Search Results (1,749)

Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (−6.3 to −6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of −10.83 and −11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization. Full article

Background/Objectives: Uncontrolled diabetes and associated comorbidities disproportionately affect African American (AA) adults. Medication adherence is key to diabetes control yet is often suboptimal, particularly among AA adults. This study examined associations between patient characteristics and adherence among AA adults with uncontrolled diabetes and compared two medication adherence instruments for predicting diabetes control. Methods: This cross-sectional analysis used baseline data from the Management of Diabetes in Everyday Life (MODEL) study, a clinical trial to improve diabetes self-care among AA adults with uncontrolled diabetes. Internal consistency of the 12-item Adherence to Medication Refills and Medications Scale for diabetes medications (ARMS-D) was evaluated by comparing its Cronbach α to the standardized Cronbach α calculated from MODEL data. Associations with variables were examined using correlations, t-tests, or ANOVA, as appropriate. Stepwise multiple regression identified predictors of diabetes control assessed by hemoglobin A1c (HbA1c). Results: Among 665 participants (mean age = 54 years, HbA1c = 10.24%; 67% female; 73% high health literacy), 75% reported perfect adherence on the Summary of Diabetes Self-Care Activities Medications Subscale (SDSCA-MS) versus 7.3% on ARMS-D. ARMS-D showed strong internal consistency (α = 0.81). Lower adherence by ARMS-D was associated with younger age, higher social complexity, and depression (all p ≤ 0.001). ARMS-D score, age, depression, and insulin, dipeptidyl peptidase 4 inhibitor, and sodium-glucose co-transporter 2 inhibitor use predicted baseline HbA1c. Conclusions: This study demonstrates that younger age, depression, and high social complexity are associated with lower medication adherence measured using the ARMS-D. Adherence gaps identified by ARMS-D may validly predict diabetes control and help guide interventions to improve diabetes care in AA adults with uncontrolled diabetes. Full article

Background/Objectives: Poly (ADP-ribose) polymerase (PARP1) has emerged as a promising therapeutic target in human breast cancer particularly in BRCA1/2 mutation carriers where a synthetic lethal interaction leads to massive tumor cell death upon specific inhibitors’ administration. Current clinically approved PARP inhibitors (Talazoparib and Olaparib) show outstanding therapeutic capabilities but suffer from severe side effects. Most importantly, some of them can cause life-threatening cardiotoxicity through hERG off-target effects. Here, we performed an extensive study to identify lead compounds with improved binding modes and favorable predicted pharmacokinetics using an integrated computational strategy. Methods: An artificial intelligence-driven drug design (AIDDISON™ v2023) workflow was employed to search ultra-large chemical space libraries for active compounds, which were then optimized via computer-aided methods to form a PARP-Tailored Database (PTD). This database was then analyzed through a virtual screening workflow, molecular docking studies, molecular dynamics (MD) simulations, MM/GBSA binding free energy calculations, DFT analysis and ADME/Tox predictions using the Schrödinger suite (v2023-2), MobaXterm v25.2, Gaussian 16.0, ProTox-3 and Pred-hERG v5.0 respectively. Results: Three compounds (1a–1c) were identified as promising candidates. Among them 1a appeared to be the most active compound with a favorable docking score (−9.488 kcal/mol) that is not only higher than 1b and 1c but also higher than that of Talazoparib (−6.778 kcal/mol). MD simulations of 1a–1c in the active site revealed an average RMSD of ~2.5–3.6 Å which is better compared to the parent Talazoparib (5.6 Å). Interestingly, on the 250 ns extended MD study, 1a exhibited a slightly reduced RMSD between 2.4 and 3.2 Å, whereas Talazoparib retained higher fluctuations of ~5 Å to 6 Å. MM/GBSA binding energy analysis indicated 1a to have better predicted binding affinity (−67.820 kcal/mol), which is also better than Talazoparib (−63.734 kcal/mol). DFT calculations showed good electronic properties and in silico ADMET studies also indicated 1a to have good drug-likeness and lower predicted hepatotoxicity and cardiotoxicity risk. Conclusions: These findings identify compound 1a as a promising lead, while compounds 1b and 1c remain viable candidates for further optimization. However, experimental validation is critical to confirm the predicted biological activity and safety profiles. Full article

