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

Autor: Janssen A; Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, Amsterdam, The Netherlands., Leebeek FWG; Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands., Cnossen MH; Department of Pediatric Hematology, Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands., Mathôt RAA; Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: CPT: pharmacometrics & systems pharmacology [CPT Pharmacometrics Syst Pharmacol] 2022 Jul; Vol. 11 (7), pp. 934-945. Date of Electronic Publication: 2022 May 27.
DOI: 10.1002/psp4.12808
Abstrakt: 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.
(© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
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