The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment.
OBJECTIVE:
To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients’ health.
METHODS:
Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption.
RESULTS:
In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration.
CONCLUSION:
This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.
FerlayJ., GLOBONCAN 2008v2.0, Cancer incidence and mortality worldwide: IARC CancerBase No. 10, 2010.
2.
CenterM.SiegelR. and JemalA., Global Cancer Facts & Figures, American Cancer Society Inc., 2011.
3.
TobiasJ.S. and HochhauserD., Cancer and its Management, John Wiley & Sons, 2014.
4.
MurphyH.JaafariH. and DorovolnyH.M., Differences in predictions of ODE models of tumor growth: A cautionary example, BMC Cancer16 (2016), 1–10.
5.
El-GoharyA., Chaos optimal control of cancer self-remission and tumor system steady states, Chaos, Solitons & Fractals37 (2008), 1305–1316.
6.
El-GoharyA. and AlwaselI.A., The chaos and optimal control of cancel model with complete unknown parameters, Chaos, Solitons & Fractals42 (2009), 2865–2874.
7.
KirschnerD. and PanettaJ.C., Modeling immunotherapy of the tumor-immune interaction, Journal of Mathematical Biology37 (1998), 1305–1316.
8.
BozkurtF. and ÖzköseF., Stability analysis of macrophage-tumor interaction with piecewise constant arguments, in: Proceedings of the AIP Conference Proceedings, 2015.
9.
ÖzköseF.YilmazS.YavuzM. and ÖztürkI., A fractional modelling of tumor-immune system interaction related to lung cancer with real data, The European Physical Journal Plus137 (2022), 1–28.
10.
GalmariniD.GalmariniC.M. and GalmariniF.C., Cancer chemotherapy: A critical analysis of its 60 years of history, Critical Reviews in Oncology/Hematology84 (2012), 181–199.
11.
ShapiroG.I. and HarperJ.W., Anticancer drug targets: cell cyle and checkpoint control, Journal of Clinical Investigation104 (1999), 1645–1953.
12.
FabbrociniG.CameliN.RomanoM.C. and MarianoM., Chemotherapy and skin reactions, Journal of Experimental & Clinical Cancer Research31 (2012), 1–6.
13.
KarinM.JobinC. and BalkwillF., Chemotherapy immunity and microbiota-a new triumvirate, Nature Medicine20 (2014), 126–127.
14.
WangY.ProbinV. and ZhouD., Cancer therapy induced residual bone marrow injury: mechanisms of induction and implication for therapy, Current Cancer Therapy Reviews2 (2006), 271–279.
15.
VasievichE.A. and HuangL., The suppressive tumor microenvironment: A challenge in cancer immunotherapy, Molecular Pharmaceutics8 (2011), 635–641.
16.
VannemanM. and DranoffG., Combining immunotherapy and targeted therapies in cancer treatment, Nature Reviews Cancer12 (2012), 237–251.
17.
GomesJ.P.AssoniA.F.PelattiM. and CoattiG., Deepening a simple question: Can MSCs be used to treat cancer?Anticancer Research37 (2017), 4747–4758.
18.
CopelanE.A., Hemotopietic stem-cell transplantation, New England Journal of Medicine354 (2006), 1813–1826.
BalduzziA.ValsecchiM.G.UderzoC. and De LorenzoP., Chemotherapy versus allogenic transplantation for very-high-rish childhood acute lymphoblastic leukaemia in first complete remission: Comparison by genetic randomization in an internal prospective study, The Lancet366 (2005), 635–641.
21.
BertholdF.BoosJ.BurdachS. and ErttmannR., Myeloablative megatherapy with autologous stem-cell rescue versus oral maintenance chemotherapy as consolidation treatment in patients with high-risk neuroblastoma: a randomised controlled trial, The Lancet Oncology6 (2005), 649–658.
22.
FermandJ.P.KatsahianS.DivineM. and LeblondV., High-dose therapy and autologous blood stem-cell transplantation compared with conventional treatment in myeloma patients aged 55 to 65 years: Long-term results of a randomized control trial from the Group Myelome-Autogreffe, Journal of Clinical Oncology23 (2005), 9227–9233.