Background/Objectives: Coagulation factor XIa (FXIa) has emerged as a promising therapeutic target for the development of safer anticoagulant therapies with reduced bleeding risk. This study aimed to develop an interpretable machine learning-driven quantitative structure–activity relationship (QSAR) framework for predicting the inhibitory activity of FXIa inhibitors and supporting virtual screening applications. Methods: A total of 3026 curated compounds retrieved from the ChEMBL database were used for regression modeling, whereas 2119 compounds were retained for classification modeling after excluding intermediate-activity molecules. Molecular descriptors were generated using RDKit, Mordred, and Morgan fingerprint representations. Following preprocessing and feature selection, multiple machine learning algorithms were systematically benchmarked. Model robustness and reliability were further evaluated using 5-fold cross-validation, scaffold-aware validation, applicability domain analysis, and Y-randomization testing. Results: Nonlinear ensemble learning approaches consistently outperformed conventional linear algorithms. The optimized HistGradientBoostingRegressor achieved the best regression performance, with an independent test-set R² value of 0.711 and an RMSE value of 0.759, whereas the optimized classification model achieved accuracies approaching 95%. SHAP analysis identified lipophilicity-related descriptors, aromatic scaffold organization, electrostatic surface properties, and molecular topology as major contributors to FXIa inhibitory activity prediction. In addition, a proof-of-concept virtual screening workflow successfully identified several candidate compounds exhibiting high predicted pKi values and elevated active-class probabilities. Conclusions: The proposed framework provides a robust, interpretable, and reproducible machine learning-driven QSAR strategy for FXIa inhibitor discovery and may facilitate future virtual screening campaigns and medicinal chemistry optimization studies targeting FXIa-associated anticoagulant drug discovery. Full article

Hepatoblastoma (HB), the most common pediatric liver cancer, exhibits marked variability in therapeutic response despite minimal genetic heterogeneity, implicating epigenetic regulation as a key driver of tumor behavior. Among these, polycomb repressor complexes (PRC) remain poorly explored as therapeutic targets. Integrative analysis of samples from patients with HB and public datasets identified BMI1, a core component of PRC1, as significantly upregulated, with high expression strongly associated with aggressive disease and poor survival. Functional screening of epigenetic inhibitors across 15 HB cell lines revealed BMI1 inhibition as the most effective therapeutic strategy, with strong concordance between in vitro predictions and in vivo responses in patient-derived xenograft (PDX) models. The BMI1 inhibitor PTC596 demonstrated the highest potency, consistently suppressing tumor growth across models. Mechanistically, PTC596 induced BMI1 degradation, reduced histone H2A ubiquitination, impaired microtubule dynamics, and restored intrinsic apoptosis by shifting the BCL2–BAX balance, leading to caspase-3/7 activation. Transcriptomic profiling confirmed apoptosis as the most significantly enriched pathway. In vivo, PTC596 markedly reduced tumor burden and proliferation while inducing pro-apoptotic signaling, without detectable toxicity. Together, these findings establish BMI1 as a critical oncogenic dependency in HB, demonstrate the value of robust preclinical tumor modeling for therapeutic validation, and identify PTC596 as a promising, mechanism-based treatment strategy. Full article

The therapeutic armamentarium for colorectal cancer (CRC) has been significantly expanded with the introduction of immunotherapy, particularly immune checkpoint inhibitors (ICIs). However, the response to immunotherapy is strongly dependent on microsatellite instability (MSI) status. Tumors with high MSI (MSI-H) and/or mismatch repair deficiency (dMMR) exhibit high tumor mutational burden (TMB), increased neoantigen load, and enhanced immunogenicity, leading to improved responses to ICIs compared with microsatellite-stable (MSS) and/or mismatch repair-proficient (pMMR) tumors. This has changed the treatment landscape of this small subgroup of metastatic CRC (mCRC), including the approval of pembrolizumab as a first-line option. In contrast, most mCRC cases are MSS/pMMR and are resistant or poorly responsive to ICIs, with no established standard immunotherapy strategy. Therefore, current approaches aim to convert these “cold” tumors into “hot,” immunologically active tumors. This review summarizes the distinct molecular basis of MSI phenotypes and their interaction with the tumor microenvironment, and provides relevant insights into current clinical evidence for prognostic and predictive biomarkers beyond MSI status, as well as novel therapeutic strategies to overcome resistance in MSS disease. Full article

