Search Results (2,955)

Background/Objectives: Cancer suffers from unresolved therapeutic challenges owing to the lack of targeted therapies and heightened recurrence risk. This study aimed to investigate the new series of hydroxamate by structurally modifying the pharmacophore of vorinostat. Methods: The present work involves the synthesis of 15 differently substituted 2H-1,2,3-triazole-based hydroxamide analogs by employing triazole ring as a cap with varied linker fragments. The compounds were evaluated for their anticancer effect, especially their anti-breast cancer response. Molecular docking and molecular dynamics simulations were conducted to examine binding interactions. Results: Results indicated that among all synthesized hybrids, the molecule VI(i) inhibits the growth of MCF-7 and A-549 cells (GI₅₀ < 10 μg/mL) in an antiproliferative assay. Compound VI(i) was also tested for cytotoxic activity by employing an MTT assay against A549, MCF-7, and MDA-MB-231 cell lines, and the findings indicate its potent anticancer response, especially against MCF-7 cells with IC₅₀ of 60 µg/mL. However, it experiences minimal toxicity towards the normal cell line (HEK-293). Mechanistic studies revealed a dual-pathway activation: first, apoptosis (17.18% of early and 10.22% of late apoptotic cells by annexin V/PI analysis); second, cell cycle arrest at the S and G2/M phases. It also promotes ROS generation in a concentration-dependent manner. The HDAC–inhibitory assay, extended in silico molecular docking, and MD simulation experiments further validated its significant binding affinity towards HDAC 1 and 6 isoforms. DFT and ADMET screening further support the biological proclivity of the title compounds. The notable biological contribution of VI(i) highlights it as a potential candidate, especially against breast cancer cells. Full article

Efflux is one of the key mechanisms used by Gram-negative bacteria to reduce internal antibiotic concentrations. These active transport systems recognize and expel a wide range of toxic molecules, including antibiotics, thereby contributing to reduced antibiotic susceptibility and allowing the bacteria to acquire additional resistance mechanisms. To date, unlike other resistance mechanisms such as enzymatic modification or target mutations/masking, efflux is challenging to detect and counteract in clinical settings, and no standardized methods are currently available to diagnose or inhibit this mechanism effectively. This review first outlines the structural and functional features of major efflux pumps in Gram-negative bacteria and their role in antibiotic resistance. It then explores various strategies used to curb their activity, with a particular focus on efflux pump inhibitors under development, detailing their structural classes, modes of action, and pharmacological potential. We discuss the main obstacles to their development, including the structural complexity and substrate promiscuity of efflux mechanisms, the limitations of current screening methods, pharmacokinetic and tissue distribution issues, and the risk of off-target toxicity. Overcoming these multifactorial barriers is essential to the rational development of less efflux-prone antibiotics or of efflux pump inhibitors. Full article

During the course of genome mining initiatives, we identified a marine-derived Micromonospora, assigned here as strain WMMD956; the genome of WMMD956 appeared to contain a number of features associated with everninomicins, well-known antimicrobial orthosomycins. In addition, LCMS-based hierarchical clustering analysis and principal component analysis (hcapca) revealed that WMMD956 displayed an extreme degree of metabolomic and genomic novelty. Dereplication of high-resolution tandem mass spectrometry (HRMS/MS) and Global Natural Product Social molecular networking platform (GNPS) analysis of WMMD956 resulted in the identification of several analogs of the previously known everninomicin. Chemical structures were unambiguously confirmed by HR-ESI-MS, 1D and 2D NMR experiments, and the use of MS/MS data. The isolated metabolites, 1–3, were evaluated for their antibacterial activity against methicillin-resistant Staphalococcus aureus (MRSA). Full article

