Background: At present, the few photothermal/chemotherapy studies about retinoblastoma that have been reported are mainly restricted to ectopic models involving subcutaneous implantation. However, eyeball is unique physiological structure, the blood-retina barrier (BRB) hinders the absorption of drug molecules through the systemic route. Moreover, the abundant blood circulation in the fundus accelerates drug metabolism. To uphold the required drug concentration, patients must undergo frequent chemotherapy sessions.
Purpose: To address these challenges above, we need to develop a secure and effective drug delivery system (FA-PEG-PDA-DOX) for the fundus.
Methods: We offered superior therapeutic efficacy with minimal or no side effects and successfully established orthotopic mouse models. We evaluated cellular uptake performance and targeting efficiency of FA-PEG-PDA-DOX nanosystem and assessed its synergistic antitumor effects in vitro and vivo. Biodistribution assessments were performed to determine the retention time and targeting efficiency of the NPs in vivo. Additionally, safety assessments were conducted.
Results: Cell endocytosis rates of the FA-PEG-PDA-DOX+Laser group became 5.23 times that of the DOX group and 2.28 times that of FA-PEG-PDA-DOX group without irradiation. The fluorescence signal of FA-PEG-PDA-DOX persisted for more than 120 hours at the tumor site. The number of tumor cells (17.2%) in the proliferative cycle decreased by 61.6% in the photothermal-chemotherapy group, in contrast to that of the saline control group (78.8%). FA-PEG-PDA-DOX nanoparticles(NPs) exhibited favorable biosafety and high biocompatibility.
Conclusion: The dual functional targeted nanosystem, with the effects of DOX and mild-temperature elevation by irradiation, resulted in precise chemo/photothermal therapy in nude mice model.
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http://dx.doi.org/10.2147/IJN.S467949 | DOI Listing |
Adv Sci (Weinh)
December 2024
Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
The anti-tumor efficacy of current pharmacotherapy is severely hampered due to the adaptive evolution of tumors, urgently needing effective therapeutic strategies capable of breaking such adaptability. Metabolic reprogramming, as an adaptive survival mechanism, is closely related to therapy resistance of tumors. Colorectal cancer (CRC) cells exhibit a high energy dependency that is sustained by an adaptive metabolic conversion between glucose and glutamine, helping tumor cells to withstand nutrient-deficient microenvironments and various treatments.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Department of Pharmacy, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China.
Drug resistance is an important factor for prostate cancer (PCa) to progress into refractory PCa, and abnormal lipid metabolism usually occurs in refractory PCa, which presents great challenges for PCa therapy. Here, a cluster of differentiation 36 (CD36) inhibitor sulfosuccinimidyl oleate sodium (CD36i) and stearoyl-CoA desaturase 1 (SCD1) siRNA (siSCD1) are selected to inhibit lipid uptake and synthesis in PCa, respectively. To this end, a multiresponsive drug delivery nanosystem, HA@CD36i-TR@siSCD1 is designed.
View Article and Find Full Text PDFBiomater Res
December 2024
Department of Neurosurgery, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu 226001, P.R. China.
Glioblastoma multiforme (GBM) is among the most challenging malignant brain tumors, making the development of new treatment strategies highly necessary. Glioma stem cells (GSCs) markedly contribute to drug resistance, radiation resistance, and tumor recurrence in GBM. The therapeutic potential of nanomaterials targeting GSCs in GBM urgently needs to be explored.
View Article and Find Full Text PDFInt J Nanomedicine
December 2024
Department of Oral Implantology, Hospital of Stomatology, Jilin University, Changchun, 130021, People's Republic of China.
ACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
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