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Multiscale Modelling of Xenobiotic Transport Through Biotissues

Sneddon, A (2021) Multiscale Modelling of Xenobiotic Transport Through Biotissues. Doctoral thesis, Liverpool John Moores University.

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In this thesis, we explore three methods that are commonly used to describe the movement of chemicals in-vivo. We employ a number of techniques to include parameters and features to account for the complex physiology of the system we model. In Chapter 2, we investigate a micro-scale model, to describe the distribution of chemicals applied to the skin topically using histology images. Due to the complex, heterogeneous nature of the skin, the model which is dependent on both space and time is solved using the finite element method. It is shown, that the model can predict the in-vitro distribution of chemicals with differing physico-chemical properties. In Chapter 3, employ a physiologically-based-pharmacokinetic (PBPK) model, to account for the systemic delivery of the percutaneous absorption of compounds. The output from the model described in Chapter 2 is then paired with this PBPK model in order to describe the distribution of xenobiotics at all stages of percutaneous absorption. The model is then used to understand how properties such as skin thickness, vehicle concentration, as well as the skin condition atopic dermatitis affect plasma concentration. Finally, Chapter 4 describes a model for the permeation and uptake of polymersomes into spheroids. Model physiological parameters are derived from in-vitro data, which is then used to understand which binding parameters have the greatest contribution to the therapeutic efficacy of the treatment. Furthermore, optimal polymersome radii are derived for a range intracellular pore sizes.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Transdermal drug delivery; PBPK; Polymersomes; Mathematical Biology; Applied Mathematics
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Divisions: Computer Science & Mathematics
Date Deposited: 08 Dec 2020 12:19
Last Modified: 13 Sep 2022 14:33
DOI or ID number: 10.24377/LJMU.t.00014148
Supervisors: Jarman, I, Colley, H and Webb, S
URI: https://researchonline.ljmu.ac.uk/id/eprint/14148
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