ArticleCPT: pharmacometrics & systems pharmacology2022
Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The trial behind it
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Who cites it
15 citing papers in PubMed.
- Development and validation of a machine learning model for individualised meropenem dosing in CRRT patients.Scientific reports · 2026Article
- Machine Learning Modeling for Predicting Infliximab Pharmacokinetics in Pediatric and Young Adult Patients With Crohn Disease: Leveraging Ensemble Modeling With Synthetic and Real-World Data.Therapeutic drug monitoring · 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
- Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing.Antimicrobial agents and chemotherapy · 2026Article
- Methodological Techniques Used in Machine Learning to Support Individualized Drug Dosing Regimens Based on Pharmacokinetic Data: A Scoping Review.Clinical pharmacokinetics · 2025Article
- Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.The AAPS journal · 2025Article
- Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review.European journal of clinical pharmacology · 2025Review
- Optimizing CMV therapy: Population pharmacokinetics and Monte Carlo simulations for letermovir and maribavir dosage.PloS one · 2025Article
- Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.Antimicrobial agents and chemotherapy · 2024Article
- A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.Antimicrobial agents and chemotherapy · 2024Article
- EEG-based finger movement classification with intrinsic time-scale decomposition.Frontiers in human neuroscience · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- Artificial intelligence in pharmacology research and practice.Clinical and translational science · 2023Review
- Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles.CPT: pharmacometrics & systems pharmacology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Everolimus is an immunosuppressant with a small therapeutic index and large between-patient variability. The area under the concentration versus time curve (AUC) is the best marker of exposure but measuring it requires collecting many blood samples. The objective of this study was to train machine learning (ML) algorithms using pharmacokinetic (PK) profiles from kidney transplant recipients, simulated profiles, or both types, and compare their performance for everolimus AUC
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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.