In silico trials for treatment of acute ischemic stroke: Design and implementation

Claire Miller*, Raymond M. Padmos, Max van der Kolk, Tamás I. Józsa, Noor Samuels, Yidan Xue, Stephen J. Payne, Alfons G. Hoekstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

An in silico trial simulates a disease and its corresponding therapies on a cohort of virtual patients to support the development and evaluation of medical devices, drugs, and treatment. In silico trials have the potential to refine, reduce cost, and partially replace current in vivo studies, namely clinical trials and animal testing. We present the design and implementation of an in silico trial for treatment of acute ischemic stroke. We propose an event-based modelling approach for the simulation of a disease and injury, where changes to the state of the system (the events) are assumed to be instantaneous. Using this approach we are able to combine a diverse set of models, spanning multiple time scales, to model acute ischemic stroke, treatment, and resulting brain tissue injury. The in silico trial is designed to be modular to aid development and reproducibility. It provides a comprehensive framework for application to any potential in silico trial. A statistical population model is used to generate cohorts of virtual patients. Patient functional outcomes are also predicted with a statistical model, using treatment and injury results and the patient's clinical parameters. We demonstrate the functionality of the event-based modelling approach and trial framework by running proof of concept in silico trials. The proof of concept trials simulate the same cohort of patients twice: once with successful treatment (successful recanalisation) and once with unsuccessful treatment (unsuccessful treatment). Ways to overcome some of the challenges and difficulties in setting up such an in silico trial are discussed, such as validation and computational limitations.

Original languageEnglish
Article number104802
Number of pages13
JournalComputers in Biology and Medicine
Volume137
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

Funding Information:
This project (INSIST; www.insist-h2020.eu ) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777072.

Publisher Copyright:
© 2021 The Author(s)

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