The conventional approach to developing medical treatments and medical devices usually covers pre-clinical and in-vitro investigations, in-vivo animal studies and clinical trials with humans. In-silico trials and virtual cohorts present a promising avenue for addressing the challenges inherent in clinical research and improving its efficiency. Despite considerable advancements in the field of in-silico trials, several notable gaps and challenges still need to be addressed, one is the limited availability of open and user-friendly statistical tools to support the specific analysis of virtual cohorts and in-silico trials. In the EU-Horizon funded project SIMCor we have developed a web application, providing a R-statistical environment supporting the validation of virtual cohorts and the application of validated cohorts for in-silico trials. It provides a practical platform for validating cohorts and has implemented existing statistical techniques that can be applied to compare virtual cohorts with real datasets. It is fully open, generic and menu driven and provides user guidance and help ( https://github.com/ecrin-github/SIMCor , https://zenodo.org/records/14718597 ).The tool has been developed according to specified user requirements and has been extensively tested and validated. Important next steps are to gain more experience with the tool in other domains and research environments and to extend its functionality.

Physiologically based pharmacokinetic (PBPK) models play a critical role in evaluating drug residue concentrations and estimating withdrawal intervals (WDIs) for food-producing animals. These models are facilitated by various programming software (e.g. R program) and predefined PBPK platforms, such as Open Systems Pharmacology (OSP) suite integrated by PK-Sim and Mobi, which offers a user-friendly graphical interface. Both R and OSP are open-source software. However, there is a lack of comparative analyses of both platforms and their potential impact on PBPK models. This study aims to evaluate the influence of different platforms on PBPK workflow, parameters selection, and output results, which is exemplified via a case study for meloxicam in chickens in both platforms. Our findings indicate that while the choice of PBPK platforms affected the workflow and input parameters, the predictive performance of established models remained consistent across both platforms. Both platforms predicted meloxicam pharmacokinetics in plasma and tissues accurately across different exposure scenarios. The PBPK-estimated WDIs under various dosing regimens from both platforms were quite similar. Notable differences between OSP suite and R were primarily observed during sensitivity analysis and parameter identification processes, especially the time consumption. This study offers insight into software variances and their implications for translating PBPK modeling knowledge between users of 2 platforms. Also, it provides a PBPK model structure template implemented in both software platforms for food safety and risk assessment in poultry and a detailed tutorial on expanding the model structure in PK-Sim and Mobi.

Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real-time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post-operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi-modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast-track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence: Level V.

Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO) is a type of mechanical circulatory support used, among others, in case of cardiogenic shock, consisting in percutaneous cannulation of the femoral artery. Despite the widespread use of this procedure in clinical practice, a deeper understanding of the complex interaction between native and ECMO output, as well as the fluid dynamics and perfusion of aorta and its branches is still required. Herein, a numerical and experimental approach is presented to model a VA-ECMO procedure on a patient-specific aortic geometry. For both approaches, cardiogenic shock was modeled by considering three different severities of left ventricular failure (mild, moderate, and severe), corresponding to a reduction in cardiac output of 30%, 50%, and 70% relative to the healthy condition, respectively. For each case, different levels of the ECMO support were simulated, ranging from 0 to 6 l/min. The performance of the VA-ECMO configuration was evaluated in terms of both afterload increase and flow at all aortic branches. Both methods highlighted the afterload increase in high levels of ECMO support. Furthermore, numerical and experimental data revealed the existence of a trade-off level of ECMO support that guarantees healthy perfusion of all vessels with the lowest afterload. This correlation opened a pathway for the definition of a tool for determining a suitable level of ECMO support on the basis of the knowledge of patient-specific data.

Cardiovascular digital twins and mechanistic models can be used to obtain new biomarkers from patient-specific hemodynamic data. However, such model-derived biomarkers are only clinically relevant if the uncertainty of the biomarkers is smaller than the variation between timepoints/patients. Unfortunately, this uncertainty is challenging to calculate, as the uncertainty of the underlying hemodynamic data is largely unknown and has several sources that are not additive or normally distributed. This violates normality assumptions of current methods; implying that also biomarkers have an unknown uncertainty. To remedy these problems, we herein present a method, with attached code, for uncertainty calculation of model-derived biomarkers using non-normal data. First, we estimated all sources of uncertainty, both normal and non-normal, in hemodynamic data used to personalize an existing model; the errors in 4D flow MRI-derived stroke volumes were 5-20 % and the blood pressure errors were 0 ± 8 mmHg. Second, we estimated the resulting model-derived biomarker uncertainty for 100 simulated datasets, sampled from the data distributions, by: 1) combining data uncertainties 2) parameter estimation, 3) profile-likelihood. The true biomarker values were found within a 95 % confidence interval in 98 % (median) of the cases. This shows both that our estimated data uncertainty is reasonable, and that we can use profile-likelihood despite the non-normality. Finally, we demonstrated that e.g. ventricular relaxation rate has a smaller uncertainty (∼10 %) than the variation across a clinical cohort (∼40 %), meaning that these biomarkers have clinical potential. Our results take us one step closer to the usage of model-derived biomarkers for cardiovascular patient characterization.

