journal article·2022-02

pH-sensitive chitosan-PEG-decorated hollow mesoporous silica nanoparticles could be an effective treatment for acute myeloid leukemia (AML)

G. Ultav, H. Tonbul, E. Tavukcuoglu, S. C. Ozturk, S. Akbas, A. Sahin, G. Esendagli, Y. Capan

Journal of Nanoparticle Research· Volume 24· Issue 2
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Abstract

Improved treatment of acute myeloid leukemia (AML) could be possible by longer retention of anticancer drugs in the bloodstream. In this study, it was aimed to obtain improved treatment against AML by providing prolonged blood levels of doxorubicin and ensuring endosomal escape by the proton sponge effect. With this aim, pH-sensitive and chitosan-poly ethylene glycol (Cs-PEG) coated doxorubicin-loaded hollow mesoporous silica nanoparticles (C-HMSN-DN) were prepared. Nanoparticles (NPs) were characterized by dynamic light scattering (DLS), zeta potential, transmission electron microscopy (TEM), X-Ray diffraction (XRD), and nitrogen adsorption-desorption isotherms. High doxorubicin encapsulation efficacy was obtained as 90%. pH-sensitive formulations were showed higher cellular uptake and found more effective against human leukemia cell line (HL60) than non-pH sensitive formulations. In vivo studies showed that Cs-PEG coating prolonged blood circulation time tremendously in comparison to unmodified nanoparticles and free doxorubicin. The designed drug delivery system (DDS) can be more effective by endosomal escape to eliminate myeloid cells which are granular cells containing a great number of lysosomes. In conclusion, we present a drug delivery system that provides a prolonged blood circulation time due to Cs-PEG coating and effective drug delivery via pH-sensitive drug release and endosomal escape for AML treatment.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Advanced biosensing and bioanalysis techniques · Dendrimers and Hyperbranched Polymers

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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