Estrogen receptor alpha (ERα) plays a vital role in the development and progression of breast cancer by regulating the expression of genes associated with cell proliferation in breast tissue. ERα inhibition is a key strategy in the prevention and treatment of breast cancer. Previous research modified chalcone compounds into pyrazoline benzenesulfonamide derivatives (Modifina) which show activity as an ERα inhibitor. This study aimed to design novel pyrazoline benzenesulfonamide derivatives (PBDs) as ERα antagonists using in silico approaches. Structure-based and ligand-based drug design approaches were used to create drug target molecules. A total of forty-five target molecules were initially designed and screened for drug likeness (Lipinski’s rule of five), cytotoxicity, pharmacokinetics and toxicity using a web-based prediction tools. Promising candidates were subjected to molecular docking using AutoDock 4.2.6 to evaluate their binding interaction with ERα, followed by molecular dynamics simulations using AMBER20 to assess complex stability. A pharmacophore model was also generated using LigandScout 4.4.3 Advanced. The molecular docking results identified PBD-17 and PBD-20 as the most promising compounds, with binding free energies (ΔG) of −11.21 kcal/mol and −11.15 kcal/mol, respectively. Both formed hydrogen bonds with key ERα residues ARG394, GLU353, and LEU387. MM-PBSA further supported these findings, with binding energies of −58.23 kJ/mol for PDB-17 and −139.46 kJ/mol for PDB-20, compared to −145.31 kJ/mol, for the reference compound, 4-OHT. Although slightly less favorable than 4-OHT, PBD-20 demonstrated a more stable interaction with ERα than PBD-17. Furthermore, pharmacophore screening showed that both PBD-17 and PBD-20 aligned well with the generated model, each achieving a match score of 45.20. These findings suggest that PBD-17 and PBD-20 are promising lead compounds for the development of a potent ERα inhibitor in breast cancer therapy. Full article

Background: Hypertension is a serious global health issue affecting over one billion adults and leading to severe complications if left unmanaged. Despite medical advancements, only a fraction of patients effectively have their hypertension under control. Among the factors that hinder adherence to hypertensive drugs are the debilitating side effects of the drugs. The lack of adherence results in poorer patient outcomes as patients opt to live with their condition, instead of having to deal with the side effects. Hence, there is a need to discover new hypertension drug molecules with better side effects to increase patient treatment options. To this end, computational methods such as artificial intelligence (AI) have become an exciting option for modern drug discovery. AI-based computational drug discovery methods generate numerous new lead antihypertensive drug molecules. However, predicting their potential side effects remains a significant challenge because of the complexity of biological interactions and limited data on these molecules. Methods: This paper presents a machine learning approach to predict the potential side effects of computationally synthesised antihypertensive drug molecules based on their molecular properties, particularly functional groups. We curated a dataset combining information from the SIDER 4.1 and ChEMBL databases, enriched with molecular descriptors (logP, PSA, HBD, HBA) using RDKit. Results: Gradient Boosting gave the most stable generalisation, with a weighted F1 of 0.80, and AUC-ROC of 0.62 on the independent test set. SHAP analysis over the cross-validation folds showed polar surface area and logP contributing the largest global impact, followed by hydrogen bond counts. Conclusions: Functional group patterns, augmented with key ADMET descriptors, offer a first-pass screen for identifying side-effect risks in AI-designed antihypertensive leads. Full article

Tellurite (TeO₃²⁻) is a highly soluble and toxic oxyanion that inhibits the growth of Escherichia coli at concentrations as low as ~1 µg/mL. This toxicity has been primarily attributed to the generation of reactive oxygen species (ROS) during its intracellular reduction by thiol-containing molecules and NAD(P)H-dependent enzymes. However, under anaerobic conditions, E. coli exhibits significantly increased tellurite tolerance—up to 100-fold in minimal media—suggesting the involvement of additional, ROS-independent mechanisms. In this study, we combined chemical-genomic screening, untargeted metabolomics, and targeted biochemical assays to investigate the effects of tellurite under both aerobic and anaerobic conditions. Our findings reveal that tellurite perturbs amino acid and nucleotide metabolism, leading to intracellular imbalances that impair protein synthesis. Additionally, tellurite induces notable changes in membrane lipid composition, particularly in phosphatidylethanolamine derivatives, which may influence biophysical properties of the membrane, such as fluidity or curvature. This membrane remodeling could contribute to the increased resistance observed under anaerobic conditions, although direct evidence of altered membrane fluidity remains to be established. Overall, these results demonstrate that tellurite toxicity extends beyond oxidative stress, impacting central metabolic pathways and membrane-associated functions regardless of oxygen availability. Full article

The protein p53, often referred to as the “guardian of the genome,” is essential for preserving cellular balance and preventing cancerous transformations. As one of the most commonly altered genes in human cancers, its impaired function is associated with tumor initiation, development, and resistance to treatment. Exploring the diverse roles of p53, which include regulating the cell cycle, repairing DNA, inducing apoptosis, reprogramming metabolism, and modulating immunity, provides valuable insights into cancer mechanisms and potential treatments. This review integrates recent findings on p53′s dual nature, functioning as both a tumor suppressor and an oncogenic promoter, depending on the context. Wild-type p53 suppresses tumors by inducing cell cycle arrest or apoptosis in response to genotoxic stress, while mutated variants often lose these functions or gain novel pro-oncogenic activities. Emerging evidence highlights p53′s involvement in non-canonical pathways, such as regulating tumor microenvironment interactions, metabolic flexibility, and immune evasion mechanisms. For instance, p53 modulates immune checkpoint expression and influences the efficacy of immunotherapies, including PD-1/PD-L1 blockade. Furthermore, advancements in precision diagnostics, such as liquid biopsy-based detection of p53 mutations and AI-driven bioinformatics tools, enable early cancer identification and stratification of patients likely to benefit from targeted therapies. Therapeutic strategies targeting p53 pathways are rapidly evolving. Small molecules restoring wild-type p53 activity or disrupting mutant p53 interactions, such as APR-246 and MDM2 inhibitors, show promise in clinical trials. Combination approaches integrating gene editing with synthetic lethal strategies aim to exploit p53-dependent vulnerabilities. Additionally, leveraging p53′s immunomodulatory effects through vaccine development or adjuvants may enhance immunotherapy responses. In conclusion, deciphering p53′s complex biology underscores its unparalleled potential as a biomarker and therapeutic target. Integrating multi-omics analyses, functional genomic screens, and real-world clinical data will accelerate the translation of p53-focused research into precision oncology breakthroughs, ultimately improving patient outcomes. Full article

Background/Objectives: Ewing sarcoma (ES), a highly aggressive bone and soft tissue cancer occurring in children and young adults, is defined by the ETS fusion oncoprotein EWS::FLI1. Although event-free survival rates remain high in ES patients with localized disease, those with metastatic or relapsed disease face poor long-term survival odds. Topoisomerase 1 (TOP1) inhibitors are commonly used therapeutics in ES relapse regimens. Methods: In this work, we used a genome-wide CRISPR knockout library screen to identify the deletion of the TOP1 gene as a mechanism for resistance to topoisomerase 1 inhibitors. Using isogenic cell line models, we performed a high-throughput small-molecule screen to discover a small molecule, GNF-7, which had an IC50 that was 10-fold lower in TOP1-deficient cells when compared to the wild-type cells. Results: The characterization of GNF-7 demonstrated the molecule was highly active in the inhibition of CSK, p38α, EphA2, Lyn, and ZAK and specifically downregulated genes induced by the EWS::FLI1 fusion oncoprotein. Conclusions: Together, these results suggest that GNF-7 or small molecules with a similar kinase profile could be effective treatments for ES patients in combination with TOP1 inhibitors or for those patients who have developed resistance to TOP1 inhibitors. Full article

