Pediatric Pharmacology

Population Pharmacokinetics of Phenytoin in Critically Ill Children

The Journal of Clinical Pharmacology XX(XX) 1–10 © 2014, The American College of Clinical Pharmacology DOI: 10.1002/jcph.417

Stefanie Hennig, PhD1, Ross Norris, PhD1,2,3, Quyen Tu, BPharm4, Karin van Breda, BSc2, Kate Riney, PhD5,6, Kelly Foster, MN7, Bruce Lister, MBBS, FANZCA, FCICM, MBA8,9, and Bruce Charles, DSc, PhD1

Abstract The objective was to study the population pharmacokinetics of bound and unbound phenytoin in critically ill children, including influences on the protein binding profile. A population pharmacokinetic approach was used to analyze paired protein-unbound and total phenytoin plasma concentrations (n ¼ 146 each) from 32 critically ill children (0.08–17 years of age) who were admitted to a pediatric hospital, primarily intensive care unit. The pharmacokinetics of unbound and bound phenytoin and the influence of possible influential covariates were modeled and evaluated using visual predictive checks and bootstrapping. The pharmacokinetics of protein-unbound phenytoin was described satisfactorily by a 1-compartment model with first-order absorption in conjunction with a linear partition coefficient parameter to describe the binding of phenytoin to albumin. The partitioning coefficient describing protein binding and distribution to bound phenytoin was estimated to be 8.22. Nonlinear elimination of unbound phenytoin was not supported in this patient group. Weight, allometrically scaled for clearance and volume of distribution for the unbound and bound compartments, and albumin concentration significantly influenced the partition coefficient for protein binding of phenytoin. The population model can be applied to estimate the fraction of unbound phenytoin in critically ill children given an individual’s albumin concentration.

Keywords phenytoin, protein binding, population pharmacokinetics, pediatrics, critical care

Phenytoin (diphenylhydantoin; PHY) was first synthesized more than a century ago and has been used worldwide as an anticonvulsant for about 60 years following its approval by the US Food and Drug Administration (FDA) in 1953. It is used as a first-line agent in the treatment and prophylaxis of seizures arising from brain injury or other conditions for critically ill children, in whom only a few pharmacokinetic (PK) studies have been reported.1–3 There are ongoing issues with respect to the determination of pediatric PHY dosage regimens that are efficacious but which produce minimal adverse effects.4 Furthermore, at therapeutic plasma concentrations, the PK of PHY may be nonlinear and

display marked variability due to capacity-limited hepatic metabolism resulting in unpredictable plasma concentrations which are disproportionate to the dose.5,6 The nonlinearity has been linked to study design, liver pathophysiology, the age of the patient, and the dose and route of administration.7 Collectively, these factors are justification for the therapeutic drug monitoring (TDM) of PHY in which the targeted concentration range for total plasma concentration is 10–20 mg/L just before the next dose.8 To rapidly achieve a target concentrations in critically ill patients often require a loading dose. In healthy subjects about nine-tenths of the PHY in plasma is bound to serum proteins, mostly albumin, but it

1

8

School of Pharmacy, Pharmacy Australia Centre of Excellence (PACE), The University of Queensland, Brisbane, Queensland, Australia 2 Australian Centre for Paediatric Pharmacokinetics, Mater Pathology Services and Mater Research Institute, Brisbane, Queensland, Australia 3 School of Pharmacy, Griffith University, Gold Coast, Queensland, Australia 4 Mater Pharmacy Services, Mater Health Services, Brisbane, Queensland, Australia 5 Neurosciences Unit, Mater Children’s Hospital, Brisbane, Queensland, Australia 6 Mater Medical Research Institute, University of Queensland, Brisbane, Queensland, Australia 7 Acute Care Stream, West Moreton Hospital and Health Service, Ipswich, Queensland, Australia

Paediatric Intensive Care Unit, Mater Children’s Hospital, Brisbane, Queensland, Australia 9 Medical School, Griffith University, Gold Coast, Queensland, Australia Submitted for publication 23 June 2014; accepted 16 October 2014. Corresponding Author: Dr Stefanie Hennig, PhD, School of Pharmacy, The University of Queensland, 20 Cornwall Street, Woolloongabba QLD 4102, Australia Email: [email protected] Present address for Dr Norris: Sydpath, Department of Clinical Pharmacology and Toxicology, St Vincent’s Hospital, Darlinghurst, NSW, Australia.

