Abstract
Objectives
The main aim of this work is to introduce a robust controller for controlling the drug dosage.
Methods
The presented work establishes a novel robust controller that controls the drug dosage and it also carried out parameters estimation. Along with this, a Regularized Error Function-based EKF (REF-EKF) is introduced for estimating the tumor cells that could be adapted for different conditions. It also assists in solving the overfitting problems, which occur during the drug dosage estimation. Moreover, the performance of the adopted controller is compared over other conventional schemes, and the attained outcomes reveal the appropriate impact of drug dosage injection on immune, normal, and tumor cells. It is also ensured that the presented controller does a robust performance on the parameter uncertainties. Moreover, to enhance the performance of the proposed system and for fast convergence, it is aimed to fine-tune the initial state of EKF optimally using a new Improved Gray Wolf Optimization (GWO) termed as Adaptive GWO (AGWO). Finally, analysis is held to validate the betterment of the presented model.
Results
The outcomes, the proposed method has accomplished a minimal value of error with an increase in time, when evaluated over the compared models.
Conclusions
Thus, the improvement of the proposed REF-EKF-AGWO model is proved from the attained results.
Research funding: None declared.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Ethical approval: The conducted research is not related to either human or animal use.
References
1. Pucci, C, Martinelli, C, Ciofani, G. Innovative approaches for cancer treatment: current perspectives and new challenges. Ecancermedicalscience 2019;13:961. https://doi.org/10.3332/ecancer.2019.961.Search in Google Scholar
2. Matsuda, C, Ishiguro, M, Teramukai, S, Kajiwara, Y, Fujii, S, Kinugasa, Y, et al.. A randomised-controlled trial of 1-year adjuvant chemotherapy with oral tegafur–uracil versus surgery alone in stage II colon cancer: SACURA trial. Eur J Canc 2018;96:54–63. https://doi.org/10.1016/j.ejca.2018.03.009.Search in Google Scholar
3. Batmani, Y, Khaloozadeh, H. Optimal drug regimens in cancer chemotherapy: a multi-objective approach. Comput Biol Med 2013;43:2089–95. https://doi.org/10.1016/j.compbiomed.2013.09.026.Search in Google Scholar
4. Wang, X, Zhang, H, Chen, X. Drug resistance and combating drug resistance in cancer. Canc Drug Resist 2019;2:141–60.10.20517/cdr.2019.10Search in Google Scholar PubMed PubMed Central
5. Rokhforoz, P, Jamshidi, AA, Sarvestani, NN. Adaptive robust control of cancer chemotherapy with extended Kalman filter observer. Inf Med Unlocked 2017;8:1–7. https://doi.org/10.1016/j.imu.2017.03.002.Search in Google Scholar
6. Chen, T, Kirkby, NF, Jena, R. Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation. Comput Methods Prog Biomed 2012;108:973–83. https://doi.org/10.1016/j.cmpb.2012.05.011.Search in Google Scholar
7. Paryad-zanjani, S, Mahjoob, MJ, Amanpour, S, Kheirbakhsh, R, Akhoundzadeh, MH. A supplemental treatment for chemotherapy: control simulation using a mathematical model with estimated parameters based on in vivo experiment. IFAC-PapersOnLine 2016;49:277–82. https://doi.org/10.1016/j.ifacol.2016.12.138.Search in Google Scholar
8. Pouchol, C, Clairambault, J, Lorz, A, Trélat, E. Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy. J Math Pure Appl 2018;116:268–308. https://doi.org/10.1016/j.matpur.2017.10.007.Search in Google Scholar
9. Baleanu, D, Jajarmi, A, Sajjadi, SS, Mozyrska, D. A new fractional model and optimal control of a tumor-immune surveillance with non-singular derivative operator. Chaos: Interdiscipl J Nonlinear Sci 2019;29:083127. https://doi.org/10.1063/1.5096159.Search in Google Scholar
10. Matsuda, C, Ishiguro, M, Teramukai, S, Kajiwara, Y, Fujii, S, Kinugasa, Y, et al.. A randomised-controlled trial of 1-year adjuvant chemotherapy with oral tegafur–uracil versus surgery alone in stage II colon cancer: SACURA trial. Eur J Canc 2018;96:54–63. https://doi.org/10.1016/j.ejca.2018.03.009.Search in Google Scholar
