Search Results (1,796)

Background: Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy, ranking third in incidence and second in cancer-related mortality. Despite therapeutic advances, challenges such as chemotherapy toxicity and drug resistance persist. Thus, there is an urgent need for novel CRC treatments. However, developing new drugs is time-consuming and resource-intensive. As a more efficient approach, drug repurposing offers a promising alternative for discovering new therapies. Methods: In this study, we screened 1068 small molecular compounds from an FDA-approved drug library in CRC cells. Menadione was selected for further study based on its activity profile. Mechanistic analysis included a cell death pathway PCR array, differential gene expression, enrichment, and network analysis. Gene expressions were validated by RT-qPCR. Results: We identified menadione as a potent anti-tumor drug. Menadione induced three programmed cell death (PCD) signaling pathways: necroptosis, apoptosis, and autophagy. Furthermore, we found that the anti-tumor effect induced by menadione in CRC cells was mediated through a key gene: MAPK8. Conclusions: By employing methods of cell biology, molecular biology, and bioinformatics, we conclude that menadione can induce multiple forms of PCD in CRC cells by activating MAPK8, providing a foundation for repurposing the “new use” of the “old drug” menadione in CRC treatment. Full article

Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections. Full article

Methicillin-resistant Staphylococcus aureus (MRSA), a multidrug-resistant pathogen, poses a significant threat to global healthcare. This review evaluates the potential of marine algal metabolites as novel antibacterial agents against MRSA. We explore the clinical importance of S. aureus, the emergence of MRSA as a “superbug”, and its resistance mechanisms, including target modification, drug inactivation, efflux pumps, biofilm formation, and quorum sensing. The limitations of conventional antibiotics (e.g., β-lactams, vancomycin, macrolides) are discussed, alongside the promise of algal-derived compounds such as fatty acids, pigments, polysaccharides, terpenoids, and phenolic compounds. These metabolites exhibit potent anti-MRSA activity by disrupting cell division (via FtsZ inhibition), destabilizing membranes, and inhibiting protein synthesis and metabolic pathways, effectively countering multiple resistance mechanisms. Leveraging advances in algal biotechnology, this review highlights the untapped potential of marine algae to drive innovative, sustainable therapeutic strategies against antibiotic resistance. Full article

Background: Salmonella infections pose a serious threat to both animal and human health worldwide. Notably, there is an increasing trend in the resistance of Salmonella to fluoroquinolones, the first-line drugs for clinical treatment. Methods: Utilizing Salmonella Typhimurium CICC 10420 as the test strain, ciprofloxacin was used for in vitro induction to develop the drug-resistant strain H1. Changes in the minimum inhibitory concentrations (MICs) of various antimicrobial agents were determined using the broth microdilution method. Transcriptomic and metabolomic analyses were conducted to investigate alterations in gene and metabolite expression. A combined drug susceptibility test was performed to evaluate the potential of exogenous metabolites to restore antibiotic susceptibility. Results: The MICs of strain H1 for ofloxacin and enrofloxacin increased by 128- and 256-fold, respectively, and the strain also exhibited resistance to ceftriaxone, ampicillin, and tetracycline. A single-point mutation of Glu469Asp in the GyrB was detected in strain H1. Integrated multi-omics analysis showed significant differences in gene and metabolite expression across multiple pathways, including two-component systems, ABC transporters, pentose phosphate pathway, purine metabolism, glyoxylate and dicarboxylate metabolism, amino sugar and nucleotide sugar metabolism, pantothenate and coenzyme A biosynthesis, pyrimidine metabolism, arginine and proline biosynthesis, and glutathione metabolism. Notably, the addition of exogenous glutamine, in combination with tetracycline, significantly reduced the resistance of strain H1 to tetracycline. Conclusion: Ciprofloxacin-induced Salmonella resistance involves both target site mutations and extensive reprogramming of the metabolic network. Exogenous metabolite supplementation presents a promising strategy for reversing resistance and enhancing antibiotic efficacy. Full article

