Population pharmacokinetics of phenytoin in critically ill children

Population Pharmacokinetic/Pharmacodynamic Modelling of Daptomycin for Schedule Optimization in Patients with Renal Impairment

The aims of this study are (i) to develop a population pharmacokinetic/pharmacodynamic model of daptomycin in patients with normal and impaired renal function, and (ii) to establish the optimal dose recommendation of daptomycin in clinical practice. Several structural PK models including linear and non-linear binding kinetics were evaluated. Monte Carlo simulations were conducted with a fixed combination of creatinine clearance (30-90 mL/min/1.73 m2) and body weight (50-100 kg). The final dataset included 46 patients and 157 daptomycin observations. A two-compartment model with first-order peripheral distribution and elimination kinetics assuming non-linear protein-binding kinetics was selected. The bactericidal effect for Gram+ strains with MIC ≤ 0.5 mg/L could be achieved with 5-12 mg/kg daily daptomycin based on body weight and renal function. The administration of 10-17 mg/kg q48 h daptomycin allows to achieve bactericidal effect for Gram+ strains with MIC ≤ 1 mg/L. Four PK samples were selected as the optimal sampling strategy for an accurate AUC estimation. A quantitative framework has served to characterize the non-linear binding kinetics of daptomycin in patients with normal and impaired renal function. The impact of different dosing regimens on the efficacy and safety outcomes of daptomycin treatment based on the unbound exposure of daptomycin and individual patient characteristics has been evaluated.

Levetiracetam Compared to Phenobarbital as a First Line Therapy for Neonatal Seizures: An Unexpected Influence of Benzodiazepines on Seizure Response

OBJECTIVES

Neonatal seizures are common complications. Phenobarbital is the agent of choice but leads to adverse neurologic outcomes. There has been increased use of newer agents like levetiracetam. The objective of this study was determining the rate of seizure resolution in neonates treated with phenobarbital or levetiracetam.

METHODS

This was a retrospective, single-center, cohort study from June 1, 2012-June 1, 2018 evaluating seizure resolution in neonates following first-line treatment with phenobarbital versus levetiracetam. Data were collected via review of the patient's charts in the electronic medical record. The primary outcome was seizure resolution without addition of a second antiepileptic agent. Logistic regression was used to assess the impact of pertinent variables.

RESULTS

Each group included 73 patients. The mean gestational age was 36.01 and 37.91 weeks for the phenobarbital and levetiracetam groups, respectively (p = 0.011). The phenobarbital group had higher rates of intraventricular hemorrhage at baseline. The median birth weight was 2750 and 3002 grams in the phenobarbital and levetiracetam groups, respectively (p = 0.10). Forty-five neonates (61.6%) achieved seizure resolution with phenobarbital compared with 30 neonates (41.1%) with levetiracetam (p = 0.01). In neonates who did not receive a benzodiazepine, seizure resolution was similar between groups (51-52%). In neonates who received a benzodiazepine, seizure resolution rate was 94.1% (16/17 neonates) for phenobarbital and 18.2% (4/22 neonates) for levetiracetam.

CONCLUSIONS

These findings suggest seizure resolution with levetiracetam, and phenobarbital may be impacted by benzodiazepine administration. If no benzodiazepine is used, these agents demonstrated similar efficacy. Further research into the pharmacodynamic interaction with benzodiazepines is necessary.

Prediction of Unbound Ceftriaxone Concentration in Children: Simple Bioanalysis Method and Basic Mathematical Equation