11. Toyooka, S, Okumura, N, Nakamura, H, Nakata, M, Yamashita, M, Tada, H, et al.. A multicenter randomized controlled study of paclitaxel plus carboplatin versus oral uracil-tegafur as the adjuvant chemotherapy in resected non–small cell lung cancer. J Thorac Oncol 2018;13:699–706. https://doi.org/10.1016/j.jtho.2018.02.015.Search in Google Scholar
12. Wu, H, Hu, H, Wan, J, Li, Y, Wu, Y, Tang, Y, et al.. Hydroxyethyl starch stabilized polydopamine nanoparticles for cancer chemotherapy. Chem Eng J 2018;349:129–45. https://doi.org/10.1016/j.cej.2018.05.082.Search in Google Scholar
13. Gibbons, A, Groarke, AM. Coping with chemotherapy for breast cancer: asking women what work. Eur J Oncol Nurs 2018;35:85–91. https://doi.org/10.1016/j.ejon.2018.06.003.Search in Google Scholar
14. Jiang, S, Liu, Y, Huang, L, Zhang, F, Kang, R. Effects of propofol on cancer development and chemotherapy: potential mechanisms. Eur J Pharmacol 2018;831:46–51. https://doi.org/10.1016/j.ejphar.2018.04.009.Search in Google Scholar
15. Sun, B, Luo, C, Cui, W, Sun, J, He, Z. Chemotherapy agent-unsaturated fatty acid prodrugs and prodrug-nanoplatforms for cancer chemotherapy. J Contr Release 2017;264:145–59. https://doi.org/10.1016/j.jconrel.2017.08.034.Search in Google Scholar
16. Wang, F, Porter, M, Konstantopoulos, A, Zhang, P, Cui, H. Preclinical development of drug delivery systems for paclitaxel-based cancer chemotherapy. J Contr Release 2017;267:100–18. https://doi.org/10.1016/j.jconrel.2017.09.026.Search in Google Scholar
17. Abbasian, M, Roudi, MM, Mahmoodzadeh, F, Eskandani, M, Jaymand, M. Chitosan-grafted-poly(methacrylic acid)/graphene oxide nanocomposite as a pH-responsive de novo cancer chemotherapy nanosystem. Int J Biol Macromol, Available online. 2018;118:1871–9. https://doi.org/10.1016/j.ijbiomac.2018.07.036.Search in Google Scholar
18. Bao, T, Seidman, AD, Piulson, L, Vertosick, E, Chen, X, Vickers, AJ, et al.. A phase IIA trial of acupuncture to reduce chemotherapy-induced peripheral neuropathy severity during neoadjuvant or adjuvant weekly paclitaxel chemotherapy in breast cancer patients. Eur J Canc 2018;101:12–9. https://doi.org/10.1016/j.ejca.2018.06.008.Search in Google Scholar
19. Kimmick, GG, Li, X, Steven, T, Fleming, SA, Sabatino, JF, Wilson, J, et al.. Risk of cancer death by comorbidity severity and use of adjuvant chemotherapy among women with locoregional breast cancer. J Geriatr Oncol 2018;9:214–20. https://doi.org/10.1016/j.jgo.2017.11.004.Search in Google Scholar
20. Kurt, B, Kapucu, S. The effect of relaxation exercises on symptom severity in patients with breast cancer undergoing adjuvant chemotherapy: an open label non-randomized controlled clinical trial. Eur J Integr Med, Available online, 3 August 2018;22:54–61. https://doi.org/10.1016/j.eujim.2018.08.002.Search in Google Scholar
21. Lai, X, Friedman, A. Mathematical modeling in scheduling cancer treatment with combination of VEGF inhibitor and chemotherapy drugs. J Theor Biol 2019;462:490–8. https://doi.org/10.1016/j.jtbi.2018.11.018.Search in Google Scholar
22. Wu, X, Liu, Q, Zhang, K, Cheng, M, Xin, X. Optimal switching control for drug therapy process in cancer chemotherapy. Eur J Contr 2018;42:49–58. https://doi.org/10.1016/j.ejcon.2018.02.004.Search in Google Scholar
23. Liang, L, Luo, H, He, Q, You, Y, Liang, J. Investigation of cancer-associated fibroblasts and p62 expression in oral cancer before and after chemotherapy. J Cranio-Maxillofacial Surg 2018;46:605–10. https://doi.org/10.1016/j.jcms.2017.12.016.Search in Google Scholar
24. Khalili, P, Vatankhah, R. Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy. Comput Biol Med 2019;109:195–206. https://doi.org/10.1016/j.compbiomed.2019.04.011.Search in Google Scholar
25. Wu, X, Liu, Q, Zhang, K, Cheng, M, Xin, X. Optimal switching control for drug therapy process in cancer chemotherapy. Eur J Contr 2018;42:49–58. https://doi.org/10.1016/j.ejcon.2018.02.004.Search in Google Scholar