Background/Objectives: Limited robust data support the use of antibiotic combinations in the treatment of orthopedic infections. However, in certain situations, the combination of antibiotics seems to be beneficial. This review aims to outline the circumstances under which a combination of antibiotics may be utilized in the treatment of orthopedic infections. Methods: We reviewed the existing guidelines on orthopedic infections and focused on situations where antibiotic combinations are recommended or proposed optionally. We chose vitro and animal studies that provide evidence for the effectiveness of several widely recommended combinations. Results: The combinations serve multiple purposes: they provide empirical coverage while awaiting microbiological results, offer targeted treatment for difficult-to-treat infections, and facilitate oral treatment primarily for staphylococcal infections. The objectives include enhancing bacterial coverage against Gram-positive and Gram-negative bacteria, achieving synergistic effects with bactericidal agents, and reducing the risk of antibiotic resistance. The review outlines specific combinations for fracture-related infections, periprosthetic joint infections, spinal infections, and anterior cruciate ligament reconstruction infections, emphasizing the importance of tailoring antibiotic choices based on local epidemiology and patient history. The review also addresses potential drawbacks of combination therapy, such as toxicity, higher costs, and drug interactions, underscoring the complexity of managing orthopedic infections effectively. Conclusions: According to the guidelines, several different proposals are made, depending in part on the countries’ epidemiology. In a well-defined situation, various authors propose either monotherapy or a combination of antibiotics. When a combination is suggested, the choice of antibiotics is based on the expected effect: broadening the spectrum, enhancing bactericidal activity, achieving a synergistic effect, or reinforcing biofilm activity to optimize the treatment. Full article

Background and Objectives: Colorectal cancer (CRC) is one of the most frequently diagnosed malignancies worldwide. Although chemotherapy is an effective treatment for colorectal cancer (CRC), its effectiveness is frequently hindered by the emergence of resistant cancer cells. Studies have demonstrated a linkage between drug resistance and the pregnane X receptor (PXR), which influences the metabolism and the transport of chemotherapeutic agents. Likewise, autophagy is also a well-established mechanism that contributes to chemotherapy resistance, and it is closely tied to tumor progression. This pre-clinical study aims to investigate the role of mtKRAS-dependent autophagy with PXR expression after treatment with Irinotecan in colorectal cancer. Methods: CRC lines were treated with specific inhibitors, such as 3-methyladeninee, hydroxychloroquine PI-103, and irinotecan hydrochloride, and subjected to various assays, including MTT for cell viability, Western blot for protein expression, siRNA-mediated PXR knock-out, and confocal microscopy for autophagic vacuole visualization. Protein quantification, gene knockdown, and subcellular localization studies were performed under standardized conditions to investigate treatment effects on autophagy and apoptosis pathways. Conclusions: Our experiments showed that PXR knockdown does not alter autophagy levels following Irinotecan treatment, but it promotes apoptotic cell death despite elevated autophagy. Moreover, late-stage autophagy inhibition reduces PXR expression, whereas induction through PI3K/AKT/mTOR inhibition leads to increased expression of PXR. Our experiments uncover a mechanism by which autophagy facilitates the nuclear translocation of the PXR, thereby promoting resistance to Irinotecan across multiple cell lines. Full article

Background/Objectives: All current FDA-approved antibody–drug conjugates (ADCs) are single-target and single-payload molecules that have limited efficacy in patients due to drug resistance. Therefore, our goal was to generate a novel ADC that was less susceptible to single points of resistance to reduce the likelihood of patient relapse. Methods: We developed a dual-targeting, dual-payload ADC by conjugating a bispecific EGFR x cMET antibody to two payloads (MMAF and SN38) that had separate mechanisms of action using a novel tri-functional linker. This dual-payload ADC was tested for potency and efficacy in dividing and nondividing in vitro cell models using multiple tumor cell types. Efficacy of the dual-payload ADC was confirmed using in vivo models. Results: Our ADC with dual MMAF and SN38 payloads was more efficacious in inhibiting cell proliferation than single-payload ADCs across multiple cancer cell lines. In addition, the dual-payload molecule inhibited nondividing cells, which were more resistant to traditional ADC payloads. The dual-payload ADC also exhibited more potent tumor growth inhibition in vivo compared to that of single-payload ADCs. Conclusions: Overall, the bispecific antibody conjugated with both the MMAF and SN38 payloads inhibited tumor growth more strongly than ADCs conjugated with MMAF or SN38 alone. Developing dual-payload ADCs could limit the impact of acquired resistance in patients as well as lower the effective dose of each payload. Full article

