The investigators retrospectively collected real-world clinical data from 3,002 patients treated with intravenous voriconazole between May 2018 and January 2025 at Quanzhou First Hospital. After applying predefined eligibility criteria, the model-development analysis included 96 patients and 240 trough concentration measurements. The cohort was predominantly male, with 74 of 96 patients male, and had a median age of 64 years, weight of 55 kg and creatinine clearance of 85.16 mL/min.

Patients initially received weight-based intravenous dosing, consisting of a 6 mg/kg loading dose every 12 hours followed by 4 mg/kg every 12 hours for maintenance. Trough samples were collected after steady state was reached, generally before the fifth dose. The researchers used nonlinear mixed-effects modeling with NONMEM to develop the population model. Because only trough concentrations were available, a two-compartment model produced substantial fitting errors, so the investigators selected a one-compartment model with linear elimination. An exponential model represented between-subject variability, while a combined additive and proportional model represented residual variability.

The final model estimated typical voriconazole clearance at 4.2 L/h and volume of distribution at 75.9 L. Stepwise covariate modeling identified creatinine clearance and C-reactive protein as significant predictors of clearance and concomitant omeprazole use as a significant predictor of volume of distribution. At a constant creatinine clearance, the model indicated that a twofold rise in C-reactive protein was associated with approximately 10% lower clearance. Body weight and several conventional markers of hepatic function, including ALT, AST, alkaline phosphatase and bilirubin, did not remain significant in the final model.

Model performance was evaluated with goodness-of-fit and visual predictive checks. In bootstrap resampling, 9,644 of 10,000 datasets converged, giving a convergence rate of 96.44%; parameter estimates from the final model were close to bootstrap medians and within their 95% confidence intervals. Visual predictive checks showed that the median observations fell within the 90% prediction interval and that the interval covered most observations. These results supported internal model stability, although the authors noted that sparse sampling and the exclusive availability of trough data limited precise estimation of volume of distribution.

The researchers then used Monte Carlo simulation to derive a trough-based dose-adjustment protocol. The simulation generated a virtual population of 4,000 subjects, with covariate distributions resampled from the study cohort, and evaluated possible maintenance-dose changes in 5-percentage-point increments, rounding the selected changes to achievable 50 mg increments. The modeled target was a steady-state trough concentration of 1.0–5.5 mg/L after up to three adjustments. For concentrations below 0.5 mg/L, the protocol proposed a 95% maintenance-dose increase; for concentrations of 0.5 to less than 1.0 mg/L, it proposed a 65% increase.

For concentrations above the target range, the simulation proposed reducing the maintenance dose by 35% when the trough was above 5.5 to 7.5 mg/L, by 55% when it was above 7.5 to 10 mg/L, and by 75% when it exceeded 10 mg/L. The simulated probability of reaching the target range after up to three adjustments was 89.2% for troughs below 0.5 mg/L, 90.3% for troughs from 0.5 to less than 1.0 mg/L, 97.9% for troughs above 5.5 to 7.5 mg/L, 98.9% for troughs above 7.5 to 10 mg/L and 99.0% for troughs above 10 mg/L. These were model-based predictions rather than observed outcomes in every dosing category.

Clinical validation included 67 hospitalized patients who received intravenous voriconazole and underwent therapeutic drug monitoring between January 2025 and January 2026. Clinical pharmacists calculated model-informed adjustments, physicians implemented the revised regimens and concentrations were measured again at steady state. Under the standard prescribing-information regimen, 31 of 67 patients, or 46.3%, reached the target range. After up to three model-guided adjustments, 61 patients, or 91.04%, had steady-state plasma concentrations within the range. Six patients remained outside the target: three were transferred to other hospitals because their underlying illnesses worsened, preventing further monitoring, and three remained below the range despite adjustment and were switched to alternative antimicrobial agents.

The findings are limited by the retrospective single-center design, a predominantly older population with limited demographic and clinical diversity, sparse trough-only sampling, and inclusion limited to intravenous dosing. Child-Pugh scores could not be incorporated because routine coagulation monitoring was unavailable, and CYP2C19 genotyping was not performed, preventing quantification of that genetic contribution. The validation was also conducted within the same hospital, and the safety profile of the model-guided regimen was not evaluated. Accordingly, the observed increase in target-range attainment supports the regimen within this retrospective setting but does not establish broader effectiveness or safety across other populations and clinical settings.

Subject of Research: Population pharmacokinetic modeling and clinical validation of trough-based intravenous voriconazole dose adjustment

Article Title: Population pharmacokinetic modeling of voriconazole based on real-world data for dosing regimen optimization

Article References: Jiang, L., & Lin, Z. (2026). Population pharmacokinetic modeling of voriconazole based on real-world data for dosing regimen optimization. Frontiers in Pharmacology, 17, Article 1979373. https://doi.org/10.3389/fphar.2026.1979373

Image Credits: AI Generated

DOI: 10.3389/fphar.2026.1979373

Keywords: voriconazole, population pharmacokinetics, therapeutic drug monitoring, dose adjustment, Monte Carlo simulation, intravenous dosing, real-world data, clinical validation, Population, pharmacokinetic, modeling, based