Multi-Model Investigation and Adaptive Estimation of the Acoustic Release of a Model Drug From Liposomes.

Abstract

This paper researches a suitable mathematical model that can reliably predict the release of a model drug (namely calcein) from biologically targeted liposomal nanocarriers triggered by ultrasound. Using mathematical models, curve fitting is performed on a set of five experimental acoustic drug release runs from Albumin-, Estrone-, and RGD-based Drug Delivery Systems (DDS). The three moieties were chosen to target specific cancers using receptor-mediated endocytosis. The best-fitting mathematical model is then enhanced using a Kalman filtering (KF) algorithm to account for the statistics of the dynamic and measurements noise sequences in predicted drug release. Unbiased drug-release estimates are realized by implementing an online noise identification algorithm. The algorithm is first deployed in a simulated environment in which it was rigorously tested and compared with the correct solution. Then, the algorithm was used to process the five experimental datasets. The results suggest that the Adaptive Kalman Filter (AKF) is exceptionally good at handling drug release estimation problems with a priori unknown or with changing noise covariances. In comparison with the KF, the AKF approach exhibited as low as a 69% reduction in the level of error in estimating the drug release state. Finally, the proposed algorithm is not computationally demanding and is capable of online estimation tasks.