Aortic dissection continues to be responsible for significant morbidity and mortality, although recent advances in medical data assimilation and in experimental and in silico models have improved our understanding of the initiation and progression of the accumulation of blood within the aortic wall. Hence, there remains a pressing necessity for innovative and enhanced models to more accurately characterize the associated pathological changes. Early on, experimental models were employed to uncover mechanisms in aortic dissection, such as hemodynamic changes and alterations in wall microstructure, and to assess the efficacy of medical implants. While experimental models were once the only option available, more recently they are also being used to validate in silico models. Based on an improved understanding of the deteriorated microstructure of the aortic wall, numerous multiscale material models have been proposed in recent decades to study the state of stress in dissected aortas, including the changes associated with damage and failure. Furthermore, when integrated with accessible patient-derived medical data, in silico models prove to be an invaluable tool for identifying correlations between hemodynamics, wall stresses, or thrombus formation in the deteriorated aortic wall. They are also advantageous for model-guided design of medical implants with the aim of evaluating the deployment and migration of implants in patients. Nonetheless, the utility of in silico models depends largely on patient-derived medical data, such as chosen boundary conditions or tissue properties. In this review article, our objective is to provide a thorough summary of medical data elucidating the pathological alterations associated with this disease. Concurrently, we aim to assess experimental models, as well as multiscale material and patient data-informed in silico models, that investigate various aspects of aortic dissection. In conclusion, we present a discourse on future perspectives, encompassing aspects of disease modeling, numerical challenges, and clinical applications, with a particular focus on aortic dissection. The aspiration is to inspire future studies, deepen our comprehension of the disease, and ultimately shape clinical care and treatment decisions.

Drug-induced changes in cardiac contractility (inotropy) can lead to cardiotoxicity, a major cause of discontinuation in drug development. Preclinical approaches to assess cardiac inotropy are imperfect, with in vitro assays limited to stem cell-derived or adult human primary cardiomyocytes. Human mechanistic in silico modelling and simulations are already successfully applied for proarrhythmia prediction, contributing to cardiac safety assessment strategies in early drug development. In this study, we investigated their ability to predict drug-induced effects on cardiac inotropy. We considered a validation set of 28 neutral/negative inotropic and 13 positive inotropic reference compounds and simulated their effects on cell contractility via ion channel inhibition and perturbation of nine biomechanical modelling parameters, respectively. For each compound, a wide range of drug concentrations was simulated in an experimentally calibrated control population of 323 human ventricular in silico cells. Simulated biomarkers indicating drug-induced inotropic effects were compared with in vitro preclinical data from the literature. Computer simulations predicted drug-induced inotropic changes observed in vitro for 25 neutral/negative inotropes and 10 positive inotropes. Predictions of negative inotropic changes were quantitatively in agreement for 86% of tested drugs. Active tension peak was identified as the biomarker with highest predictive potential. This study describes the validation and application of an in silico cardiac electromechanical model for drug safety evaluation, combining ion channel inhibition data and information on potential inotropic mechanisms to predict inotropic changes. Furthermore, a route for its integration as part of a preclinical drug safety assessment strategy is outlined.

Personal body armor, such as ballistic protective plates (BPPs), plays a vital role in protecting the torso against high-energy impacts, yet Behind Armor Blunt Trauma (BABT) remains a concern. BABT can inflict damage on critical organs, particularly the heart and lungs. This study investigates the protective performance of BPP materials and padding configurations. We employed a finite element (FE) model of the torso, incorporating detailed anatomical features, to simulate non-penetrating impacts on the heart and lungs. Three BPP materials - Kevlar-29, Ultra-High-Molecular-Weight Polyethylene (UHMWPE), and Alumina, were analyzed with and without 6 mm and 12 mm padding layers against a 5.56 mm bullet impact at 500 m/s. The results demonstrated that Alumina plates provided superior protection, resulting in 186% lower peak strain and 229% lower peak stress in the heart compared to Kevlar and UHMWPE. The addition of padding further reduced strains and stresses, with 12 mm padding yielding average reductions of 44% in peak strain and 36% in peak stress in the heart. Similarly, in the lungs, 12 mm padding led to reductions of 38% in peak strain and 34% in peak stress. The model was validated against experimental force measurements using a life-sized torso phantom, demonstrating strong agreement with piezoelectric sensor measurements (less than 4% difference). These findings underscore the importance of selecting appropriate BPP materials and padding thickness to minimize biomechanical impacts on vital organs.