2 is the unbound drug that is pharmacologically active at the effect site(s) and correlates best with efficacy and toxicity.9 Based on an unbound fraction of 0.1, the therapeutic range for unbound PHY has been scaled back proportionally to 1–2 mg/L; however, the unbound fraction can vary markedly,4,10,11 and may be difficult to predict in some clinical circumstances.9,12 Consequently, targeting the total concentration is valid only if the unbound fraction remains constant;13 thus the routine measurement of total plasma PHY concentrations is justified only if the inter-patient variability in protein binding is small. As PHY has a low capacity for extraction by the liver, the relationship between the total drug concentration and the unbound concentration of PHY can change markedly if factors such as drug interaction or significantly altered pathophysiology impacts upon protein binding and, therefore, the PK.14 An example of the latter is hypoalbuminemia, which commonly occurs in critically ill children, and which can act as a marker for morbidity and mortality in these patients.15 Attempts to “correct” the total PHY concentration for albumin concentration, in order to estimate theoretical “unbound” concentrations using a nomogram approach (eg, Sheiner– Tozer equation16), are often unreliable in directing PHY therapy in critically ill children with hypoalbuminemia.12 The use of unbound PHY concentration data has been advocated previously for TDM,13 PK, and pharmacodynamics (PD) studies.9–11,14,17 However, for reasons of cost, logistics, and others there has been very little published data on the PK of unbound PHY in critically ill children despite the fact that physical protein separation techniques such as ultrafiltration provide reliable unbound concentrations which are not biased by varying protein concentration or centrifugation time.18 One paper described the PK of unbound PHY using a nonlinear model of elimination,6 whereas another used a model which resembled ours in some respects but focused primarily on drug-drug interactions.19 The primary aim of this study was to investigate the pharmacokinetics of PHY in critically ill children using a population PK (mixed-effects) modeling approach, with the specific focus on the disposition of the protein-unbound drug. Other aims were as follows: to determine the protein binding profile of PHY using a compartmental partition coefficient; to estimate the absolute oral bioavailability of PHY; and to identify and quantify factors which explained the PK variability of PHY in these patients.

Methods Patients and Data Collection Ethical approval was obtained to study children who were admitted to the pediatric critical care unit of the Mater Children’s Hospital (Brisbane, Queensland, Australia) between November 2006 and October 2009. Patients who

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were prescribed PHY as part of their treatment and whose guardian signed an informed consent form were included in the study. Failure in obtaining consent as well as no PHY measurements taken as part of routine clinical care resulted in exclusion from the study. Patients were receiving PHY (Dilantin1 [Pfizer] oral formulation was a 30 mg/5 mL pediatric suspension, IV formulation: 100 mg/2 mL ampoules) for convulsions arising from a variety of causes, including serious head injury. IV fosphenytoin was not used in this study, as it is not registered in Australia. As this study was observational and did not pre-specify dose or blood sampling times, the majority of samples were drawn at or near the trough as per normal TDM practice for PHY, or occasionally for other routine clinical investigation. Plasma PHY concentrations, dose, time of the dose, route of administration and infusion duration (where applicable), sampling date and time, weight, age, sex, serum biochemistry, and reason(s) for critical care admission were recorded. Analytical Method Samples were assayed in the Australian Centre for Paediatric Pharmacokinetics laboratories using a validated reverse-phase high performance liquid chromatography (HPLC) with UV detection method. To obtain plasma ultrafiltrate, plasma (150 mL) was centrifuged at 1200g for 30 minutes using Amicon Centrifree YM-30 centrifugal filter units (Merck Millipore, Billerica, Massachusetts) with a 30,000 MW pore cut-off. Samples, devices, and the centrifuge were equilibrated to room temperature (24  2 °C) prior to ultracentrifugation in accord with ultracentrifugation protocols reported previously for PHY binding studies.11,12,18–20 Samples were assayed for PHY content using the following method: To a teflon-lined screw-cap pyrex glass tube (100 mm  13 mm) was added either an unknown sample, standard or control (50 mL), 1 M HCl (25 mL), and extraction solvent comprising 5% (v/v) isopropyl alcohol in chloroform (500 mL) containing 2 mg/L of internal standard (5-(4-methylphenyl)5-phenylhydantoin). The contents were vortex-mixed for 60 seconds at ambient temperature followed by brief centrifugation (300g) to give sufficient phase separation. The organic layer was carefully decanted into a glass tube, evaporated to dryness at 60 °C under a gentle stream of nitrogen gas, and the residue was reconstituted in mobile phase (100 mL) prior to injection into the HPLC. The instrumentation comprised a model LC-20 HPLC system with a diode array detector set to 210 nm (Shimadzu Australia, Sydney, NSW, Australia). The mobile phase of ammonium phosphate buffer (15 mM, pH 7.2):methanol: acetonitrile (58:10:32) was pumped at 0.2 mL/min through a Prevail C18 column (150  2.1 mm i.d., 3 mm particles; Alltech Associates, Deerfield, Illinois). The run time was 12.5 minutes. Plasma samples, collected in EDTA tubes, were assayed against standards prepared in

Hennig et al

blank EDTA plasma for total PHY; ultrafiltrates were assayed against standards prepared in isotonic pH 7.4 disodium hydrogen orthophosphate buffer (0.1 M phosphate containing 0.3% w/v NaCl). Preliminary comparison of standard curves prepared identically in ultrafiltrates from blank plasma and isotonic buffer yielded the following data: slopes were 0.0478 in ultrafiltrates and 0.0482 in buffer, and intercepts were 0.0031 in ultrafiltrates and 0.0007 in buffer, with an r2 of 0.998 for ultrafiltrates and 0.996 for buffer. The assay was validated and showed linearity for total PHY from 0.05 to 30 mg/L (r2 ¼ 0.999; Y ¼ 0.0472X þ 0.0007), and for unbound PHY from 0.05 to 3.0 mg/L (r2 ¼ 0.999; Y ¼ 0.0507X þ 0.0000786). The selectivity was demonstrated by the absence of interfering peaks equivalent to 0.005 mg/L PHY or greater in blank matrices for both unbound and total assays. Accuracy for total PHY was better than 104% at 0.10 mg/L and better than 105% at 20 mg/L, and for unbound PHY accuracy was better than 103% at 0.05 mg/L and better than 99% at 3.0 mg/L. Imprecision, measured as coefficient of variation (%), was 15% at 0.1 mg/L and 3% at 20 mg/L for total PHY, and 9% at 0.05 mg/L and 4% at 3.0 mg/L for unbound PHY. The lower and upper limits of quantification were 0.05 mg/L and 30 mg/L, respectively. Samples containing more than 30 mg/L were diluted in blank matrix and re-assayed (ie, total PHY samples were diluted in blank plasma and unbound PHY samples were diluted in buffer as used to prepare the standards). Pharmacokinetic Modeling The population modeling was conducted using NONMEM1 version 7.2,21 Intel FORTRAN compiler, and PsN1

