Polymer Concentration Maximizes Encapsulation Efficiency in Electrohydrodynamic Mixing Nanoprecipitation

Abstract

Encapsulation in self-assembled block copolymer (BCP) based nanoparticles (NPs) is a common approach to enhance hydrophobic drug solubility, and nanoprecipitation processes in particular can yield high encapsulation efficiency (EE). However, guiding principles for optimizing polymer, drug, and solvent selection are critically needed to facilitate rapid design of drug nanocarriers. Here, we evaluated the relationship between drug-polymer compatibility and concentration ratios on EE and nanocarrier size. Our studies employed a panel of four drugs with differing molecular structures (i.e., coumarin 6, dexamethasone, vorinostat/SAHA, and lutein) and two BCPs [poly(caprolactone)-b-poly(ethylene oxide) (PCL-b-PEO) and poly(styrene)-b-poly(ethylene oxide) (PS-b-PEO)] synthesized using three nanoprecipitation processes [i.e., batch sonication, continuous flow flash nanoprecipitation (FNP), and electrohydrodynamic mixing-mediated nanoprecipitation (EM-NP)]. Continuous FNP and EM-NP processes demonstrated up to 50% higher EE than batch sonication methods, particularly for aliphatic compounds. Drug-polymer compatibilities were assessed using Hansen solubility parameters, Hansen interaction spheres, and Flory Huggins interaction parameters, but few correlations were EE observed. Although some Hansen solubility (i.e., hydrogen bonding and total) and Flory Huggins interaction parameters were predictive of drug-polymer preferences, no parameter was predictive of EE trends among drugs. Next, the relationship between polymer: drug molar ratio and EE was assessed using coumarin 6 as a model drug. As polymer:drug ratio increased from <1 to 3–6, EE approached a maximum (i.e., ∼51% for PCL BCPs vs. ∼44% PS BCPs) with Langmuir adsorption behavior. Langmuir behavior likely reflects a formation mechanism in which drug aggregate growth is controlled by BCP adsorption. These data suggest polymer:drug ratio is a better predictor of EE than solubility parameters and should serve as a first point of optimization.

Using a systematic and quantitative approach to generate new insights into drug loading of PLGA nanoparticles using nanoprecipitation

“…It is believed that blank PLGA NPs prepared by nanoprecipitation are formed in three stages, involving NPs nucleation, growth through aggregation, and eventually the formation of kinetically locked nanoparticles after a characteristic aggregation time scale 4,18,54–57 However, the situation may be complicated by the presence of drug which could interfere with nucleation and growth and may itself form crystals if supersaturation conditions occur. In order to gain an insight into the possible mechanisms involved during the mixing process for solvent and anti-solvent and the role of drug it is necessary to consider both the kinetic and thermodynamic factors involved.…”

“…6,10,[12][13][14] However, fewer studies have examined the impact of these parameters on drug loading. [15][16][17][18] In addition, the drug entrapment by PLGA NPs has tended to be treated in the literature on a descriptive basis by demonstrating the impact of each processing parameter on a specic drug and application. However, there is a lack of generalised conclusions regarding the impact of the processing parameters on the drug entrapment mechanism and efficiency.…”

Cytotoxicity Enhancement in MCF-7 Breast Cancer Cells with Depolymerized Chitosan Delivery of α-Mangostin

“…A standard curve was obtained using the different concentrations (2–20 µg/mL) measured at 245 nm. The EE and DL of AMG present in NPs were calculated using the following Equations (3) and (4) [ 43 , 44 ]: …”

Cytotoxicity Enhancement of α-Mangostin with Folate-Conjugated Chitosan Nanoparticles in MCF-7 Breast Cancer Cells

“…The LE and LC of AM contained in NPs were determined using Equations ( 1) and (2), respectively [95,96]:…”