Characterization of nanomedicines’ surface coverage using molecular probes and capillary electrophoresis

Abstract

A faithful characterization of nanomedicine (NM) is needed for a better understanding of their in vivo outcomes. Size and surface charge are studied with well-established methods. However, other relevant parameters for the understanding of NM behavior in vivo remain largely inaccessible. For instance, the reactive surface of nanomedicines, which are often grafted with macromolecules to decrease their recognition by the immune system, is excluded from a systematic characterization. Yet, it is known that a subtle modification of NMs' surface characteristics (grafting density, molecular architecture and conformation of macromolecules) is at the root of major changes in the presence of biological components. In this work, a method that investigates the steric hindrance properties of the NMs' surface coverage based on its capacity to exclude or allow adsorption of well-defined proteins was developed based on capillary electrophoresis. A series of proteins with different molecular weights (MW) were used as molecular probes to screen their adsorption behavior on nanoparticles bearing different molecular architectures at their surface. This novel strategy evaluating to some degree a functionality of NMs can bring additional information about their shell property and might allow for a better perception of their behavior in the presence of biological components. The developed method could discriminate nanoparticles with a high surface coverage excluding high MW proteins from nanoparticles with a low surface coverage that allowed high MW proteins to adsorb on their surface. The method has the potential for further standardization and automation for a routine use. It can be applied in quality control of NMs and to investigate interactions between proteins and NM in different situations.

Understanding the interplay between surface properties and the aspect ratio of ellipsoidal nanomaterials

“…NS composed of PIBCA core and polysaccharide shell were prepared using an anionic emulsion polymerization of isobutyl cyanoacrylate monomer and the polysaccharides in an acidic medium. This method was adapted from previous reports using other polysaccharides, including chitosan [14,15], dextran [16], and hyaluronan [17]. Non-labeled NS and FITC-labeled NS were prepared by dissolving 69 mg of each polysaccharide (FITC-labeled or not) in 5 mL of dilute nitric acid at a concentration of 0.2 M under magnetic stirring (300 rpm) in a glass tube at 40 °C for 30 min.…”

“…Spherical particles composed of poly(isobutyl cyanoacrylate) (PIBCA) were obtained by polymerizing isobutyl cyanoacrylate monomer and polysaccharides. [14][15][16][17] Shape-persistent E-NM with a high aspect ratio (Γ = 3.4) were achieved through the physical deformation of NS. [12] The E-NM shape was stable for 6 months at different temperatures (4 °C, 20 °C, and 40 °C).…”

The use of data mining and machine learning in nanomedicine: a survey

“…[9] shows a meta-analysis of clinical and preclinical studies comparing the anticancer efficacy of liposomal versus conventional non-liposomal doxorubicin. Results are controversial, demon-strating enhanced therapeutic efficacy of liposomal vs. free doxorubicin in pre-clinical studies but not in clinical studies [14][15][16][17][18][19][20].…”

“…The set of original variables, often with an high number of members with repetitions and redundancy, is replace with a lower number of new uncorrelated variables that can be view as variable profiles, latent in the original dataset. PCA components need to be interpreted: analysis is not automated and the new profiles need to be discovered considering relationships between old and new variables [14]. studied relationships between size and surface coverage change in nanomedicines and their behaviour in vivo.…”