3 version 3.5.1.22 Structural model parameter estimates, interindividual variability (IIV), and residual unexplained variability (RUV) were obtained by first-order conditional estimation with interaction (FOCE þ I). Structural base models were initially explored followed by covariate model development. In developing the structural model, it was assumed that the binding of PHY to albumin was capacity-unlimited due to the large albumin concentrations which act as a “sink.” Therefore, the distribution of PHY between protein and plasma water (protein-unbound drug) was described by invoking a linear partition coefficient parameter which was adapted from an approach used previously to describe the maternal-foetal distribution of metformin23 (see Figure 1). In the model, drug enters compartment 2 by first-order absorption from the gut (compartment 1), or directly by intravenous infusion. Protein-unbound PHY (Cu) in compartment 2 distributes to body tissues having an apparent volume of V2, and to compartment 3 with volume V3 (approximated by plasma albumin volume), and which represents PHY that is protein-bound (Cb) and in equilibrium with compartment 2. The ratio of Cb to Cu is represented by a partition coefficient parameter (Pub). Cb was calculated by subtraction of the assayed Cu in plasma ultrafiltrate from the corresponding assayed total plasma PHY concentrations. Cu is irreversibly cleared from compartment 2 mainly by liver metabolism. As there were intravenous data from some patients, the absolute oral bioavailability (F) could be estimated. F was restrained to a range of feasible values (0–1) using a logit function. The PK model was developed simultaneously for intravenous and oral data. Both 1- and 2-compartment models for partitioning

Figure 1. Pharmacokinetic model for protein-unbound (compartment 2) and protein-bound (compartment 3) phenytoin. The unbound:bound disposition model is analogous to a linear 2-compartment model after oral administration to the gut and intravenous drug administration via infusion to the central compartment. The measured concentration of unbound phenytoin (Cu) at any given time is equal to A2/V2, where A2 is the amount of unbound drug and V2 is the volume of distribution in (2). Unbound phenytoin is cleared irreversibly from (2) by a first-order process (CL ¼ K  V2). Firstorder equilibration between (2) and (3) is described by Q ¼ V3  keq, where Q is the intercompartmental clearance, V3 is the estimated volume of (3), and keq is the equilibrium rate constant. The concentration of protein-bound phenytoin (Cb) at any given time is given by A3/V3  PUB, where PUB is the binding partition coefficient between the unbound (2) and the bound (3) compartment, and A3 is the amount of bound phenytoin.

4 of PHY from compartment 2 to compartment 3 were tested in describing the Cu data. Following an exploratory sensitivity analysis, the half-life of equilibration (Teq) of PHY between plasma water and albumin binding sites was fixed to 0.4 seconds during model development. This model assumes very rapid bi-directional movement of drug between the central compartment (ie, proteinunbound PHY in plasma water) and the binding compartment (ie, albumin-bound PHY). The inter-individual variability (IIV) was modeled exponentially and interoccasion variability (IOV) was modeled by an additional random effects parameters24, as described below: Pij ¼ Ppop  eðhi;p þkj;p Þ where Pjj represents the estimate of a parameter P for subject i on occasion j about the typical population value (Ppop). Parameter hi,P is a random variable distributed with a mean value of 0 and variance of v2P which represents the IIV of P in the population. Parameter k is a random variable, was assumed to be sampled from a normal distribution of mean value 0 and a variance of p2, representing the variability of P on different occasions. An occasion was defined as a dose, or sequential doses, followed by at least one observation. The residual unexplained variability (RUV) was estimated using proportional, additive, and combined error models; separately for Cb and Cu data using the second data item option (L2) in NONMEM which takes into account correlated observations. All available covariates were screened initially for potential significant covariateparameter relationships via graphical inspection of individual empirical Bayesian pharmacokinetic parameter estimates (of the derived base model, including the allometric weight relation) versus covariates. Potential identified covariates were added to the structural model and were considered to have improved the fit of the model to the data if it was biologically plausible and there was a decrease in the objective function value (OFV) generated by NONMEM. For nested models, the difference between a pair of OFV values when a covariate was included (full model), then excluded (reduced model), approximates the Chi-square (X2) statistic which can be tested for significance (X21, 0.05 ¼ 3.84). Weight was used as the initial covariate on all clearance and volume parameters allometrically scaled.25 Covariates representing continuous data items were screened separately using linear, power, and exponential functions in which the parametrization was centered on the population median value. For model exploration and diagnostics goodness-of-fit (GOF) plots and prediction-corrected visual predictive checks (VPC) were used. The bootstrap 95% confidence intervals around the final population model parameters were obtained using an automated nonparametric bootstrap with sample replacement (n ¼ 1,500 runs).