Alpha-Synuclein (α-Syn) is a presynaptic neuronal protein implicated in the pathogenesis of Parkinson’s disease (PD) and other synucleinopathies, primarily through its aggregation into insoluble fibrils. The extended α-Syn half-life necessitates treatment durations that are incompatible with efficient high-throughput drug screening, can risk compound stability or cause cellular toxicity. To address this, we inserted a PEST sequence, a motif known to promote rapid protein degradation, at the C-terminus of the SNCA gene using CRISPR/Cas9 to create a novel cell line with reduced α-Syn half-life. This modification accelerates α-Syn turnover, providing a robust model for studying α-Syn dynamics and offering a platform that is applicable to other long-lived proteins. Our results demonstrate a six-fold reduction in α-Syn half-life, enabling the rapid detection of changes in protein levels and facilitating the identification of molecules that modulate α-Syn production and degradation pathways. Using inhibitors of the proteasome, transcription, and translation further validated the model’s utility in examining various mechanisms that impact protein levels. This novel cell line represents a significant advancement for studying α-Syn dynamics and offers promising avenues to develop therapeutics for α-synucleinopathies. Future research should focus on validating this model in diverse experimental settings and exploring its potential in high-throughput screening applications. Full article

Estrogens regulate many physiological processes in the human body, including the cardiovascular system. Importantly, Estradiol (E2) exerts its vascular protective actions, in part, by promoting endothelial repair via induction of endothelial cell (EC) proliferation, migration and angiogenesis. Recent evidence that microRNAs (miRNAs) play an important role in vascular health and disease as well as in regulating Estrogen actions in many cell types. We hypothesize that E2 may mediate its vascular protective actions via the regulation of miRNAs. Following initial screening, we found that E2 downregulates the levels of miR-193a-3p in ECs. Moreover, miR-193a-3p downregulation by miR-193a-3p-antimir mimicked the effects as E2 on EC growth, migration, and capillary formation. Restoring miR-193a-3p levels with mimics after E2 treatment abrogated the vasculogenic actions of E2, suggesting a key role of miR-193a-3p in E2-mediated EC-growth-promoting effects. We further investigated the cellular mechanisms involved and found that miR-193a-3p inhibits angiogenesis by blocking phosphoinositide-3-kinase (PI3K)/Akt-vascular endothelial growth factor (VEGF) and Activin receptor-like kinase 1 (ALK1)/SMAD1/5/8 signaling in ECs, both pathways that are important in E2-mediated vascular protection. Additionally, using reverse transcription polymerase chain reaction (RT-PCR), we demonstrate that E2 downregulates miR-193a-3p in ECs via Estrogen Receptor (ER)α, but not ERβ or G protein-coupled estrogen receptor (GPER). Moreover, these actions occur post-transcriptionally, as the expression of pri-miR-193a-3p was not affected. The anti-angiogenic actions of miR-193a-3p were also observed in in vivo Matrigel implant-based capillary formation studies in ovariectomized mice where E2 induced capillary formation, and these effects were abrogated in the presence of miR-193a-3p, but not in the control mimic. Assessment of miR-193a-3p levels in plasma collected from in vitro fertilization (IVF) subjects with low and high E2 levels showed significantly lower miR-193a-3p levels in responders during the high E2 period. Hence, our findings provide the first evidence that miR-193a-3p mimic inhibits angiogenesis whereas its antimir is angiogenic. Importantly, E2 mediates its regenerative actions on ECs/capillary formation by downregulating endogenous miR-193a-3p expression. Both miR-193a-3p mimic or antimir may represent important therapeutic molecules to prevent or to induce endothelial function in treating pathophysiologies associated with capillary growth. Full article

