Polymer-Functionalized Magnetic Nanoparticles for Targeted Quercetin Delivery: A Potential Strategy for Colon Cancer Treatment - Macedo, Júlia Borges de; Bueno, Julia Narayana Schoroeder; Kanunfre, Carla Cristine; Miranda, José Ricardo de Arruda; Bakuzis, Andris Figueiroa; Ferrari, Priscileila Colerato (MDPI, 2025)
Background/Objectives: Nanoparticle-based drug delivery systems improve pharmacokinetic aspects, including controlled release and drug targeting, increasing therapeutic efficacy, and reducing toxicity in conventional colon cancer treatment. The superparamagnetism of magnetic nanoparticles (MNP) appears to be a potential alternative for magnetothermal therapy, inducing tumor cell death by an external magnetic field. Therefore, this study aimed to develop chitosan (CS) and folate-chitosan (FA-CS)-coated MNP to improve the stability and targeting of the system for quercetin (Q) delivery. Methods: After FA-CS synthesis and 32 factorial design, polymer-functionalized MNPs were produced for quercetin loading, characterized, and evaluated by drug dissolution and cytotoxicity assay. Results: The factorial design indicated the positive influence of CS on MNPs' Zeta potential, followed by the CS-temperature interaction. Optimized formulations had hydrodynamic diameters of 122.32 ± 8.56 nm, Zeta potentials of +30.78 ± 0.8 mV, and loading efficiencies of 80.45% (MNP-CS-Q) and 54.4% (MNP-FA-CS-Q). The 24 h drug release was controlled in MNP-CS-Q (up to 6.4%) and MNP-FA-CS-Q (up to 7.7%) in a simulated tumor medium, with Fickian diffusion release mechanism correlated to the Korsmeyer-Peppas model (R > 0.99). The cytotoxicity assay in HCT-116 showed a higher (p < 0.001) dose-dependent antitumor effect of quercetin-loaded MNP compared to free drug, with IC50s of 1.46 (MNP-CS) and 1.30 µg·mL-1 (MNP-FA-CS). Conclusions: Therefore, this study contributes to the development of biomedical nanotechnology and the magnetic debate by highlighting the antitumor potential of quercetin magnetic nanoparticles. The experimental design allows the discussion of critical manufacturing variables and the determination of optimal parameters for the formulations.