ArticleFrontiers in systems biology2023
Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules.
Article in Frontiers in systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.NPJ systems biology and applications · 2026Article
- Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration-Time Profiles.CPT: pharmacometrics & systems pharmacology · 2026Article
- 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
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Modernizing Preclinical Drug Development: The Role of New Approach Methodologies.ACS pharmacology & translational science · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans.Journal of medicinal chemistry · 2025Article
- Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK-Based Approaches.Clinical and translational science · 2025Article
- Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.Scientific reports · 2025Article
- Food contaminants: mechanisms of toxicity, computational assessment, and mitigation.Frontiers in toxicology · 2025Review
- Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.Archives of toxicology · 2024Article
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.Clinical and translational science · 2024Article
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of a new molecule's exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using
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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 Socratic graph.