ArticleCPT: pharmacometrics & systems pharmacology2022
Introduction of an artificial neural network-based method for concentration-time predictions.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.CPT: pharmacometrics & systems pharmacology · 2026Article
- The Empirical Bayes Variational Autoencoder-A Neural ODE Approach for Population Modeling in Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration-Time Profiles.CPT: pharmacometrics & systems pharmacology · 2026Article
- Machine Learning Models Reveal New Risk Factors for Sub-/Supra-Therapeutic Concentrations of Sirolimus in Children with Vascular Anomalies.Drug design, development and therapy · 2026Article
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- QSP-Copilot: An AI-Augmented Platform for Accelerating Quantitative Systems Pharmacology Model Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Evaluation of the Accuracy and Reliability of Responses Generated by Artificial Intelligence Related to Clinical Pharmacology.Journal of clinical medicine · 2025Article
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?Drug metabolism and disposition: the biological fate of chemicals · 2025Review
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Learning pharmacometric covariate model structures with symbolic regression networks.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Spatial-temporal simulation for hospital infection spread and outbreaks of Clostridioides difficile.Scientific reports · 2023Article
- Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol.CPT: pharmacometrics & systems pharmacology · 2022Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Introduction of an artificial neural network-based method for concentration-time predictions.CPT: pharmacometrics & systems pharmacology · 2022Article
- Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry.Frontiers in pharmacology · 2022Article
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Authors and funding
4 authors.
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Abstract
Pharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well-defined equations and estimate physiologically interpretable parameters, have not changed substantially during the past decades. Although the methods have proven their usefulness, they are often resource intensive and require a high level of expertise. We investigated whether a method based on artificial neural networks (ANNs) may provide an alternative approach for the prediction of concentration-time curve to supplement the gold standard methods. In this work, we used simulated data to overcome the requirement for a large clinical training data set, implemented a pharmacologically reasonable network architecture to improve extrapolation to different dosing schemes, and used transfer learning to quickly adapt the predictions to new patient groups. We demonstrate that ANNs are able to learn the shape of concentration-time curves and make individual predictions based on a short sequence of PK measurements. Furthermore, an ANN trained on simulated data was applied to real clinical data and was demonstrated to extrapolate to different dosing schemes. We also adapted the ANN trained on simulated healthy subjects to simulated hepatic impaired patients through transfer learning. In summary, we demonstrate how ANNs could be leveraged in a PK workflow to efficiently make individual concentration-time predictions, and we discuss the current limitations and advantages of such an ANN-based method.
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