In this opinion piece, we examine the pivotal role that uncertainty quantification (UQ) plays in informing clinical decision-making processes. We explore challenges associated with healthcare data and the potential barriers to the widespread adoption of UQ methodologies. In doing so, we highlight how these techniques can improve the precision and reliability of medical evaluations. We delve into the crucial role of understanding and managing the uncertainties present in clinical data (such as measurement error), diagnostic tools and treatment outcomes. We discuss how such uncertainties can impact decision-making in healthcare and emphasize the importance of systematically analysing them. Our goal is to demonstrate how effectively addressing and decoding uncertainties can significantly enhance the accuracy and robustness of clinical decisions, ultimately leading to better patient outcomes and more informed healthcare practices.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps. In an era of personalized medicine, patient-specific modelling, and digital twins, a lack of UQ understanding and appropriate implementation of UQ methodology limits the success of modelling and simulation in a clinical setting. The main contribution of our review article is to emphasize the importance and current deficiencies of UQ in the development of computational frameworks for healthcare and biological systems. As the introduction to the special issue on this topic, we provide an overview of UQ methodologies, their applications in non-biological and biological systems and the current gaps and opportunities for UQ development, as later highlighted by authors publishing in the special issue.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

In silico testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in particular when functional, such as biodegradable, implant materials are investigated. In this work, we expand and refine suitable existing mathematical models of bone growth and magnesium-based implant degradation based on ordinary differential equations. We show that we can simulate the implant degradation, as well as the osseointegration in terms of relative bone volume fraction and changes in bone ultrastructure when applying the model to experimental data from titanium and magnesium-gadolinium implants for healing times up to 32 weeks. An additional sensitivity analysis highlights important parameters and their interactions. Moreover, we show that the model is predictive in terms of relative bone volume fraction with mean absolute errors below 6%.

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualized clinical pathways. Different imaging modalities may have various fields of view and are sensitive to signals from other tissues/organs, or both. Hence, missing/partially overlapping anatomical information is often available across individuals. We introduce a generative shape model for multipart anatomical structures, learnable from sets of unpaired datasets, i.e., where each substructure in the shape assembly comes from datasets with missing or partially overlapping substructures from disjoint subjects of the same population. The proposed generative model can synthesize complete multipart shape assemblies coined virtual chimeras (VCs). We applied this framework to build VCs from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a graph neural network-based generative shape compositional framework, which comprises two components, a part-aware generative shape model that captures the variability in shape observed for each structure of interest in the training population and a spatial composition network that assembles/composes the structures synthesized by the former into multipart shape assemblies (i.e., VCs). We also propose a novel self-supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance (MR) images in the UK Biobank (UKBB). When trained with complete and partially overlapping data, our approach significantly outperforms a principal component analysis (PCA)-based shape model (trained with complete data) in terms of generalizability and specificity. This demonstrates the superiority of the proposed method, as the synthesized cardiac virtual populations are more plausible and capture a greater degree of shape variability than those generated by the PCA-based shape model.

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.

Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (SRLV) embedded in a hybrid (in silico- in vitro) simulator of the cardiovascular system is presented, along with its experimental and computational analysis.

A SRLV phantom, developed from a patient's CT scan using polyvinyl alcohol (PVA), is embedded in a hybrid cardiovascular simulator. We present an activation method in which the hydraulic pressure external (P e ( t ) ) to the SRLV is continuously adapted to regulate the left ventricular volume (V i ( t ) ), considering the geometry and material behavior of the SRLV and the left ventricular pressure (P i ( t ) ). This activation method is verified using a finite element (FE) model of the SRLV and validated in the hybrid simulator. Different hemodynamic profiles are presented to test the flexibility of the method.

Both the FE model and hybrid simulator could represent the desired in silico data (P i ( t ) ,V i ( t ) ) with the implemented activation method, with deviations below 8.09% in the FE model and mainly < 10% errors in the hybrid simulator. Only two measurements out of 32 exceeded the 10% threshold due to simulator setup limitations.

