In the literature: November 2025 highlights

Cortés‐Ríos, Javiera, et al. “A Step‐by‐Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.” CPT: Pharmacometrics & Systems Pharmacology (2025).

In silico clinical trials (ISCT) are computational frameworks that employ mathematical models to generate virtual patients and simulate their responses to new treatments, treatment regimens, or medical devices via simulations mirroring real-world clinical trials. ISCTs are an important component of the model-informed drug development (MIDD) framework for optimizing therapies, treatment personalization, informing regulatory decisions, and accelerating overall drug development by enhancing R&D productivity. However, the emergence of complex models, such as quantitative systems pharmacology (QSP) models, presents significant challenges for their effective implementation. Guidelines for conducting ISCTs have been published to address these challenges, focusing on algorithms and credibility frameworks for generating plausible virtual patients and calibrating virtual populations. However, it is not straightforward to apply existing workflows to models where parameter distributions and correlations are estimated using nonlinear mixed effects (NLME) population fitting approaches, a common practice in the pharmaceutical industry when individual-patient-level data is available. Here, we illustrate a modeling workflow for conducting ISCTs with NLME models, detailing key considerations, methods, and challenges at each step. We demonstrate the practical implementation of this workflow through two examples to showcase its broad applicability: (1) a simple model predicting tumor growth in response to chemotherapy and (2) a more complex mechanistic QSP model of hepatitis B virus infection that captures the physiological mechanisms underlying treatment response with standard-of-care therapies.

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Lukas, Louis P., et al. “Exploring synthetic controls in rare diseases with a proof of concept in spinal cord injury.” BMC medicine 23.1 (2025): 581.

Successfully completing clinical trials for rare and heterogeneous disorders, like spinal cord injuries (SCI), remains challenging, thereby reducing the ability to test and translate promising preclinical findings. We propose synthetic controls, derived from data-driven predictions of recovery in patients undergoing standard treatments, to mitigate these challenges, in particular related to patient recruitment.

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Jayakumar, Prakash, et al. “Shared decision making using digital twins in knee osteoarthritis care: a randomized clinical trial of an AI-enabled decision aid versus education alone on decision quality, physical function, and user experience.” EClinicalMedicine 89 (2025).

Patient decision aids (DAs) improve decision quality during shared decision-making (SDM) for patients seeking care for knee osteoarthritis (OA). However, few DAs incorporate the ‘digital twin’ concept where comprehensive data are applied to computational models to generate dynamic virtual simulations and predictions to augment decision-making in real-time. We developed an artificial intelligence-enabled DA (AI-DA) that generated digital twins using patient reported outcome measurements (PROMs) and clinical data to enhance SDM by providing personalized predictions of risks and benefits for patients with knee OA considering total knee arthroplasty (TKA). We assessed the impact of the AI-DA on patient- and process-level outcomes.

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Faivre-Finn, Corinne, et al. “The concept of virtual clinical trials: A game changer in radiation oncology research?.” Radiotherapy and Oncology (2025): 111264.

Virtual clinical trials use computer simulations (e.g. radiation physics and radiobiology) and/or real-world data (e.g. patient anatomy and tissue characteristics) to model radiation oncology treatments, addressing limitations of traditional clinical trials. They aim to enhance subsequent trial design, improve patient selection, and reduce risk of unsuccessful trials. AI promises to boost their precision, enabling faster, more efficient research that keeps pace with evolving technologies and better reflects real-world clinical practice.

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Segarra-Queralt, Maria, et al. “The Role of Anticoagulants After Left Atrial Appendage Occlusion.” JACC: Case Reports (2025): 105838.

Left atrial appendage occlusion prevents thromboembolic events in atrial fibrillation patients with bleeding risk, but device-related thrombosis (DRT) remains a challenge.

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Hoogervorst, Lotje A., et al. “Development of a minimum checklist to assess the quality of evidence produced using registry data for the evaluation of medical device safety and performance.” BMJ surgery, interventions, & health technologies 7.1 (2025): e000364.

Medical device registries in Europe report limited information about their structure and methodological characteristics. This hinders their utility for evaluation of medical device safety and performance under the Medical Device Regulation. This study aimed to define a minimum checklist of items necessary for regulators to assess the quality of evidence produced using registry data for the evaluation of medical device safety and performance.

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Chen, Bohua, et al. “In silico clinical trials in drug development: a systematic review.” Therapeutic Innovation & Regulatory Science (2025): 1-17.

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and this http URL for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging-related research while rare and pediatric diseases were only addressed in 14 articles and 5 trials, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.

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Esposito, Susanna, et al. “Digital Twins in Pediatric Infectious Diseases: Virtual Models for Personalized Management.” Journal of Personalized Medicine 15.11 (2025): 514.

Digital twins (DTs), virtual replicas that integrate mechanistic modeling with real-time clinical data, are emerging as powerful tools in healthcare with particular promise in pediatrics, where age-dependent physiology and ethical considerations complicate infectious disease management. This narrative review examines current and potential applications of DTs across antimicrobial stewardship (AMS), diagnostics, vaccine personalization, respiratory support, and system-level preparedness. Evidence indicates that DTs can optimize antimicrobial therapy by simulating pharmacokinetics and pharmacodynamics to support individualized dosing, enable Bayesian therapeutic drug monitoring, and facilitate timely de-escalation. They also help guide intravenous-to-oral switches and treatment durations by integrating host-response markers and microbiological data, reducing unnecessary antibiotic exposure. Diagnostic applications include simulating host–pathogen interactions to improve accuracy, forecasting clinical deterioration to aid in early sepsis recognition, and differentiating between viral and bacterial illness. Immune DTs hold potential for tailoring vaccination schedules and prophylaxis to a child’s unique immune profile, while hospital- and system-level DTs can simulate outbreaks, optimize patient flow, and strengthen surge preparedness. Despite these advances, implementation in routine pediatric care remains limited by challenges such as scarce pediatric datasets, fragmented data infrastructures, complex developmental physiology, ethical concerns, and uncertain regulatory frameworks. Addressing these barriers will require prospective validation, interoperable data systems, and equitable design to ensure fairness and inclusivity. If developed responsibly, DTs could redefine pediatric infectious disease management by shifting practice from reactive and population-based toward proactive, predictive, and personalized care, ultimately improving outcomes while supporting AMS and health system resilience.

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Iqbal, Jeffrey David, et al. “A consensus statement on the use of digital twins in medicine.” npj Digital Medicine 8.1 (2025): 484.

Digital Health Technologies represent a marked shift from current medical technologies in use, the approach to health and healthcare and stakeholders engaged in healthcare delivery. What the digitalized future of medicine will look like and how it should be governed is unclear. A participatory process with interdisciplinary expert groups developed scenarios of Artificial Intelligence use in medicine and recommendations on their governance. The process included a patient-consumer focus group and the recommendations were validated by a representative population survey in Switzerland. Digital twins were identified as a pivotal innovation for personalized healthcare, with 62% of the Swiss population expressing interest, though 87% oppose mandatory use. Additionally, 75% view the state as responsible for ensuring necessary infrastructure. Digital twins are seen as an opportunity to support both the healthcare provider as well as patient-consumer directly in different modes of use and along functions, prevention, diagnosis, prognosis, and therapy.