External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models

Hwang, Michael F.; Beechinor, Ryan J.; Wade, Kelly C.; Benjamin, Daniel K.; Smith, P. Brian; Hornik, Christoph P.; Capparelli, Edmund V.; Duara, Shahnaz; Kennedy, Kathleen A.; Cohen‐Wolkowiez, Michael; González, Daniel

doi:10.1128/aac.01352-17

Cited by 16 publications

(14 citation statements)

References 19 publications

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“…Clearance from a simulated population of 1,000 infants increased from 0.003 to 0.074 L/hour/kg with increasing PMA, with greater variability observed in older infants ( Figure ). The mean ratio was calculated of PBPK‐derived clearance to clearance derived from a previously developed PopPK model as well as to clearance reported in product labeling for infants 26–29 weeks GA . With the exception of the 26‐week PMA group, the mean ratios fell within the twofold range ( Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…Comparison of PBPK model‐derived fluconazole clearance to clearance derived from a Pop PK model and clearance reported by the product label. Circles represent the mean ratio of PBPK ‐derived clearance to Pop PK ‐derived clearance estimates from a previous study . The model parameters from the PPRU study Pop PK model were used to generate the Pop PK ‐derived individual empirical Bayesian clearance estimates.…”

Section: Resultsmentioning

confidence: 99%

“…Because there are no estimates of fluconazole clearance for infants determined through intensive sampling and noncompartmental methods, the PBPK clearance estimates were compared with estimates obtained from an external evaluation of two PopPK models previously developed for the prophylaxis and PPRU trials as well as to clearance listed in the Food and Drug Administration product label (mean [% coefficient of variation] 0.180 [35%] and 0.218 [31%] mL/minute/kg at 1.5 and 7.5 days from birth, respectively, for infants 26–29 weeks GA) . Infants in the virtual PBPK population were stratified by postmenstrual age (PMA), the sum of both GA and PNA.…”

Section: Methodsmentioning

confidence: 99%

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Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants

Gerhart

Watt

Edginton

et al. 2019

CPT Pharmacom & Syst Pharma

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Fluconazole is used to treat hematogenous Candida meningoencephalitis in preterm and term infants. To characterize plasma and central nervous system exposure, an adult fluconazole physiologically‐based pharmacokinetic (PBPK) model was scaled to infants, accounting for age dependencies in glomerular filtration and metabolism. The model was optimized using 760 plasma samples from 166 infants (median postmenstrual age (range) 28 weeks (24–50)) and 27 cerebrospinal fluid ( CSF ) samples from 22 infants ( postmenstrual age 28 weeks (24–33)). Simulations evaluated achievement of the surrogate efficacy target of area under the unbound concentration‐time curve ≥ 400 mg • hour/L over the dosing interval in plasma and CSF using dosing guidelines. Average fold error of predicted concentrations was 0.73 and 1.14 for plasma and CSF , respectively. Target attainment in plasma and CSF was reached faster after incorporating a loading dose of 25 mg/kg. PBPK modeling can be useful in exploring CNS kinetics of drugs in children.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants

Gerhart

Watt

Edginton

et al. 2019

CPT Pharmacom & Syst Pharma

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“…In a prospective multicenter study of extremely low birth weight infants over 30% of premature infants with candidiasis died prior to discharge, despite antifungal treatment. Moreover, 73% of the surviving infants experienced either neurodevelopmental impairment post infection or died at long-term follow-up (12). The mortality rate usually reported is approximately 20% but it can reach up to 40% (13).…”

Section: Treatment and Outcomesmentioning

confidence: 99%

Fluconazole Prophylaxis of Candida Infections in Preterm Neonates

Boselova

Nikolinyova

Lucanova

et al. 2019

Acta Medica Martiniana

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Infections belong to the most serious health problems in neonates. Invasive candidiases are one of the leading causes of mortality and morbidity in Neonatal intensive care units (NICUs). A more cautious approach is adequate when dealing with fungal infections in premature neonates. Sometimes it is necessary to cure an infection at the very beginning just before manifestation of clinical symptoms. Neonatal colonization due to Candida albicans or non-albicans Candidae predisposes to invasive candidiasis. Pregnancies complicated by preterm delivery should be considered for screening and treatment of maternal Candida colonization to decrease the occurrence of neonatal fungal colonization and its consequences. It is important to prevent infection to spread among patients and avoid complications. Prophylaxis in neonates must be safe and effective. Most authors prefer selective prophylaxis. Fluconazole is the drug of choice for prophylaxis in extremely low birth weight (ELBW) neonates. The prophylaxis is beneficial especially in NICUs with high rates of invasive candidiases. The authors describe benefits and trends in prophylaxis. They also summarize evidence on timing, dosing, and effect of fluconazole prophylaxis.

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“…Recently, numerous studies have evaluated the predictive capability of popPK models [30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

Ryu

Jung

Jiao

et al. 2020

Preprint

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Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

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External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models

Cited by 16 publications

References 19 publications

Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants

Physiologically‐Based Pharmacokinetic Modeling of Fluconazole Using Plasma and Cerebrospinal Fluid Samples From Preterm and Term Infants

Fluconazole Prophylaxis of Candida Infections in Preterm Neonates

External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

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