The pharmacological activity of ceftriaxone depends on the unbound concentration. However, direct measurement of unbound concentrations is obstructive, and high individual variability of the unbound fraction of ceftriaxone was shown in children. We aim to evaluate and validate a method to predict unbound ceftriaxone concentrations in pediatric patients. Ninety-five pairs of concentrations (total and unbound) from 92 patients were measured by the bioanalysis method that we developed. The predictive performance of the three equations (empirical in vivo equation, disease-adapted equation, and multiple linear regression equation) was assessed by the mean absolute prediction error (MAPE), the mean prediction error (MPE), the proportions of the prediction error within ±30% (P ₃₀) and ±50% (P ₅₀), and linear regression of predicted versus actual unbound levels (R ²). The average total and unbound ceftriaxone concentrations were 126.18 ± 81.46 μg/ml and 18.82 ± 21.75 μg/ml, and the unbound fraction varied greatly from 4.75% to 39.97%. The MPE, MAPE, P ₃₀, P ₅₀, and R ² of the empirical in vivo equation, disease equation, and multiple linear equation were 0.17 versus 0.00 versus 0.06, 0.24 versus 0.15 versus 0.27, 63.2% versus 89.5% versus 74.7%, 96.8% versus 97.9% versus 86.3%, and 0.8730 versus 0.9342 versus 0.9315, respectively. The disease-adapted equation showed the best predictive performance. We have developed and validated a bioanalysis method with one-step extraction pretreatment for the determination of total ceftriaxone concentrations, and a prediction equation of the unbound concentration is recommended. The proposed method can facilitate clinical practice and research on unbound ceftriaxone in children. (This study has been registered at ClinicalTrials.gov under identifier NCT03113344.).

Comparisons of Four Protein-Binding Models Characterizing the Pharmacokinetics of Unbound Phenytoin in Adult Patients Using Non-Linear Mixed-Effects Modeling

BACKGROUND AND OBJECTIVE

Phenytoin is extensively protein bound with a narrow therapeutic range. The unbound phenytoin is pharmacologically active, but total concentrations are routinely measured in clinical practice. The relationship between free and total phenytoin has been described by various binding models with inconsistent findings. Systematic comparison of these binding models in a single experimental setting is warranted to determine the optimal binding behaviors.

METHODS

Non-linear mixed-effects modeling was conducted on retrospectively collected data (n = 37 adults receiving oral or intravenous phenytoin) using a stochastic approximation expectation-maximization algorithm in MonolixSuite-2019R2. The optimal base structural model was initially developed and utilized to compare four binding models: Winter-Tozer, linear binding, non-linear single-binding site, and non-linear multiple-binding site. Each binding model was subjected to error and covariate modeling. The final model was evaluated using relative standard errors (RSEs), goodness-of-fit plots, visual predictive check, and bootstrapping.

RESULTS

A one-compartment, first-order absorption, Michaelis-Menten elimination, and linear protein-binding model best described the population pharmacokinetics of free phenytoin at typical clinical concentrations. The non-linear single-binding-site model also adequately described phenytoin binding but generated larger RSEs. The non-linear multiple-binding-site model performed the worst, with no identified covariates. The optimal linear binding model suggested a relatively high binding capacity using a single albumin site. Covariate modeling indicated a positive relationship between albumin concentration and the binding proportionality constant.

CONCLUSIONS

The linear binding model best described the population pharmacokinetics of unbound phenytoin in adult subjects and may be used to improve the prediction of free phenytoin concentrations.

Evaluation of Two Fosphenytoin Loading Dose Regimens and Monitoring in Infants and Neonates Less Than Six Months of Age

OBJECTIVES

The objectives of the study were to compare the free serum concentrations after different fosphenytoin loading dose strategies in patients younger than 6 months old and to investigate the frequency of seizure cessation following a loading dose of fosphenytoin.

METHODS

This retrospective cohort study included neonates and infants admitted to a 150-bed children's hospital between August 1, 2014, and February 1, 2018. Patients were included if they were younger than 6 months old and had a postload free phenytoin serum concentration collected during the specified time frame. Patients were identified through a database query screening for the inclusion criteria. Patients were separated into 2 groups with the 15 mg/kg group as per protocol and the 20 mg/kg group as noted in common practice. Data collection included demographic information, fosphenytoin dose, time of administration of the fosphenytoin loading dose, time of sampling, free phenytoin serum concentration results, concomitant antiepileptic agents, albumin serum concentration, and total bilirubin serum concentration.

RESULTS

Forty-one patients were included for analysis, 12 in the 15 mg/kg group and 29 in the 20 mg/kg group. The average free phenytoin concentration after the loading dose was 2.45 ± 0.54 mg/L in the 15 mg/kg group and 2.52 ± 0.66 mg/L in the 20 mg/kg group. Seizure cessation after the fosphenytoin loading dose was achieved in 3 of 12 (25%) patients in the 15 mg/kg group and in 13 of 29 (45%) patients in the 20 mg/kg group (p = 0.305).

