Here are some interesting recently published papers from our community. If you have published an interesting article in the field of in silico medicine, send it to us: we will insert it in this section of the newsletter!
Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review - IEEE Journal of Biomedical and Health Informatics. DOI: 10.1109/JBHI.2021.3090469
Authors: Marco Viceconti, Luca Emili, Payman Afshari, Eulalie Courcelles, Cristina Curreli, Nele Famaey, Liesbet Geris, Marc Horner, Maria Cristina Jori, Alexander Kulesza, Axel Loewe, Michael Neidlin, Markus Reiterer, Cecile F Rousseau, Giulia Russo, Simon J Sonntag, Emmanuelle M Voisin, Francesco Pappalardo
The term "In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process...
Read the full paper: https://ieeexplore.ieee.org/document/9462824
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Arrhythmogenic Effects of Genetic Mutations Affecting Potassium Channels in Human Atrial Fibrillation: A Simulation Study - Frontiers in Physiology - DOI: 10.3389/fphys.2021.681943
Authors: Rebecca Belletti, Lucia Romero et al.
Genetic mutations in genes encoding for potassium channel protein structures have been recently associated with episodes of atrial fibrillation in asymptomatic patients. The aim of this study is to investigate the potential arrhythmogenicity of three gain-of-function mutations related to atrial fibrillationā€”namely, KCNH2 T895M, KCNH2 T436M, and KCNE3-V17Mā€”using modeling and simulation of the electrophysiological activity of the heart. A genetic algorithm was used to tune the parametersā€™ value of the original ionic currents to reproduce the alterations experimentally observed caused by the mutations...
Read the full paper: https://pubmed.ncbi.nlm.nih.gov/34135774/
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A Prototype QSP Model of the Immune Response to SARS-CoV-2 for Community Development - CPT: Pharmacometrics & Systems Pharmacology. DOI: 10.1002/psp4.12574
Authors: Wei Dai, Rohit Rao, Anna Sher, Nessy Tania, Cynthia J Musante, Richard Allen
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic requires the rapid development of efficacious treatments for patients with life-threatening coronavirus disease 2019 (COVID-19). Quantitative systems pharmacology (QSP) models are mathematical representations of pathophysiology for simulating and predicting the effects of existing or putative therapies. The application of model-based approaches, including QSP, have accelerated the development of some novel therapeutics. Nevertheless, the development of disease-scale mechanistic models can be a slow process, often taking years to be validated and considered mature...
Read the full paper: https://pubmed.ncbi.nlm.nih.gov/33217169/
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Explanation and Use of Uncertainty Obtained by Bayesian Neural Network Classifiers for Breast Histopathology Images - IEEE Transactions on Medical Imaging
Authors: Ponkrshnan Thiagarajan, Pushkar Khairnar, Susanta Ghosh
Women worldwide are afflicted by breast cancer, a widely occuring health issue that causes a large number of deaths. This paper aims to review and present several approaches to identify breast cancers using ML algorithms and Biosensors. The objective is to investigate the application of multiple algorithms based on Machine Learning approach and biosensors for early breast cancer detection. Biosensors and machine learning are needed to identify cancers based on microscopic images, that is why automation is needed.
Read the full paper: https://digitalcommons.mtu.edu/michigantech-p/15453/
