Optimal drug administration of mixed cytotoxic and immunostimulating agents for cancer treatment via multi-objective optimization

Authors

DOI:

https://doi.org/10.5902/2179460X87129

Keywords:

Cancer Treatment, Chemotherapy, Immunotherapy, Optimal Control, Multi-objective Optimization

Abstract

Cancer represents a significant concern in terms of global public health, standing out as one of the main causes of death and a barrier to the advancement of life expectancy. The costs associated with cancer treatment have grown above the rate of inflation, driven by the increase in the number of new patients diagnosed, costs of materials and drugs involved, and inefficiency of care, which is becoming increasingly complex and uncoordinated. The mixed administration of immunotherapy and chemotherapy drugs plays a key role in cancer treatment. However, such treatments combination can present challenges arising from the complex interactions between these two therapeutic modalities. This work aims to identify the optimal combination of treatments that allows for minimizing both the tumor volume and the adverse effects resulting from the joint administration of drugs through a multi-objective optimization approach, establishing guidelines for optimal drug administration in the context of combined immunotherapy and chemotherapy.

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Author Biographies

Maicon de Paiva Torres, Instituto Politécnico do Rio de Janeiro - UERJ

Maicon de Paiva Torres holds a degree in Mechanical Engineering (2010-2014), a master's degree (2016-2018) and a doctorate (2019-2023) in Computational Modeling from the State University of Rio de Janeiro (UERJ). He was awarded the Grade 10 Master's Scholarship from the Carlos Chagas Filho Research Support Foundation of the State of Rio de Janeiro (FAPERJ). Throughout his academic career, he worked on projects in the line of research in Applied Mathematics and Scientific Computing, with applications in shielding calculations for neutrons, vibrations, heat transfer and determining the optimal administration of medicines for the treatment of cancer. He currently works as a Project Engineer at Altec. He has experience in the areas of numerical analysis, stochastic optimization and inverse problems.

Géssica Ramos da Silva, Instituto Politécnico do Rio de Janeiro - UERJ

Doctor in Computational Modeling from the State University of Rio de Janeiro (Polytechnic Institute). During the doctoral program, she worked in the research field of "Applied Mathematics and Scientific Computing," employing multi-objective optimization in the estimation of parameters for thermodynamic models, with a focus on cubic equations of state. She holds a master's degree in Computational Modeling from the State University of Rio de Janeiro (Polytechnic Institute). During the master's program, she worked in the research field of "Thermofluidodynamics, Porous Media, and Particle Transport," employing the Smoothed Particle Hydrodynamics method in the simulation of problems involving free interfaces, with a focus on the dynamics of droplet formation, oscillation, and coalescence. She holds a bachelor's degree in Mechanical Engineering from the State University of Rio de Janeiro (Polytechnic Institute), having received the Academic Dignity certificate with honors Cum Laude, and a Bachelor's degree in Mathematics from the Federal Fluminense University. During her undergraduate studies in Mechanical Engineering, she participated in three research projects: in the first (PIBIC-UERJ), she worked in the mass transport area with an emphasis on determining kinetic parameters of adsorption through the use of inverse optimization algorithms; in the second (FAPERJ), she developed a project focusing on the modeling of experiments in the analysis of corrosion results in metallic materials; and, in the third, she developed a computational simulator for the calculation of optimal insulation thickness in oil ducts under submarine conditions.

Fran Sérgio Lobato, Universidade Federal de Uberlândia

Born in Araguari-MG, he obtained his Bachelor's degree in Chemical Engineering in 2002 from the Faculty of Chemical Engineering at the Federal University of Uberlândia, where he completed his Master's thesis, focusing on the Algebraic-Differential Optimal Control Theory, in 2004. He completed his Ph.D. thesis in 2008 at the Faculty of Mechanical Engineering at the Federal University of Uberlândia, where he worked with the Differential Evolution Algorithm applied to multi-objective problems. He is currently an associate professor at the Federal University of Uberlândia, a permanent faculty member of the Graduate Program in Modeling and Optimization at the Federal University of Goiás, Catalão Campus, and a permanent faculty member of the Graduate Program in Mechanical Engineering at the Federal University of Uberlândia, Santa Mônica Campus. His areas of interest include: i) Algebraic-Differential Optimal Control Theory with Floating Index, with applications in various areas, mainly in biotechnological processes and in medicine for the development of strategies for carcinoma treatment; ii) Classical, Heuristic, Structural, and Nature-Inspired Optimization Methods; iii) Parameter updating of evolutionary algorithms using Chaotic Search Models and the Convergence Rate Concept based on Population Homogeneity Concept; iv) Inverse Problems; v) Design of Multi-objective Engineering Systems; vi) Treatment of Robust Optimization Problems; and vii) Treatment of Optimization Problems with Reliability.

Gustavo Barbosa Libotte, Instituto Politécnico do Rio de Janeiro - UERJ

He holds a bachelor's degree in Computer Engineering (2010-2014), a master's degree (2014-2015), and a Ph.D. (2016-2020) in Computational Modeling from the State University of Rio de Janeiro. He was awarded the Nota 10 Doctoral Scholarship from the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro. He was a postdoctoral fellow in the Institutional Training Program (PCI/CNPq) at the National Laboratory for Scientific Computing (2020-2021) and at the Federal University of Rio de Janeiro (2021-2022), through the C-Ação Emergencial COVID-19 (FAPERJ) call, where he conducted research on the epidemiological modeling of COVID-19. Currently, he is an adjunct professor in the Department of Computational Modeling at the Polytechnic Institute of the State University of Rio de Janeiro (IPRJ/UERJ) and a permanent faculty member of the Graduate Program in Computational Modeling at IPRJ/UERJ. He is the titular faculty representative of the Computational Modeling Postgraduate Committee at the Polytechnic Institute, elected for the term from 01/09/2023 to 31/08/2025. Since 2023, he has been a member of the ATLAS experiment, operating at the LHC particle accelerator at CERN. He has experience in the area of numerical analysis and stochastic optimization, with emphasis on evolutionary algorithms, parameter estimation, and uncertainty analysis.

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Published

2024-11-07

How to Cite

Torres, M. de P., Silva, G. R. da, Lobato, F. S., & Libotte, G. B. (2024). Optimal drug administration of mixed cytotoxic and immunostimulating agents for cancer treatment via multi-objective optimization. Ciência E Natura, 46(esp. 1), e87129. https://doi.org/10.5902/2179460X87129

Issue

Vol. 46 No. esp. 1 (2024): ERMAC e ENMC

Section

Special Edition 1

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