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!
PLoS Computational Biology: Credibility assessment of in silico clinical trials for medical devices
Pras Pathmanathan et al
Abstract
In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus ‘synthetic’ virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA’s Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.
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Frontiers in Medicine: Toward trustworthy medical device in silico clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges
Kenneth I Aycock et al
Abstract
Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.
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JACC: Clinical Electrophysiology: Optimizing the Distribution of Ablation Lesions to Prevent Postablation Atrial Tachycardia: A Personalized Digital-Twin Study
Kensuke Sakata et al
Abstract
Background
Although targeting atrial fibrillation (AF) drivers and substrates has been used as an effective adjunctive ablation strategy for patients with persistent AF (PsAF), it can result in iatrogenic scar-related atrial tachycardia (iAT) requiring additional ablation. Personalized atrial digital twins (DTs) have been used preprocedurally to devise ablation targeting that eliminate the fibrotic substrate arrhythmogenic propensity and could potentially be used to predict and prevent postablation iAT.
Objectives
In this study, the authors sought to explore possible alternative configurations of ablation lesions that could prevent iAT occurrence with the use of biatrial DTs of prospectively enrolled PsAF patients.
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Bulletin of Mathematical Biology: Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes
Jana L. Gevertz & Joanna R. Wares
Abstract
Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.
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International Journal of Molecular Sciences: In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review
Ana Francisca T. Gomes et al
Abstract
The Target-Based Virtual Screening approach is widely employed in drug development, with docking or molecular dynamics techniques commonly utilized for this purpose. This systematic review (SR) aimed to identify in silico therapeutic targets for treating Diabetes mellitus (DM) and answer the question: What therapeutic targets have been used in in silico analyses for the treatment of DM? The SR was developed following the guidelines of the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis, in accordance with the protocol registered in PROSPERO (CRD42022353808). Studies that met the PECo strategy (Problem, Exposure, Context) were included using the following databases: Medline (PubMed), Web of Science, Scopus, Embase, ScienceDirect, and Virtual Health Library. A total of 20 articles were included, which not only identified therapeutic targets in silico but also conducted in vivo analyses to validate the obtained results. The therapeutic targets most frequently indicated in in silico studies were GLUT4, DPP-IV, and PPARγ. In conclusion, a diversity of targets for the treatment of DM was verified through both in silico and in vivo reassessment. This contributes to the discovery of potential new allies for the treatment of DM.
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Computer Methods in Applied Mechanics and Engineering: A neural network finite element approach for high speed cardiac mechanics simulations
Shruti Motiwale et al
Abstract
Comprehensive image-based computational modeling pipelines are being actively developed for high-fidelity patient-specific cardiac simulations. However, conventional simulation techniques pose a challenge in this regard, primarily because of their excessively slow performance. We have developed a Neural Network Finite Element (NNFE) approach for high-speed cardiac mechanics simulations that can produce accurate simulation results within seconds (Journal of Biomechanical Engineering 144.12 (2022): 121010.). The method utilized neural networks to learn the displacement solution; and finite elements for defining the problem domain, specifying the boundary conditions, and performing numerical integrations. The NNFE method does not rely on use of traditional FEM simulations, experimental data, or reduced order modeling approaches, and has been successfully applied to hyperelastic boundary value problems using a potential energy formulation. In the present work we extended the NNFE approach to a prolate spheroid model of the left ventricle as a starting point for more complex cardiac simulations. We incorporated spatially varying fiber structures and utilized a Fung-type material model that included active contraction along the local myofiber axis. As cardiac mechanics are non-conservative problems with path-dependent pressure boundary conditions, we developed a new NNFE formulation based on classical virtual work principles. Importantly, the resultant NNFE cardiac model was trained over the complete physiological functional range of pressure, volume, and myofiber active stress. The final trained cardiac model predicted the displacement solution over the cardiac cycle for any physiological condition without retraining with a mean nodal displacement error of 0.023±0.019mm. Similar agreement accuracy was found for the stress and strain results. The NNFE model trained within 2.25 h and predicted the complete pressure–volume response within 30 s, whereas the FE model took approximately 5 h. This study successfully demonstrates the potential of the NNFE method to simulate cardiac mechanics with high speed and accuracy over the complete physiological functional space.
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Computers in Biology and Medicine: The onset of coarctation of the aorta before birth: Mechanistic insights from fetal arch anatomy and haemodynamics
Uxio Hermida et al
Abstract
Accurate prenatal diagnosis of coarctation of the aorta (CoA) is challenging due to high false positive rate burden and poorly understood aetiology. Despite associations with abnormal blood flow dynamics, fetal arch anatomy changes and alterations in tissue properties, its underlying mechanisms remain a longstanding subject of debate hindering diagnosis in utero. This study leverages computational fluid dynamics (CFD) simulations and statistical shape modelling to investigate the interplay between fetal arch anatomy and blood flow alterations in CoA. Using cardiac magnetic resonance imaging data from 188 fetuses, including normal controls and suspected CoA cases, a statistical shape model of the fetal arch anatomy was built. From this analysis, digital twin models of false and true positive CoA cases were generated. These models were then used to perform CFD simulations of the three-dimensional fetal arch haemodynamics, considering physiological variations in arch shape and blood flow conditions across the disease spectrum. This analysis revealed that independent changes in the shape of.
the arch and the balance of left-to-right ventricular output led to qualitatively similar haemodynamic alterations. Transitioning from a false to a true positive phenotype increased retrograde flow through the aortic isthmus. This resulted in the appearance of an area of low wall shear stress surrounded by high wall shear stress values at the flow split apex on the aortic posterior wall opposite the ductal insertion point.
Our results suggest a distinctive haemodynamic signature in CoA characterised by the appearance of retrograde flow through the aortic isthmus and altered wall shear stress at its posterior side. The consistent link between alterations in shape and blood flow in CoA suggests the need for comprehensive anatomical and functional diagnostic approaches in CoA. This study presents an application of the digital twin approach to support the understanding of CoA mechanisms in utero and its potential for improved diagnosis before birth.
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AHA/ASA Journal: Left Atrial Roof Enlargement Is a Distinct Feature of Heart Failure With Preserved Ejection Fraction
Sören J. Backhaus et al
Abstract
Background
It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF.
Methods
Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e′, >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization.
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AHA/ASA Journal: Differentiating Left Ventricular Remodeling in Aortic Stenosis From Systemic Hypertension
Masliza Mahmod et al
Abstract
Background
Left ventricular (LV) hypertrophy occurs in both aortic stenosis (AS) and systemic hypertension (HTN) in response to wall stress. However, differentiation of hypertrophy due to these 2 etiologies is lacking. The aim was to study the 3-dimensional geometric remodeling pattern in severe AS pre- and postsurgical aortic valve replacement and to compare with HTN and healthy controls.
Methods
Ninety-one subjects (36 severe AS, 19 HTN, and 36 healthy controls) underwent cine cardiac magnetic resonance. Cardiac magnetic resonance was repeated 8 months post-aortic valve replacement (n=18). Principal component analysis was performed on the 3-dimensional meshes reconstructed from 109 cardiac magnetic resonance scans of 91 subjects at end-diastole. Principal component analysis modes were compared across experimental groups together with conventional metrics of shape, strain, and scar.
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