Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
International Journal for Numerical Methods in Biomedical Engineering: Translation of Animal Study to Human: In Silico Based Development of Implantable Pulmonary Artery Pressure Sensor
Leonid Goubergrits et al
Abstract
Implantable pulmonary artery pressure sensors (PAPS) might impose a flow-induced risk of thrombus formation in the pulmonary artery (PA). To assess this risk, an in silico study-enhanced animal study with 20 sensors implanted in 10 pigs had previously been conducted. In the in silico study, PAPS were virtually implanted mimicking real implantations, based upon data acquired by CT. This animal in silico study investigated changes in hemodynamics caused by PAPS using image-based computational fluid dynamics (CFD). However, porcine and human PA differ significantly in geometry and hemodynamics. To investigate the transferability of animal in silico study findings toward human conditions, we propose a parallel in silico human study. Based on a similarity analysis (L1 norm for 8 geometric features) human PA geometries with the least difference to 10 porcine PA were selected. PAPS were virtually implanted in human PA as close as possible, mimicking the implantation configuration of the animal study. Finally, a numerical flow analysis of the hemodynamic changes due to PAPS implantation was done. Comparing human and porcine PA, we found significantly larger left and right PA diameters in humans, whereas no differences were found for main PA diameters and bifurcation angle. Comparing hemodynamic boundary conditions, we found a significantly smaller heart rate and a significantly higher peak systolic main PA flow rate in humans, whereas no significant differences for cardiac output were found. The human in silico PAPS study found no relevant changes in hemodynamics increasing the risk of thrombus formation after sensor implantation. This is also valid for PAPS that were non-optimally implanted. Thus, despite differences between species, findings of the in silico animal study were confirmed by the human in silico study.
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Computers in Biology and Medicine: In silico estimation of thrombogenic risk after left atrial appendage excision: Towards digital twins in atrial fibrillation
Carlos Albors et al
Abstract
The left atrial appendage (LAA) is a highly variable, pouch-like structure in the left atrium prone to thrombus formation, especially in atrial fibrillation (AF) patients. In silico cardiac models can help characterize the LAA's complex morphology and hemodynamics, aiding in identifying pro-thrombotic areas. This study assessed atrial hemodynamics and thrombus formation risk after LAA excision and compared with optimal synthetic excisions and occluder placements in high thrombogenic-risk cases.
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The Lancet Digital Health: Medical digital twins: enabling precision medicine and medical artificial intelligence
Christoph Sadée et al
Abstract
The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.
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Clinical Pharmacology and Therapeutics: Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures
Javiera Cortés-Ríos et al
Abstract
In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We propose a virtual trial framework for chronic Hepatitis B, accurately simulating clinical protocols, patient characteristics, and endpoints using a mechanistic mathematical model. Clinical trial simulations with this model successfully captured functional cure with standard-of-care therapies (nucleos(t)ide analogs and pegylated interferon) as well as complex clinical observations, facilitating mechanistic hypothesis generation and suggesting biomarker signatures that may predict treatment outcomes. In silico trials revealed that responders exhibited enhanced cytotoxic immunity and significant serum-alanine transaminase increases, suggesting a potential response biomarker. However, a higher baseline Hepatitis B surface antigen did not proportionately increase cytotoxic antiviral immune responses, indicating a potential immune ceiling but in the face of increasing systemic antigen burden, therefore culminating in a lower treatment response. Virtual patients enabled the generation of large virology biomarker synthetic datasets, which empowered a machine learning model to predict functional cure in virtual patients with ~ 95% accuracy. This underscores the potential of in silico trials in enhancing clinical trials, generating mechanistic hypotheses, and accelerating chronic Hepatitis B drug development.
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ALTEX: From cellular perturbation to probabilistic risk assessments
Alexandra Maertens et al
Abstract
Chemical risk assessment is evolving from traditional deterministic approaches to embrace probabilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxicological risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.
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