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arXiv · 2610.06227

APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins

Romain Ferrara·Martin Soucail·Victor Gertner·Adil Moussali·Joris Cocquebert·Sandrine Oziel-Taieb·Julien Nicolas·Florence Gattacceca·Mihaela van der Schaar·Sébastien Benzekry
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Abstract

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.

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Romain Ferrara, Martin Soucail, Victor Gertner, Adil Moussali, Joris Cocquebert, Sandrine Oziel-Taieb, Julien Nicolas, Florence Gattacceca, Mihaela van der Schaar, Sébastien Benzekry. 2026-10-05. APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins. https://arxiv.org/abs/2610.06227

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