ArticleScientific reports2023
Adapting physiologically-based pharmacokinetic models for machine learning applications.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Navigating the complexity of oral peptide delivery: challenges and strategies to enhance oral bioavailability.Frontiers in drug delivery · 2026Review
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- The Future of Medicine: AI and ML Driven Drug Discovery Advancements.Current topics in medicinal chemistry · 2025Review
- Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?Drug metabolism and disposition: the biological fate of chemicals · 2025Review
- A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.Pharmaceutical research · 2024Review
- Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics.Toxics · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.
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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.