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.

中文翻译：

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模型药物从脂质体的声释放的多模型研究和自适应估计。

本文研究了一种合适的数学模型，该模型可以可靠地预测超声触发的生物靶向脂质体纳米载体中模型药物（即钙黄绿素）的释放。使用数学模型，对基于白蛋白，雌酮和RGD的药物输送系统（DDS）的五个实验声学药物释放过程进行了曲线拟合。使用受体介导的内吞作用选择这三个部分以靶向特定癌症。然后，使用卡尔曼滤波（KF）算法增强最适合的数学模型，以说明预测的药物释放过程中动态和测量噪声序列的统计信息。通过实施在线噪声识别算法，可以实现无偏见的药物释放估计。该算法首先部署在经过严格测试的模拟环境中，并与正确的解决方案进行比较。然后，该算法用于处理五个实验数据集。结果表明，自适应卡尔曼滤波器（AKF）非常擅长处理先验未知或噪声协方差不断变化的药物释放估计问题。与KF相比，AKF方法在估计药物释放状态时的误差水平降低了69％。最后，提出的算法对计算的要求不高，并且能够在线估计任务。结果表明，自适应卡尔曼滤波器（AKF）非常擅长处理先验未知或噪声协方差不断变化的药物释放估计问题。与KF相比，AKF方法在估计药物释放状态时的误差水平降低了69％。最后，提出的算法对计算的要求不高，并且能够在线估计任务。结果表明，自适应卡尔曼滤波器（AKF）非常擅长处理先验未知或噪声协方差不断变化的药物释放估计问题。与KF相比，AKF方法在估计药物释放状态时的误差水平降低了69％。最后，提出的算法对计算的要求不高，并且能够在线估计任务。