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Predictive performance of a gentamicin population pharmacokinetic model in neonates receiving full-body hypothermia.

ABSTRACT: BACKGROUND:Population pharmacokinetic (popPK) models derived from small pharmacokinetics (PK) studies in neonates are often underpowered to detect clinically important characteristics that drive dosing. External validation of such models is crucial. In this study, the predictive performance of a gentamicin popPK model in neonates receiving hypothermia was evaluated. METHODS:A previously published gentamicin popPK model was developed in neonates with hypoxic ischemic encephalopathy undergoing hypothermia using a retrospective single-institution (University of California-San Francisco) data set. The predictive performance of this model was evaluated in an external retrospective data set from the University of California-San Francisco (validation A) and another from Duke University (validation B). Both institutions used the same hypothermia protocol and collected similar clinical and PK data. Gentamicin dosing and samples were collected per routine care. Predictive performance was evaluated by quantifying the accuracy and precision of model predictions and using simulation-based diagnostics to detect bias in predictions. RESULTS:Forty-one neonates (n = 18 validation A; n = 23 validation B) with median (range) gestational age of 40 weeks (33-42) and birth weight of 3.3 kg (1.9-4.6) and 76 samples (55% troughs, 33% and 28% drawn at 24 and 36 hours after dose, respectively) were analyzed. The model adequately predicted gentamicin concentrations from the same institution (validation A; median average fold error = 1.1 and numerical prediction distribution error P > 0.05) but underpredicted concentrations from the outside institution (validation B; median average fold error = 0.6 and numerical prediction distribution error P < 0.05). CONCLUSIONS:The model demonstrated adequate predictive performance for an external data set in the same institution but not from an outside institution. Larger sample sizes, use of data from multiple institutions, and external evaluation in development of popPK models in neonates may improve generalizability of dosing recommendations arising from single-institution studies.

SUBMITTER: Sampson MR

PROVIDER: S-EPMC4166612 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Project description:IntroductionThe pharmacokinetics of vancomycin are highly variable among neonates, which makes dosing challenging in this population. However, adequate drug exposure is critical, especially when treating methicillin-resistant Staphylococcus aureus (MRSA) infections. Utilization of population pharmacokinetic models and Bayesian methods offers the potential for developing individualized therapeutic approaches. To meet this need, a neonatal vancomycin population pharmacokinetic model was recently published. The current study sought to externally evaluate the predictive performance and generalizability of this model.MethodsA retrospective chart review of neonates who received vancomycin and had ≥1 peak and ≥1 trough concentrations at five Intermountain Healthcare neonatal intensive care units from 2006 to 2013 was performed and served as the external validation cohort. The published population pharmacokinetic model was implemented in NONMEM 7.2 with the structural and variance parameter values set equal to the estimates reported previously. The model was then used to predict the first peak and trough concentration for each neonate in the validation cohort and the model prediction error and absolute prediction error were calculated. Normalized prediction distribution errors (NPDE) were also evaluated.ResultsA total of 243 neonates were studied with a median postmenstrual age of 33 (range: 23-54) weeks and a median weight of 1.6 (range: 0.4-6.8) kg. The model predicted the observed vancomycin concentrations with reasonable precision. For all vancomycin concentrations, the median prediction error was -0.8 (95% CI: -1.4 to -0.4) mg/L and the median absolute prediction error was 3.0 (95% CI: 2.7-3.5) mg/L. No trends in NPDE across weight, postmenstrual age, serum creatinine, or time after dose were observed.ConclusionAn evaluation of a recently published neonatal vancomycin population pharmacokinetic model in a large external dataset supported the predictive performance and generalizability of the model. This model may be useful in evaluating neonatal vancomycin dosing regimens and estimating the extent of drug exposure.

Project description:Background and objectiveAmikacin is preferred in treating Gram-negative infections in neonates and it has a narrow therapeutic window. The population pharmacokinetic modeling approach can aid in designing optimal dosage regimens for amikacin in neonates. In this study, we attempted to identify the suitable population pharmacokinetic model from the published reports for the study population from an Indian setting.MethodsPublished population pharmacokinetic studies for amikacin in neonates were identified. Data on structural models and typical pharmacokinetic parameters were extracted from the studies. For the clinical study, neonates who met the inclusion criteria were enrolled in the study from the NICU, Kasturba Medical College, Manipal, during Jan 2020 to March 2022. Drug concentrations were estimated, and demographic and clinical data were collected. Identified population pharmacokinetic models were used to predict the amikacin concentrations in neonates. Predicted concentrations were compared against the observed concentrations. Differences between predicted and observed concentrations were quantified using statistical measures. The population pharmacokinetic model, which was able to predict the data well, is considered a suitable model for the study population. Dosing regimens were suggested for neonates using the pharmacometric simulation approach generated by the selected model.ResultsA total of 43 plasma samples were collected from 31 neonates. Twelve population pharmacokinetic models were found for amikacin in neonates. The predictive performance of the 12 studies was performed using clinical data. A two-compartment model reported by Illamola et al. predicted the amikacin concentrations better than other models. Illamola et al. reported creatinine clearance and body weight as the significant covariates impacting the pharmacokinetic parameters of amikacin. This model was able to predict the clinical data with 29.97% and 0.686 of relative median absolute prediction error and relative root mean square error, respectively, which is the best among the published models. The Illamola et al. model was selected as the final model to perform pharmacometric simulations for the subjects with different combinations of creatinine clearance and body weight. Dosage regimens were designed to attain target therapeutic concentrations for the virtual subjects and a nomogram was developed.ConclusionsThe population pharmacokinetic model reported by the Illamola et al. model was selected as the final model to explain the clinical data with the lowest relative median absolute prediction error and relative root mean square error when compared with other models. An amikacin nomogram was developed for the neonates whose creatinine clearance and body weight ranged between 10 and 90 mL/min and between 2 and 4 kg, respectively. A developed nomogram can assist clinicians to design an optimal dosage regimen of amikacin for term neonates.

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Predictive performance of a gentamicin population pharmacokinetic model in neonates receiving full-body hypothermia.

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