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

Objectives 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. Methods 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. Results 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%. Conclusion The hybrid model proposed significantly improved isavuconazole AUC estimation over MAP-BE based on the sole C24h and may improve dose adjustment.

Mots clés

fungal infections isavuconazole machine learning population pharmacokinetics iohexol Bayesian estimator glomerular filtration rate Machine Learning

Domaines

Pharmacologie

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https://unilim.hal.science/hal-04414322

Soumis le : lundi 29 janvier 2024-13:32:24

Dernière modification le : jeudi 14 novembre 2024-16:30:02

Dates et versions

hal-04414322 , version 1 (29-01-2024)

Identifiants

HAL Id : hal-04414322 , version 1
DOI : 10.1007/s11095-023-03507-y

Citer

Alexandre Destere, Pierre Marquet, Marc Labriffe, Milou-Daniel Drici, Jean-Baptiste Woillard. A Hybrid Algorithm Combining Population Pharmacokinetic and Machine Learning for Isavuconazole Exposure Prediction. Pharmaceutical Research, 2023, 40 (4), pp.951-959. ⟨10.1007/s11095-023-03507-y⟩. ⟨hal-04414322⟩

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