The activation method effectively allows to represent various pressure-volume loops, as verified numerically, and validated experimentally in the hybrid simulator. This work presents a high-fidelity platform designed to simulate cardiovascular conditions, offering a robust foundation for future testing of cardiovascular medical devices under physiological conditions.

In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI).

A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population.

By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67 mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98).

The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.

Mechanistic process models play an increasingly important role in biopharmaceutical process development and manufacturing in supporting process design, characterization, and informing process control strategies. Despite the potential of mechanistic models, there is currently no clear consensus or regulatory guideline on their qualification, i.e. the processes of determining whether a model is suitable to support decision making in process development. In this work, a systematic and risk-based qualification framework for mechanistic models in biopharmaceutical process development is introduced. The framework integrates key concepts from other modeling frameworks and guidelines such as the ASME V&V 40 published by the American Society of Mechanical Engineers (ASME) and preliminary considerations in process models published by Quality Innovation Group (QIG) of the European Medicines Agency (EMA). Key concepts of the proposed framework are discussed using two case studies, including a model-informed optimization of a biopharmaceutical ultrafiltration and diafiltration process and a model-informed control strategy of a chromatography polishing step. The suggested framework can act as a foundation for dialogue and guide for other modelers in biopharmaceutical process development. It holds the capability to harmonize modeling procedures throughout the industry and establish an agreement on the qualification of mechanistic models in biopharmaceutical process development.

Hip fracture prevention approaches like prophylactic augmentation devices have been proposed to strengthen the femur and prevent hip fracture in a fall scenario. The aim of this study was to validate the finite element model (FEM) of specimens augmented by prophylactic intramedullary nailing in a simulated sideways fall impact against ex vivo experimental data. A dynamic inertia-driven sideways fall simulator was used to test six cadaveric specimens (3 females, 3 males, age 63-83 years) prophylactically implanted with an intramedullary nailing system used to augment the femur. Impact force measurements, pelvic deformation, effective pelvic stiffness, and fracture outcomes were compared between the ex vivo experiments and the FEMs. The FEMs over-predicted the effective pelvic stiffness for most specimens and showed variability in terms of under- and over-predicting peak impact force and pelvis compression depending on the specimen. A significant correlation was found for time to peak impact force when comparing ex vivo and FEM data. No femoral fractures were found in the ex vivo experiments, but two specimens sustained pelvic fractures. These two pelvis fractures were correctly identified by the FEMs, but the FEMs made three additional false-positive fracture identifications. These validation results highlight current limitations of these sideways fall impact models specific to the inclusion of an orthopaedic implant. These FEMs present a conservative strategy for fracture prediction in future applications. Further evaluation of the modelling approaches used for the bone-implant interface is recommended for modelling augmented specimens, alongside the importance of maintaining well-controlled experimental conditions.

Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods for solving ECG inverse problems, their validation strategies, their clinical applications, and their future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.

Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials.

This paper presents a review protocol for preimplementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials.

We propose a scoping review protocol that follows an enhanced Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to investigate the scope and research gaps in the in silico evaluation of algorithm-based CDS models-specifically CDS decision-making end points and objectives, evaluation metrics used, and simulation paradigms used to assess potential impacts. The databases searched are PubMed, Embase, CINAHL, PsycINFO, Cochrane, IEEEXplore, Web of Science, and arXiv. A 2-stage screening process identified pertinent articles. The information extracted from articles was iteratively refined. The review will use thematic, trend, and descriptive analyses to meet scoping aims.

We conducted an automated search of the databases above in May 2023, with most title and abstract screenings completed by November 2023 and full-text screening extended from December 2023 to May 2024. Concurrent charting and full-text analysis were carried out, with the final analysis and manuscript preparation set for completion in July 2024. Publication of the review results is targeted from July 2024 to February 2025. As of April 2024, a total of 21 articles have been selected following a 2-stage screening process; these will proceed to data extraction and analysis.

We refined our data extraction strategy through a collaborative, multidisciplinary approach, planning to analyze results using thematic analyses to identify approaches to in silico evaluation. Anticipated findings aim to contribute to developing a unified in silico evaluation framework adaptable to various clinical workflows, detailing clinical decision-making characteristics, impact measures, and reusability of methods. The study's findings will be published and presented in forums combining artificial intelligence and machine learning, clinical decision-making, and health technology impact analysis. Ultimately, we aim to bridge the development-deployment gap through in silico evaluation-based potential impact assessments.

