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A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction.

Authors
  • Destere, Alexandre1, 2
  • Marquet, Pierre1, 3
  • Labriffe, Marc1, 3
  • Drici, Milou-Daniel2
  • Woillard, Jean-Baptiste4, 5
  • 1 Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France. , (France)
  • 2 Department of Pharmacology and Pharmacovigilance Center, Côte d'Azur University Medical Center, Nice, France. , (France)
  • 3 Department of Pharmacology Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France. , (France)
  • 4 Pharmacology and Transplantation, INSERM U1248, Université de Limoges, 2 Rue du Pr Descottes, 87000, Limoges, France. [email protected]. , (France)
  • 5 Department of Pharmacology Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France. [email protected]. , (France)
Type
Published Article
Journal
Pharmaceutical Research
Publisher
Springer-Verlag
Publication Date
Apr 01, 2023
Volume
40
Issue
4
Pages
951–959
Identifiers
DOI: 10.1007/s11095-023-03507-y
PMID: 36991227
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Maximum a posteriori Bayesian estimation (MAP-BE) based on a limited sampling strategy and a population pharmacokinetic (POPPK) model is used to estimate individual pharmacokinetic parameters. Recently, we proposed a methodology that combined population pharmacokinetic and machine learning (ML) to decrease the bias and imprecision in individual iohexol clearance prediction. The aim of this study was to confirm the previous results by developing a hybrid algorithm combining POPPK, MAP-BE and ML that accurately predicts isavuconazole clearance. A total of 1727 isavuconazole rich PK profiles were simulated using a POPPK model from the literature, and MAP-BE was used to estimate the clearance based on: (i) the full PK profiles (refCL); and (ii) C24h only (C24h-CL). Xgboost was trained to correct the error between refCL and C24h-CL in the training dataset (75%). C24h-CL as well as ML-corrected C24h-CL were evaluated in a testing dataset (25%) and then in a set of PK profiles simulated using another published POPPK model. A strong decrease in mean predictive error (MPE%), imprecision (RMSE%) and the number of profiles outside ± 20% MPE% (n-out20%) was observed with the hybrid algorithm (decreased in MPE% by 95.8% and 85.6%; RMSE% by 69.5% and 69.0%; n-out20% by 97.4% and 100% in the training and testing sets, respectively. In the external validation set, the hybrid algorithm decreased MPE% by 96%, RMSE% by 68% and n-out20% by 100%. The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment. © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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