ReviewPharmaceutical research2024
A Combination of Machine Learning and PBPK Modeling Approach for Pharmacokinetics Prediction of Small Molecules in Humans.
Review in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Immune cell-derived membrane nanovesicles: A promethean fire for autoimmune disease therapy through immune cell mimicry.Bioactive materials · 2026Review
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Physiologically based pharmacokinetic modeling to predict human pharmacokinetics of a novel mithramycin analog for Ewing sarcoma.Cancer chemotherapy and pharmacology · 2026Article
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- New Frontiers of Drug Development Through the Use of New Approach Methodologies.The AAPS journal · 2026Review
- Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration-Time Profiles.CPT: pharmacometrics & systems pharmacology · 2026Article
- PBPK modeling predicts rilzabrutinib dose adjustments using BTK occupancy for CYP3A4 interactions and hepatic impairment.Scientific reports · 2026Article
- In vitro digestion models in food chemistry: advancements, challenges, and applications in nutrient bioaccessibility and bioavailability.Food chemistry: X · 2026Review
- Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments.Pharmaceutics · 2026Review
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- Physiologically Based Pharmacokinetic Modeling to Predict Human Pharmacokinetics of a Novel Mithramycin Analog for Ewing Sarcoma.Research square · 2026Article
- Adaptive AI framework for pharmacokinetics using GATs, transformers, and AutoML.Molecular diversity · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Review
- Article
- AI-Enabled Reduction of Animal Use in Cardiovascular Translational Medicine: Regulatory and Technological Perspectives.Life (Basel, Switzerland) · 2025Review
- Opportunities for machine learning and artificial intelligence in physiologically-based pharmacokinetic (PBPK) modeling.Advanced drug delivery reviews · 2025Review
- Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.AAPS PharmSciTech · 2025Review
- Utilizing Minimal Physiologically Based Pharmacokinetic Modeling to Bridge Bevacizumab Pharmacokinetics from Adult to Pediatric Patients.The AAPS journal · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRecently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.
methodsThis study developed a whole-body PBPK model and ML models of plasma protein fraction unbound (
resultsResults showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0
conclusionThe ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.
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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.