Approximately 30% of all human cancers are driven by mutations in RAS genes, KRAS, HRAS, and NRAS, resulting in the constitutive activation of RAS proteins and stimulation of MAPK/AKT signaling. Non-mutant, i.e., wild-type (WT) RAS can also become activated through mechanisms such as gene amplification or excessive stimulation by mutated or overexpressed receptor tyrosine kinases (e.g., EGFR), thereby promoting cancer progression. Mutant or activated RAS contributes to multiple hallmarks of cancer, including unchecked cellular proliferation, reprogrammed cellular metabolism, immunosuppression, and metastasis. Hence, RAS is of immense clinical importance, with hundreds of laboratories studying various aspects of RAS biology or developing RAS inhibitors. There is perhaps no greater unmet medical need in oncology than the need for a broadly efficacious but safe inhibitor of mutant and activated RAS. Mutant-specific KRAS G12C inhibitors have shown promising therapeutic efficacy, leading to FDA approval of sotorasib and adagrasib, although their use is limited to patients with the relatively rare G12C KRAS mutation. Mutant-specific KRAS inhibitors are also susceptible to adaptive resistance, in part, due to secondary RAS mutations, and compensatory signaling from WT RAS isozymes. A pan-RAS inhibitor capable of blocking all RAS isozymes, regardless of the underlying mutation, offers the potential for broader efficacy and capacity to avert resistance. While just a few years ago, pan-RAS inhibitors were predicted to be severely toxic or even fatal, the apparent safety profile of RMC-6236 (daraxonrasib), a pan-RAS inhibitor currently in clinical trials, suggests otherwise. Indeed, pan-RAS inhibitors are now considered by many in the RAS field to be the most promising class in development. In this review, we summarize the evolution and current status of pan-RAS and pan-KRAS inhibitors in preclinical and clinical development and highlight emerging human-relevant tumor models that are advancing preclinical evaluation. Full article

Background: While Immune Checkpoint Inhibitors (ICIs) have significantly improved outcomes in lung cancer (LC), clinical responses remain heterogeneous. Static tissue biomarkers, like PD-L1, and tumor mutational burden (TMB) are limited by intratumoral heterogeneity and the inability to track temporal changes. This review aims to evaluate the current state and future potential of liquid biopsy as a dynamic tool for patient selection, treatment monitoring, and the identification of resistance mechanisms in LC immunotherapy. Methods: A literature search was conducted in the PubMed database up to March 2026. We identified 65 eligible publications, including clinical trials, observational studies, and systematic reviews, focusing on liquid biopsy analytes such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and soluble immune mediators. Results: Liquid biopsy provides a “pooled” representation of the total tumor burden, overcoming the spatial limitations of tissue biopsy. Key findings include that dynamic changes in ctDNA and bTMB can predict molecular progression weeks before radiological assessment; blood-based PD-L1 monitoring (soluble, exosomal, or on CTCs) correlates with survival outcomes and offers a real-time readout of immune checkpoint activity; novel markers like tumor-macrophage fusion (TMF) cells and cytokine signatures provide unique insights into the systemic immune microenvironment. Conclusions: Liquid biopsy is evolving from a complementary diagnostic tool into a central pillar of precision immuno-oncology. Although technical standardization remains a challenge, the integration of multi-omic blood-based biomarkers represents the future of personalized lung cancer management. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by the accumulation of the toxic protein amyloid-β, formation of tau-containing neurofibrillary tangles, neuroinflammation, and synaptic dysfunction, highlighting the need for new therapeutic strategies capable of modulating multiple pathological pathways simultaneously. In this study, a structure-based in silico approach was applied to evaluate the multi-target potential of two previously reported pyrrole-based compounds (pyrrole 1 and pyrrole 2) with known monoamine oxidase-B (MAO-B) inhibitory activity and low neurotoxicity. Molecular docking studies were performed against a panel of key AD-related targets, including GSK-3β, APP, MAO-B, BACE1, AChE, BChE, COX-2, GABA-B receptor, NMDA receptor, and E3 ubiquitin ligase CHIP, using Glide XP docking. The results revealed that compound pyrrole 1 may have favorable predicted binding affinities across several targets, with relatively strong docking scores for GSK-3β and COX-2. The binding mode analysis indicated that pyrrole 1 adopts poses consistent with interaction patterns commonly observed for ATP-competitive GSK-3β inhibitors and COX-2 ligands. In silico ADMET profiling using the software SwissADME and ProTox 3.0 indicated distinct pharmacokinetic and safety profiles for the two compounds, with pyrrole 2 showing superior drug-likeness and predicted blood–brain barrier penetration, while pyrrole 1 displayed a more favorable overall toxicity profile. Collectively, these findings identify pyrrole 1 as a theoretically promising multi-target candidate for AD requiring experimental validation, while providing a strong structural basis for further optimizations and subsequent experimental confirmation. Full article