Drug resistance remains a critical barrier to effective treatment in several cancers, particularly triple-negative breast cancer (TNBC). Estrogen receptor α36 (ERα36), a variant of the estrogen receptor in ER-negative breast cancer cells, plays important roles in cancer cell proliferation. We investigated the role of ERα36 in regulating multidrug resistance protein 1 (MDR1) in MDA-MB-231 human breast cancer cells. The activation of ERα36 by BSA-conjugated estradiol (BSA-E2) increased cell viability under Adriamycin exposure, suggesting its involvement in promoting drug resistance. BSA-E2 treatment significantly reduced the intracellular rhodamine-123 levels by activating the MDR1 efflux function, which was linked to increased MDR1 transcription and protein expression. The mechanical ERα36-mediated BSA-E2-induced activation of EGFR and downstream signaling via c-Src led to an activation of the Akt/ERK pathways and transcription factors, NF-κB and CREB. Additionally, ERα36 is involved in activating Wnt/β-catenin pathways to induce MDR1 expression. The silencing of ERα36 inhibited the BSA-E2-induced phosphorylation of Akt and ERK, thereby reducing MDR1 expression via downregulation of NF-κB and CREB as well as Wnt/β-catenin signaling. These findings demonstrated that ERα36 promotes MDR1 expression through multiple non-genomic signaling cascades, including Akt/ERK-NF-κB/CREB and Wnt/β-catenin pathways, and highlight the role of ERα36 as a promising target to enhance chemotherapeutic efficacy in TNBC. Full article

RKIP and LKB1, encoded by PEBP1 and STK11, respectively, have emerged as key regulators of cancer pathophysiology. However, their role in shaping tumor progression through modulation of the tumor microenvironment (TME) is not yet fully understood. To address this, we performed a comprehensive pan-cancer analysis using TCGA transcriptomic data across 33 cancer types, grouped by their tissue of origin. We investigated PEBP1/STK11 co-expression and its association with transcriptomic reprogramming in major TME components, including immune, mechanical, metabolic, and hypoxic subtypes. Our results revealed both positive and inverse correlations between PEBP1/STK11 co-expression and TME-related molecular signatures, which did not align with classical cancer categorizations. In a subset of tumors, PEBP1/STK11 co-expression was significantly associated with improved overall survival and reduced mortality (HR < 1). Notably, we predominantly observed inverse correlations with pro-inflammatory and immunosuppressive chemokines, immune checkpoints, extracellular matrix components, and key regulators of epithelial-to-mesenchymal transition. In contrast, we found positive associations with anti-inflammatory chemokines and their receptors. Importantly, PEBP1/STK11 co-expression was consistently linked to reduced expression of drug resistance genes and greater chemosensitivity across multiple tumor types. Our findings underscore the co-expression of PEBP1 and STK11 as a promising target for future studies aimed at elucidating its potential as a biomarker for prognosis and therapeutic response in precision oncology. Full article

Tumor drug resistance, a major cause of treatment failure, involves complex multi-gene networks, remodeling of signaling pathways, and interactions with the tumor microenvironment. Yin Yang 1 (YY1) is a critical oncogene overexpressed in many tumors and mediates multiple tumor-related processes, such as cell proliferation, metabolic reprogramming, immune evasion, and drug resistance. Notably, YY1 drives resistance through multiple mechanisms, such as upregulation of drug efflux, maintenance of cancer stemness, enhancement of DNA repair capacity, modulation of the tumor microenvironment, and epithelial–mesenchymal transition, thereby positioning it as a pivotal regulator of drug resistance. This review examines the pivotal role of YY1 in resistance, elucidating its molecular mechanisms and clinical relevance. We demonstrate that YY1 inhibition could effectively reverse drug resistance and restore therapeutic sensitivity across various treatment modalities. Importantly, we highlight the promising potential of YY1-targeted strategies, particularly combined with anti-tumor agents, to overcome resistance barriers. Furthermore, we discuss critical translational considerations for advancing these combinatorial approaches into clinical practice. Full article

