000 compounds was mined to hunt putative EBNA1 inhibitors using docking computations and molecular dynamics simulations (MDS). On the basis of binding energy (ΔGbinding) estimations over 200 ns MDS, UMHMNP351444649 and UMHMNP134128179 revealed a greater binding affinity towards EBNA1 compared to KWG, with ΔGbinding values of −35.6, −33.3, and −32.4 kcal/mol, respectively. Structural and energetical investigations of UMHMNP351444649 and UMHMNP134128179 complexed with EBNA1 were inspected, unveiling the great constancy of these inhibitors within the EBNA1 binding site. Moreover, the identified MNPs demonstrated favorable physicochemical and medicinal chemistry characteristics. Finally, density functional theory calculations were executed, and the results assured the outcomes obtained from docking computations and MDS. These findings proposed UMHMNP351444649 and UMHMNP134128179 as potential anti-EBV drug candidates that warrant further in-vitro and in-vivo assays.