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Results Patients and Data Collection The data from 32 patients (21 male) comprised 292 PHY concentrations, made up of respective pairs of proteinunbound (n ¼ 146) and protein-bound (n ¼ 146) concentrations. Study patient’s demographics are presented in Table 1. Patients were on average 6.9 years old, with 11 patients being under 2 years old, 11 patients between 2 and 11 years old, and 10 patients >11 years old. Patients had received PHY for prophylactic or therapeutic treatment intravenously (19.5% of all study doses) or orally (80.5% of all study doses); 8 children received PHY by both routes at different times. Oral PHY was administered mainly via a nasogastric tube and occasionally via a transpyloric tube. The use of the oral administration route did not indicate that patients were not still critically ill, but that these patients did not receive an acute bowel surgery. The indications for PHY were either prevention or control of seizures (including status epilepticus) with the seizures themselves being due to a range of underlying etiologies, including acute brain injury (eg, hypoxic ischemic encephalopathy, traumatic and infective brain injury, brain injury from a motor vehicle accident, brain tumor, or hemorrhage eg, cerebral aneurysm), or etiologies that had onset at a time remote to the intensive care unit admission (eg, longstanding brain structural abnormality as seen in cerebral palsy). The mean (SD) total PHY concentration was 7.56  6.62 mg/L, and the protein-unbound concentration was 0.88  0.79 mg/L. The mean protein-unbound fraction was 0.12  0.03. The total and unbound PHY concentrations were highly correlated (r2 ¼ 0.931). For paired PHY determinations, the distribution (% of total data) of the protein-unbound fraction was 0.12 (35%). The mean serum albumin concentration was 35.0  9.33 g/L. The serum albumin concentration changed during the study observation period in 70% of patients, with an average (range) absolute change of 26% (2%–54%).

Table 1. Characteristics of Study Patients Characteristic a

Male/female Phenytoin IV dose (mg/kg) Phenytoin PO dose (mg/kg) Samples (PO/IV)a Occasionsa Protein-unbound concentration (mg/L) Total concentration (mg/L) Age (yr) Weight (kg) Albumin (g/L) a

Number.

Mean  SD

Range

21/11 5.4  5.7 3.3  1.8 146 (115/31) 3.5 0.88  0.79 7.66  6.6 6.9  5.9 27.9  21.2 35.0  9.3

– 1.7–22.0 0.7–10.0 – 1–8 (0.009–4.88) (0.036–44.4) 0.08–17.1 4.0–80.0 11.0–48.0

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Population PK Modeling The time course of PHY concentration in plasma after zero-order intravenous infusion or first-order oral dosing was best described by a 1-compartment model in conjunction with a partition coefficient parameter (PUB) describing the equilibrium distribution of PHY from plasma water to albumin (Figure 1, Table 2). For proteinunbound PHY, the typical population clearance (CL) was 14.0 L/h/70 kg, the volume of distribution (V) was 447 L/ 70 kg; F was 63%. A nonlinear (Michaelis-Menten) model was not supported. The typical population PUB value was 8.23, from which a typical fu of 0.108 was calculated as fu ¼ 1  (1 þ 8.23). The volume of distribution (V3) of the drug bound to albumin was scaled from the typical plasma volume of 2.8 L in a 70 kg healthy adult where WT is the current body weight of the subject, and agrees with a median plasma volume of 40 mL/kg estimated in young children.26 Initial attempts to estimate the absorption rate constant (ka) of PHY could not be supported in this study because there was very little data in the first few hours after dosing. Therefore, ka was fixed to 0.167 hours 1 which was the typical value reported previously in a pediatric PHY study in this hospital.1 A sensitivity analysis conducted using ka values between 0.1 and 0.3, at increments of 0.025, showed that a slightly lower but

statistically insignificant change in the OFV occurred when the ka was fixed to 0.225; therefore this value was used in all subsequent modeling. The IIV values estimated for CL, V2, PUB, ka, and F were high (47%–180%), even after covariate inclusion, which most likely reflected the heterogeneous patient cohort. Parameter estimation was unstable when the IIV on F was included; therefore this was not pursued in subsequent modeling. Nonetheless, it was evident that a considerable amount of the IOV on CL was due to the IOV on F; the addition of the IIV on F resulted in a decrease in the OFV of 23, whereas a reduction in the IIV on CL was noted indicating that variability seen in CL and V were most likely due to variability in F. The IOV was modeled separately on CL, V2, and F, but was supported only for CL in which the OFV was reduced by 46 points, the IIV was reduced from 64.5% to 59.9%, and the RUV reduced from 37.5% to 24.6%. A proportional error model was found to best describe the RUV which was 13.6% for unbound PHY and 10.3% for bound PHY, with an additional proportional error contribution of 17.4% for all observations in the final model. After assessing all biologically plausible parametercovariate relationships graphically, the following covariates were tested in the model: albumin, weight, lean

Table 2. Parameter Estimates for Unbound Phenytoin from the Final Population Model and the Bootstrap Internal Validation Procedure Bootstrap Results (n ¼ 1,500) Parameter OFV Fixed effects parameters Clearance CL (L/h/70 kg) Volume of distribution V2 for PHYu (L/70 kg) Partition coefficient unbound-bound (PUB) Volume of distribution V3 for PHYb (L/70 kg)a Percent change in PUB with every 1 g/L albumin deviation from 35 g/L (PUB_COV) (%) Absorption rate constant ka (/h)b Bioavailability F1 (%) Random effects parameters (CV%) IIVCL IIVV2 IIVPUB IIVka IIVFc IOVCL Residual unexplained variability Proportional (%) Assay error for PHYu (%) Assay error for PHYb (%) a

Final Model

Median

58.3

68.3

274.0

87.7

14.0 447.0 8.23 2.8 0.73

13.5 444.5 8.23

8.9 269.3 7.81

18.2 647.6 8.66

0.72

0.14

0.225 63

62

95% Confidence Intervals (2.5%–97.5%)

31

1.5

100

47.7 84.7 9.9 180.8 87.7 60.0

45.5 85.7 8.6 175.8 86.0 59.7

1.1 55.2 0.7 7.2 30.8 41.4

69.6 144.5 14.3 270.8 163.3 74.3

17.4 13.6 10.3

17.6 13.1 10.7

10.5 9.6 6.0

21.8 16.0 15.7

Estimated in earlier models; however fixed in the final model. Fixed after sensitivity pffiffiffiffiffiffiffiffiffiffianalysis. ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c IIVF ðCV%Þ  TVF  ð1 TVFÞ  vF  100. OFV ¼ objective function value, equal to minus twice the maximum logarithm of the likelihood of the data, PUB_COV is representing the change of the typical PUB (TVPUB) with every 1 g/L that serum albumin (ALB) is different from the median serum albumin of 35 g/L, TVPUB ¼ PUB*(1þ PUB_COV *(ALB-35)), PHYu ¼ unbound phenytoin; PHYb ¼ bound phenytoin. b

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body weight, age, serum creatinine concentration, and renal function estimated using the Schwartz formula. In the final model only WT on CL and V2 together with ALB on PUB were included as statistically significant covariates in the model. In the final model CL, V2, and V3 were scaled allometrically to body weight, reducing the IIV on CL and V2 to 11% and 50.6%, respectively. The final covariate model for individual clearance (CLi) and volume of distribution (V2i) of unbound PHY and volume of distribution for bound PHY (V3i) was defined as CLi ¼ CL  ðWT=70Þ0:75  eðhi;CL þkj;CL Þ V2i ¼ V2  ðWT=70Þ  eðhi;V2 Þ V3i ¼ 2:8  ðWT=70Þ where CL and V2 represent the population average values for clearance and volume of distribution of unbound PHY and hi;CL ; kj;CL ; hi;CL the IIV and IOV for CL and IIV for V2, respectively, which are listed in Table 2. Plasma albumin concentrations were related to PUB using linear and power functions, with the linear model producing the largest change ( 8.5) in the OFV and therefore retained. In the final model, for an individual having a serum albumin concentration of 35 g/L, the typical value of PUB changed by 0.73% with a 1 g/L deviation from a reference level 35 g/L according to the equations: PUB ð%Þ ¼ h8:23  ð1 þ 0:00737  ½ALB

35ŠÞi  100

and; f u ¼ 1  h1 þ PUBi Thus, a patient with ALB concentration greater than 35 g/L will show a relatively increased PHY binding and consequently decreased protein-unbound PHY, and viceversa for ALB concentrations less than 35 g/L. The addition of ALB as a covariate in the model reduced the IIV on PUB from 9.7% to 9.0%. The relationship between the protein-unbound fraction (%) and albumin concentration as described by the final model is displayed graphically in Figure 2. The GOF plots for the final model were satisfactory and did not show trends indicating major misspecification of the model (Figure S1 in Supplemental Digital Content —Appendix). The VPCs for both the unbound and bound PHY concentrations showed that the median line summarizing the observed data was overlayed with the median simulated prediction line, with most of the data being symmetrically distributed about the median (Figure 3). Slight over-predictions at the 5th and the 95th percentile were noted at sampling times soon after dosing probably due to the very sparse data at these times. The median values for all estimated parameters from the bootstrap analysis were very similar to the estimates from

Figure 2. The relationship between the protein-unbound fraction (%) and albumin concentration as described by the final model (solid line), and that observed in individual patients (circles).

the final model (Table 2) and were within their respective 95th percentile ranges. The confidence intervals around the IIV parameters from the bootstrap estimations related to CL, ka, and F were large, which was probably due to an insufficient amount of data to confidently estimate these parameters. Predicted total PHY concentrations (ie, predicted protein-unbound plus predicted protein-bound concentrations) were highly correlated with measured total PHY observations (Figure 4), which further established a satisfactory predictive performance. Collectively, these diagnostics confirmed the suitability of the final model to describe the PK of the unbound and the bound PHY in this population. The code in the NMTRAN control file for the final model is shown in Supplemental Digital Content—Appendix.

Discussion A population modeling approach for the analysis of the data was applied here for 3 main reasons; first, due to ethical, logistical, and procedural limitations, we had no input into the limited numbers of blood samples per patient collected opportunistically for therapeutic drug monitoring purposes; second, a population PK analysis can handle the unstructured and unbalanced dosage and blood sampling patterns which typically occurs with these patients as their clinical condition changes, often rapidly and unpredictably; third, a specific goal and indeed a major advantage of a population PK analysis is to identify and quantify sources of variability in PK response (eg, drug clearance) which can influence dosage selection and contribute to individualized prescribing of drugs such as

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Figure 3. Visual predictive check for protein-unbound (left panel) and protein-bound (right panel) phenytoin for the final model. The measured unbound and the calculated bound phenytoin concentrations are represented as gray dots. The upper, middle, and lower are representing the 95th, 50th, and 5th percentile of the observations (solid lines), respectively, and the upper, middle, and lower are representing the 95th, 50th, and 5th percentile of the simulated data (dashed line), respectively, from the final model. The gray shaded areas are the 90%CI for the simulated data’s percentiles for each bin.

PHY which have a narrow therapeutic window. We were thus able to achieve the main goal of the study within these restrictions, ie, to investigate the disposition of proteinunbound PHY in critically ill children.

Figure 4. Observed total phenytoin concentration (mg/L) vs. modelpredicted total phenytoin concentration (mg/L) estimated using the final model. The line of unity for perfect agreement is represented by the dashed line; the linear regression line of best fit of the data is represented by the solid line (r2 ¼ 0.9639).

A 1-compartment linear model clearly provided the best fit to the data, despite the fact that the disposition of PHY has often been described by Michaelis-Menten kinetics due to capacity-limited hepatic metabolism in which the systemic clearance changes with plasma drug concentration.5,6,27,28 This difference could be due to several factors; first, in the present study blood was drawn in the early stages of PHY treatment; therefore the plasma concentrations may not have accumulated subsequently to higher and potentially saturable steady-state levels; second, our patients were young (1 month to 17 years), where liver function is expected to be good notwithstanding any other pathophysiological influences; third, low and often sub-therapeutic PHY concentrations have been reported in adults29 and in children20 with acute brain injury or specifically traumatic brain injury, perhaps due to increased clearance via alteration in protein binding or stress-induced stimulation of hepatic metabolic capacity. In support, at least 1 previous study found that a linear 1compartment model best described PHY kinetics in neonates and infants with seizures.1 Our approach differed fundamentally from others in that the model contained 2 different “compartments” representing protein-bound drug and protein-unbound drug, with drug transfer (“partitioning”) between these compartments, with only the unbound drug being cleared from the body. Presently, there was on average 8.23 times more PHY bound to plasma protein than existed in the

8 unbound form, and that the percent of the unbound to total PHT concentration was approximately 11%, which agreed with a previous report at comparable albumin concentrations in critically ill children.12 Importantly, our results confirmed that alterations in albumin concentration change the relationship between the total drug concentration and the unbound concentration of PHY. The model presented here showed a linear relationship between the partition coefficient and albumin concentration, which translates to a nonlinear relationship between fu and albumin concentration (since, fu ¼ 1/(1 þ PUB)). Compared to our results, others reported greater increases in fu in hypoalbuminemia.12,30 We suspect that a power model may provide a better prediction of fu in severe hypoalbuminemia, but unfortunately our data set contained only a few patients having serum albumin concentrations less than 25 g/L. Therefore, until more data from patients having very low albumin concentrations are collected, the final reported (linear) model should be restricted to the range of albumin concentrations presently used. Additionally, the influence of the drug-drug interactions with other antiepileptic drugs and phenytoin was not feasible due to the relatively modest number of patients; however this was addressed previously by Joerger et al.19 Consequently, measuring unbound PHY concentrations directly is preferable and clinically more valuable than using an equation to predict unbound observations. A recent case study showed how total PHY concentrations are unreliable and do not correlate to unbound concentration measured in patients undergoing hemodialysis.31 Indeed the use of such nomograms depends on the accuracy and precision of the population pharmacokinetic parameters used to construct them, and on the assumption that the study population and the subjects in whom the nomogram was developed are similar. For example, the SheinerTozer equation can only reliably be used in patients without clinically significant renal impairment, or in patients who do not receive other drugs that are extensively albumin-bound. It was shown previously that the prediction of unbound PHY concentration using the Sheiner-Tozer equation was biased and it significantly underestimated the observed unbound PHY concentrations.32,33 We also evaluated the predictive performance, according to Sheiner and Beal34 of both the Sheiner-Tozer equation and the present model to predict an individual’s unbound phenytoin concentration in the data set. The relative prediction error and the root mean squared error was 0.3% and 0.15 mg/L for our model, and 21.0% and 0.32 mg/L when using the Sheiner-Tozer equation, respectively. However, this comparison is limited in that the same data used to evaluate the predictive performance were also used to develop the model; this should be repeated in future studies using an external data set. Consequently, in laboratories which do not report unbound PHY concentration data for TDM purposes, fu