The global impact of the COVID-19 crisis has underscored the need for novel therapeutic candidates capable of efficiently targeting essential viral proteins. Existing therapeutic strategies continue to encounter limitations such as reduced efficacy against emerging variants, safety concerns, and suboptimal pharmacodynamics, which emphasize the potential of natural-origin compounds as supportive agents with immunomodulatory, anti-inflammatory, and antioxidant benefits. The present study significantly advances prior molecular docking research through comprehensive virtual screening of structurally related analogs derived from antiviral phytochemicals. These compounds were evaluated specifically against the SARS-CoV-2 main protease (3CLpro) and papain-like protease (PLpro). Utilizing chemical similarity algorithms via the ChEMBL database, over 600 candidate molecules were retrieved and subjected to automated docking, interaction pattern analysis, and comprehensive ADMET profiling. Several analogs showed enhanced binding scores relative to their parent scaffolds, with CHEMBL1720210 (a shogaol-derived analog) demonstrating strong interaction with PLpro (−9.34 kcal/mol), and CHEMBL1495225 (a 6-gingerol derivative) showing high affinity for 3CLpro (−8.04 kcal/mol). Molecular interaction analysis revealed that CHEMBL1720210 forms hydrogen bonds with key PLpro residues including GLY163, LEU162, GLN269, TYR265, and TYR273, complemented by hydrophobic interactions with TYR268 and PRO248. CHEMBL1495225 establishes multiple hydrogen bonds with the 3CLpro residues ASP197, ARG131, TYR239, LEU272, and GLY195, along with hydrophobic contacts with LEU287. Gene expression predictions via DIGEP-Pred indicated that the top-ranked compounds could influence biological pathways linked to inflammation and oxidative stress, processes implicated in COVID-19’s pathology. Notably, CHEMBL4069090 emerged as a lead compound with favorable drug-likeness and predicted binding to PLpro. Overall, the applied in silico framework facilitated the rational prioritization of bioactive analogs with promising pharmacological profiles, supporting their advancement toward experimental validation and therapeutic exploration against SARS-CoV-2. Full article

Peptides play a crucial role in the development of pharmaceuticals and functional foods. Multiple studies have shown that natural bioactive peptides possess antioxidant, antihypertensive, anti-tumor, and anti-inflammatory activities. Marine bioactive peptides, especially those sourced from fish, constitute a substantial reservoir of these molecules. Although considerable research has been undertaken on fish-derived peptides, studies specifically concerning those from tuna are limited. Tuna, a marine fish of high nutritional value, generates substantial by-product waste during fishing and processing. Therefore, it is essential to conduct an evaluation of the advancements in study on tuna-derived active peptides and to offer a perspective on the direction of future investigations. This review integrates prospective bioactive peptides derived from tuna and reports contemporary strategies for their investigation, including extraction, purification, screening, identification, and activity evaluation procedures, including Yeast Surface Display (YSD) and molecular docking. This review seeks to promote the continued investigation and application of bioactive peptides derived from tuna. Full article

Phosphatidylinositol 3-kinase alpha (PI3Kα) is frequently mutated in head and neck squamous cell carcinoma (HNSCC), leading to the constitutive activation of the PI3K/Akt pathway, which promotes tumor cell proliferation, survival, and metastasis. PI3Kα allosteric inhibitors demonstrate therapeutic potential as both monotherapy and combination therapy, particularly in patients with PIK3CA mutations or resistance to immunotherapy, through the precise targeting of mutant PI3Kα. Compared to ATP-competitive PI3Kα inhibitors such as Alpelisib, the allosteric inhibitor RLY-2608 exhibits enhanced selectivity for mutant PI3Kα while minimizing the inhibition of wild-type PI3Kα, thereby reducing side effects such as hyperglycemia. To date, no allosteric PI3Kα inhibitors have been approved for clinical use. To develop novel PI3Kα inhibitors with improved safety and efficacy, we employed a scaffold hopping approach to structurally modify RLY-2608 and constructed a compound library. Based on the structural information of the PI3Kα allosteric site, we conducted the systematic virtual screening of 11,550 molecules from databases to identify lead compounds. Through integrated approaches, including molecular docking studies, target validation, druggability evaluation, molecular dynamics simulations, and metabolic pathway and metabolite analyses, we successfully identified a promising novel allosteric PI3Kα inhibitor, H-18 (3-(2-chloro-5-fluorophenyl)isoindolin-1-one). H-18 has not been previously reported as a PI3Kα inhibitor, and provides an excellent foundation for subsequent lead optimization, offering a significant starting point for the development of more potent PI3Kα allosteric inhibitors. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article