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Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates.

ABSTRACT: This study aims to investigate the clinical and demographic factors influencing gentamicin pharmacokinetics in a large cohort of unselected premature and term newborns and to evaluate optimal regimens in this population.All gentamicin concentration data, along with clinical and demographic characteristics, were retrieved from medical charts in a Neonatal Intensive Care Unit over 5 years within the frame of a routine therapeutic drug monitoring programme. Data were described using non-linear mixed-effects regression analysis ( nonmem®).A total of 3039 gentamicin concentrations collected in 994 preterm and 455 term newborns were included in the analysis. A two compartment model best characterized gentamicin disposition. The average parameter estimates, for a median body weight of 2170?g, were clearance (CL) 0.089?l?h(-1) (CV 28%), central volume of distribution (Vc ) 0.908?l (CV 18%), intercompartmental clearance (Q) 0.157?l?h(-1) and peripheral volume of distribution (Vp ) 0.560?l. Body weight, gestational age and post-natal age positively influenced CL. Dopamine co-administration had a significant negative effect on CL, whereas the influence of indomethacin and furosemide was not significant. Both body weight and gestational age significantly influenced Vc . Model-based simulations confirmed that, compared with term neonates, preterm infants need higher doses, superior to 4?mg?kg(-1) , at extended intervals to achieve adequate concentrations.This observational study conducted in a large cohort of newborns confirms the importance of body weight and gestational age for dosage adjustment. The model will serve to set up dosing recommendations and elaborate a Bayesian tool for dosage individualization based on concentration monitoring.

SUBMITTER: Fuchs A

PROVIDER: S-EPMC4243884 | biostudies-other | 2014 Nov

REPOSITORIES: biostudies-other

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Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates.

Fuchs Aline A Guidi Monia M Giannoni Eric E Werner Dominique D Buclin Thierry T Widmer Nicolas N Csajka Chantal C

British journal of clinical pharmacology 20141101 5

<h4>Aim</h4>This study aims to investigate the clinical and demographic factors influencing gentamicin pharmacokinetics in a large cohort of unselected premature and term newborns and to evaluate optimal regimens in this population.<h4>Methods</h4>All gentamicin concentration data, along with clinical and demographic characteristics, were retrieved from medical charts in a Neonatal Intensive Care Unit over 5 years within the frame of a routine therapeutic drug monitoring programme. Data were des ...[more]

PMID: 24938850

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Project description: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.

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.

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:ObjectivesTo assess the value of single and serial fetal biometry for the prediction of small- (SGA) and large-for-gestational-age (LGA) neonates delivered preterm or at term.MethodsA cohort study of 3,971 women with singleton pregnancies was conducted from the first trimester until delivery with 3,440 pregnancies (17,334 scans) meeting the following inclusion criteria: 1) delivery of a live neonate after 33 gestational weeks and 2) two or more ultrasound examinations with fetal biometry parameters obtained at ?36 weeks. Primary outcomes were SGA (<5th centile) and LGA (>95th centile) at birth based on INTERGROWTH-21st gender-specific standards. Fetus-specific estimated fetal weight (EFW) trajectories were calculated by linear mixed-effects models using data up to a fixed gestational age (GA) cutoff (28, 32, or 36 weeks) for fetuses having two or more measurements before the GA cutoff and not already delivered. A screen test positive for single biometry was based on Z-scores of EFW at the last scan before each GA cut-off so that the false positive rate (FPR) was 10%. Similarly, a screen test positive for the longitudinal analysis was based on the projected (extrapolated) EFW at 40 weeks from all available measurements before each cutoff for each fetus.ResultsFetal abdominal and head circumference measurements, as well as birth weights in the Detroit population, matched well to the INTERGROWTH-21st standards, yet this was not the case for biparietal diameter (BPD) and femur length (FL) (up to 9% and 10% discrepancy for mean and confidence intervals, respectively), mainly due to differences in the measurement technique. Single biometry based on EFW at the last scan at ?32 weeks (GA IQR: 27.4-30.9 weeks) had a sensitivity of 50% and 53% (FPR = 10%) to detect preterm and term SGA and LGA neonates, respectively (AUC of 82% both). For the detection of LGA using data up to 32- and 36-week cutoffs, single biometry analysis had higher sensitivity than longitudinal analysis (52% vs 46% and 62% vs 52%, respectively; both p<0.05). Restricting the analysis to subjects with the last observation taken within two weeks from the cutoff, the sensitivity for detection of LGA, but not SGA, increased to 65% and 72% for single biometry at the 32- and 36-week cutoffs, respectively. SGA screening performance was higher for preterm (<37 weeks) than for term cases (73% vs 46% sensitivity; p<0.05) for single biometry at ?32 weeks.ConclusionsWhen growth abnormalities are defined based on birth weight, growth velocity (captured in the longitudinal analysis) does not provide additional information when compared to the last measurement for predicting SGA and LGA neonates, with both approaches detecting one-half of the neonates (FPR = 10%) from data collected at ?32 weeks. Unlike for SGA, LGA detection can be improved if ultrasound scans are scheduled as close as possible to the gestational-age cutoff when a decision regarding the clinical management of the patient needs to be made. Screening performance for SGA is higher for neonates that will be delivered preterm.

Dataset Information

Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates.

Publications

Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates.

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