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Melanoma is one of the most aggressive types of solid cancer, originating in melanocytes. Due to its complex and heterogeneous nature, it can respond very differently to treatment. For many years, researchers have used standard two-dimensional cell cultures to evaluate drug efficacy and understand the cellular and molecular biology of this disease, but 2D cultures have limitations compared to 3D cultures when it comes to mimicking the tumor microenvironment in the body. Rodent models are often used to understand melanoma progression and develop new effective treatments, but they do not accurately represent human physiology. Ex vivo modelling of melanoma could significantly improve our understanding and predict treatment outcomes. Efforts have been directed toward developing reliable models that accurately mimic melanoma in its appropriate tissue environment, including spheroid formation, tumor organoids, bio-printed tissue constructs, and microfluidic devices. This review provides a comprehensive exploration of 3D models used in drug screening for targeted therapy in melanoma by screening 120 studies and critically discussing 22 key research publications. Moreover, we provide details of drug screening accuracy and therapeutic efficacy of melanoma 3D models and identify current challenges to propose future directions for enhancing 3D model-based drug screening.
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http://dx.doi.org/10.1007/s12015-025-10870-3 | DOI Listing |
Chembiochem
September 2025
School of Biological and Chemical Sciences, Ryan Institute, University of Galway, University Road, Galway, H91 TK33, Ireland.
Activated B-cell diffuse large B-cell lymphoma (ABC-DLBCL) is an aggressive cancer with poor response to standard chemotherapy. In search of new therapeutic leads, a library of 435 fractions prepared from the Irish marine biorepository was screened against 2 ABC-DLBCL cell lines (TMD8 and OCI-Ly10) and a non-cancerous control cell line (CB33). Active fractions are prioritized based on potency and selectivity.
View Article and Find Full Text PDFOpen Res Eur
September 2025
Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, 1870, Denmark.
Background: Innovative antibiotic discovery strategies are urgently needed to successfully combat infections caused by multi-drug-resistant bacteria.
Methods: We employed a direct screening approach to identify compounds with antimicrobial and antimicrobial helper-drug activity against Gram-positive and Gram-negative bacteria. We used this platform in two different strains of methicillin-resistant (MRSA) and aminoglycoside-resistant strains of to screen for antimicrobials compounds, which potentiate the activity of aminoglycoside antibiotics.
Rev Med Liege
September 2025
Service de Diabétologie, Nutrition et Maladies métaboliques, CHU Liège, Belgique.
Type 1 diabetes (T1D) is an autoimmune chronic disease that leads to the destruction of pancreatic beta cells and thus requires lifelong insulin therapy. Constraints and adverse events associated to insulin therapy are well known as well as the risk of long-term complications linked to chronic hyperglycaemia. Symptomatic T1D is preceded by a preclinical asymptomatic period, which is characterized by the presence of at least two auto-antibodies against beta cell without disturbances of blood glucose control (stage 1) or, in addition to immunological biomarkers, by the presence of mild dysglycaemia reflecting a defect of early insulin secretion (stage 2).
View Article and Find Full Text PDFChembiochem
September 2025
School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, P. R. China.
The ATPase caseinolytic protease X (ClpX), forming the ClpXP complex with caseinolytic protease P (ClpP), is essential for mitochondrial protein homeostasis. While ClpP targeting is a recognized anticancer strategy, the role of ClpX in cancer remains underexplored. In pancreatic ductal adenocarcinoma (PDAC), elevated CLPX expression correlates with poor prognosis, suggesting its oncogenic function.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, Pavia 27100, Italy.
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric.
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