26. Pouchol, C, Clairambault, J, AlexanderLorz, Trélat, E. Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy. J Math Pure Appl 2018;116:268–308. https://doi.org/10.1016/j.matpur.2017.10.007.Search in Google Scholar
27. Rokhforoz, P, Jamshidi, AA, Sarvestani, NN. Adaptive robust control of cancer chemotherapy with extended Kalman filter observer. Inf Med Unlocked 2017;8:1–7. https://doi.org/10.1016/j.imu.2017.03.002.Search in Google Scholar
28. Toyooka, S, Okumura, N, Nakamura, H, Nakata, M, Yamashita, M, Tada, H, et al.. A multicenter randomized controlled study of paclitaxel plus carboplatin versus oral uracil-tegafur as the adjuvant chemotherapy in resected non–small cell lung cancer. J Thorac Oncol 2018;13:699–706. https://doi.org/10.1016/j.jtho.2018.02.015.Search in Google Scholar
29. Schattler, H, Ledzewicz, U. Optimal control for mathematical models of cancer therapies. Springer; 2010.Search in Google Scholar
30. Mohite, UL, Hirenkumar, G. Robust controller for cancer chemotherapy dosage using nonlinear kernel-based error function. Bio Algorithm Med Syst 2020;17:20190056.10.1515/bams-2019-0056Search in Google Scholar
31. Utkarsha. Non-linear kernel-based error function for extended Kalman filter oriented robust control of cancer chemotherapy. In Communication.Search in Google Scholar
32. Akhlaghi, S, Zhou, N, Huang, Z. Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. Systems and Control; 2017.10.1109/PESGM.2017.8273755Search in Google Scholar
33. Mirjalili, S, Mirjalili, SM, Lewis, A. Grey Wolf optimizer. Adv Eng Software 2014;69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.Search in Google Scholar
34. Wagh, MB, Gomathi, N. Improved GWO-CS algorithm-based optimal routing strategy in VANET. J Netw Commun Syst 2019;2:34–42.Search in Google Scholar
35. Rajakumar, BR. Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis. Int J Comput Sci Eng 2013;8:180–93. https://doi.org/10.1504/ijcse.2013.053087.Search in Google Scholar
36. Rajakumarm, BR. Impact of static and adaptive mutation techniques on the performance of Genetic Algorithm. Int J Hybrid Intell Syst 2013;10:11–22. https://doi.org/10.3233/his-120161.Search in Google Scholar
37. Swamy, SM, Rajakumar, BR, Valarmathi, IR. Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with Cauchy mutation. In: IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013). Red Hook, NY: Curran; 2015.10.1049/ic.2013.0361Search in Google Scholar
38. George, A, Rajakumar, BR. APOGA: an adaptive population pool size based genetic algorithm. In: AASRI Procedia - 2013 AASRI Conference on Intelligent Systems and Control (ISC 2013). Vancouver, Canada: Elsevier Procedia; vol 4; 2013:288–96 pp.10.1016/j.aasri.2013.10.043Search in Google Scholar
39. Rajakumar, BR, George, A. A new adaptive mutation technique for genetic algorithm. In: Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) Coimbatore, India. Coimbatore: Institute of Electrical and Electronics Engineers ( IEEE ); vol 1–7; 2012:18–20 pp.10.1109/ICCIC.2012.6510293Search in Google Scholar
40. Badr, EM, Salam, MA, Ahmed, H. Optimizing support vector machine using the Gray Wolf optimizer algorithm for breast cancer detection; 2019.Search in Google Scholar
41. Roy, RG. Rescheduling based congestion management method using hybrid Grey Wolf optimization - grasshopper optimization algorithm in power system. J Comput Mech Power Syst Contr 2019;2:9–18.10.46253/jcmps.v2i1.a2Search in Google Scholar
42. Jadhav, AN, Gomathi, N. DIGWO: hybridization of dragonfly algorithm with improved Grey Wolf optimization algorithm for data clustering. Multimed Res 2019;2:1–11.Search in Google Scholar
43. Sreedharan, NPN, Ganesan, B, Raveendran, R, Sarala, P, Dennis, B. Grey Wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biom 2018;7:490–9. https://doi.org/10.1049/iet-bmt.2017.0160.Search in Google Scholar
44. Utkarsha. Robust controller for cancer chemotherapy dosage using non-linear kernel-based error function. In communication.Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston