In the literature: October 2026 highlights
Discover the recently published papers from our community!
Balmus, Maximilian, et al. “In silico clinical trials in drug development: virtual patients, applications, and regulatory convergence.” Clinical Pharmacology & Therapeutics (2026).
Conventional clinical trials remain the benchmark for evaluating therapeutic safety and efficacy, yet they are constrained by escalating costs, withdrawal over the extended follow-up periods, recruitment difficulties, ethical limits, and a restricted ability to characterize heterogeneous populations. In silico clinical trials, which use computational models of patient physiology to simulate the effect of interventions across a virtual cohort, have emerged as a complementary paradigm that is efficient, scalable, and mechanistically informed. However, the maturity of in silico clinical trial applications varies considerably between physiological systems. This review pursues three aims. First, we introduce a common taxonomy for virtual patients and in silico trials, spanning levels of model personalization, from fully synthetic populations through hybrid cohorts to patient-specific digital twins, and levels of abstraction, from compartment-based models to whole-organ anatomically accurate models. Second, we survey applications across multiple organ systems, drawing on illustrative examples that expose markedly different degrees of modeling maturity, from comparatively established cardiac and hepatic safety and efficacy studies to areas where mechanistic models remain early in development. Third, we examine the regulatory landscape, tracing its evolution towards risk-informed credibility assessment, and the recent harmonization of model-informed drug development guidance. Although personalized modeling and regulatory pathways are evolving, they often do not converge within a unified framework. Their wider adoption will require robust evaluation against experimental and clinical evidence to demonstrate their predictive reliability.
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Martorella, Francesca, et al. “Modeling Strategies for Neurodegenerative Diseases: In Vitro, In Vivo and In Silico Frameworks.” Biomedicines (2026).
Neurodegenerative diseases (NDs) comprise a heterogeneous group of disorders characterized by complex etiologies, multifactorial pathogenic mechanisms, and remarkable clinical and molecular variability. Their increasing prevalence poses an important challenge to healthcare systems, society and the global economy. Despite decades of research, effective disease-modifying therapies remain elusive, highlighting the urgent need for reliable disease models capable of unraveling pathogenic mechanisms and supporting the development of personalized therapeutic strategies. Modeling NDs remains inherently challenging, since disease onset and progression arise from the interplay among genetic factors, environmental exposures, aging, and individual pathological conditions. Consequently, similar insults may lead to distinct molecular trajectories and clinical outcomes in different subjects. Reproducing this heterogeneity, together with the prolonged temporal evolution that characterizes NDs over decades in humans, represents a major limitation of current experimental models. Here, we provide a comprehensive overview of the modeling strategies currently available for the study of NDs, spanning conventional two-dimensional cellular systems, advanced patient-derived three-dimensional organoids, complementary in vivo animal models, and rapidly evolving in silico approaches. For each model category, we discuss its historical development, current applications, future perspectives, and, critically, principal strengths and limitations in reproducing disease biology and supporting translational research. We emphasize that the growing synergy between in vitro, in vivo, and in silico methodologies, empowered by multimodal data integration and artificial intelligence, is expected to accelerate biomarker discovery, improve disease modeling, reduce experimental burden, and ultimately foster the development of personalized disease-modifying therapies for NDs.
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Bevis, Laura, et al. “Clinical perspectives on the virtual human twin: an overview of engagement and adoption challenges.” Progress in Biomedical Engineering (2026).
Digital twins (DTs) in healthcare have the potential to transform patient care, but their adoption depends critically on the perspectives of clinical professionals, patients, and the public. As part of the Ecosystem for Digital Twins in Healthcare (EDITH) initiative, we undertook a programme of targeted engagement to understand, explore and integrate these perspectives into the development of the European virtual human twin (VHT). Methods included surveys, breakout sessions at public ecosystem meetings, and three dedicated workshops with clinicians and patient representatives covering cardiovascular and musculoskeletal applications. Key findings emphasised the need for early and sustained end-user involvement to ensure that DTs in healthcare address unmet clinical needs, improve patient outcomes, and demonstrate cost-effectiveness. Participants identified practical challenges in data access, validation, regulation, and integration into hospital systems, as well as broader socio-economic and trust-related concerns. Opportunities were also recognised for using DTs to influence patient behaviour, enhance preventive care, and support medical education. From these insights, we identified recommendations across six themes: inclusion, clinical relevance, integration and validation, trust and equity, patient involvement, and incentives. The outcomes of this work provide an important step towards improving clinical interest, engagement, and eventual uptake in DTs in healthcare. Incorporating these perspectives into the VHT roadmap has strengthened its alignment with real-world clinical priorities and patient needs, supporting EDITH’s vision of a sustainable and clinically impactful DT ecosystem. Further work is needed to extend this research across a wider range of clinical and patient stakeholder groups, and additional complex, multi-parametric clinical environments such as oncology.
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Ghosh, Rajdeep, et al. “Prediction of compressive strength of vertebral body with metastatic lesions based on quantitative computed tomography-based subject-specific finite element models.” Bone (2026): 117961.
