In Silico Trials and NAMs for Translational Digital Health

Therapeutic development and clinical translation face major obstacles because preclinical models and invasive clinical measurements do not always predict human response, benefit, or safety accurately. Roughly 85 to 90 percent of drug candidates entering clinical testing fail to reach approval, reflecting the limits of current predictive tools rather than a lack of scientific effort.Digital health and informatics-driven approaches are changing this picture. Advances in Modeling & Simulation (M&S), Model-Informed Drug Development (MIDD), In Silico Trials (IST), and other New Approach Methodologies (NAMs) generate human-relevant, regulatory-aligned evidence by connecting computational models with digital health data, imaging, multi-omics, real-world data, and clinical informatics pipelines. Regulators including the FDA and EMA are increasingly developing guidance and regulatory pathways that support the use of these frameworks in drug development and clinical decision-making, reflected in MIDD guidance and the FDA Modernization Act 2.0. This Research Topic focuses on how that regulatory-aligned digital evidence is generated, validated, and integrated into decision-making pipelines, rather than on the underlying wet-lab or animal-model science itself.This Research Topic examines how New Approach Methodologies (NAMs) and digital health informatics pipelines — including in silico simulations, mechanistic models, AI-assisted workflows, and data from organoid and organ-on-chip platforms — can be structured, validated, and connected to support translational digital health. We aim to highlight informatics frameworks that reduce dependence on animal models, minimize invasive procedures, and improve translation of biomedical discoveries into clinically actionable, regulatory-relevant evidence across drug development, therapeutic evaluation, diagnostics, medical-device workflows, and clinical decision support. Submissions should keep the digital methodology and data pipeline central, treating biological or experimental data as an input to be modelled or validated computationally rather than as the primary focus.Suitable themes include, but are not limited to:• In Silico Trials for translational medicine: simulation-based clinical trial frameworks and supporting data pipelines predicting efficacy, toxicity, patient response, and outcomes.• MIDD as a regulatory decision-support tool: computational frameworks integrating pharmacology, physiology, and clinical data to inform regulatory and clinical decisions, rather than to report underlying pharmacological findings.• Digital pipelines for NAMs: informatics infrastructure and computational workflows that structure and validate organ-on-chip or other human-relevant data for modeling and decision-making, reducing reliance on animal models.• Bridging preclinical models to clinical trials: informatics methods converting early-stage biological, imaging, multi-omics, or mechanistic data into structured, clinically usable predictions.• Bridging invasive and non-invasive measurements: digital biomarker pipelines correlating invasive gold-standard measurements with non-invasive biomarkers, imaging, wearable signals, or routine clinical data.• AI-assisted mechanistic modeling: hybrid workflows combining machine learning with mechanistic simulation for prediction, model acceleration, uncertainty estimation, or patient stratification.• Digital health informatics pipelines: data standards, integration workflows, and decision-support systems allowing simulation outputs to be used in clinical or translational settings.• Model credibility, validation, and regulatory readiness: frameworks demonstrating the trustworthiness, reproducibility, and regulatory relevance of simulation-based evidence.• Case studies in digital translational decision-making: examples where in silico or informatics-driven evidence contributed to drug development, therapeutic evaluation, safety assessment, medical-device workflows, patient monitoring, or non-invasive clinical decision support. Topic editors: Alon Malka-Markovitz, Mohammed Cherkaoui, IGNACIO LUGONES Source: https://www.frontiersin.org/research-topics/84508/in-silico-trials-and-nams-for-translational-digital-health