Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling

Alexander Janssen*, Frank W.G. Leebeek, Marjon H. Cnossen, Ron A.A. Mathôt,

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real-world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more accurately predicts a priori drug concentrations compared to a previous NLME model. In addition, we show that our model correctly describes the changing drug concentration over time. By adopting pharmacokinetic principles, the DCM allows for simulation of different treatment strategies and enables therapeutic drug monitoring.

Original languageEnglish
Pages (from-to)934-945
Number of pages12
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume11
Issue number7
DOIs
Publication statusPublished - Jul 2022

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