DERR1-10.2196/63875.

In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal "new approach methodologies" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.

Transparent and accurate reporting in early phase dose-finding (EPDF) clinical trials is crucial for informing subsequent larger trials. The SPIRIT statement, designed for trial protocol content, does not adequately cover the distinctive features of EPDF trials. Recent findings indicate that the protocol contents in past EPDF trials frequently lacked completeness and clarity. To address this gap, the international consensus-driven SPIRIT-DEFINE checklist was developed through a robust methodological framework for guideline development, with the aim to improve completeness and clarity in EPDF trial protocols. The checklist builds on the SPIRIT statement, adding 17 new items and modifying 15 existing ones.The SPIRIT-DEFINE explanation and elaboration (E&E) document provides comprehensive information to enhance understanding and usability of the SPIRIT-DEFINE checklist when writing an EPDF trial protocol. Each new or modified checklist item is accompanied by a detailed description, its rationale with supportive evidence, and examples of good reporting curated from EPDF trial protocols covering a range of therapeutic areas and interventions. We recommend utilising this paper alongside the SPIRIT statement, and any relevant extensions, to enhance the development and review of EPDF trial protocols.By facilitating adoption of the SPIRIT-DEFINE statement for EPDF trials, this E&E document can promote enhancement of methodological rigour, patient safety, transparency, and facilitate the generation of high-quality, reproducible evidence that will strengthen the foundation of early phase research and ultimately improve patient outcomes.

This work is a further extension of the SPIRIT-DEFINE study, which obtained no external funding. The principal investigator (CY) used internal staff resources, together with additional resources from external partners, to conduct this study. The SPIRIT-DEFINE study is a component of the DEFINE project, which also developed the MRC/NIHR funded CONSORT-DEFINE guidance. ICR-CTSU receives programmatic infrastructure funding from Cancer Research UK (C1491/A25351; CTUQQR-Dec22/100004), which has contributed to accelerating the advancement and successful completion of this work.

Early phase dose-finding (EPDF) trials are key in the development of novel therapies, with their findings directly informing subsequent clinical development phases and providing valuable insights for reverse translation. Comprehensive and transparent reporting of these studies is critical for their accurate and critical interpretation, which may improve and expedite therapeutic development. However, quality of reporting of design characteristics and results from EPDF trials is often variable and incomplete. The international consensus-based CONSORT-DEFINE (Consolidated Standards for Reporting Trials Dose-finding Extension) statement, an extension of the CONSORT statement for randomised trials, was developed to improve the reporting of EPDF trials. The CONSORT-DEFINE statement introduced 21 new items and modified 19 existing CONSORT items.This CONSORT-DEFINE Explanation and Elaboration (E&E) document provides important information to enhance understanding and facilitate the implementation of the CONSORT-DEFINE checklist. For each new or modified checklist item, we provide a detailed description and its rationale with supporting evidence, and present examples from EPDF trial reports published in peer-reviewed scientific journals. When reporting the results of EPDF trials, authors are encouraged to consult the CONSORT-DEFINE E&E document, together with the CONSORT and CONSORT-DEFINE statement papers, and adhere to their recommendations. Widespread adoption of the CONSORT-DEFINE statement is likely to enhance the reporting quality of EPDF trials, thus facilitating the peer review of such studies and their appraisal by researchers, regulators, ethics committee members, and funders.

This work is a further extension of the CONSORT-DEFINE study, which was funded by the UK Medical Research Council (MRC)-National Institute for Health and Care Research (NIHR) Methodology Research Programme (MR/T044934/1). The Clinical Trials and Statistics Unit at The Institute of Cancer Research (ICR-CTSU) receives programmatic infrastructure funding from Cancer Research UK (C1491/A25351; CTUQQR-Dec 22/100 004), which has contributed to accelerating the advancement and successful completion of this work.

This State of the Future Review describes and discusses the potential transformative power of digital twins in cardiac electrophysiology. In this 'big picture' approach, we explore the evolution of mechanistic modelling based digital twins, their current and immediate clinical applications, and envision a future where continuous updates, advanced calibration, and seamless data integration redefine clinical practice of cardiac electrophysiology. Our aim is to inspire researchers and clinicians to embrace the extraordinary possibilities that digital twins offer in the pursuit of precision medicine.

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.

