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首页> 外文期刊>Technology in cancer research & treatment. >Using artificial neural networks and model predictive control to optimize acoustically assisted Doxorubicin release from polymeric micelles.
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Using artificial neural networks and model predictive control to optimize acoustically assisted Doxorubicin release from polymeric micelles.

机译：使用人工神经网络和模型预测控制来优化声学协助的阿霉素从聚合物胶束中的释放。

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摘要

We have been developing a drug delivery system that uses Pluronic P105 micelles to sequester a chemotherapeutic drug--namely, Doxorubicin (Dox)--until it reaches the cancer site. Ultrasound is then applied to release the drug directly to the tumor and in the process minimize the adverse side effects of chemotherapy on non-tumor tissues. Here, we present an artificial neural network (ANN) model that attempts to model the dynamic release of Dox from P105 micelles under different ultrasonic power intensities at two frequencies. The developed ANN model is then utilized to optimize the ultrasound application to achieve a target drug release at the tumor site via an ANN-based model predictive control. The parameters of the controller are then tuned to achieve good reference signal tracking. We were successful in designing and testing a controller capable of adjusting the ultrasound frequency, intensity, and pulse length to sustain constant Dox release.
机译：我们一直在开发一种药物输送系统，该系统使用Pluronic P105胶束隔离一种化学治疗药物-即阿霉素（Dox）-直至到达癌症部位。然后应用超声波将药物直接释放到肿瘤，并在此过程中将化学疗法对非肿瘤组织的不利副作用降至最低。在这里，我们提出了一个人工神经网络（ANN）模型，该模型试图模拟在两个频率下不同超声功率强度下P105胶束中Dox的动态释放。然后，通过基于ANN的模型预测控制，将开发的ANN模型用于优化超声应用，以实现肿瘤部位的目标药物释放。然后调整控制器的参数以实现良好的参考信号跟踪。我们成功设计并测试了一种控制器，该控制器能够调节超声频率，强度和脉冲长度，以维持恒定的Dox释放。

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