The influence of amphiphilic quaternary ammonium palmitoyl glycol chitosan (GCPQ) polymer composition on oil-loaded nanocapsule architecture

The hydrophobicity governs the nanoarchitecture formation of oil-loaded quaternary ammonium palmitoyl glycol chitosan (GCPQ)-nanocapsules. Mathematical models were applied to obtain a predictive equation to produce different particle sizes which nanoarchitecture depended on DP, DQ, cHLB, and oil concentration. Thus, multiple – or single – oil cores with small particles stabilizing polymer shells could be observed depending on the composition. [Display omitted] Predicting the exact nature of the self-assembly of amphiphilic molecules into supramolecular structures is of utmost importance for a variety of applications, but this is a challenge for nanotechnology. The amphiphilic drug delivery polymer-N-palmitoyl-N-monomethyl-N,N-dimethyl-N,N,N-trimethyl-6-O-glycolchitosan (GCPQ) self-assembles in aqueous media to form nanoparticles. This work aimed to develop a systematic predictive mathematical model on the eventual nature of oil-loaded GCPQ-nanoparticles and to determine the main independent variables that affect their nanoarchitecture following self-assembly. GCPQ polymers were produced with varying degree of palmitoylation (DP, 5.7–23.8 mol%), degree of quaternization (DQ, 7.2–22.7 mol%), and molecular weight (MW, 11.2–44.2 kDa) and their critical hydrophilic-lipophilic balance (cHLB) optimized to produce oil-loaded nanocapsules. Non-linear mathematical models (Particle size (nm) = 466.05 − 5.64DP − 6.52DQ + 0.13DQ2 − 0.03 MW2 − 14.48cHLB + 0.48cHLB2) were derived to predict the nanoparticle sizes (R2 = 0.998, R2adj = 0.995). Smaller nanoparticle sizes (148–157 nm) were obtained at high DP, DQ, and cHLB values, in which DP was the main independent variable responsible for nanoparticle size. Single or multiple-oil cores with small particles stabilizing polymer shells could be observed depending on the oil volume. Nanoparticle architectures, especially the nature of the oil-core(s), were driven by the DP, DQ, cHLB, and oil concentration. Here, we have developed a predictive model that may be applied to understand the nanoarchitecture of oil-loaded GCPQ-nanoparticles.