Use of Model Predictive Control and Artificial Neural Networks to Optimize the Ultrasonic Release of a Model Drug From Liposomes

Abstract

The use of echogenic liposomes to deliver chemotherapeutic agents for cancer treatment has gained wide recognition in the last 20 years. Cancerous cells can develop multiple drug resistance (MDR), in part, due to the drop in concentration of chemotherapeutic agents below the therapeutic levels inside the tumor. This suggests that MDR can be reduced by controlling the level of drug release in the diseased area. In this paper, a model predictive controller based on neural networks is proposed to maintain a constant chemotherapeutic release at the cancer site. The proposed system was able to follow the set point by varying the U.S. intensity within preset constraints. The system simulated model is viable and it showed a high average fit when stimulated with variable input variations, indicating the robustness of the nonlinear model. By maintaining a constant release of the drug so that the concentration level is above a certain threshold, we hope to reduce cancer resistance towards chemotherapeutic agents.