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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Cite this article
APA 7
Ferrara, R., Soucail, M., Gertner, V., Moussali, A., Cocquebert, J., Oziel-Taieb, S., Nicolas, J., Gattacceca, F., Schaar, M. V. D., & Benzekry, S. (2026). APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins. https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins
MLA 9
Ferrara, Romain, et al. "APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins." https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins.
Chicago (author–date)
Ferrara, Romain, Martin Soucail, Victor Gertner, Adil Moussali, Joris Cocquebert, Sandrine Oziel-Taieb, Julien Nicolas, Florence Gattacceca, Mihaela van der Schaar, and Sébastien Benzekry. 2026. "APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins." https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins.
Harvard
Ferrara, R., Soucail, M., Gertner, V., Moussali, A., Cocquebert, J., Oziel-Taieb, S., Nicolas, J., Gattacceca, F., Schaar, M. V. D. and Benzekry, S. (2026) 'APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins', Available at: https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins.
Vancouver
Ferrara R, Soucail M, Gertner V, Moussali A, Cocquebert J, Oziel-Taieb S, et al. APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins. https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins
IEEE
R. Ferrara, M. Soucail, V. Gertner, A. Moussali, J. Cocquebert, S. Oziel-Taieb, J. Nicolas, F. Gattacceca, M. V. D. Schaar, and S. Benzekry, "APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins," https://omanscience.com/en/articles/apod-reasoning-guided-agentic-population-ordinary-differential-equation-discovery-for-pharmacological-digital-twins.