Dual drug-loaded biofunctionalized amphiphilic chitosan nanoparticles: Enhanced synergy between cisplatin and demethoxycurcumin against multidrug-resistant stem-like lung cancer cells

Lung cancer kills more humans than any other cancer and multidrug resistance (MDR) in cancer stem like cells (CSC) is emerging as a reason for failed treatments. One concept that addresses this root cause of treatment failure is the utilization of nanoparticles to simultaneously deliver dual drugs to cancer cells with synergistic performance, easy to envision - hard to achieve. (1) It is challenging to simultaneously load drugs of highly different physicochemical properties into one nanoparticle, (2) release kinetics may differ between drugs and (3) general requirements for biomedical nanoparticles apply. Here self-assembled nanoparticles of amphiphilic carboxymethyl-hexanoyl chitosan (CHC) were shown to present nano-microenvironments enabling simultaneous loading of hydrophilic and hydrophobic drugs. This was expanded into a dual-drug nano-delivery system to treat lung CSC. CHC nanoparticles were loaded/chemically modified with the anticancer drug cisplatin and the MDR-suppressing Chinese herbal extract demethoxycurcumin, followed by biofunctionalization with CD133 antibody for enhanced uptake by lung CSC, all in a feasible one-pot preparation. The nanoparticles were characterized with regard to chemistry, size, zeta potential and drug loading/release. Biofunctionalized and non-functionalized nanoparticles were investigated for uptake by lung CSC. Subsequently the cytotoxicity of single and dual drugs, free in solution or in nanoparticles, was evaluated against lung CSC at different doses. From the dose response at different concentrations the degree of synergy was determined through Chou-Talalay's Plot. The bio-functionalized nanoparticles promoted synergistic effects between the drugs and were highly effective against MDR lung CSC. The efficacy and feasible one-pot preparation suggests preclinical studies using relevant disease models to be justified. (C) 2016 Elsevier B.V. All rights reserved.