Voriconazole (VRCZ) exhibits highly variable pharmacokinetics. Although CYP2C19 poor metabolizers are common in Japan, routine genetic testing is rarely performed in daily clinical practice. Consequently, existing population pharmacokinetic (Pop-PK) models that lack genetic data require external validation in Japanese clinical settings. This study aimed to identify the most accurate nongenetic Pop-PK model for this population and elucidate the factors influencing prediction errors. We retrospectively analyzed 174 adult inpatients who received VRCZ within the first 14 d of therapy. Eight Pop-PK models were evaluated using prediction error metrics and specific clinical criteria. For the best-performing model, normalized prediction distribution error (NPDE) analysis was conducted. Subsequently, a classification and regression tree (CART) analysis with leave-one-out cross-validation (LOOCV) was performed to identify factors associated with prediction accuracy. Model H, incorporating the albumin-bilirubin (ALBI) score, performed best (relative root mean square error 2.97, criteria 33.91%, median prediction error (MDPE) -29.06%). However, NPDE analysis (mean 0.774, variance 2.394) revealed residual systematic underprediction and substantial unmodeled variability. CART analysis identified C-reactive protein (CRP) as the primary determinant of accuracy. While the best model (age, platelet count, serum creatinine, and CRP) achieved a training accuracy of 69.5%, internal validation via LOOCV yielded an accuracy of 56.9% and a multi-class area under the receiver operating characteristic curve of 0.568. While the ALBI-score-based Model H is the most accurate, predictions remain limited in high-inflammation states due to inherent stochastic variability. Stratifying patients by baseline CRP level is crucial for optimizing the initial VRCZ dosing.
Uemura et al. (Wed,) studied this question.