Growth hormone (GH) signaling has been implicated in tumor progression and therapy resistance across multiple cancer types, yet its role in bladder cancer remains largely unexplored. In this study, we investigated the impact of GH and its receptor (GHR) on therapy resistance and disease progression in urothelial carcinoma (UC) through integrated transcriptomic and in vitro analyses. Transcriptomic profiling of The Cancer Genome Atlas bladder cancer cohort revealed that high tumoral GHR expression was associated with differential upregulation of genes involved in drug efflux, epithelial-to-mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. Notably, elevated GHR levels correlated with significantly reduced overall survival in patients with UC. In parallel, in vitro experiments demonstrated that GH promotes chemoresistance in UC cell lines via upregulation of ATP-binding cassette-containing (ABC) transporters and activation of EMT. GH also modulated ECM-remodeling-associated genes in a chemotherapy-dependent manner, including matrix metalloproteinases and tissue inhibitors of metalloproteinases. Importantly, these effects were abrogated by Pegvisomant, a GHR antagonist, indicating the functional relevance of GH/GHR signaling in the mediation of these phenotypes. Collectively, our findings support a mechanistic role for GH signaling in driving therapy resistance and tumor aggressiveness in bladder cancer and suggest GHR antagonism as a potential therapeutic strategy to improve treatment outcomes. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers worldwide, characterized by late diagnosis, aggressive progression, and poor response to conventional monotherapies. Combination therapies have emerged as a promising approach to overcome multidrug resistance (MDR), enhance efficacy, and target the complex tumor microenvironment (TME). Nanoparticle-based drug delivery systems (DDSs) have gained significant attention for their ability to co-deliver multiple agents with controlled release profiles. This review comprehensively examines nanoparticle-based platforms developed for PDAC combination therapies, focusing on small-molecule drugs. The systems discussed are drawn from studies published between 2005 and 2025. Full article

Epilepsy remains a major therapeutic challenge, with approximately one-third of patients experiencing drug-resistant epilepsy (DRE) despite the availability of multiple antiseizure medications (ASMs). This review aims to evaluate emerging ASMs—cenobamate, fenfluramine, ganaxolone, ezogabine (retigabine), and perampanel—with a focus on their mechanisms of action, pharmacological profiles, and potential role in precision medicine. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science to identify preclinical and clinical studies evaluating the pharmacodynamics, pharmacokinetics, efficacy, and safety of the selected ASMs. Relevant trials, reviews, and mechanistic studies were reviewed to synthesize the current understanding of their application in DRE and specific epilepsy syndromes. Each ASM demonstrated unique mechanisms targeting hyperexcitability, including the modulation of γ-aminobutyric acid receptor A (GABA-A) receptors, sodium and potassium channels, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA receptors), and serotonin systems. These mechanisms correspond with specific pathophysiological features in syndromes such as Dravet and Lennox–Gastaut. Evidence from clinical trials supports their use as adjunctive therapies with generally favorable tolerability, though adverse events and variable efficacy profiles were noted. The mechanistic diversity of these emerging ASMs supports their value in personalized epilepsy management, particularly in treatment-resistant cases. While the promise of precision medicine is evident, further studies are required to address challenges related to long-term safety, cost, and equitable access. 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

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer tumor immune microenvironment. Breast cancer stemness was calculated using one-class logistic regression. Twelve main cell clusters were identified, and the subsequent three subsets of Regulatory T cells with different differentiation states were identified as being closely related to immune regulation and metabolic pathways. A prognostic risk model including MEA1, MTFP1, PASK, PSENEN, PSME2, RCC2, and SH2D2A was generated through the intersection between Regulatory T cell differentiation-related genes and stemness-related genes using LASSO and univariate Cox regression. The patient’s total survival times were predicted and validated with AUC of 0.96 and 0.831 in both training and validation sets, respectively; the immunotherapeutic predication efficacy of prognostic signature was confirmed in four ICI RNA-Seq cohorts. Seven drugs, including Ethinyl Estradiol, Epigallocatechin gallate, Cyclosporine, Gentamicin, Doxorubicin, Ivermectin, and Dronabinol for prognostic signature, were screened through molecular docking and found a synergistic effect among drugs with deep learning. Our prognostic signature potentially paves the way for overcoming immune resistance, and blocking the interaction between cancer stemness and Tregs may be a new approach in the treatment of breast cancer. Full article