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

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.

Modeling and Bias-Robust Estimation of the Acoustic Release of Chemotherapeutics from Liposomes.

This paper models the acoustic drug release of chemotherapeutics from liposomes using a kinetic model that accounts for systematic biases affecting the drug delivery process. An optimal stochastic filter is then proposed to provide robust estimates of the percent drug released. Optimality is guaranteed by accurately identifying the underlying statistical noise characteristics in experimental data. The estimator also quantifies the bias in the release, exhibited by the experimental data. Drug release is experimentally measured as a change in fluorescence upon the application of ultrasound. First, a first-order kinetic model is proposed to model the release, which is aided by a bias term to account for the fact that full release is not achieved under the conditions explored in this study. The noise structure affecting the process dynamics and the measurement process is then identified in terms of the statistical covariance of the measured quantities. The identified covariance magnitudes are then utilized to estimate the dynamics of drug release as well as the bias term. The identified a priori knowledge is used to implement an optimal Kalman filter, which was initially tested in a simulation environment. The experimental datasets are then fed into the filter to estimate the state and identify the bias. Experiments span a number of ultrasonic power densities for liposomes. The results suggest that the proposed algorithm, the optimal Kalman filter, performs well in modeling acoustically activated drug release from liposomes.

Identification of the Uncertainty Structure to Estimate the Acoustic Release of Chemotherapeutics From Polymeric Micelles.

This paper estimates the acoustic drug release from micelles after accurately identifying the underlying statistical noise characteristics in experimental data. The drug release is measured as a change in fluorescence as ultrasound is applied. First, the noise structure affecting the process dynamics and the measurement process is identified in terms of statistical covariance of the aforementioned quantities. Then, the identified covariance magnitudes are utilized to estimate the dynamics of drug release. The performance of different filters is investigated. The identified a priori knowledge is used to implement an optimal Kalman filter, a multi-hypothesis Kalman filter, and a variant of the full information estimator (moving horizon estimator) to the problem at hand. The proposed algorithms are initially deployed in a simulation environment, and then the experimental data sets are fed into the algorithms to validate their performance. Experiments span a number of ultrasonic power densities for both non-targeted and targeted polymeric micelles (the targeting being accomplished using the folate moiety). The results suggest that the proposed algorithm, the optimal Kalman filter, performs better than the other two in all tests performed.