Current drug targets最新文献

Ferroptosis is a form of oxidative, iron-dependent, non-apoptotic cell death characterized by the accumulation of lipid peroxides and the depletion of glutathione. Ferroptosis plays a significant role in human cancer and is essential in neurological disorders, including neurodegeneration, stroke, and neurotrauma. One of the key challenges in cancer research is how to effectively kill cancer cells while leaving healthy cells intact. Cancer cells often have defects in cell death executioner mechanisms, which is one of the main reasons for therapy resistance. To enable growth, cancer cells exhibit an increased iron demand compared with normal, non-cancer cells. This iron dependency can make cancer cells more vulnerable to iron-catalyzed necrosis, referred to as ferroptosis. It is a newly identified regulated form of cell death, which is thought to play a major role in neurodegenerative diseases. The mechanisms of ferroptosis in several neurological disorders are discussed in detail in this article. It also provides an overview of emerging medications that target ferroptosis in the treatment of neurological disorders. It also highlights the variations and connections between the different cell death pathways implicated in neurological disorders. Clarifying the function of ferroptosis in the brain will help us better understand the mechanisms behind neurological disorders and offer possible strategies for both acute and long-term neurological illness prevention and therapy. Consequently, we provide an overview and brief description of the main pathways involved in ferroptosis in this review, focusing on its regulation and its dual roles as a tumor suppressor and an oncogenic process in various human malignancies. The identification of FDA-approved drugs as ferroptosis inducers has raised high expectations for ferroptosis as a promising new approach to killing therapy-resistant cancers.

Introduction: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and the MET exon 14 skipping mutation is a key oncogenic driver, which promotes tumor progression and provides a new direction for precision therapy.

Methods: A systematic search of English-language literature and clinical trial data related to the MET exon 14 skipping mutation from 2020-2025 was performed to summarize the role of the mutation and therapeutic advances.

Results: DNA-based next-generation sequencing (NGS), RNA-based NGS, and RT-qPCR were employed as the main detection methods. Preclinical models confirmed that mutations promote tumor progression by activating the RAS/MAPK pathway. Clinical trials have reported objective remission rates (ORR) of 46-68% for first-line treatment with MET inhibitors in NSCLC patients harboring MET exon 14 skipping mutations.

Discussion: MET exon 14 skipping mutation as a therapeutic target for NSCLC has made significant progress, and MET inhibitors are more advantageous than chemotherapy and immunotherapy, and have been recommended by national and international guidelines as a first-line treatment option. Additionally, NGS technology has the potential to dynamically monitor tumor evolution and drugresistant mutations, thereby helping to realize precision medicine.

Conclusion: The MET exon 14 skipping mutation is an important target for the precision treatment of NSCLC, and MET-TKIs have remarkable efficacy but a prominent problem with drug resistance. The construction of a precision medicine system encompassing diagnosis, treatment, and drug resistance management through multi-omics research, technological innovation, and international collaboration is a key direction for improving prognosis.

Introduction: Conventional cancer therapies are limited by systemic toxicity, poor selectivity, and drug resistance. Bacterial proteins, such as azurin, represent a promising alternative due to their tumor selectivity, low immunogenicity, and multifunctional mechanisms. This review highlights recent progress in azurin-based anticancer strategies, including mechanisms of action, structural modifications, and integration with peptide systems, nanotechnology, and gene therapy.

Methods: A search for articles using the keywords "azurin, cancer" was conducted on the Google Scholar and PubMed databases, with an emphasis on the years 2023-2024.

Results: Azurin and its peptide derivative p28 selectively target cancer cells by stabilizing p53, inducing apoptosis, and arresting the cell cycle, while also modulating key signaling pathways. Structural features of azurin enable interactions with multiple molecular targets, and p28 enhances cellular uptake and sensitizes tumors to chemotherapeutics. Advanced delivery platforms, including engineered bacteria (E. coli Nissle 1917, S. typhimurium VNP-20009), chimeric peptides, and nanocarriers, improve tumor targeting and therapeutic outcomes. Preclinical models and clinical trials demonstrate low toxicity and efficacy against various solid tumors and gliomas.

