Cancerous diseases are being responsible for the death of many around the globe. Treating these illnesses pose a significant challenge to the medical professionals. While conventional methods, chemotherapy or radiotherapy for example, provide a remedy to the issue their side effects are not negligible. In the past few decades new methods have emerged, which could hinder the strength of the side effects and most remarkably, antiangiogenic therapy can make a notable difference in every day cancer treatment. While the technique has many advantages the cost of treatments are often expensive due to the non-personalized administration medical protocols. In this paper a model based solution is provided which aims to lower the medical expenses during the treatment by creating personalized administration plans with the help of control engineering.
Cancer therapy optimization is an issue that can be solved using the control engineering approach. An optimal therapy generation algorithm is presented and tested using a tractable mouse model of breast cancer. The optimized therapeutic protocol is calculated in a closed-loop manner at fixed time instants, twice in a week. The controller consists of a nonlinear model predictive controller which uses the state estimation of a moving horizon estimator. The estimator also computes parameter estimates of the prediction model such that the time varying nature of tumor evolution can be captured. Results show that remission can be induced in a 28-day interval using the algorithm.
Nonlinear Model Predictive Control (NMPC) is utilized to compute optimal administration protocols for chemotherapeutic treatment. By using model-based methods, the side effects of the drug can be mitigated in conjunction with a decrease in treatment expenses. The designed controller was able to provide the protocol in the form of impulses that can model administration by injection, using a smooth approximation of the Dirac delta distribution. For the implementation of the NMPC algorithm, Direct Multiple Shooting (DMS) was chosen so that the computational time of the problem remains reasonable. Numerical effects on the stability of the computation were discussed, with a solution for each issue present. The controller was also tested on virtual patients, with data from mice experiments, which concluded in applicable treatment protocols.
In this paper a novel control strategy is introduced in order to create robust and adaptive control approach for type 1 diabetes mellitus. This approach uses Robust Fixed Point Transformations which hinders the negative effect of inherent model uncertainties and measurement disturbances. The results are validated by extensive simulation on the proposed control algorithm from which conclusions were drawn.
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