Towards personalised drug delivery from coronary stents

Context
Coronary Heart Disease is set to remain the leading cause of mortality and morbidity worldwide for the foreseeable future. It is characterised by a thickening of the arteries which supply blood to the heart tissue, which can lead to a heart attack. This is now commonly treated by percutaneous coronary intervention (PCI), where a drug-eluting stent (DES) is permanently implanted to restore blood flow through the artery. Despite their success, these medical devices do not perform as well in some cases, most notably in those patients with more than one diseased coronary artery [1] and/or with existing co-morbidities [2]. Even within the same patient, a DES may work perfectly in one diseased vessel, whilst an identical DES implanted in a different vessel may fail due to restenosis, resulting in re-occlusion of the vessel. The reason for this variable performance remains unclear, thus hampering industry in their efforts to provide personalised stent treatments that leading clinicians are now calling for [3].

Aims and objectives
The overall aim of this project is to use mathematical and computational modelling to investigate the important of the inclusion of patient-specific factors in models of drug release from stents. To achieve this overall aim the specific objectives will include: a review the DES and atherosclerosis literature to assess the state of the art and to develop a deeper understanding of the pathology and biology; providing advice to experimentalists for any required experiments; the assessment of patient-specific medical images and their incorporation into mathematical models; the development of novel mathematical and computational models of drug release from stents in patient-specific environments; the use of the models to provide guidance to industry/clinicians on stent design/choice on a patient-specific basis.

Methodology
This project will develop novel mathematical and computational methods for assessing the importance of the inclusion of patient-specific factors in modelling drug release from stents. The methods will include, but are not limited to: analytical mathematical techniques such as non-dimensionalisation, perturbation techniques, small and large time approximations; finite difference and finite element numerical methods; and derivation of geometries from imaging.
Potential Impact, Applications and benefits
The models that will be developed in this project have the potential to lay the foundations for personalised drug-delivery from coronary stents. This could lead to a paradigm shift in how stent manufactures and clinicians approach PCI.

Alignment with EPSRC Strategies and Research Areas
This research sits neatly within the healthcare technologies theme, covering Medical Device Design and Innovation, Optimising Treatments, Novel Computational and Mathematical Methods and Developing Future Therapies.


References
1. Serruys, P.W., et al., Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. N Engl J Med, 2009. 360(10): p. 961-72.
2. Farkouh, M.E., et al., Strategies for multivessel revascularization in patients with diabetes. N Engl J Med. 367(25): p. 2375-84.
3. Kolandaivelu, K., B.B. Leiden, and E.R. Edelman, Predicting response to endovascular therapies: dissecting the roles of local lesion complexity, systemic comorbidity, and clinical uncertainty. J Biomech, 2014. 47(4): p. 908-21.
4. McKittrick, C.M., et al., Modelling the Impact of Atherosclerosis on Drug Release and Distribution from Coronary Stents. Annals of Biomedical Engineering, 2015: p. 1-11.