Discussion: Evidence supports azurin as a versatile anticancer agent with unique advantages over conventional therapies. Its compatibility with delivery innovations enhances precision and minimizes systemic toxicity. However, further optimization, large-scale clinical validation, and long-term safety studies are required.

Conclusion: Azurin and its derivatives provide a promising platform for anticancer therapy, offering tumor specificity, low toxicity, and synergy with multiple treatment modalities. Their integration into advanced delivery and genetic systems may significantly improve cancer treatment and recurrence prevention.

Introduction: The therapeutic candidate CPPs are short amino acid sequences that can deliver attractive therapeutic molecules across cellular membranes. Hence, CPPs can provide the backbone and mechanisms for drug tailoring and diagnostics.

Methods: A structured dataset consisting of 473 confirmed CPPs derived from the EnDM-CPP database was analyzed. Four sequence descriptors were computed: Position Relative Incidence Matrix (PRIM), Reverse PRIM (RPRIM), Accumulative Absolute Position Incidence Vector (AAPIV), and Reverse AAPIV. These feature vectors were used to train and test three deep learning architectures: Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. Self-consistency testing, independent testing, and 10-fold crossvalidation were used for model evaluation. Additionally, SHAP values were employed to explain the XAI sequences and identify the most important sequence components.

Results: The best score during cross-validation was achieved by the CNN model, with an accuracy of 99.05%, followed by the DNN. In contrast, the LSTM model achieved an accuracy of 95.24%, a substantial drop from the former, suggesting it may be suffering from overfitting. In general, all models were able to predict outputs with reasonable accuracy given the structured input features. Biologically relevant descriptors were highlighted by the SHAP analysis, which improved the transparency of the predictions.

Discussion: The performance of the CNN indicates its ability to process structured sequence-based biological data effectively. The drop in accuracy with the LSTM model suggests insufficient training data or lack of proper regularization. The use of SHAP enhances explainability by linking features to biological properties. While in vitro or in vivo validation has not yet been performed, the current analysis suggests that in silico CPP prediction is feasible.

Conclusion: Deep learning in this case has created a framework that can accurately identify CPPs. The CNN model also has additional features that make it ideal for use in drug delivery, molecular diagnostics, and personalized medicine. SHAP-based XAI further adds explainability, increasing trust in the model's predictions.

Introduction: This review aims to comprehensively discuss the emerging role of artificial intelligence (AI) and three-dimensional (3D) printing in the design and development of personalized polymeric nanocomposite-based drug delivery systems. The focus is on how integrating these technologies enhances the precision, efficacy, and customization of pharmacotherapy compared with conventional formulations.

Methodology: An extensive literature survey was conducted using databases such as PubMed, Scopus, Web of Science, and ScienceDirect, focusing on publications from the past decade. Peerreviewed studies, reviews, and reports on polymeric nanocomposites, AI-based formulation design, and 3D-printed drug delivery systems were critically analyzed. The collected data were synthesized to elucidate design principles, fabrication methods, and the synergistic application of AI and 3D printing in personalized medicine.

Results: Polymeric nanocomposites have demonstrated superior performance in targeted and controlled drug release due to their adaptable physicochemical properties and biocompatibility. The application of AI enables predictive modeling, optimization of formulation parameters, and patient stratification through data-driven algorithms. Concurrently, 3D printing enables the fabrication of patient-specific dosage forms and implants with programmable drug-release profiles. Together, these technologies enable the development of individualized therapeutic systems that enhance treatment outcomes and minimize adverse effects.

Discussion: This synergistic incorporation of AI and additive manufacturing tackles some of the main obstacles in precision medicine by diminishing trial-and-error in formulation, enhancing reproducibility, and promoting better outcomes during treatment. Such multidisciplinary applications are most promising in cancer, diabetes, neurodegenerative, infectious, and cardiovascular diseases.

