Model-based clinical dose optimization for phenobarbital in neonates: An illustration of the importance of data sharing and external validation

Völler, Swantje; Flint, Robert B.; Stolk, L. M. L.; Degraeuwe, Pieter; Simons, Sinno; Pokorna, Paula; Burger, David M.; Groot, Ronald de; Tibboel, Dick; Knibbe, Catherijne A. J.

doi:10.1016/j.ejps.2017.05.026

Cited by 25 publications

(65 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In general, phenobarbital is used for generalized and partial convulsive seizures and has widespread use in the neonatal population . Data for phenobarbital have been published in the neonatal population, but, despite the long history and large amount of use in the pediatric population, the pharmacokinetics of phenobarbital have not been quantified across the pediatric age spectrum . Neonatal patients can have significant changes in organ function secondary to growth and maturation, which can affect the pharmacokinetic profile of medications .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Data for phenobarbital have been published in the neonatal population, but, despite the long history and large amount of use in the pediatric population, the pharmacokinetics of phenobarbital have not been quantified across the pediatric age spectrum. [3][4][5][6] Neonatal patients can have significant changes in organ function secondary to growth and maturation, which can affect the pharmacokinetic profile of medications. 7,8 In addition, medical interventions, such as wholebody hypothermia in neonates with hypoxic ischemic encephalopathy, may also alter the disposition of medications in pediatric patients.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phenobarbital population pharmacokinetics across the pediatric age spectrum

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phenobarbital population pharmacokinetics across the pediatric age spectrum

et al. 2018

View full text Add to dashboard Cite

show abstract

“…A dataset from a previously published population pharmacokinetic analysis of phenobarbital in term and preterm neonates was used [18]. This dataset contained phenobarbital plasma concentrations collected during therapeutic drug monitoring from 53 neonates up to 80 h after the last phenobarbital dose.…”

Section: Case Studymentioning

confidence: 99%

“…This dataset contained phenobarbital plasma concentrations collected during therapeutic drug monitoring from 53 neonates up to 80 h after the last phenobarbital dose. The weight of these neonates ranged from 0.45 to 4.5 kg and had a median value of 2.7 kg [18].…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

The Influence of Normalization Weight in Population Pharmacokinetic Covariate Models

et al. 2018

Self Cite

View full text Add to dashboard Cite

In covariate (sub)models of population pharmacokinetic models, most covariates are normalized to the median value; however, for body weight, normalization to 70 kg or 1 kg is often applied. In this article, we illustrate the impact of normalization weight on the precision of population clearance (CL pop ) parameter estimates. The influence of normalization weight (70, 1 kg or median weight) on the precision of the CL pop estimate, expressed as relative standard error (RSE), was illustrated using data from a pharmacokinetic study in neonates with a median weight of 2.7 kg. In addition, a simulation study was performed to show the impact of normalization to 70 kg in pharmacokinetic studies with paediatric or obese patients. The RSE of the CL pop parameter estimate in the neonatal dataset was lowest with normalization to median weight (8.1%), compared with normalization to 1 kg (10.5%) or 70 kg (48.8%). Typical clearance (CL) predictions were independent of the normalization weight used. Simulations showed that the increase in RSE of the CL pop estimate with 70 kg normalization was highest in studies with a narrow weight range and a geometric mean weight away from 70 kg. When, instead of normalizing with median weight, a weight outside the observed range is used, the RSE of the CL pop estimate will be inflated, and should therefore not be used for model selection. Instead, established mathematical principles can be used to calculate the RSE of the typical CL (CL TV ) at a relevant weight to evaluate the precision of CL predictions. Key PointsNormalization to a weight outside the observed weight range (e.g. 70 kg normalization in a paediatric study) can increase the uncertainty of parameter estimates in pharmacokinetic covariate models.The predictive performance of pharmacokinetic models and their covariate submodels is unaffected by weight normalization.When normalizing outside the observed covariate range, the RSEs of the corresponding population estimates should generally not be used for model evaluation. The RSE of the typical parameter at a relevant covariate value can be used instead.Sebastiaan Goulooze and Swantje Völler contributed equally to this work.

show abstract

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

Reeve,

On,

Havla

et al. 2023

Cochrane Database of Systematic Reviews

View full text Add to dashboard Cite

Model-based clinical dose optimization for phenobarbital in neonates: An illustration of the importance of data sharing and external validation

Abstract: Data-sharing can help to successfully develop and validate population pharmacokinetic models in neonates. From the results it seems that both PNA and bodyweight are required to guide dosing of phenobarbital in term and preterm neonates.

Cited by 25 publications

References 34 publications

Phenobarbital population pharmacokinetics across the pediatric age spectrum

Phenobarbital population pharmacokinetics across the pediatric age spectrum

The Influence of Normalization Weight in Population Pharmacokinetic Covariate Models

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

Contact Info

Product

Resources

About