Abdur Rahman, Salman (2020) An investigation into the drug release mechanisms of polymeric solid dispersions. Doctoral thesis, University of East Anglia.
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Abstract
Personalised polypills, which includes multiple drugs in a single pill tailored for individual patients, has gained a lot of research interests with the emergence of pharmaceutical 3D printing. A distinct feature of polypill is to be able to release each drug in a controlled manner. However, currently, there are limited tools to aid the design of such solid dosage forms with desired drug release kinetics. In this work, the drug release mechanisms of a wide range of solid dispersions formed using polymers and model drugs covering a wide range of physicochemical properties were investigated to generate a large dataset with an attempt to develop a simulation strategy for achieving a desired drug release profile.
Building a dataset and using the dataset toward simulation building the data to be reproducible and reliable. The sources of errors throughout the manufacturing and the performance measurements of 3D printed example solid dosage forms were first investigated to assess the reproducibility and reliability of the experimental data generated to build the dataset. This was the focus of chapter 3. Thereafter, chapter 4 systematically investigated the behaviour of a wide range of pure polymers to enable the prediction of the behaviour of polymer blends. The polymer behaviour studied include hydration, swelling, and erosion. Addition of the drug and investigating the effect on formulation behaviour was the focus of chapter 5. Chapter 6 used statistical approaches such as principal component analysis as a factor reduction technique and K-means clustering to classify the behaviour of the polymer-drug dispersions. These statistical approaches successfully demonstrated that correlating polymer behaviours and drug release profiles can be used to predict the selection of polymer(s) for a given drug to achieve a desired drug release profile. Further upscaling of the dataset is crucial to enhance analysis.
Item Type: | Thesis (Doctoral) |
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Faculty \ School: | Faculty of Science > School of Pharmacy |
Depositing User: | Chris White |
Date Deposited: | 17 Mar 2021 09:43 |
Last Modified: | 17 Mar 2021 09:43 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/79488 |
DOI: |
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