ArticleCPT: pharmacometrics & systems pharmacology2025
Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Deep-Learning: An Emerging Tool to Support Model-Informed Drug Development.Clinical and translational science · 2026Article
- The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- 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
- Deep-Learning- versus Hypothesis-Driven Modeling in Model-Informed Drug Development: A PK/PD Case Study.Journal of clinical pharmacology · 2026Article
- Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics.CPT: pharmacometrics & systems pharmacology · 2026Article
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- Leveraging Neural ODEs for Population Pharmacokinetics of Dalbavancin in Sparse Clinical Data.Entropy (Basel, Switzerland) · 2025Article
- Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.CPT: pharmacometrics & systems pharmacology · 2025Article
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5 authors.
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Abstract
Neural ordinary differential equations (NODEs) are an emerging machine learning (ML) method to model pharmacometric (PMX) data. Combining mechanism-based components to describe "known parts" and neural networks to learn "unknown parts" is a promising ML-based PMX approach. In this work, the implementation of low-dimensional NODEs in two widely applied PMX software packages (Monolix and NONMEM) is explained. Inter-individual variability is introduced to NODEs and proposals for the practical implementation of NODEs in such software are presented. The potential of such implementations is shown on various demonstrational datasets available in the Monolix model library, including pharmacokinetic (PK), pharmacodynamic (PD), target-mediated drug disposition (TMDD), and survival analyses. All datasets were fitted with NODEs in Monolix and NONMEM and showed comparable results to classical modeling approaches. Model codes for demonstrated PK, PKPD, TMDD applications are made available, allowing a reproducible and straight-forward implementation of NODEs in available PMX software packages.
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