The therapeutic landscape of advanced and recurrent endometrial cancer (EC) has evolved substantially in recent years due to the integration of molecular classification and novel systemic therapies. This review summarizes current treatment strategies in advanced EC, focusing on immunotherapy, targeted therapies, and molecularly guided approaches. Immune checkpoint inhibitors (ICIs) have become a cornerstone of treatment, particularly in mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) tumors, where durable clinical benefit has been observed. Recent phase III trials demonstrated that the addition of ICIs to platinum-based chemotherapy significantly improves progression-free survival in the first-line setting, especially in dMMR disease, with more modest but clinically meaningful benefit in mismatch repair-proficient (pMMR) tumors. In the post-platinum setting, combinations such as pembrolizumab plus lenvatinib have expanded treatment options for pMMR patients, despite increased toxicity. Advances in molecular profiling, including the ProMisE classification, are increasingly guiding treatment personalization. Emerging therapies, including PARP inhibitors and antibody–drug conjugates targeting HER2 and Trop-2, are showing promising activity. Despite these advances, challenges remain regarding resistance mechanisms, optimal treatment sequencing, and predictive biomarkers beyond MMR status. Full article

Plant-derived bioactive peptides have garnered widespread interest for their functions in managing obesity and associated metabolic disorders. This study investigated the lipid-lowering activity and underlying mechanisms of VPIIMH, a hexapeptide derived from Chlorella pyrenoids, using in vitro enzymatic assays, Caenorhabditis elegans models, and network pharmacology. In vitro, VPIIMH acted as a reversible non-competitive inhibitor of pancreatic lipase, achieving an inhibition rate of 43.17 ± 1.47% at 8.0 mg/mL. Molecular docking revealed that this inhibition likely occurs through ionic bonds between VPIIMH and PL (1LPB) at Arg256. In a high-fat C. elegans model, treatment with 0.5 mg/mL VPIIMH significantly reduced fat accumulation by 37.2% and triglyceride levels by 26.9%. Furthermore, VPIIMH extended the lifespan of C. elegans under oxidant stress by 40.3% and under heat stress by 17.5%. Network pharmacology predicted that VPIIMH targets nine core proteins, which were classified into three synergistic modules: the SIRT1-PPAR for core regulation, the RAS for systemic coordination, and the inflammatory target (CCR5, MMP9, EGFR) for microenvironment support. This study elucidates the multi-target and multi-pathway mechanism of VPIIMH, suggesting its potential application in combating obesity and related lipid metabolism disorders. These findings provide a scientific basis for the development of VPIIMH as a functional food ingredient targeting metabolic health. Full article

Background/Objectives: Machine learning has emerged as a transformative force in drug discovery, revolutionizing traditional research paradigms and profoundly improving the efficiency, cost-effectiveness, and speed of the drug development cycle for novel drugs. Colorectal cancer is one of the most prevalent malignant tumors and imposes a heavy burden on global public health due to its high morbidity, mortality, and poor prognosis. Cyclooxygenase-2 (COX-2) is a key therapeutic target of colorectal cancer and has been extensively applied in the development of novel anti-colorectal cancer drugs. Methods: In this study, we systematically compared the performance of Random Forest Classifier (RFC), deep learning (DL), and graph neural network (GNN) models, including GAT (Graph Attention Network), GCN (Graph Convolutional Network), and MPNN (Message Passing Neural Network), with diverse features in the classification task of COX-2 inhibitors, based on a custom COX-2 inhibitors dataset and a Traditional Chinese Medicine (TCM)-derived compound library. The optimal model was subsequently used to screen for potential COX-2 inhibitors. Additionally, the key substructures governing COX-2 inhibitory activity were also identified and analyzed. Finally, the prioritized candidate compounds underwent experimental validation. Results: Both RFC and DL models outperformed GNN models. Through further comparative analysis of models’ predictive performance, the RFC model was ultimately verified as the optimal model for activity screening of TCM-derived compounds. The molecular interactions and binding affinities between predicted candidate compounds and COX-2 were further investigated. Finally, the selected lead compound, dehydrocostus lactone, was experimentally confirmed to possess potent COX-2 inhibitory activity. Conclusions: This study highlights that the RFC model is highly effective in screening bioactive components from TCM under small-dataset conditions, providing a solid foundation for subsequent related research in this field. Full article

