ArticleJournal of pharmacokinetics and pharmacodynamics2024
Low-dimensional neural ODEs and their application in pharmacokinetics.
Article in Journal of pharmacokinetics and pharmacodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 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
- Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.CPT: pharmacometrics & systems pharmacology · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Innovative applications of artificial intelligence technology in pharmacometrics.European journal of clinical pharmacology · 2026Review
- 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
- LSTM-Based Prediction of Human PK Profiles and Parameters for Intravenous Small Molecule Drugs Using ADME and Physicochemical Properties.CPT: pharmacometrics & systems pharmacology · 2025Article
- 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
- Article
- Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.Clinical and translational science · 2025Review
- Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.CPT: pharmacometrics & systems pharmacology · 2025Article
- Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Robust parameter estimation and identifiability analysis with hybrid neural ordinary differential equations in computational biology.NPJ systems biology and applications · 2024Article
- Subcellular Drug Distribution: Exploring Organelle-Specific Characteristics for Enhanced Therapeutic Efficacy.Pharmaceutics · 2024Review
- On inductive biases for the robust and interpretable prediction of drug concentrations using deep compartment models.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Bridging pharmacology and neural networks: A deep dive into neural ordinary differential equations.CPT: pharmacometrics & systems pharmacology · 2024Review
- Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.Clinical pharmacokinetics · 2024Article
- Applying neural ordinary differential equations for analysis of hormone dynamics in Trier Social Stress Tests.Frontiers in genetics · 2024Article
- Machine Learning and Artificial Intelligence in Nanomedicine.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application. In this manuscript, we introduce the functionality of NODEs and develop specific low-dimensional NODE structures based on PK principles. We discuss two challenges of NODEs, overfitting and extrapolation to unseen data, and provide practical solutions to these problems. We illustrate concept and application of our proposed low-dimensional NODE approach with several PK modeling examples, including multi-compartmental, target-mediated drug disposition, and delayed absorption behavior. In all investigated scenarios, the NODEs were able to describe the data well and simulate data for new subjects within the observed dosing range. Finally, we briefly demonstrate how NODEs can be combined with mechanistic models. This research work enhances understanding of how NODEs can be applied in PK analyses and illustrates the potential for NODEs in the field of pharmacology and pharmacometrics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.