REVIEW article
Front. Med. Technol.
Sec. Cardiovascular Medtech
Volume 7 - 2025 | doi: 10.3389/fmedt.2025.1546417
This article is part of the Research TopicEvolution in Cardiovascular MedTechView all 6 articles
Drug Coated Balloons in Percutaneous Coronary Intervention: how can computational modelling help inform evolving clinical practice?
Provisionally accepted- 1University of Glasgow, Glasgow, United Kingdom
- 2Golden Jubilee National Hospital, Clydebank, United Kingdom
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Drug-coated balloons (DCB) represent an emerging therapeutic alternative to drug-eluting stents (DES) for the treatment of coronary artery disease (CAD). Among the key advantages of DCB over DES are the absence of a permanent structure in the vessel and the potential for fast and homogeneous drug delivery. While DCB were first introduced for treatment of in-stent restenosis (ISR), their potential wider use in percutaneous coronary intervention (PCI) has recently been explored in several randomized clinical trials, including for treatment of de novo lesions. Moreover, new hybrid techniques that combine DES and DCB are being investigated to more effectively tackle complex cases. Despite the growing interest in DCB within the clinical community, the mechanisms of drug exchange and the interactions between the balloon, the polymeric coating and the vessel wall are yet to be fully understood. It is, therefore, perhaps surprising that the number of computational (in-silico) models developed to study interventions involving these devices is small, especially given the mechanistic understanding that has been gained from computational studies of DES procedures over the last two decades. In this paper, we discuss the current and emerging clinical approaches for DCB use in PCI and review the computational models that have been developed thus far, underlining the potential challenges and opportunities in integrating in-silico models of DCB into clinical practice.
Keywords: Percutaneous Coronary Intervention, Drug coated balloons, Drug Transport, lesion preparation, Computational modelling, In-silico analysis
Received: 16 Dec 2024; Accepted: 18 Mar 2025.
Copyright: © 2025 Renon, Ramses, Aggarwal, Good and McGinty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sean McGinty, University of Glasgow, Glasgow, United Kingdom
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