The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi-scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular-scale data, particularly single-cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular-scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal-scale physiology data to create multi-system, multi-scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

Virtual evaluation of medical therapy through human-based modelling and simulation can accelerate and augment clinical investigations. Treatment of the most common cardiac arrhythmia, atrial fibrillation (AF), requires novel approaches. This study prospectively evaluates and mechanistically explains three novel pharmacological therapies for AF through in silico trials, including single and combined SK and K2P channel block. AF and pharmacological action were assessed in a large cohort of 1000 virtual patients, through 2962 multiscale simulations. Extensive calibration and validation with experimental and clinical data support their credibility. Sustained AF was observed in 654 virtual patients. In this cohort, cardioversion efficacy increased to 82% (535 of 654) through combined SK+K2P channel block, from 33% (213 of 654) and 43% (278 of 654) for single SK and K2P blocks, respectively. Drug-induced prolongation of tissue refractoriness, dependent on the virtual patient's ionic current profile, explained cardioversion efficacy (atrial refractory period increase: 133.0 ± 48.4 ms for combined vs. 45.2 ± 43.0 and 71.0 ± 55.3 ms for single SK and K2P block, respectively). Virtual patients cardioverted by SK channel block presented lower K2P densities, while lower SK densities favoured the success of K2P channel inhibition. Both ionic currents had a crucial role on atrial repolarization, and thus a synergism resulted from the multichannel block. All three strategies, including the multichannel block, preserved atrial electrophysiological function (i.e. conduction velocity and calcium transient dynamics) and thus its contractile properties (safety). In silico trials identify key factors determining treatment success and the combined SK+K2P channel block as a promising strategy for AF management. KEY POINTS: This is a large-scale in silico trial study involving 2962 multiscale simulations. A population of 1000 virtual patients underwent three treatments for atrial fibrillation. Single and combined SK+K2P channel block were assessed prospectively. The multi-ion channel inhibition resulted in 82% cardioversion efficacy. In silico trials have broad implications for precision medicine.

Inclisiran, an siRNA targeting hepatic PCSK9 mRNA, administered twice-yearly (after initial and 3-month doses), substantially and sustainably reduced LDL-cholesterol (LDL-C) in Phase III trials. Whether lowering LDL-C with inclisiran translates into a reduced risk of major adverse cardiovascular events (MACE) is not yet established. In-silico trials applying a disease computational model to virtual patients receiving new treatments allow to emulate large scale long-term clinical trials. The SIRIUS in-silico trial programme aims to predict the efficacy of inclisiran on CV events in individuals with established atherosclerotic cardiovascular disease (ASCVD).

A knowledge-based mechanistic model of ASCVD was built, calibrated, and validated to conduct the SIRIUS programme (NCT05974345) aiming to predict the effect of inclisiran on CV outcomes. The SIRIUS Virtual Population included patients with established ASCVD (previous myocardial infarction (MI), previous ischemic stroke (IS), previous symptomatic lower limb peripheral arterial disease (PAD) defined as either intermittent claudication with ankle-brachial index <0.85, prior peripheral arterial revascularization procedure, or vascular amputation) and fasting LDL-C ≥ 70 mg/dL, despite stable (≥4 weeks) well-tolerated lipid-lowering therapies.SIRIUS is an in-silico multi-arm trial programme. It follows an idealized crossover design where each virtual patient is its own control, comparing inclisiran to (i) placebo as adjunct to high-intensity statin therapy with or without ezetimibe, (ii) ezetimibe as adjunct to high-intensity statin therapy, (iii) evolocumab as adjunct to high-intensity statin therapy and ezetimibe.The co-primary efficacy outcomes are based on the time to the first occurrence of any component of 3P-MACE (composite of CV death, nonfatal MI, or nonfatal IS) and time to occurrence of CV death over 5 years.

The SIRIUS in-silico trial programme will provide early insights regarding potential effect of inclisiran on MACE in ASCVD patients, several years before the availability of the results from ongoing CV outcomes trials (ORION-4 and VICTORION-2-P).

Clinicaltrials.gov identifier: NCT05974345.

