ArticleJournal of pharmacokinetics and pharmacodynamics2022
Population pharmacokinetic model selection assisted by machine learning.
Article in Journal of pharmacokinetics and pharmacodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.CPT: pharmacometrics & systems pharmacology · 2026Article
- Population Pharmacokinetic Models at the Forefront of Precision Dosing.Pharmaceutics · 2026Article
- Machine learning estimation of FVIII pharmacokinetic parameters in Chinese children with severe Hemophilia A.NPJ systems biology and applications · 2026Article
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- A machine learning approach to population pharmacokinetic modelling automation.Communications medicine · 2025Article
- Machine Learning Prediction and Validation of Plasma Concentration-Time Profiles.Molecular pharmaceutics · 2025Article
- Effect of Cumulative Exposure on the Efficacy of Paroxetine: A Population Pharmacokinetic-Pharmacodynamic and Machine Learning Analyses.CPT: pharmacometrics & systems pharmacology · 2025Article
- ADME Properties in Drug Delivery.Pharmaceutics · 2025Article
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Leveraging Clinical Data to Enhance the Performance Evaluation of Ceftriaxone Population Pharmacokinetic Models in Children.Clinical pharmacokinetics · 2025Article
- Selecting the Best Pharmacokinetic Models for a Priori Model-Informed Precision Dosing with Model Ensembling.Clinical pharmacokinetics · 2024Article
- Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.Drugs · 2024Review
- Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.CPT: pharmacometrics & systems pharmacology · 2024Article
- Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.Clinical and translational science · 2024Article
- Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.The AAPS journal · 2024Article
- Learning pharmacometric covariate model structures with symbolic regression networks.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Machine Learning and Artificial Intelligence in PK-PD Modeling: Fad, Friend, or Foe?Clinical pharmacology and therapeutics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.
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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.