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dc.contributor.authorVillasana, Minayaen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorAguilar, Sorayaen_UK
dc.description.abstractObjectives: This study extends a previous mathematical model of cancer cytotoxic chemotherapy, which considered cycling tumor cells and interactions with the immune system, by incorporating a different type of drug: a cytostatic agent. The effect of a cytostatic drug is to arrest cells in a phase of their cycle. In consequence, once tumor cells are arrested and synchronized they can be targeted with a cytotoxic agent, thus maximizing cell kill fraction and minimizing normal cell killing. The goal is to incorporate the new drug into the chemotherapy protocol and devise optimal delivery schedules. Methods: The problem of designing efficient combined chemotherapies is formulated as an optimal control problem and tackled using a state-of-the-art evolutionary algorithm for real-valued encoding, namely the covariance matrix adaptation evolution strategy. Alternative solution representations and three formulations of the underlying objective function are proposed and compared. Results: The optimization problem was successfully solved by the proposed approach. The encoding that enforced non-overlapping (simultaneous) application of the two types of drugs produced competitive protocols with significant less amount of toxic drug, thus achieving better immune system health. When compared to treatment protocols that only consider a cytotoxic agent, the incorporation of a cytostatic drug dramatically improved the outcome and performance of the overall treatment, confirming in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy. Conclusion: We conclude that the proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies.en_UK
dc.relationVillasana M, Ochoa G & Aguilar S (2010) Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy. Artificial Intelligence in Medicine, 50 (3), pp. 163-173.
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectEvolutionary algorithmsen_UK
dc.subjectEvolution strategiesen_UK
dc.subjectSingular optimal controlen_UK
dc.subjectTumor modelen_UK
dc.subjectDelay differential equation systemen_UK
dc.subjectCytostatic drugen_UK
dc.subjectCytotoxic drugen_UK
dc.subjectCombined chemotherapyen_UK
dc.subjectCancer chemotherapyen_UK
dc.subjectCancer Chemotherapyen_UK
dc.subjectTumors Chemotherapyen_UK
dc.titleModeling and optimization of combined cytostatic and cytotoxic cancer chemotherapyen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[artificial intelligence medicine.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.citation.jtitleArtificial Intelligence in Medicineen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationUniversidad Simon Bolivaren_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversidad de Carabobo, Venezuelaen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorVillasana, Minaya|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorAguilar, Soraya|en_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.filenameartificial intelligence medicine.pdfen_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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