ReviewAdvanced drug delivery reviews2025
Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.
Review in Advanced drug delivery reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Re-engineering insulin for oral delivery: structural modifications, advanced formulation strategies, and future directions.Drug delivery · 2026Review
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Construction of a curated human pharmacokinetics database for molecular fragment analysis and machine learning applications.Drug metabolism and disposition: the biological fate of chemicals · 2026Article
- Pharmacogenomics and Epigenetic Regulation Transforming Pediatric Precision Therapeutics.Journal of personalized medicine · 2026Review
- Therapeutic peptides and proteins: Status and developments in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Monitoring of dual-drug combination therapy in pediatric epilepsy patients: a machine learning model for simultaneous VPA-LEV concentration-dose prediction.Journal of translational medicine · 2026Article
- Utility of Modeling and Simulations in Drug Development: Contrasting Japan, US and UK Descriptions of PBPK Modeling from Package Inserts.The AAPS journal · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool for quantitating and understanding the fate of drug and drug carriers in complex living systems. It is particularly valuable in situations where data are difficult to obtain due to cost, time, or ethical constraints. Recent advances in PBPK modeling have greatly improved their accuracy in modeling in vivo and clinical data, especially in special populations (e.g., pediatric and geriatric subjects), which consequently enhanced their utility in drug development. Nevertheless, current PBPK models remain limited by our ability to ascertain complex biological mechanisms and/or physiological processes, often resulting in many critical but unknown parameters or parameters with large uncertainty. Machine learning (ML) and applications of broader artificial intelligence (AI) tools that facilitate parameter estimation, model learning, database mining, and uncertainty quantification not only offer the potential to address the shortcomings of PBPK modeling, but also introduce opportunities for enabling earlier use of PBPK modeling in the drug development process. Here, we summarize ML-influenced advances in PBPK modeling and discuss our expectations of the likely avenues for future ML/AI contributions to PBPK modeling.
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.