Accurate segmentation of medical images is critical for generating patient-specific models suitable for computational analyses, particularly in the context of transcatheter aortic valve implantation (TAVI). This study aimed to quantify the accuracy of the segmentation process from medical images of TAVI patients to understand the uncertainty in patient-specific geometries. We also quantified discrepancies between actual and CT-related diameter measurements due to artifacts and intra-observer variability. Segmentation accuracy was assessed using both synthetic phantom models and patient-specific data. The impact of voxelization and CT scanner resolution on segmentation accuracy was evaluated, while the intersection over union (IoU) metric was used to compare the consistency of different segmentation methodologies. The voxelization process introduced a marginal error (<1%) in phantom models relative to CAD models. CT scanner resolution impacted segmented model accuracy only after a 7.5-fold increase in voxel size compared to the baseline medical image. IoU analysis revealed higher segmentation accuracy for calcification (93.4 ± 3.1 %) compared to the aortic wall (85.4 ± 8.4 %) and native valve leaflets (75.5 ± 6.3 %). Discrepancies in THV diameter measurements highlighted a ∼5 % error due to metallic artifacts, with variability among observers and at different THV heights. Errors due to voxel size, segmentation methodologies and CT-related artifacts can impact the reliability of patient-specific geometries and ultimately computational predictions used to asses clinical outcomes and enhance decision-making. This study underscores the importance of accurate segmentation and its standardization for patient-specific modeling of TAVI simulations.

The objective of this study is to validate a novel workflow for implementing patient-specific finite element (FE) simulations to virtually replicate the Transcatheter Aortic Valve Implantation (TAVI) procedure.

Seven patients undergoing TAVI were enrolled. Patient-specific anatomical models were reconstructed from pre-operative computed tomography (CT) scans and subsequentially discretized, considering the native aortic leaflets and calcifications. Moreover, high-fidelity models of CoreValve Evolut R and Acurate Neo2 valves were built. To determine the most suitable material properties for the two stents, an accurate calibration process was undertaken. This involved conducting crimping simulations and fine-tuning Nitinol parameters to fit experimental force-diameter curves. Subsequently, FE simulations of TAVI procedures were conducted. To validate the reliability of the implemented implantation simulations, qualitative and quantitative comparisons with post-operative clinical data, such as angiographies and CT scans, were performed.

For both devices, the simulation curves closely matched the experimental data, indicating successful validation of the valves mechanical behaviour. An accurate qualitative superimposition with both angiographies and CTs was evident, proving the reliability of the simulated implantation. Furthermore, a mean percentage difference of 1,79 ± 0,93 % and 3,67 ± 2,73 % between the simulated and segmented final configurations of the stents was calculated in terms of orifice area and eccentricity, respectively.

This study shows the successful validation of TAVI simulations in patient-specific anatomies, offering a valuable tool to optimize patients care through personalized pre-operative planning. A systematic approach for the validation is presented, laying the groundwork for enhanced predictive modeling in clinical practice.

Cardiovascular diseases remain a leading cause of morbidity and mortality worldwide and require extensive investigation through in-vitro studies. Mock Circulatory Loops (MCLs) are advanced in-vitro platforms that accurately replicate physiological and pathological hemodynamic conditions, while also allowing for precise and patient-specific data collection. Particle Image Velocimetry (PIV) is the standard flow visualization technique for in-vitro studies, but it is costly and requires strict safety measures. High-power Light Emitting Diode illuminated PIV (LED-PIV) offers a safer and cheaper alternative.

In this study, we aim to demonstrate the feasibility of a Hybrid-MCL integrated with a LED-PIV system for the investigation of Abdominal Aortic Aneurysm (AAA) compliant phantoms. We considered two distinct AAA models, namely, an idealized model and a patient-specific one under different physiological flow and pressure conditions.

The efficacy of the proposed setup for the investigation of AAA hemodynamics was confirmed by observing velocity and vorticity fields across multiple flow rate scenarios and regions of interest.

The findings of this study underscore the potential impact of Hybrid-MCL integrated with a LED-PIV system on enhancing the affordability, accessibility, and safety of in-vitro CVD investigations.

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation-perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.

Whereas studying the aortic valve in isolation has facilitated the development of life-saving procedures and technologies, the dynamic interplay of the aortic valve and its surrounding structures is vital to preserving their function across the wide range of conditions encountered in an active lifestyle. Our view is that these structures should be viewed as an integrated functional unit, here referred to as the aortic valve apparatus (AVA). The coupling of the aortic valve and root, left ventricular outflow tract, and blood circulation is crucial for AVA's functions: unidirectional flow out of the left ventricle, coronary perfusion, reservoir function, and support of left ventricular function. In this review, we explore the multiscale biological and physical phenomena that underlie the simultaneous fulfillment of these functions. A brief overview of the tools used to investigate the AVA, such as medical imaging modalities, experimental methods, and computational modeling, specifically fluid-structure interaction (FSI) simulations, is included. Some pathologies affecting the AVA are explored, and insights are provided on treatments and interventions that aim to maintain quality of life. The concepts explained in this article support the idea of AVA being an integrated functional unit and help identify unanswered research questions. Incorporating phenomena through the molecular, micro, meso, and whole tissue scales is crucial for understanding the sophisticated normal functions and diseases of the AVA.

