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Estimation of Drug and Tumor Properties Using Novel Hybrid Meta-Heuristic Methods Publisher Pubmed



Mirchi P1 ; Soltani M1, 2, 3, 4, 5
Authors
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Authors Affiliations
  1. 1. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
  2. 2. Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran
  3. 3. Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
  5. 5. Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada

Source: Journal of Theoretical Biology Published:2020


Abstract

One of the major drawbacks in mathematical modeling of the drug delivery in living species is application of a common value for a specific property such as diffusion coefficient of drug in tissue, while this property is unique for each person or species. Therefore, knowledge on the species-specific values of these properties can improve the process of drug delivery and treatment. Inverse problem methods can achieve these unique properties for each specimen. Estimation of the individual-specific drug and tumor parameters requires the evaluation of the drug concentration (the concentration of medical images) within the tumor tissue. Accordingly, in this paper, first, the drug transport equation in tumor is determined. Then, the sensitivity analysis is conducted to determine the appropriate area for selecting the drug concentration to estimate the drug and tumor parameters. Finally, the parameters estimated by meta-heuristic and hybrid meta-heuristic methods are compared. To enhance the validity of the methods, two different drug transport models are examined. The results indicate that the hybrid methods gave rise to more precise estimations, especially the hybrid particle swarm optimization (PSO) method with whale optimization algorithm (WOA) which offer more appropriate responses in the parameters estimation of two models. © 2019
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