Modeling of In vitro Release Kinetics of Ultrasound-triggered Targeted Liposomes

Abstract

Ultrasound-triggered, targeted liposomes are a promising drug delivery system as they potentially improve the clinical outcomes of chemotherapy while reducing associated side effects. However, the common drug release models used in pharmaceutical studies do not accurately fit the in-vitro release profiles found in stimuli-responsive or externally-activated drug delivery systems. This presents a challenge for these drug delivery systems, as predictable and stable release profiles are necessary for clinical work. Ideally, the design process of targeted nanoparticles should predict the release behavior of the nanoparticles, at least in-vitro and, eventually, in vivo. This requires models that incorporate properties that influence sonosensitivity of the drug delivery particles. In this work, we perform a comprehensive model fitting of a large data set of liposomal release data with 7 targeting moieties in addition to the control (Albumin, cRGD, Estrone, Hyaluronic acid, Herceptin, Lactobionic acid, and Transferrin) under ultrasound release protocols using low frequency (20 kHz) ultrasound at 6.2, 9 and 10 mW/cm² as well as high frequencies (1.07 MHz and 3 MHz) at power densities of 10.5 (1 MHz), 50 (1 MHz), and 173 W/cm² (3 MHz). The release models we use are Zero-order, First-order, Higuchi, Korsmeyer Peppas, Weibull, Hixon Crowell, Baker Lonsdale, Gompertz, Hopfenberg, the novel Lu Hagen model and a third-order polynomial fit. We then establish the models that fit best under varying conditions of ultrasound release frequencies and power densities. Additionally, we show an empirical mathematical relationship between the molecular weight and pKa value (negative log of the acid dissociation constant) of the moieties and the release coefficients for two single coefficient models, the zero-order model and a fixed power Korsmeyer Peppas model. This is an important step in modeling the responsiveness of targeted liposomes and enables better prediction of in vitro liposomal release performance.