A Real-Time Plasma Concentration Prediction Model for Voriconazole in
Elderly Patients via Machine Learning Combined with Population
Pharmacokinetics
Abstract
Background Voriconazole (VCZ) is a first-line treatment drug for
invasive fungal disease, with narrow therapeutic window and significant
inter-individual variability. VCZ is primarily metabolized by liver,
which declines with age. For elderly patients, physiological and
pathological factors lead to more pronounced fluctuations in VCZ plasma
concentrations. Thus, it is crucial to establish a model that accurately
predicts VCZ plasma concentration in elderly patients. Methods A
retrospective study was performed incorporating 32 variables, including
population pharmacokinetics (PPK) parameters derived from the PPK model.
Recursive Feature Elimination with Cross-Validation was used for feature
selection. Multiple algorithms were selected and combined into an
ensemble model, and the model was interpreted by Shapley Additive
exPlanations. Results The predictive performance of machine learning was
greatly improved after inclusion of PPK parameters. The composition of
XGBoost, RF, and CatBoost (1:1:8) with the highest R2 (0.828) was
determined as the final ensemble model. Feature selection greatly
simplified the model from 31 variables to 9 variables without
compromising its performance. The R2, mean square error, mean absolute
error, and accuracy (± 30%) of external validation were 0.633, 1.094,
2.286, and 71.05%, respectively. Conclusions Our study is the first to
include PPK parameters as new factors for machine learning modeling to
predict VCZ plasma concentrations in elderly patients. The model
underwent optimization through feature selection. Our model provides a
reference for individualized dosing of VCZ in clinical practice,
enhancing the efficacy and safety of VCZ treatment in elderly patients.
Keywords: voriconazole, elderly patients, machine learning, population
pharmacokinetics, precision medicine