Pathologic vertebral fractures are a major complication in metastatic spine disease. However, current clinical scores, such as Spinal Instability Neoplastic Score (SINS), show limited predictive capability, particularly within the indeterminate range where most clinical uncertainty lies. This study aimed to develop and evaluate quantitative computed tomography (qCT)-based subject-specific finite element (SSFE) models to predict vertebral strength in presence of different metastatic lesion types. Twelve ex vivo human spine segments, each containing one metastatic (n = 12) and one adjacent control vertebra (n = 12), were scanned using qCT and calibrated using a calibration phantom. Homogenised nonlinear finite element models were developed with spatially heterogeneous, isotropic, density-dependent material properties and loaded under uniaxial compression corresponding to 1.9% apparent strain. Ultimate failure load, stiffness, and strain distributions were compared between metastatic and control vertebrae. Predicted failure load ranged from 0.2 kN to 6.2 kN (median (IQR): 1.10 (0.64-2.68) kN metastatic; 1.21 (0.69-2.84) kN control), with no statistically significant difference between groups (p > 0.05). Normalised failure load varied widely, reflecting lesion-specific mechanical heterogeneity. Lytic lesions generally weakened vertebrae, whereas mixed and blastic lesions occasionally enhanced strength, likely due to localised sclerosis or reactive bone formation. High compressive axial strains (greater than 0.019) were frequently concentrated near the endplates, particularly in lytic vertebrae. qCT-derived bone mineral density strongly correlated with failure load (R2 = 0.74-0.77). These findings highlight the complexity of metastatic vertebral mechanics and demonstrate that qCT-based SSFE modelling provides a quantitative framework for assessing fracture risk, complementing conventional imaging-based tools.
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Sips, Fianne, et al. “A Conceptual Translational Bench-to-Bedside Cardiac Safety Framework for Predicting Drug-Induced QT Interval Prolongation from Molecular Structure to Clinical Data.” International Journal of Molecular Sciences 27.20 (2026): 9312.
QT interval prolongation is associated with an increased risk of torsades de pointes (TdP), a potentially life-threatening ventricular arrhythmia, making assessment of cardiac repolarization a critical component of drug development. This work demonstrates how integrated in silico methodologies can be used to predict and quantitatively characterize QT liability across the drug discovery and development continuum. A proof-of concept translational workflow combining molecular structure analysis, in vitro pharmacology, animal data, and clinical exposure-response modeling was evaluated using the retrospective case examples verapamil and dofetilide. Molecular structure- and pharmacology-based assessments predicted hERG channel interaction and potential QT risk for both compounds. While initial in silico analyses suggested a high QT liability for verapamil and dofetilide, mechanistic electrophysiological modeling differentiated their effects. For verapamil, concurrent calcium channel inhibition offset hERG-mediated effects, resulting in predicted reductions in action potential duration and no clinically relevant QT prolongation. In contrast, dofetilide produced concentration-dependent increases in action potential duration, consistent with its known proarrhythmic potential. Translation of nonclinical findings to humans accurately predicted the absence of clinically meaningful QT effects for verapamil and identified QT prolongation exceeding the regulatory 10 ms threshold for dofetilide at therapeutic concentrations. Drug exposure and pharmacokinetic properties could be anticipated based solely on molecular structure. This conceptual workflow demonstrates how integrated quantitative in silico approaches can support earlier identification, characterization, and communication of QT risk, enabling informed decision-making and improved development efficiency from bench to bedside.
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Zingaro, Alberto, et al. “Advancing aortic stenosis assessment: Validation of fluid–structure interaction models against 4D flow MRI data.” The Journal of Physiology (2026).
Systematic in vivo validations of computational models of the aortic valve (AV) remain scarce, despite successful validation against in vitro data. Utilizing a combination of computed tomography and 4D flow magnetic resonance imaging data, we developed patient-specific fluid-structure interaction models of the AV immersed in the aorta for five patients in the pre-transcatheter AV replacement configuration. Our computational models are subjected to rigorous validation against 4D flow measurements. Our results demonstrate the models’ capacity to accurately replicate flow dynamics. In addition, we illustrate how computational models can serve as valuable cross-checks to reduce noise and erratic behaviour of in vivo data. Crucially, our validated models enable the measurement of additional critical quantities essential for a comprehensive understanding of aortic stenosis (AS) and its treatments: we compute the blood residence time, enhancing precision and personalization in assessing the probability of thrombus formation within the aorta. This study represents a significant step towards integrating in silico technologies into real clinical contexts, providing a robust framework for improving AS diagnosis and the design of next-generation AV bioprostheses. KEY POINTS: Patient-specific fluid-structure interaction computational models of the aortic valve are developed for five patients in pre-transcatheter aortic valve replacement configuration. A synergistic approach involving in silico models and in vivo data is utilized, including computed tomography and 4D flow magnetic resonance imaging. A patient-specific calibration strategy is introduced to identify the aortic valve Young’s modulus, leveraging in vivo flow-derived metrics in combination with patient-specific valve and aortic geometries. The computational models are validated against in vivo 4D flow measurements, demonstrating their ability to replicate flow dynamics accurately. The potential of computational models to cross-check and reduce noise in in vivo data is highlighted, providing additional critical physiological quantities for comprehensive aortic stenosis assessment, such as blood residence time, important for thrombus formation evaluation.
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Muñoz-Moya, Estefano, et al. “Novel finite element fully coupled multi-species mechano-transport simulations approach of the Intervertebral Disc.” Computer Methods and Programs in Biomedicine (2026): 109531.
Intervertebral disc (IVD) degeneration (IDD) is closely linked to impaired nutrient transport within the avascular disc, critically affecting cell viability and tissue health. Finite element (FE) modeling of mechano-metabolic-transport phenomena is widely used to explore disc nutrition, degeneration, and metabolic stress. However, traditional sequential coupling approaches remain limited by reduced accuracy, long runtimes, and implementation complexity. This study aimed to develop and validate an efficient and accurate multi-species transport simulation method integrated within mechanical analyses, thereby overcoming existing limitations for large-scale patient-personalized (PP) cohort simulations.
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