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Stochastic computational modelling of complex drug delivery systems

Bezbradica, Marija (2013) Stochastic computational modelling of complex drug delivery systems. PhD thesis, Dublin City University.

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Abstract

As modern drug formulations become more advanced, pharmaceutical companies face the need for adequate tools to permit them to model complex requirements and to reduce unnecessary adsorption rates while increasing the dosage administered. The aim of the research presented here is the development and application of a general stochastic framework with agent-based elements for building drug dissolution models, with a particular focus on controlled release systems. The utilisation of three dimensional Cellular Automata and Monte Carlo methods, to describe structural compositions and the main physico-chemical mechanisms, is shown to have several key advantages: (i) the bottom up approach simplifies the definition of complex interactions between underlying phenomena such as diffusion,polymer degradation and hydration, and the dissolution media; (ii) permits straightforward extensibility for drug formulation variations in terms of supporting various geometries and exploring effects of polymer composition and layering; (iii) facilitates visualisation, affording insight on system structural evolution over time by capturing successive stages of dissolution. The framework has been used to build models simulating several distinct release scenarios from coated spheres covering single coated erosion and swelling dominated spheres as well as the influence of multiple heterogeneous coatings. High-performance computational optimisation enables precision simulations of the very thin coatings used and allows fast realisation of model state changes. Furthermore, theoretical analysis of the comparative impact of synchronous and asynchronous Cellular Automata and the suitability of their application to pharmaceutical systems is performed. Likely parameter distributions from noisy in vitro data are reconstructed using Inverse Monte Carlo methods and outcomes are reported.

Item Type:Thesis (PhD)
Date of Award:November 2013
Refereed:No
Additional Information:Under auspices of IRCSET Enterprise Partnership Award Scheme with Sigmoid Pharma
Supervisor(s):Ruskin, Heather J. and Crane, Martin
Uncontrolled Keywords:Drug Delivery Systems; dissolution models; stochastic framework; agent-basis; complex drug formulations; high performance computing
Subjects:Mathematics > Mathematical models
Medical Sciences > Biomechanics
Mathematics > Stochastic analysis
Computer Science > Computer simulation
DCU Faculties and Centres:Research Initiatives and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:IRCSET EPS
ID Code:18379
Deposited On:26 Nov 2013 16:00 by Heather Ruskin. Last Modified 10 Jun 2015 01:02

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