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Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.

ABSTRACT: Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC0- t). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC0- t values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg · h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.

SUBMITTER: Germovsek E

PROVIDER: S-EPMC4958175 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.

Germovsek Eva E Kent Alison A Metsvaht Tuuli T Lutsar Irja I Klein Nigel N Turner Mark A MA Sharland Mike M Nielsen Elisabet I EI Heath Paul T PT Standing Joseph F JF

Antimicrobial agents and chemotherapy 20160722 8

Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the conc ...[more]

PMID: 27270281

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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:IntroductionVortioxetine is an antidepressant primarily metabolized by the polymorphic enzyme cytochrome P450 (CYP) 2D6. A population pharmacokinetic (popPK) model of vortioxetine and its CYP2D6-dependent metabolite was recently published.ObjectiveThe aim of the current study was to assess the predictive performance of the popPK model using vortioxetine concentration measurements from a clinical setting. Furthermore, the study aimed to evaluate the ability of different CYP2D6 phenotype classification systems to provide accurate concentration predictions.MethodsOverall, 1388 patients receiving vortioxetine treatment were identified from a therapeutic drug monitoring (TDM) database in Oslo, Norway; 334 CYP2D6-genotyped patients with 502 serum concentrations of vortioxetine, analysed by a validated ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) method, were retrospectively included. The performance of the vortioxetine popPK model was tested on the clinical data from the TDM database.ResultsOverall, the model had a good ability to predict vortioxetine concentrations measured in clinical practice, with a slight tendency to overpredict concentrations. Using simulation-based diagnostics, 76% of the prediction-corrected TDM concentrations were within the 90% prediction interval based on 1000 simulated data sets. Prediction-based diagnostics showed the best performance for CYP2D6 poor and ultrarapid metabolizers, with a median prediction error (MDPE) of 12% and 23%, respectively, while the poorest performance was observed for normal metabolizers, with an MDPE of 66%. In the comparison of different CYP2D6 phenotype classification systems, the use of differentiated activity scores for decreased function alleles did not improve the concentration predictions. Grouping the CYP2D6 genotypes into the four conventional phenotype groups provided predictions closest to the TDM measured concentrations.ConclusionTDM data provide a unique insight into real-world clinical practice with vortioxetine. The tendency of the popPK model to overpredict vortioxetine concentrations measured in TDM may be attributed to several factors, including poor treatment compliance for some patients and, to a lesser extent, lack of information on patient characteristics and misspecified CYP2D6 alleles. To optimize personalized therapy with vortioxetine, real-world clinical data sets originating from different ethnicities need to be studied in the future.

Project description:Therapeutic hypothermia (TH) is standard treatment for neonates (≥36 weeks) with perinatal asphyxia (PA) and hypoxic-ischemic encephalopathy. TH reduces mortality and neurodevelopmental disability due to reduced metabolic rate and decreased neuronal apoptosis. Since both hypothermia and PA influence physiology, they are expected to alter pharmacokinetics (PK). Tools for personalized dosing in this setting are lacking. A neonatal hypothermia physiology-based PK (PBPK) framework would enable precision dosing in the clinic. In this literature review, the stepwise approach, benefits and challenges to develop such a PBPK framework are covered. It hereby contributes to explore the impact of non-maturational PK covariates. First, the current evidence as well as knowledge gaps on the impact of PA and TH on drug absorption, distribution, metabolism and excretion in neonates is summarized. While reduced renal drug elimination is well-documented in neonates with PA undergoing hypothermia, knowledge of the impact on drug metabolism is limited. Second, a multidisciplinary approach to develop a neonatal hypothermia PBPK framework is presented. Insights on the effect of hypothermia on hepatic drug elimination can partly be generated from in vitro (human/animal) profiling of hepatic drug metabolizing enzymes and transporters. Also, endogenous biomarkers may be evaluated as surrogate for metabolic activity. To distinguish the impact of PA versus hypothermia on drug metabolism, in vivo neonatal animal data are needed. The conventional pig is a well-established model for PA and the neonatal Göttingen minipig should be further explored for PA under hypothermia conditions, as it is the most commonly used pig strain in nonclinical drug development. Finally, a strategy is proposed for establishing and fine-tuning compound-specific PBPK models for this application. Besides improvement of clinical exposure predictions of drugs used during hypothermia, the developed PBPK models can be applied in drug development. Add-on pharmacotherapies to further improve outcome in neonates undergoing hypothermia are under investigation, all in need for dosing guidance. Furthermore, the hypothermia PBPK framework can be used to develop temperature-driven PBPK models for other populations or indications. The applicability of the proposed workflow and the challenges in the development of the PBPK framework are illustrated for midazolam as model drug.

Project description:Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16-16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053-0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.

Dataset Information

Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.

Publications

Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.

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