Metastases increase the risk of fracture when affecting the femur. Consequently, clinicians need to know if the patient's femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing models were tested for reproducibility. This is a critical issue to address in order to apply the technique on a large cohort around the world to help evaluate bone metastatic fracture risk in patients. The aim of this study is then to evaluate 1) the reproducibility 2) the transposition of the reproduced model to another dataset and 3) the global sensitivity of one of the most promising models of the literature (original model).

The model was reproduced based on the paper describing it and discussion with authors to avoid reproduction errors. The reproducibility was evaluated by comparing the results given in the original model by the original first team (Leuven, Belgium) and the reproduced model made by another team (Lyon, France) on the same dataset of CT-scans of ex vivo femurs. The transposition of the model was evaluated by comparing the results of the reproduced model on two different datasets. The global sensitivity analysis was done by using the Morris method and evaluates the influence of the density calibration coefficient, the segmentation, the orientations and the length of the femur.

The original and reproduced models are highly correlated (r2 = 0.95), even though the reproduced model gives systematically higher failure loads. When using the reproduced model on another dataset, predictions are less accurate (r2 with the experimental failure load decreases, errors increase). The global sensitivity analysis showed high influence of the density calibration coefficient (mean variation of failure load of 84 %) and non-negligible influence of the segmentation, orientation and length of the femur (mean variation of failure load between 7 and 10 %).

This study showed that, although being validated, the reproduced model underperformed when using another dataset. The difference in performance depending on the dataset is commonly the cause of overfitting when creating the model. However, the dataset used in the original paper (Sas et al., 2020a) and the Leuven's dataset gave similar performance, which indicates a lesser probability for the overfitting cause. Also, the model is highly sensitive to density parameters and automation of measurement may minimize the uncertainty on failure load. An uncertainty propagation analysis would give the actual precision of such model and improve our understanding of its behavior and is part of future work.

Distraction osteogenesis (DO) is a bone regenerative maneuver, which is conventionally done with external fixators and, more recently, with telescopic intramedullary nails. Despite the proven effectiveness, external approaches are intrusive to the patient's life while intramedullary nailing damages the growth plates, making them unsuitable for pediatric patients. An internal DO plate fixator (IDOPF) was developed for pediatric patients to address these limitations. The objective of this study was to test the hypothesis that the IDOPF can withstand a partial weight bearing scenario and create a favorable mechanical microenvironment at the osteotomy gap for bone regeneration as the device elongates. A finite element model of a surrogated long bone diaphysis osteotomy fixation by means of the IDOPF was created and subjected to axial compression, bending and torsion. As the osteotomy gap increased from 2 mm to 20 mm, under compression, The average axial interfragmentary strains decreased from 2.33% to 0.35%. Stress increased from 179 MPa to 281 MPa at the contact interfaces of the telescopic compartments, which exceeded the endurance limit of stainless steel (270 MPa) but was below its yield limit (415 MPa). These results demonstrate, that the IDOPF can withstand a partial load bearing scenario and provide a stable biomechanical environment conductive to bone healing. However, high contact stresses at the telescopic interfaces of the device are likely to cause wear, as is frequently reported in telescopic fixators. This study is a step towards refining the IDOPF design for clinical use.

Fabry disease (FD) is an X-linked lysosomal disease whose ultimate consequences are the accumulation of sphingolipids and subsequent inflammatory events, mainly at the endothelial level. The outcomes include different nervous system manifestations as well as multiple organ damage. Despite the availability of known biomarkers, early detection of FD remains a medical need. This study aimed to develop an in silico model based on machine learning to identify candidate vascular and nervous system proteins for early FD damage detection at the cellular level. A combined systems biology and machine learning approach was carried out considering molecular characteristics of FD to create a computational model of vascular and nervous system disease. A data science strategy was applied to identify risk classifiers by using 10 K-fold cross-validation. Further biological and clinical criteria were used to prioritize the most promising candidates, resulting in the identification of 36 biomarker candidates with classifier abilities, which are easily measurable in body fluids. Among them, we propose four candidates, CAMK2A, ILK, LMNA, and KHSRP, which have high classification capabilities according to our models (cross-validated accuracy ≥ 90%) and are related to the vascular and nervous systems. These biomarkers show promise as high-risk cellular and tissue damage indicators that are potentially applicable in clinical settings, although in vivo validation is still needed.