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A materials science-inspired paradigm to predict the physical stability of amorphous drugs
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Barnard, Trent (2023) A materials science-inspired paradigm to predict the physical stability of amorphous drugs. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3970577
Abstract
Amorphous drugs have gained attention as a promising alternative to crystalline formulations due to their ability to enhance solubility. However, ensuring the physical stability of amorphous drugs is critical for successful commercialisation. Unfortunately, predicting the timescale of crystallisation for amorphous drugs is challenging. To address this problem, machine learning models can be developed to predict the physical stability of amorphous drugs.
This study presents methodological advancements in using molecular dynamics simulations to develop machine learning models for predicting the crystallisation tendency of amorphous drugs. The study develops and computes solid-state descriptors that capture the dynamic properties of the amorphous phase and complements the traditional singlemolecule descriptors commonly used in quantitative structure-activity relationship (QSAR) models. We have also specifically focused on a particular molecular glass to gain insights into the dynamical properties of materials similar to amorphous drugs.
The results show that the use of molecular simulations as a tool to enrich the traditional machine learning paradigm for drug design and discovery can lead to high accuracy in predicting the physical stability of amorphous drugs. The net result of this work is an improvement over the state-of-the-art in predicting the crystallisation tendency of amorphous drugs.
Item Type: | Thesis (PhD) | ||||
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Subjects: | Q Science > QA Mathematics R Medicine > RM Therapeutics. Pharmacology R Medicine > RS Pharmacy and materia medica T Technology > TA Engineering (General). Civil engineering (General) |
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Library of Congress Subject Headings (LCSH): | Drugs -- Design -- Molecular aspects -- Research, Drugs -- Design -- Mathematical models, Machine learning -- Research, Amorphous substances, Crystallization, Pharmaceutical technology | ||||
Official Date: | May 2023 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Mathematics Institute | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Sosso, Gabriele | ||||
Format of File: | |||||
Extent: | vii, 148 pages : illustrations | ||||
Language: | eng |
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