ArticleFrontiers in pharmacology2025
Precision dosing of voriconazole in immunocompromised children under 2 years: integrated machine learning and population pharmacokinetic modeling.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Narrow Therapeutic Index Drugs: Regulatory Frameworks, Bioequivalence, and Model-Informed Precision Dosing.Pharmaceutics · 2026Review
- Model-informed vancomycin precision dosing by population pharmacokinetics combined with machine learning algorithms.British journal of clinical pharmacology · 2026Article
- Variance-Consistent Covariate Modeling from Posterior Summaries in Population Pharmacokinetics.Pharmaceutical research · 2026Article
- Machine learning combined with population pharmacokinetics: a hybrid model for predicting the plasma concentration of linezolid in critically ill pediatric patients.Frontiers in pharmacology · 2026Article
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
- An Interpretable PK-Informed Hybrid Model for Voriconazole Exposure Prediction: Roles of CYP2C19 Genotype and Inflammation.Drug design, development and therapy · 2026Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to develop an individualized dosing strategy for voriconazole (VRZ) in children under 2 years of age by integrating machine learning (ML) and population pharmacokinetic (PopPK) modeling. Methods: This retrospective observational study included 76 eligible pediatric patients for model development, analyzing their baseline characteristics and laboratory parameters. A population pharmacokinetic (PopPK) model using NONMEM Results: A total of 76 pediatric patients were enrolled for model development, consisting of 58 males (76.3%) and 18 females (23.7%), with a median age of 11 months and a median weight of 8.05 kg. We analyzed 110 therapeutic drug monitoring (TDM) samples of VRZ from these participants. A one-compartment model with first-order absorption and elimination described the population pharmacokinetics of VRZ. Population estimates for apparent clearance (CL/F) and volume of distribution (V/F) were 17.9 L/h/70kg (RSE, 10.8%) and 788 L/70kg (RSE, 15.4%), respectively. An XGBoost model accurately predicted voriconazole concentrations (R Conclusion: This study emphasized the importance of personalized treatment in utilizing VRZ for children under 24 months. The XGBoost model demonstrated potential in identifying an initial dose recommendation for VRZ.
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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.