ArticleToxicological sciences : an official journal of the Society of Toxicology2023
Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
What it found
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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
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antisense Versus Antigene in the Computer-Aided Design of Triplex-Forming Oligonucleotides (TFO): Insights from a Dual-Method Review, Combining Bibliometric and Systematic Review.International journal of molecular sciences · 2025Pooled it
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Neuroimmune pharmacotherapy across the skin-brain axis: mechanisms, therapeutic targets, and AI-enabled precision approaches.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- PK-Informed Microphysiological Systems: From Dynamic Dosing to Quantitative In Vitro-In Vivo Translation.Pharmaceutics · 2026Review
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Interactions between cytochrome P450 enzymes and traditional Chinese herbs: A comprehensive review.Journal of the Chinese Medical Association : JCMA · 2026Review
- Advancing Pediatric Dose Scaling: Strategies, Modeling Approaches, and Clinical Applications.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 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
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Age-Driven Physiologically Based Pharmacokinetic Modeling of Empagliflozin: Toward Precision Dosing in Youth.ACS pharmacology & translational science · 2026Article
- Epigenetic vulnerability in endocrine disruption: bridging mechanisms, models, and environmental inequities.Clinical epigenetics · 2026Review
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Structural Optimization of Pterostilbene, a Promising Lead Molecule, and Evaluation of Its Derivatives via ADMET Prediction and In Vitro/In Vivo Anti-Cerebral Ischemic Activity.International journal of molecular sciences · 2026Article
- Pharmacokinetics and Exploratory Exposure-Response Analysis of Chikusetsusaponin IVa in Myocardial Ischemia/Reperfusion-Injured Rats.Pharmaceuticals (Basel, Switzerland) · 2026Article
- The architecture of computational antiviralism: a multi-scale framework from molecular targeting to viral ecosystem engineering.Molecular diversity · 2026Review
- Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments.Pharmaceutics · 2026Review
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
26 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
2 authors.
Funding
Abstract
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.
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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.