23.
DreylingM.LenzG.HosterE. and Van HoofA., Early consolidation by myeloablative radiochemotherapy followed by autologous stem cell transplantation in first remission significantly prolongs progression-free survival in mantle-cell lymphoma: results of a prospective randomized trial of the European MCL Network, Blood105 (2005), 2677–2684.
24.
NitzU.A.MohrmannS.FischerJ. and LindemannW., Comparison of rapidly cycled tandem high-dose chemotherapy plus peripheral-blood stem-cell support versus dose-dense conventional chemotherapy for adjuvant treatment of high-risk breast cancer: Results of a multicentre phase III trial, The Lancet366 (2005), 1935–1944.
25.
LeeR.H.OhJ.Y.ChoiH. and BazhanovN., Therapeutic factors secreted by mesenchymal stromal cells and tissue repair, Journal of Cellular Biochemistry112 (2011), 3073–3078.
26.
ChangJ.C., Cancer stem cells: Role in tumor growth, recurrence, metastasis, and treatment resistance, Medicine95 (2016).
27.
CoddA.S.KanasekiT.TorigoT. and TabiZ., Cancer stem cells as targets for immunotherapy, Immunology153 (2017), 304–314.
28.
BatlleE. and CleversH., Cancer stem cells revisited, Nature medicine23 (2017), 1124–1134.
29.
ItikM.SalamciM.U. and BanksS.P., Optimal control of drug therapy in cancer treatment, Nonlinear Analysis: Theory, Methods & Applications71 (2009), 1473–1486.
30.
bin Mohd ZainM.Z.KanesanJ.ChuahJ.H. and DhanapalS., A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization, Applied Soft Computing70 (2018), 680–700.
31.
ZietsS. and NicolliniC., Mathematical approaches to optimization of cancer chemotherapy, Bulletin of Mathematical Biology41 (1979), 305–324.
32.
AlqudahM.A., Cancer treatment by stem cells and chemotherapy as a mathematical model with numerical simulations, Alexandra Engineering Journal59 (1957), 1953–1957.
33.
FanZ.LiW.CaiX. and LiH., Push and pull search for solving constrained multi-objective optimization problems, Swarm and Evolutionary Computation44 (2018), 665–679.
34.
DebK.PratapA.AgarwalS. and MeyarivanT., A fast and elitest multiobjective genetic algorithm: NSGA-II, IEEE Transaction on Evoltionary Computation6 (2002), 182–197.
35.
FonsecaC.M. and FlemingP.J., An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation1 (1993), 1–23.
36.
BäckT. and SchwefelH., An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation44 (2018), 665–679.
37.
LiH.SunJ.ZhangQ. and ShuiY., Adjustment of weight vectors of penalty-based boundary intersection method in MOEA/D, International Conference on Evolutionary Multi-Criterion Optimization (2019), 91–100.
38.
XieY.QiaoJ.WangD. and YinB., A novel decomposition -based multiobjective evolutionary algorithm using improved multiple adaptive dynamic selection strategies, Information Sciences556 (2021), 472–494.
39.
ZitzlerE.LaumannsM. and ThieleL., SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report, 103, 2001.
40.
KhareV.YaoX. and DebK., Performance scaling of multi-objective evolutionary algorithms, in: International Conference on Evolutionary Multi-Criterion Optimization, 2003, pp. 376–390.
41.
TsaiC.Y. and KaoI.-W., Particle swarm optimization with selective particle regeneration for data clustering, Expert Systems with Applications38 (2011), 6565–6576.
42.
BradyR. and EnderlinH., Mathematical models of cancer: When to predict novel therapies and when not to, Bull. Math. Biol.81 (2019), 3722–3731.
43.
HedengrenJ., APMonitor Modeling Language. apmonitor.com (accessed on April 2013).
44.
HedengrenJ.ShishavanR.A.PowellK.M. and EdgarT.F., Nonlinear modeling, estimation and predictive control in APMonitor, Computers & Chemical Engineering80 (2014), 133–148.