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can readily be estimated from the population model we developed with the important proviso that the target population is similar to that from the present study subjects. Additionally, the present model can be incorporated into available software used for Bayesian forecasting (eg, TCIWorks; http://www.tciworks.info). Despite many studies on the PK of PHY, there is surprisingly little data on the oral absorption in patients, and this significant gap in knowledge is being recognized as outlined in a recent published commentary.35 In particular, nothing has been reported on oral bioavailability of PHY in critically ill children, and as some of our patients had received intravenous and oral PHY we had the opportunity to investigate the oral bioavailability, especially since critically ill patients can have impaired gastrointestinal function36 including compromised gastric emptying and motility.37 Several of our patients had acute brain injury from a variety of causes including severe traumatic head injury which can adversely affect gastric emptying.38,39 The population bioavailability was lower and more variable than reported in healthy adult volunteers,40 and in Japanese hospital outpatients,27 but was similar to that in young infants.1 Although there are several methods for the assessment of gastric emptying in critically ill patients,41 no such data for correlating gastric emptying with oral bioavailability in individual patients were available. In conclusion, a population PK model for PHY in children under critical care has been successfully developed and validated. The fraction of PHY which is unbound to serum protein increases in a nonlinear profile as albumin concentration decreases, and vice versa. This relationship can be used to estimate unbound PHY concentrations in critically ill children when the unbound drug cannot be measured during therapeutic drug monitoring, but it requires validation in prospective clinical studies. The absolute bioavailability of PHY is less than in several other patient groups and should be taken into account when switching between administrations; the reason for the reduced availability of orally administered PHY in critically ill children warrants further inquiry. Acknowledgments The authors thank the pharmacists from Mater Pharmacy Services, Brisbane, Australia who contributed to the data collection and also the study patients and their parents/guardians. The contributions by Dr Geoffrey Wallace (Mater Children’s Hospital), Dr Michael Barras (Royal Brisbane and Women’s Hospital), Ms Rani George, and Ms Hana Yassien Alraman (Australian Centre for Paediatric Pharmacokinetics) are acknowledged. Dr Hennig had full access to all of the study data; she contributed to the acquisition of the data, and takes full responsibility for all aspects of the pharmacometric data analysis and for compilation of the final manuscript. Dr Norris

Hennig et al and Ms van Breda undertook assay development, validation, and analyzed the samples obtained from the patients. Dr Norris and Dr Charles contributed to the conceptualization and planning of the study, data acquisition analysis of samples, data analysis and interpretation, and the drafting and critique of the manuscript for important intellectual content. Authors Tu, Foster, Riney, and Lister contributed to patient recruitment, data acquisition, and critical revision of the manuscript.

Declaration of Conflicting Interests The authors declare no conflicts of interest with respect to any aspect of this study.

Funding The NONMEM software licence was supported by the Australian Centre for Pharmacometrics. The study was partly funded by the Mater Children’s Hospital Golden Casket Research Fund.

References 01. Al Za’abi M, Lanner A, Xiaonian X, Donovan T, Charles B. Application of routine monitoring data for determination of the population pharmacokinetics and enteral bioavailability of phenytoin in neonates and infants with seizures. Ther Drug Monit. 2006; 28:793–799. 02. Battino D, Estienne M, Avanzini G. Clinical pharmacokinetics of antiepileptic drugs in paediatric patients. Part II. Phenytoin, carbamazepine, sulthiame, lamotrigine, vigabatrin, oxcarbazepine and felbamate. Clin Pharmacokinet. 1995;29:341–369. 03. Bauer LA, Blouin RA. Phenytoin Michaelis-Menten pharmacokinetics in Caucasian paediatric patients. Clin Pharmacokinet. 1983;8: 545–549. 04. Cheng A, Banwell B, Levin S, Seabrook JA, Freeman D, Rieder M. Oral dosing requirements for phenytoin in the first three months of life. J Popul Ther Clin Pharmacol. 2010;17:e256–261. 05. Grasela TH, Sheiner LB, Rambeck B, et al. Steady-state pharmacokinetics of phenytoin from routinely collected patient data. Clin Pharmacokinet. 1983;8:355–364. 06. Deleu D, Aarons L, Ahmed IA. Estimation of population pharmacokinetic parameters of free-phenytoin in adult epileptic patients. Arch Med Res. 2005;36:49–53. 07. Patsalos PN, Berry DJ, Bourgeois BF, et al. Antiepileptic drugs–best practice guidelines for therapeutic drug monitoring: a position paper by the subcommission on therapeutic drug monitoring, ILAE Commission on Therapeutic Strategies. Epilepsia. 2008;49:1239–1276. 08. Rall T, Scheifer L. Drugs effective in the therapy of the epilepsies. In: Gilman AG, Rall TW, Nies AS, Taylor P, eds. Godman & Gilman’s The Pharmacological Basis of Therapeutics. New York: Macmillan Publishing Company; 1990:454–456. 09. Burt M, Anderson DC, Kloss J, Apple FS. Evidence-based implementation of free phenytoin therapeutic drug monitoring. Clin Chem. 2000;46:1132–1135. 10. Peterson GM, McLean S, Aldous S, Von WRJ, Millingen KS. Plasma protein binding of phenytoin in 100 epileptic patients. Br J Clin Pharmacol. 1982;14:298–300. 11. Kilpatrick CJ, Wanwimolruk S, Wing LM. Plasma concentrations of unbound phenytoin in the management of epilepsy. Br J Clin Pharmacol. 1984;17:539–546. 12. Wolf GK, McClain CD, Zurakowski D, Dodson B, McManus ML. Total phenytoin concentrations do not accurately predict free phenytoin concentrations in critically ill children. Pediatr Crit Care Med. 2006;7:434–439; quiz 440.

