Bridging the Worlds of Pharmacometrics and Machine Learning

Kamilė Stankevičiūtė , Jean-Baptiste Woillard (1, 2) , Richard Peck (3) , Pierre Marquet (4) , Mihaela van der Schaar (5)

1 P&T - Pharmacologie et Transplantation
2 Service de Pharmacologie, toxicologie et pharmacovigilance [CHU Limoges]
3 University of Liverpool
4 Pharmacology & Transplantation, INSERM U1248, Université de Limoges, Limoges, France.
5 UCLA - University of California [Los Angeles]

Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.

Keywords: pharmacometrics artificial intelligence machine learning simulations model informed precision dosing; pharmacometrics; artificial intelligence; machine learning; simulations; model informed precision dosing

Domains: Pharmacology

Kamilė Stankevičiūtė, Jean-Baptiste Woillard, Richard Peck, Pierre Marquet, Mihaela van der Schaar. Bridging the Worlds of Pharmacometrics and Machine Learning. Clinical Pharmacokinetics, 2023, 62 (11), pp.1551-1565. ⟨10.1007/s40262-023-01310-x⟩. ⟨hal-04489693⟩