ArticleFrontiers in pharmacology2026
Exposure prediction and dose optimization of polymyxin B based on bayesian and machine learning.
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: To explore the application scenarios of maximum Methods: Two sets of simulations based on the population pharmacokinetic (PopPK) model developed for PMB were used for the development and testing of Bayesian and XGBoost models in four scenarios. The predictive performances of MAP-BE and XGBoost for the area under the concentration-time curve (AUC) over 12-h intervals at steady state were evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ( Results: Both MAP-BE and XGBoost accurately estimate the AUC Conclusion: This study provides evidence for the application scenario of MAP-BE and XGBoost for predicting the AUC of PMB, which assists in selecting better approaches when predicting drug exposure with available therapeutic drug monitoring information to guide the adjustment of dosing regimens in the clinic.
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