Germline loss-of-function mutations in BRCA1 and BRCA2 markedly increase susceptibility to breast, ovarian, and other cancers. Mechanistically, BRCA2 facilitates RAD51 recruitment to sites of DNA damage, whereas BRCA1 regulates homologous recombination repair (HRR) through double-strand break resection and broader DNA damage response signaling. These insights underpin targeted therapies such as poly (ADP-ribose) polymerase inhibitors (PARPis), which induce synthetic lethality in homologous recombination-deficient tumors. Clinically, PARPis have demonstrated significant benefit in BRCA1/2-mutated breast, ovarian, pancreatic, and prostate cancers. However, resistance remains a major obstacle, with secondary intragenic BRCA1/2 mutations restoring partial protein function representing a prominent mechanism. Despite therapeutic advances, critical gaps persist in understanding how specific BRCA1/2 domains and residual protein activities contribute to tumorigenesis and treatment response. In this review, we summarize the structural and functional domains of BRCA1/2, their pathogenic mutation profiles, and therapeutic strategies targeting BRCA1/2-deficient cancers. Despite therapeutic advances, critical gaps persist in understanding how specific BRCA1/2 domains and residual protein activities contribute to tumorigenesis and treatment response. This review emphasizes the need for functional studies of BRCA1/2 variants to refine risk prediction and develop mutation-tailored therapies. Full article

Acetylcholinesterase (AChE) inhibition remains a key therapeutic strategy in the management of neurodegenerative disorders such as Alzheimer’s disease. In this study, the inhibitory potential of the gastric pentadecapeptide BPC-157 and two newly designed hybrid analogs, CIARA-1 and CIARA-2, was investigated for the first time. The hybrid peptides were rationally designed by combining a BPC-157-derived fragment with an arginine-containing C-terminal sequence to enhance interactions with the enzyme’s active and peripheral binding sites. Enzyme kinetics were evaluated using a modified Ellman assay, and inhibition parameters were determined through Lineweaver–Burk analysis. All tested compounds exhibited a competitive mechanism of inhibition, as evidenced by increased Michaelis–Menten constant (K_m) values with unchanged maximum velocity (V_max), indicating competition with the substrate at the catalytic site of AChE. Among the tested compounds, CIARA-1 demonstrated the highest inhibitory potency, reflected by the lowest inhibition constant (K_i = 0.24 mM) and IC₅₀ value (2.52 mM), followed by CIARA-2 (K_i = 0.29 mM; IC₅₀ = 2.73 mM) and BPC-157 (K_i = 0.48 mM; IC₅₀ = 2.80 mM). These findings were consistent with molecular modeling predictions, supporting stronger binding interactions for CIARA-1. Despite significantly lower potency compared to clinically used AChE inhibitors, the studied peptides represent a promising scaffold for further optimization. Overall, this work demonstrates that BPC-157 and its hybrid analogs act as reversible competitive AChE inhibitors, with enhanced activity observed for structurally modified derivatives. The results highlight the potential of peptide-based hybrid molecules as multifunctional candidates in the development of novel therapeutics targeting cholinergic dysfunction. Full article

The type VI secretion system (T6SS) is a contact-dependent, contractile multiprotein complex widely distributed among Gram-negative bacteria. It mediates the translocation of effector proteins into bacterial competitors and eukaryotic host cells, promoting environmental fitness and contributing to virulence. In Salmonella, five pathogenicity islands encoding T6SSs (SPI-6, SPI-19, SPI-20, SPI-21, and SPI-22) have been described, along with an expanding repertoire of associated effector proteins. However, their global diversity and distribution remain incompletely resolved due to limited genomic sampling. To address this, we analyzed a curated dataset of 490 Salmonella genomes representing 45 serotypes. T6SS regions were identified using SecreT6, revealing that SPI-6 is widely distributed, whereas SPI-19, SPI-20, and SPI-21 are restricted to a subset of serotypes. SPI-20 and SPI-21 were exclusively found in S. enterica subsp. arizonae and diarizonae, while SPI-22 was absent from all analyzed genomes. All open reading frames within T6SS clusters were then analyzed for effector prediction and functional annotation. This approach recovered 32 out of 45 previously described T6SS effectors and identified several novel candidates. These included a cytidine deaminase with predicted DNase activity in SPI-6: two candidate nuclease effectors in SPI-19 with DNase and RNase activities, and four putative effectors in SPI-21, including enzymes with predicted peptidoglycan hydrolase activity, a potential inhibitor of eukaryotic ATPases, and a membrane pore-forming toxin. Additionally, a putative phospholipase effector was identified within a VgrG-associated genomic island in a subset of S. enterica subsp. diarizonae isolates. Collectively, these findings expand the known repertoire of Salmonella T6SS effector proteins and highlight their functional diversity. Full article