Kontsioti, Elpida 
ORCID: 0000-0002-4053-0220
(2023)
Improved Signal Detection of Drug-Drug Interactions in the Post-Marketing Phase: Reference Sets, Quantitative Methods and Systems Pharmacology Aspects.
PhD thesis, University of Liverpool.
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Abstract
In contemporary medicine, the continuous introduction of new medicines to the market has significantly contributed to disease prevention and improved patient outcomes. However, the administration of medicines is hindered by the emergence of unforeseen adverse effects, often observed during late-stage clinical studies or following authorisation. This has resulted in notable drug withdrawals due to these unexpected side effects. Of particular concern are the side effects resulting from drug-drug interactions (DDIs). The empirical study of DDIs before the drugs enter the market is challenging due to the limited number of co-prescribed drugs typically included in late-stage clinical trials. Also, computational methods for identifying potential DDIs during drug development are not capable of successfully capturing all adverse DDIs that can occur in clinical practice. Therefore, post-marketing surveillance plays a crucial role in detecting and monitoring DDIs, making pharmacovigilance an integral part of the drug lifecycle. To address these challenges, this thesis proposes a comprehensive approach consisting of four stages to enhance signal detection activities for DDIs in the post-marketing setting. Firstly, the level of agreement on DDI information across major online drug information resources is assessed. The assessment results show considerable variation in the interacting drug pairs of the examined resources, together with variability in the categorisation of severity and clinical management recommendations for the included DDIs. Such variability presents potentially deleterious consequences for patient safety and demonstrates a need for harmonisation and standardisation of the information available on drug information resources. In the second stage, a normalised reference set called CRESCENDDI (Clinically-relevant REference Set CENtred around Drug-Drug Interactions) is introduced. This publicly available dataset provides comprehensive information on DDIs and the individual behaviour of interacting drugs, facilitating research in signal detection methodologies and enabling quantitative performance evaluation. The third stage seeks to investigate the impact of confounding factors on existing signal detection methodologies. It is concluded that reference sets populated with some of the examined confounding factors can significantly impact the performance evaluation metrics, potentially altering the conclusions regarding which methodologies are perceived to perform best. The final stage proposes a novel signal detection method built upon a Bayesian hypothesis testing framework and combined with a systems pharmacology network to refine potential DDI signals and assess their biological plausibility. The results of this study showcase the potential of systems pharmacology to enhance signal detection in pharmacovigilance, with DDIs being an important and promising area of application. In conclusion, this thesis presents a comprehensive framework that addresses the challenges of signal detection of DDIs. By focusing on data standardisation, reference set development, signal detection method building and signal refinement using biological plausibility aspects, the findings and tools developed in this thesis offer valuable insights for enhancing pharmacovigilance and ultimately promoting better healthcare outcomes.
| Item Type: | Thesis (PhD) |
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| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Dec 2023 15:39 |
| Last Modified: | 07 Dec 2023 15:40 |
| DOI: | 10.17638/03176530 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3176530 |
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