Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours. (June 2017)

• Record Type: Journal Article
• Authors: Sharifi, N.; Ozgoli, S.; Ramezani, A.
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 13
• Page End: 19
• Publication Date: 2017-06
• Subjects: Cancer -- Chemotherapy -- Effector cells -- Immunotherapy -- Multiple model predictive control; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.012
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A drug administration method for mixed cancer therapy is presented. The multiple model predictive controller is designed not only for the chemotherapy but also for the immune boost to avoid drug toxicity and autoimmune. Some main constraints are considered to evade drugs' side effects. Low amount of doses of drugs in each instance are required. Tumor regression is led. Abstract: Background: 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. Methods: 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. Results: 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 suitable results with a perceptible reduction in medicine side effects. Conclusion: It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth.