Synthesis of High Drug Loading Nanocarriers & Image Analysis Characterization of Spherical Microparticles

Abstract

I. Drug Loading: Flash NanoPrecipitation (FNP) is a scalable process by which hydrophobic active pharmaceutical ingredients (APIs) and block copolymers quickly assemble into nanocarriers (NCs) through a precipitation-stabilization process. However, it has been difficult to achieve API loading above 50% without sacrificing the size or stability of the NCs. Previously, we have shown that drug loading can be increased in the FNP process by splitting it into two mixing stages, precipitating the API in the first stage, and then encapsulating it in the block copolymer for the second. This new process was coined sequential FNP (sFNP). At the same time (but independently), highly soluble, hydrophilic APIs have been capsulated by converting them into hydrophobic salts through a method called ion-pairing. This project will further explore both sFNP and ion pairing to create stable, highly loaded particles of previously-unencapsulated ion-paired APIs.

II. Image Analysis: Size distribution of engineered particles is often the most important factor when determining its quality. It is customary to report average particle size and batch polydispersity when characterizing a particle batch. Gathering this data, however, can still be time consuming or expensive due to traditional imaging and data collection methods. Here, we create a MATLAB program that can quickly generate size data using inexpensive light microscope images of spherical microparticle batches. This program generates size distribution histograms and polydispersity reports in under a minute by analyzing these microscope images of particles. Unlike traditional image analysis software like ImageJ, this MATLAB based program accurately detects and estimates the size of clustered and overlapping spherical particles and thresholds the image without falsely counting detecting smudges and dark regions. Using these methods, we were able to reach detection rates of up to 95% for images with over 1000 particles.