Multi-scale, Multi-Organ Intervention Modeling and Virtual Clinical Trials

Advances in mechanistic modeling, machine learning, and biomedical data integration are making it possible to move beyond “one-size-fits-all” evidence and toward patient-level predictions that can support treatment selection and dosing in real-world care. This Research Topic focuses on multi-scale, multi-organ intervention modeling and virtual clinical trials built around digital patient models—computational representations that link patient features (e.g., genetics, comorbidities, physiology, imaging, labs, and longitudinal history) to underlying biology and, ultimately, to drug response.

We welcome contributions that develop or apply digital patient models to connect molecular and cellular mechanisms to organ and whole-body outcomes across therapeutic areas. A key aim is to identify and validate predictive biomarkers (mechanistic, statistical, or hybrid) that enable patient stratification—surfacing who is most likely to benefit, who may not respond, and who may be at higher risk of adverse events. By simulating interventions across heterogeneous digital cohorts, virtual clinical trials can complement traditional studies by exploring dosing strategies, trial design choices, and treatment sequences, while also supporting translation from controlled settings to routine clinical practice.

This Research Topic invites work spanning multi-scale modeling (from signaling pathways to tissue and organ function), multi-organ and systems pharmacology, and model-informed drug development. Submissions may include new methods, validation studies, translational applications, and perspectives on implementation and governance.

Topics of interest include (but are not limited to):

o Digital patient and digital twin approaches that integrate multi-omic, clinical, and real-world data

o Multi-scale, multi-organ models linking mechanism to PK/PD and clinical endpoints

o Virtual clinical trial design: synthetic cohorts, eligibility criteria, endpoints, and external controls

o Predictive biomarker discovery and validation for response, toxicity, and dose optimization

o Patient stratification and individualized dosing (including adaptive strategies and treatment sequencing)

o Model calibration, uncertainty quantification, interpretability, and robustness across populations

o Hybrid mechanistic–ML frameworks and causal approaches for intervention prediction

o Use cases in complex, multi-organ diseases (e.g., oncology, immunology, cardiometabolic, infectious disease)

o Regulatory, ethical, and practical considerations for deploying patient-level prediction models in clinical settings

By bringing together modeling, data science, pharmacology, and clinical translation, this Research Topic aims to accelerate a shift from population averages to actionable, patient-specific predictions—supporting better treatment choices, safer dosing, and more efficient evidence generation.