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Issue No. 06 - Nov.-Dec. (2012 vol. 9)
ISSN: 1545-5963
pp: 1595-1606
N. Kazmi , Sch. of Comput., Eng. & Inf. Sci., Northumbria Univ., Newcastle upon Tyne, UK
M. A. Hossain , Sch. of Comput., Eng. & Inf. Sci., Northumbria Univ., Newcastle upon Tyne, UK
R. M. Phillips , Inst. of Cancer Therapeutics, Univ. of Bradford, Bradford, UK
ABSTRACT
Bioreductive drugs are a class of hypoxia selective drugs that are designed to eradicate the hypoxic fraction of solid tumors. Their activity depends upon a number of biological and pharmacological factors and we used a mathematical modeling approach to explore the dynamics of tumor growth, infusion, and penetration of the bioreductive drug Tirapazamine (TPZ). An in-silico model is implemented to calculate the tumor mass considering oxygen and glucose as key microenvironmental parameters. The next stage of the model integrated extra cellular matrix (ECM), cell-cell adhesion, and cell movement parameters as growth constraints. The tumor microenvironments strongly influenced tumor morphology and growth rates. Once the growth model was established, a hybrid model was developed to study drug dynamics inside the hypoxic regions of tumors. The model used 10, 50 and 100 μM as TPZ initial concentrations and determined TPZ pharmacokinetic (PK) (transport) and pharmacodynamics (cytotoxicity) properties inside hypoxic regions of solid tumor. The model results showed that diminished drug transport is a reason for TPZ failure and recommend the optimization of the drug transport properties in the emerging TPZ generations. The modeling approach used in this study is novel and can be a step to explore the behavioral dynamics of TPZ.
INDEX TERMS
Tumors, Biological system modeling, Drugs, Mathematical model, Electronic countermeasures, Computational modeling,microenvironment and Tirapazamine, Extra cellular matrix, Hypoxia, mathematical modeling
CITATION
N. Kazmi, M. A. Hossain, R. M. Phillips, "A Hybrid Cellular Automaton Model of Solid Tumor Growth and Bioreductive Drug Transport", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1595-1606, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.118
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