Presentation (CTI)

OBJECTIVES: Virtual cohorts and synthetic patients are gaining traction to address data scarcity, privacy and ethical constraints in drug development. Quinten Health in partnership with INSERM/CONSTANCES, one of the largest general population cohorts in EU is developing a regulatory-grade, data-integrative, disease-specific approach to accelerate and de-risk drug development and evaluation using AI, machine-learning (ML) and predictive modeling. The tool supports: 1) identifying patient profiles for clinical decision, 2) characterizing disease progression, 3) emulating standard of care (SoC) - treatment effect and exposure, 4) simulating alternative scenarios (e.g. population, timing), 5) modeling cost to identify drivers and optimize resources.
METHODS: The tool is based on the French CONSTANCES cohort (220,000+ adults), collecting health, socioeconomic, and biological data linked with insurance and hospital records. It uses disease-specific models (eg COPD, obesity) and methods such as clustering for patient identification, Bayesian / survival models, neural networks for disease progression and SoC, Markov models for medico-economic analysis. Generative adversarial networks GANs support tool development which produces HTAs-relevant outcomes, including quality of life, healthcare resource use, and direct medical costs.
RESULTS: Based on pharmaceutical sponsors’ priorities, initial target indications include asthma, COPD, and obesity. A feasibility assessment evaluated the potential to generate HTA-grade evidence using the CONSTANCES-SNDS dataset. While CONSTANCES may underrepresent severe cases in some diseases (e.g. COPD), it offers long-term follow-up and access to valuable para-clinical variables. Linkage with SNDS adds value by enabling reconstruction of care pathways, treatments, and outcomes over time.
CONCLUSIONS: The CONSTANCES-SNDS integration offers a unique dataset combining depth, breadth, and long follow-up, making it fit-for-purpose for early decision-making in drug development, access, and evaluation. By aligning regulatory-grade data with HTA and payer expectations, it provides a credible, scalable foundation to simulate real-world impact, reduce uncertainty, and inform strategic choices from target population definition to market access planning.