Oct 5, 2026, q-bio.QMJ/K move · Enter open · S save
Romain Ferrara, Martin Soucail, Victor Gertner, Adil Moussali+6
COMPutational pharmacology and clinical Oncology (COMPO), Inria Center at University Côte d’Azur, Cancer Research Center of Marseille, Marseille, France · Department of Nuclear Medicine, Institut Paoli-Calmettes, ENETS Center of Excellence, IPC NET Center, Marseille, France · Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France+3
Establishing ordinary differential equations (ODEs) describing population data is a fundamental part of mathematical modeling in pharmacology, crucial to developing digital twins. However, doing so from sparse, noisy data is a slow, expert-driven task. Existing automated methods either search a restricted model space or ignore population inter-individual variability. Here we introduce APOD (Agentic Population ODE Discovery), a language-model agent that iteratively reasons over biological knowledge and fit diagnostics in an open-ended search space to discover a population digital twin (PDT), i.e., a shared ODE system with between-subject variability. On synthetic pharmacokinetic and tumor-dynamics benchmarks, APOD recovered ground-truth structures in 94-100% of runs, 12-fold faster in median than an established library-based search. On real cohorts it converged to valid structures, and proposed a PDT of radioligand-therapy-induced platelet dynamics that predicts thrombocytopenia from first-cycle data and simulates alternative dosing schedules that lower the predicted risk of toxicity.