External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models.

Published online

Journal Article

Fluconazole is an antifungal agent used for the treatment of invasive candidiasis, a leading cause of morbidity and mortality in premature infants. Population pharmacokinetic (PK) models of fluconazole in infants have been previously published by Wade et al. (Antimicrob Agents Chemother 52:4043-4049, 2008, https://doi.org/10.1128/AAC.00569-08) and Momper et al. (Antimicrob Agents Chemother 60:5539-5545, 2016, https://doi.org/10.1128/AAC.00963-16). Here we report the results of the first external evaluation of the predictive performance of both models. We used patient-level data from both studies to externally evaluate both PK models. The predictive performance of each model was evaluated using the model prediction error (PE), mean prediction error (MPE), mean absolute prediction error (MAPE), prediction-corrected visual predictive check (pcVPC), and normalized prediction distribution errors (NPDE). The values of the parameters of each model were reestimated using both the external and merged data sets. When evaluated with the external data set, the model proposed by Wade et al. showed lower median PE, MPE, and MAPE (0.429 μg/ml, 41.9%, and 57.6%, respectively) than the model proposed by Momper et al. (2.45 μg/ml, 188%, and 195%, respectively). The values of the majority of reestimated parameters were within 20% of their respective original parameter values for all model evaluations. Our analysis determined that though both models are robust, the model proposed by Wade et al. had greater accuracy and precision than the model proposed by Momper et al., likely because it was derived from a patient population with a wider age range. This study highlights the importance of the external evaluation of infant population PK models.

Full Text

Duke Authors

Cited Authors

  • Hwang, MF; Beechinor, RJ; Wade, KC; Benjamin, DK; Smith, PB; Hornik, CP; Capparelli, EV; Duara, S; Kennedy, KA; Cohen-Wolkowiez, M; Gonzalez, D

Published Date

  • December 2017

Published In

Volume / Issue

  • 61 / 12

PubMed ID

  • 28893774

Pubmed Central ID

  • 28893774

Electronic International Standard Serial Number (EISSN)

  • 1098-6596

Digital Object Identifier (DOI)

  • 10.1128/AAC.01352-17

Language

  • eng

Conference Location

  • United States