A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.
Antimicrob Agents Chemother
; 68(5): e0141523, 2024 May 02.
Article
em En
| MEDLINE
| ID: mdl-38501807
ABSTRACT
Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.
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Texto completo:
1
Temas:
ECOS
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Financiamentos_gastos
Bases de dados:
MEDLINE
Assunto principal:
Método de Monte Carlo
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Teorema de Bayes
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Daptomicina
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Área Sob a Curva
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Aprendizado de Máquina
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Antibacterianos
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Antimicrob Agents Chemother
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
França