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Innovative payloads for ADCs in cancer treatment: moving beyond the selective delivery of chemotherapy.
Ther Adv Med Oncol
January 2025
Division of New Drugs and Early Drug Development, European Institute of Oncology, IRCCS, Via Giuseppe Ripamonti 435, Milan 20141, Italy.
Antibody-drug conjugates (ADCs) have emerged as a transformative approach in cancer therapy by enhancing tumor targeting and minimizing systemic toxicity compared to traditional chemotherapy. Initially developed with chemotherapy agents as payloads, ADCs have now incorporated alternative payloads, such as immune-stimulating agents, natural toxins, and radionuclides, to improve therapeutic efficacy and specificity. A significant advancement in ADC technology is the integration of Proteolysis Targeting Chimeras (PROTACs), which enable the precise degradation of cellular targets involved in tumorigenesis.
View Article and Find Full Text PDFRefractory fungal infection: Three case reports highlighting good practice.
Med Mycol Case Rep
December 2024
Public Health Wales Mycology Reference Laboratory, University Hospital of Wales, Heath Park Way, CF14 4XW, Cardiff, United Kingdom.
Refractory invasive fungal disease is a significant clinical problem, with high morbidity, mortality and costs. The complex causes of refractory infection include breakthrough infection due to antifungal resistance (both innate and acquired), suboptimal therapy and impaired immune responses in critically ill or immunocompromised patients. This case series details three reports on the identification and management of refractory fungal infections, two cases of azole resistance and one case of resistant candidiasis, highlighting the importance of accurate diagnosis, monitoring, implementation of biomarkers (serological markers, PCR), antifungal susceptibility testing and antifungal stewardship to optimise management and minimise risks of emergence of drug resistance.
View Article and Find Full Text PDFImpact of SWCNT-conjugated senna leaf extract on breast cancer cells: A potential apoptotic therapeutic strategy.
Open Life Sci
December 2024
Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
Breast cancer (BC) has a prevalence rate of 21.8% among Saudi women and ranks as the third leading cause of death in Western nations. Nanotechnology offers innovative methods for targeted BC therapy, and this study explores the use of single-walled carbon nanotubes (SWCNTs) for delivering the senna leaf extract.
View Article and Find Full Text PDFEfficacy of injectable platelet-rich fibrin (i-PRF) as a novel vehicle for local drug delivery in non-surgical periodontal pocket therapy: A randomized controlled clinical trial.
J Adv Periodontol Implant Dent
September 2024
Department of Periodontics, Saveetha Dental College & Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India.
Background: The vehicle in a local drug delivery (LDD) system plays a vital role in delivering the active drug component at the diseased site. Liquid/injectable platelet-rich fibrin (i-PRF), an autologous fibrin matrix, might be used as a vehicle to enmesh drugs and deliver locally at the periodontally diseased sites. This study evaluated the efficacy of the drug (ciprofloxacin [Cip])-loaded i-PRF as a LDD system adjunct to subgingival debridement in subjects with periodontal pockets.
View Article and Find Full Text PDFPrediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning.
J Cheminform
January 2025
PROMOCS Laboratory, Department of Chemistry and Chemical Technologies, University of Calabria, Arcavacata di Rende (CS), Italy.
Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, and absorb within the therapeutic window (600-850 nm). Traditional prediction methods for these light absorption properties, including Time-Dependent Density Functional Theory (TDDFT), are often computationally intensive and time-consuming. In this study, we explore a machine learning (ML) approach to predict the light absorption in the region of the therapeutic window of platinum, iridium, ruthenium, and rhodium complexes, aiming at streamlining the screening of potential photoactivatable prodrugs.
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