ArticleCPT: pharmacometrics & systems pharmacology2021
Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr.
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 25 papers.
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Who cites it
25 citing papers in PubMed.
- Model-Informed Precision Dosing of Protein Kinase Inhibitors: Evaluation of Model-Averaging and Flattened Priors Methods.Clinical pharmacokinetics · 2026Trial
- Evaluating Vaginal Microbiome Effects on Antiretroviral Exposure in the Female Genital Tract Using Pharmacometric and Machine Learning Approaches.Clinical pharmacology and therapeutics · 2026Article
- Ceftaroline pharmacokinetics on ECMO, a pediatric case report.International journal of antimicrobial agents · 2026Article
- Population pharmacokinetics of dalbavancin: external validation, model averaging, and implications for precision dosing in prolonged therapy.Antimicrobial agents and chemotherapy · 2026Article
- Advancing Precision Dosing of 5-FU: Population PK Model Development, Limited Sampling Strategies, and Fit-for-Use Application.Clinical pharmacokinetics · 2026Article
- Clinical Model-Informed Precision Dosing Consult Service for Accelerating Personalized Medication in Pediatric Patients.Clinical pharmacology and therapeutics · 2026Review
- Raltegravir Plasma Exposure: A Machine Learning-Based Model for its Prediction Using Limited Sampling Strategy.The AAPS journal · 2026Article
- Model-informed precision dosing of carboplatin in cancer patients by leveraging myelosuppression data from electronic health records.British journal of clinical pharmacology · 2026Article
- Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing.Antimicrobial agents and chemotherapy · 2026Article
- Benefit of plasma cystatin C for carboplatin individual dosing in a patient with spinal amyotrophy.Cancer chemotherapy and pharmacology · 2025Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Numerical Verification of Tucuxi, a Promising Bayesian Adaptation Tool for Model-Informed Precision Dosing.CPT: pharmacometrics & systems pharmacology · 2025Article
- Assessing the Potential of Generative Artificial Intelligence Models to Assist Experts in the Development of Pharmacokinetic Models.Advanced pharmaceutical bulletin · 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
- Model-Informed Dose Optimization of Pazopanib in Real-World Patients with Cancer.Clinical pharmacokinetics · 2025Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.The AAPS journal · 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
- A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction.Pharmaceutical research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model-informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP-BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, "test" models with different features were coded, for example, first-order and zero-order absorption, lag time, time-varying covariates, Michaelis-Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter-individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP-BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven "real" previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose-related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP-BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.
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