A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics. Issue 188 (30th March 2022)

• Record Type: Journal Article
• Authors: Corti, Anna; Colombo, Monika; Rozowsky, Jared M.; Casarin, Stefano; He, Yong; Carbonaro, Dario; Migliavacca, Francesco; Rodriguez Matas, Jose F.; Berceli, Scott A.; Chiastra, Claudio
• Is Part Of: Journal of the Royal Society interface. Volume 19:Issue 188(2022)
• Publication Date: 2022-03-30
• Subjects: lower-limb peripheral arteries -- restenosis -- multiscale modelling -- agent-based modelling -- computational fluid dynamics -- mechanobiology; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2021.0871
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.