CONCLUSIONS

The study demonstrates that a traditional range of fosphenytoin loading dose (15-20 mg/kg) led to elevated postloading dose free phenytoin serum concentrations in the majority of patients with a seizure cessation rate of approximately 39%. The question remains as to what the optimal dose and target concentration should be in this patient population to achieve the best efficacy without risking associated toxicities.

Nonlinear protein binding of phenytoin in clinical practice: Development and validation of a mechanistic prediction model

AIMS

To individualize treatment, phenytoin doses are adjusted based on free concentrations, either measured or calculated from total concentrations. As a mechanistic protein binding model may more accurately reflect the protein binding of phenytoin than the empirical Winter-Tozer equation that is routinely used for calculation of free concentrations, we aimed to develop and validate a mechanistic phenytoin protein binding model.

METHODS

Data were extracted from routine clinical practice. A mechanistic drug protein binding model was developed using nonlinear mixed effects modelling in a development dataset. The predictive performance of the mechanistic model was then compared with the performance of the Winter-Tozer equation in 5 external datasets.

RESULTS

We found that in the clinically relevant concentration range, phenytoin protein binding is not only affected by serum albumin concentrations and presence of severe renal dysfunction, but is also concentration dependent. Furthermore, the developed mechanistic model outperformed the Winter-Tozer equation in 4 out of 5 datasets in predicting free concentrations in various populations.

CONCLUSIONS

Clinicians should be aware that the free fraction changes when phenytoin exposure changes. A mechanistic binding model may facilitate prediction of free phenytoin concentrations from total concentrations, for example for dose individualization in the clinic.

Fosphenytoin Population Pharmacokinetics in the Acutely Ill Pediatric Population

OBJECTIVE

The purpose of this study is to describe the pharmacokinetics of phenytoin in pediatric patients receiving fosphenytoin.

DESIGN

Retrospective, population pharmacokinetic analysis.

SETTING

Emergency department or PICU of a large tertiary care children's hospital.

PATIENTS

Patients less than 19 years old who received fosphenytoin in the PICU or emergency center for treatment of seizures from January 2011 to June 2017 were included.

INTERVENTIONS

Population pharmacokinetic analysis was performed with NONMEM v7.3 (Icon Plc, Dublin, Ireland). Simulation was performed to determine optimal loading dose and maintenance dosing regimens.

MEASUREMENTS AND MAIN RESULTS

A total of 536 patients (55.4% male; median age, 3.4 yr [interquartile range, 0.92-8.5 yr]) met study criteria. Fosphenytoin was administered at median 15.1 mg/kg/dose (interquartile range, 6.3-20.7 mg/kg/dose). Mean serum concentrations of 17.5 ± 7.8 mg/L were at a median 4.2 hours (interquartile range, 2.5-7.8 hr) after a dose. A pharmacokinetic model with two compartments, allometrically scaled fat-free mass on all parameters, and serum creatinine and concomitant phenobarbital use on clearance had the best fit. Simulation demonstrated that a 20 mg/kg loading dose followed by 6 mg/kg/dose every 8 hours had the greatest percentage of concentrations in the 10-20 mg/L range, with reduced doses to achieve therapeutic in patients with reduced kidney function.

CONCLUSIONS

A loading dose of 20 mg/kg followed by 6 mg/kg/dose every 8 hours based on fat-free mass is a reasonable empiric strategy for attainment and maintenance of therapeutic trough concentrations. Concomitant phenobarbital use may increase clearance of phenytoin and fosphenytoin dose reductions should occur in patients with reduced kidney function.

Impact of Obesity on Fosphenytoin Volume of Distribution in Pediatric Patients

The impact of body habitus on fosphenytoin pharmacokinetics is poorly understood in pediatric patients. This retrospective, single-center review examined differences in fosphenytoin volume of distribution (V_D) between children with normal and obese body habitus. From 2013 to 2015, patients 2 to 18 years of age who received a loading dose of fosphenytoin were identified. Thirty-seven patients met inclusion criteria. Mean total serum phenytoin concentration was 25.3 ± 6.5 μg/mL in the nonobese group and 29.5 ± 7.6 μg/mL in the obese group ( P = .09). V_D was not significantly different between obese and nonobese groups, 0.92 ± 0.26 L/kg and 0.97 ± 0.48 L/kg ( P = .76), respectively. In contrast to adult studies, these data suggest that fosphenytoin dose adjustments for obese children may be unnecessary.