ArticleCPT: pharmacometrics & systems pharmacology2021
A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.
Article in CPT: pharmacometrics & systems pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Model-Informed Precision Dosing of Protein Kinase Inhibitors: Evaluation of Model-Averaging and Flattened Priors Methods.Clinical pharmacokinetics · 2026Trial
- Multi-model averaging improves the performance of model-guided infliximab dosing in patients with inflammatory bowel diseases.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Multisite Analysis of Vancomycin Concentration Predictions in a Bayesian Software in Adult and Pediatric Patients With and Without Acute Kidney Injury.Pharmacotherapy · 2026Article
- Evaluation of a model averaging algorithm for model-informed precision dosing in the context of parameter misspecifications.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Toward the Future: A Race-Agnostic, Bayesian Approach to Defining Glomerular Filtration for Personalized Drug Dosing.Clinical pharmacology and therapeutics · 2026Review
- Evaluation and Improvement of Specialized Vancomycin Pharmacokinetic Models for Pediatric Cardiovascular Intensive Care Unit and Pediatric Oncology Patients.Clinical pharmacokinetics · 2026Article
- Uncertainty undermines the validity of antimicrobial pharmacodynamics.Journal of pharmacokinetics and pharmacodynamics · 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
- Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.Clinical pharmacology and therapeutics · 2026Article
- Predictive Performance of Bayesian Methods to Forecast Vancomycin Concentration for Therapeutic Drug Monitoring in Critically Ill Pediatric Patients.Pharmaceutics · 2026Article
- Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Pharmacokinetics of polymyxin drugs in special populations.Frontiers in pharmacology · 2026Review
- Hybrid mechanistic-machine learning PK/PD models with digital biomarkers: from cage to clinic.Frontiers in pharmacology · 2026Review
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Machine Learning-Based Model Selection and Averaging Outperform Single-Model Approaches for a Priori Vancomycin Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Model-informed drug repurposing of proton pump inhibitors for the prevention of oxaliplatin induced peripheral neuropathy: A real-world data analysis and pharmacometrics approach.Cancer chemotherapy and pharmacology · 2025Article
- Population Pharmacokinetics Model of Thioguanine in Patients with Inflammatory Bowel Disease.Clinical pharmacokinetics · 2025Article
- Hybrid Population PK-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.bioRxiv : the preprint server for biology · 2025Article
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2 authors.
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Abstract
Model-informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowledge about the individual, in the form of drug levels or pharmacodynamic biomarkers, with prior knowledge of the drug PK in the general population. Use of "flattened priors" (FPs), in which the weight of the model priors is reduced relative to observations about the patient, has been previously proposed to estimate individual PK parameters in instances where the patient is poorly described by the PK model. However, little is known about the predictive performance of FPs and when to apply FPs in MIPD. Here, FP is evaluated in a data set of 4679 adult patients treated with vancomycin. Depending on the PK model, prediction error could be reduced by applying FPs in 42-55% of PK parameter estimations. Machine learning (ML) models could identify instances where FPs would outperform MAPs with a specificity of 81-86%, reducing overall root mean squared error (RMSE) of PK model predictions by 12-22% (0.5-1.2 mg/L) relative to MAP alone. The factors most indicative of the use of FPs were past prediction residuals and bias in past PK predictions. A more clinically practical minimal model was developed using only these two features, reducing RMSE by 5-18% (0.20-0.93 mg/L) relative to MAP. This hybrid ML/PK approach advances the precision dosing toolkit by leveraging the power of ML while maintaining the mechanistic insight and interpretability of PK models.
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