9 13. Benet LZ, Hoener BA. Changes in plasma protein binding have little clinical relevance. Clin Pharmacol Ther. 2002;71:115–121. 14. Calvo R, Lukas JC, Rodriguez M, Leal N, Suarez E. The role of unbound drug in pharmacokinetics/pharmacodynamics and in therapy. Curr Pharm Des. 2006;12:977–987. 15. Horowitz IN, Tai K. Hypoalbuminemia in critically ill children. Arch Pediatr Adolesc Med. 2007;161:1048–1052. 16. Martin E, Tozer TN, Sheiner LB, Riegelman S. The clinical pharmacokinetics of phenytoin. J Pharmacokinet Biopharm. 1977;5: 579–596. 17. Zielmann S, Mielck F, Kahl R, et al. A rational basis for the measurement of free phenytoin concentration in critically ill trauma patients. Ther Drug Monit. 1994;16:139–144. 18. McMillin GA, Juenke J, Dasgupta A. Effect of ultrafiltrate volume on determination of free phenytoin concentration. Ther Drug Monit. 2005;27:630–633. 19. Joerger M, Huitema AD, Boogerd W, van dSJJ, Schellens JH, Beijnen JH. Interactions of serum albumin, valproic acid and carbamazepine with the pharmacokinetics of phenytoin in cancer patients. Basic Clin Pharmacol Toxicol. 2006;99:133–140. 20. Stowe CD, Lee KR, Storgion SA, Phelps SJ. Altered phenytoin pharmacokinetics in children with severe, acute traumatic brain injury. J Clin Pharmacol. 2000;40:1452–1461. 21. NONMEM User’s Guides. (1989-2009). Version 7. Ellicott City, MD: Icon Development Solutions; 2009. 22. Lindbom L, Pihlgren P, Jonsson EN. PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed. 2005;79:241–257. 23. Charles B, Norris R, Xiao X, Hague W. Population pharmacokinetics of metformin in late pregnancy. Ther Drug Monit. 2006; 28:67–72. 24. Karlsson MO, Sheiner LB. The importance of modeling interoccasion variability in population pharmacokinetic analyses. J Pharmacokinet Biopharm. 1993;21:735–750. 25. Holford NH. A size standard for pharmacokinetics. Clin Pharmacokinet. 1996;30:329–332. 26. Anthony MY, Goodall SR, Papouli M, Levene MI. Measurement of plasma volume in neonates. Arch Dis Child. 1992;67:36–40. 27. Yukawa E, Higuchi S, Aoyama T. Population pharmacokinetics of phenytoin from routine clinical data in Japan. J Clin Pharm Ther. 1989;14:71–77. 28. Miller R, Rheeders M, Klein C, Suchet I. Population pharmacokinetics of phenytoin in South African black patients. S Afr Med J. 1987;72:188–190. 29. Boucher BA, Rodman JH, Jaresko GS, Rasmussen SN, Watridge CB, Fabian TC. Phenytoin pharmacokinetics in critically ill trauma patients. Clin Pharmacol Ther. 1988;44:675–683. 30. Heine RT, van Maarseveen EM, van der Westerlaken MM, et al. The quantitative effect of serum albumin, serum urea, and valproic acid on unbound phenytoin concentrations in children. J Child Neurol. 2013;29:803–810. 31. Bezzaoucha S, Merghoub A, Lamarche C, et al. Hemodialysis effects on phenytoin pharmacokinetics. Eur J Clin Pharmacol. 2014;70:499–500. 32. Beck DE, Farringer JA, Ravis WR, Robinson CA. Accuracy of three methods for predicting concentrations of free phenytoin. Clin Pharm. 1987;6:888–894. 33. Bolt J, Gorman SK. Precision, bias, and clinical utility of the Sheiner–Tozer equation to guide phenytoin dosing in critically ill adults. J Clin Pharmacol. 2013;53:451–455. 34. Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm. 1981;9:503–512. 35. Bergen DC. Pharmacokinetics of phenytoin: reminders and discoveries. Epilepsy Curr. 2009;9:102–104.

10 36. Baue AE. The role of the gut in the development of multiple organ dysfunction in cardiothoracic patients. Ann Thorac Surg. 1993;55:822–829. 37. Zaloga GP. Bedside method for placing small bowel feeding tubes in critically ill patients. A prospective study. Chest. 1991;100:1643– 1646. 38. Frost EA. The physiopathology of respiration in neurosurgical patients. J Neurosurg. 1979;50:699–714. 39. Power I, Easton JC, Todd JG, Nimmo WS. Gastric emptying after head injury. Anaesthesia. 1989;44:563–566.

The Journal of Clinical Pharmacology / Vol XX No XX (2014) 40. Gugler R, Manion CV, Azarnoff DL. Phenytoin: pharmacokinetics and bioavailability. Clin Pharmacol Ther. 1976;19:135–142. 41. Moreira TV, McQuiggan M. Methods for the assessment of gastric emptying in critically ill, enterally fed adults. Nutr Clin Pract. 2009;24:261–273.

Supporting Information Additional supporting information may be found in the online version of this article at the publisher’s web-site.

Population pharmacokinetics of phenytoin in critically ill children.

The objective was to study the population pharmacokinetics of bound and unbound phenytoin in critically ill children, including influences on the prot...
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