ArticleCPT: pharmacometrics & systems pharmacology2024
Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Residual Error Coding in NONMEM Can Mislead Diagnostic Residuals: Impact of W Definition on IWRES, WRES, and CWRESI.Pharmaceutics · 2026Article
- Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability.CPT: pharmacometrics & systems pharmacology · 2024Article
- A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration-Time Datasets: Application to Gevokizumab mPBPK Model.Pharmaceutics · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose of the method is the generation of realistic virtual patient profiles and the quantification of the extent of model misspecification, by introducing an appropriate metric, to be used as an additional diagnostic of model quality. The proposed methodology consists of the following steps: After developing a PopPK model, the individual residual errors IRES = DV-IPRED, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain IRES
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