ArticleiScience2021
Neural-ODE for pharmacokinetics modeling and its advantage to alternative machine learning models in predicting new dosing regimens.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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.
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Who cites it
69 citing papers in PubMed.
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- 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
- Causal inference and digital twins: a roadmap for the future of clinical trials.NPJ digital medicine · 2026Review
- Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Innovative applications of artificial intelligence technology in pharmacometrics.European journal of clinical pharmacology · 2026Review
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026Review
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Hybrid mechanistic-machine learning PK/PD models with digital biomarkers: from cage to clinic.Frontiers in pharmacology · 2026Review
- From time-series deep learning to physics-constrained population pharmacokinetics: AI-driven prediction of drug concentrations and exposure for model-informed dosing.Frontiers in pharmacology · 2026Review
- A Numerical Comparison of Petri Net and Ordinary Differential Equation SIR Component Models.IEEE access : practical innovations, open solutions · 2026Article
- Neural Controlled Differential Equation and Its Application in Pharmacokinetics and Pharmacodynamics.CPT: pharmacometrics & systems pharmacology · 2026Article
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Redefining Parameter Estimation and Covariate Selection via Variational Autoencoders: One Run Is All You Need.CPT: pharmacometrics & systems pharmacology · 2025Article
- 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 machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
9 more citing papers are in PubMed but not listed here.
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
Forecasting pharmacokinetics (PK) for individual patients is a fundamental problem in clinical pharmacology. One key challenge is that PK models constructed using data from one dosing regimen must predict PK data for different dosing regimen(s). We propose a deep learning approach based on neural ordinary differential equations (neural-ODE) and tested its generalizability against a variety of alternative models. Specifically, we used the PK data from two different treatment regimens of trastuzumab emtansine. The models performed similarly when the training and the test sets come from the same dosing regimen. However, for predicting a new treatment regimen, the neural-ODE model showed substantially better performance. To date, neural-ODE is the most accurate PK model in predicting untested treatment regimens. This study represents the first time neural-ODE has been applied to PK modeling and the results suggest it is a widely applicable algorithm with the potential to impact future studies.
Indexed as
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What Socratic 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 Socratic graph.