Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours

Computer Methods and Programs in Biomedicine
N SharifiA Ramezani

Abstract

Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to sui...Continue Reading

Citations

Jan 16, 2021·Drug Delivery and Translational Research·Richa SharmaDeepak Joshi
Oct 24, 2020·IET Systems Biology·Mostafa NazariMohammad Hadi Noori Skandari
Jan 25, 2020·Computer Methods and Programs in Biomedicine·Omar ShindiAnand Ramanathan

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