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Bridging the Worlds of Pharmacometrics and Machine Learning

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Abstract

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.

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Acknowledgments

We thank Zhaozhi Qian, Alexander Terenin, Maciej Wiatrak, and Haoting Zhang for their helpful reviews and suggestions. KS thanks AstraZeneca for research funding.

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Author notes

  1. Kamilė Stankevičiūtė and Jean-Baptiste Woillard contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK

    Kamilė Stankevičiūtė

  2. INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France

    Jean-Baptiste Woillard & Pierre Marquet

  3. Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France

    Jean-Baptiste Woillard & Pierre Marquet

  4. Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK

    Richard W. Peck

  5. Pharma Research and Development, Roche Innovation Center, Basel, Switzerland

    Richard W. Peck

  6. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

    Mihaela van der Schaar

  7. The Alan Turing Institute, London, UK

    Mihaela van der Schaar

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  1. Kamilė Stankevičiūtė
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Correspondence to Jean-Baptiste Woillard.

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Conflicts of Interest/Competing Interests

Jean-Baptiste Woillard, Pierre Marquet, and Mihaela van der Schaar have no conflicts of interest that are directly relevant to the content of this article. Kamilė Stankevičiūtė receives research funding from AstraZeneca. Richard W. Peck is an employee and stockholder of F. Hoffmann-La Roche.

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Authors’ Contributions

JBW, KS, and RWP conceptualized the ideas, JWB and KS conducted the research and wrote the manuscript, KS made the figures, RWP and PM reviewed and edited the manuscript, and MvdS acquired the funding and supervised the project.

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Stankevičiūtė, K., Woillard, JB., Peck, R.W. et al. Bridging the Worlds of Pharmacometrics and Machine Learning. Clin Pharmacokinet 62, 1551–1565 (2023). https://doi.org/10.1007/s40262-023-01310-x

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