Mathematical and computational models of drug transport in tumours

Groh, C.M., Hubbard, Matthew E., Jones, P.F., Loadman, P.M., Periasamy, N., Sleeman, B.D., Smye, S.W., Twelves, C.J. and Phillips, R.M. (2014) Mathematical and computational models of drug transport in tumours. Interface, 11 (94). 20131173/1-20131173/14. ISSN 1742-5662

Full text not available from this repository.


The ability to predict how far a drug will penetrate into the tumour microenvironment within its pharmacokinetic (PK) lifespan would provide valuable information about therapeutic response. As the PK profile is directly related to the route and schedule of drug administration, an in silico tool that can predict the drug administration schedule that results in optimal drug delivery to tumours would streamline clinical trial design. This paper investigates the application of mathematical and computational modelling techniques to help improve our understanding of the fundamental mechanisms underlying drug delivery, and compares the performance of a simple model with more complex approaches. Three models of drug transport are developed, all based on the same drug binding model and parametrized by bespoke in vitro experiments. Their predictions, compared for a ‘tumour cord’ geometry, are qualitatively and quantitatively similar. We assess the effect of varying the PK profile of the supplied drug, and the binding affinity of the drug to tumour cells, on the concentration of drug reaching cells and the accumulated exposure of cells to drug at arbitrary distances from a supplying blood vessel. This is a contribution towards developing a useful drug transport modelling tool for informing strategies for the treatment of tumour cells which are ‘pharmacokinetically resistant’ to chemotherapeutic strategies.

Item Type: Article
Keywords: Computational modelling; Mathematical modelling; Drug delivery; Drug transport; Drug binding; Pharmacokinetic profiles
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number:
Depositing User: Hubbard, Matthew
Date Deposited: 24 Feb 2017 15:12
Last Modified: 04 May 2020 16:44

Actions (Archive Staff Only)

Edit View Edit View