Conclusion: The integration of AI and 3D printing represents a transformative advancement in personalized nanocomposite drug delivery. These interdisciplinary approaches collectively enable precise control over drug release kinetics, dosage customization, and formulation design. Future developments focusing on regulatory standardization, ethical data use, and large-scale clinical translation will further accelerate the adoption of AI- and 3D-printing-assisted personalized drug delivery systems in clinical practice. While these technologies hold great potential for personalized and precise therapeutics, their clinical translation remains challenged by regulatory validation, manufacturing reproducibility, and data transparency requirements.

Introduction: The multifactorial pathogenesis of Alzheimer's Disease (AD) makes effective prevention and treatment challenging. Integrative medicine presents a promising approach by complementing conventional treatments with neuroprotective nutraceuticals. This study investigates the individual and combinatorial effects of turmeric, a neuroprotective herb, and donepezil, an acetylcholinesterase (AChE) inhibitor, on cognitive functions in a scopolamine-induced amnesic mouse model.

Methods: Molecular interactions of curcuminoids and donepezil with AChE were analyzed using AutoDock Vina and AMBER22. In animal model studies, amnesia was induced in BALB/c mice via subcutaneous injections of scopolamine (1mg/kg/day) for a duration of 25 days. From the 11th day onwards, the turmeric rhizome powder (20mg/kg/day) and donepezil (0.5mg/kg/day) were orally administered as monotherapies or in combination. Cognitive functions were assessed through behavior tests.

Results: Molecular docking and dynamics simulations revealed that curcuminoids (curcumin, bisdemethoxycurcumin, and desmethoxycurcumin) inhibited AChE more effectively than donepezil. Animal studies demonstrated significant enhancements in spatial, reference, recognition, and contextual fear memories, with both turmeric and donepezil monotherapies, and their combination therapy. No significant differences were observed between monotherapies, and no additive effect was evident in the combination therapy.

Discussion: Co-administration of turmeric and donepezil did not yield a significant additive effect on cognitive improvement in scopolamine-induced amnesic mice. Turmeric monotherapy showed cognitive improvements comparable to those of donepezil monotherapy, highlighting its potential as a candidate therapy for donepezil-resistant AD.

Conclusion: Turmeric monotherapy improved cognitive performance similarly to donepezil monotherapy in scopolamine-induced amnesic mice. These preliminary findings require further research, including dose optimization and clinical trials, to establish their clinical relevance.

Introduction: Glioblastoma multiforme (GBM) is one of the most aggressive and treatment-resistant brain tumors. Conventional therapies often fail to produce durable clinical responses due to rapid progression and therapeutic resistance. Drug repurposing offers a promising strategy to identify effective combinations using existing drugs.

Methods: This study evaluated the synergistic effects of Memantine (MEM), an NMDA receptor antagonist, and Nicotinamide (NAM), a form of vitamin B3, in U87MG GBM cells. The Chou- Talalay fixed-ratio (1:1) design was employed across five concentration levels. Cytotoxicity and anti-migratory effects were assessed after 24 h of treatment, and data were analyzed using CompuSyn (v2.0).

Results: The MEM-NAM combination exhibited significant synergistic cytotoxicity with a combination index (CI) of 0.45 ± 0.08. The IC₅₀ values were 48.2 ± 2.6 μM for Memantine and 1297.90 ± 2.46 μM for Nicotinamide. The combination also markedly inhibited GBM cell migration compared with single-drug treatments.

Discussion: The synergistic interaction between MEM and NAM suggests a complementary mechanism that enhances anti-proliferative and anti-migratory responses in GBM cells. This supports the therapeutic potential of repurposed drug combinations in overcoming tumor resistance.

Conclusion: The Memantine-Nicotinamide combination demonstrates significant in vitro synergy and anti-migratory effects against glioblastoma cells, warranting further investigation in preclinical and clinical models to validate its translational potential.

Introduction: Long noncoding RNAs (lncRNAs) are a unique class of RNA molecules that do not code for proteins but play a significant role in regulating various cellular processes. This article aims to contribute to a better understanding of lncRNA biology in NSCLC and to inspire further research that will improve the diagnosis, treatment, and prevention of this deadly disease.

Methods: A literature review was performed using PubMed, Scopus, and Web of Science to analyze studies on lncRNAs in NSCLC published between 2015 and 2025.

Results: In NSCLC, lncRNAs are commonly dysregulated and can function as either oncogenic or tumor-suppressive regulators, contributing to cancer development and progression. They regulate gene expression directly, act as competitive endogenous RNAs that bind to microRNAs and proteins, and interact with RNA-binding proteins. Furthermore, lncRNAs influence the tumor immune microenvironment, alter cellular metabolism, maintain cancer stem cell properties, and induce angiogenesis by activating specific signaling pathways. LncRNAs have attracted increasing attention for their roles as diagnostic and prognostic biomarkers in liquid biopsies and for their therapeutic relevance in NSCLC.

Discussion: This review examines the critical roles and diverse functions of lncRNAs, emphasizing their clinical applicability, recent advances in research, existing challenges, novel approaches, and prospects for NSCLC treatment.

Conclusion: By synthesizing recent findings, this study highlights the significant role of lncRNAs in carcinogenesis, encourages further research in this field, and uncovers the complex network of lncRNA-mediated regulatory effects that could lead to innovative diagnostic tools and therapeutic interventions.

Introduction: Estrogen deficiency in postmenopausal women influences several physiological processes, notably affecting the gut-brain axis (GBA). Emerging evidence from both preclinical and clinical studies suggests that the loss of estrogen following menopause contributes to GBA dysfunction. The present review aims to explore the clinical and preclinical evidence linking estrogen deficiency-induced gut dysbiosis with GBA dysfunction in postmenopausal women.

Methods: A literature survey was conducted using scientific databases such as PubMed, Google Scholar, ResearchGate, and Semantic Scholar to evaluate studies focused on estrogen's role in modulating GBA dysfunction using keywords such as estrogen, GBA, menopause, gut dysbiosis, and GM. Both experimental and observational studies were considered to synthesize current findings.

Results: Estrogen deficiency has been shown to alter the composition and diversity of GM, impair gut barrier function, and dysregulate immune responses involving T cells and microglia within the GIT and CNS. These disruptions are associated with cognitive decline, emotional disturbances, and neurodegenerative conditions. Evidence supports a strong association between menopause-related estrogen loss, gut microbial imbalance, and GBA dysfunction.

Discussion: The estrogen-GBA plays a crucial role in postmenopausal health, and phytoestrogenmediated modulation of GM offers a promising therapeutic approach supported by preclinical evidence. However, limited clinical data and population variability highlight the need for well-designed human studies to validate these findings.

Conclusion: Targeting GM modulation presents a promising therapeutic strategy for mitigating GBA dysfunction in postmenopausal women. This review consolidates existing evidence and highlights the need for further research into microbiota-based interventions to alleviate estrogen deficiency- related neurophysiological disorders.

In recent years, Molecular Dynamics (MD) simulations have emerged as a cornerstone in molecular biology and antiviral drug discovery, driven by the growing demand for high-resolution insights into biomolecular behaviour. This surge in relevance stems from the need to understand complex molecular mechanisms at an atomic scale, an area where traditional experimental techniques often face limitations. MD simulations offer a powerful computational framework capable of capturing the dynamic behaviour of proteases and other biomolecules with unparalleled spatial and temporal resolution. As a result, they have become instrumental in elucidating protein inhibition mechanisms, unveiling the molecular basis of various diseases, and guiding the rational design and optimization of therapeutic agents. In this review, we synthesize recent advances in the application of MD simulations to the study of protease inhibitors, highlighting their transformative impact on drug discovery and structural biology.