Estimation of Population Pharmacokinetic Parameters of Free-Phenytoin in Adult Epileptic Patients

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.

Electronic supplementary material

The online version of this article (10.1007/s40268-020-00323-2) contains supplementary material, which is available to authorized users.

Phenytoin-loaded lipid-core nanocapsules improve the technological properties and in vivo performance of fluidised bed granules

Lipid-core nanocapsules (LNCs) were recently reported by our group as a suitable binder system to produce fluidised bed granules. However, there is still a lack of knowledge about the influence of using these nanocarriers loaded with a drug on the properties of the granules and their in vivo performance. Therefore, this study was designed to produce innovative fluidised bed granules containing phenytoin-loaded LNCs (LNC_PHT) as a strategy to evaluate the influence of the presence of the drug-loaded nanocarriers on their in vitro and in vivo properties. Granules were produced using a mixture of maltodextrin and phenytoin (1:0.004 w/w) as substrate. They were prepared by fluid bed granulation using water or LNC_PHT as the liquid binder, affording good yields (73-82%) of granules with low moisture content (<5%). Granules prepared with LNC_PHT had larger mean size (122 μm) compared to maltodextrin primary particles (50 μm) due to the formation of solid bridges. Moreover, the use of LNC_PHT as the liquid binder improved their powder flow properties. The nanocarriers were recovered after aqueous dispersion (3.00 mg.mL^-1 of PHT) with a redispersibility close to 90%. After reconstitution in water, granules containing LNC_PHT showed an improved dissolution behaviour compared to those prepared without them. In addition, they showed a higher mucoadhesive effect due to a combined effect of the LNC_PHT and maltodextrin in the interactions with porcine intestinal mucosa. Regarding the in vivo studies, granules containing the combination of non-encapsulated PHT and PHT-loaded lipid-core nanocapsules increased the latency to seizures compared to placebo granules, showing effective anticonvulsant effect in mice. In conclusion, the use of drug-loaded nanocapsules as binder is an encouraging approach to produce fluidised bed mucoadhesive granules with improved technological properties and in vivo performance.

The Evolution of Population Pharmacokinetic Model of Oral Phenytoin for Early Seizure Prophylaxis Post-Craniotomy

BACKGROUND

This study aimed to re-establish a Population Pharmacokinetic (PPK) model of oral phenytoin to further optimize the individualized medication regimen based on our previous research.

METHODS

Patients with intracranial malignant tumor requiring craniotomy were prospectively enrolled according to the inclusion criteria. Genotypes of CYP2C9*1 or *3 and CYP2C19*1, *2 or *3 were determined by real time PCR (TaqMan probe) method. Serum concentrations of phenytoin on the 4th and 7th day after oral administration were determined using fluorescence polarization immunoassay. The PPK parameters were estimated using Nonlinear Mixed Effects Models (NONMEM) and internal validation was performed using bootstraps. The predictive performance of the final model was evaluated by Normalized Predictive Distribution Errors (NPDEs) and diagnostic goodness- of-fit plots.

RESULTS

A total of 390 serum samples were collected from 170 patients in PPK model building group. The population typical values for Vm, Km and the apparent volume of distribution (V) in the final model were 17.5 mg/h, 6.41 mg/L and 54.8 L, respectively. Internal validation by bootstraps showed that the final model was stable and reliable. NPDEs with a normal distribution and a scatterplot with symmetrical distribution showed that the final model had good predictive capability. Individualized dose regimens of additional 40 patients in the external validation group were designed by the present final PPK model. The percentages of patients with serum concentrations within the therapeutic range were 61.53% (24/39) on the 4th day and 94.87% (37/39) on the 7th day, which were higher than the 39.33% (59/150) and 52.10% (87/167) of above 170 patients (P < 0.0001).

CONCLUSION

The present PPK final model for oral phenytoin may be used to further optimize phenytoin individualized dose regimen to prevent early seizure in patients after brain injury if patient characteristics meet those of the population studied.

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.

Use of Therapeutic Drug Monitoring, Electronic Health Record Data, and Pharmacokinetic Modeling to Determine the Therapeutic Index of Phenytoin and Lamotrigine

BACKGROUND

Defining a drug's therapeutic index (TI) is important for patient safety and regulating the development of generic drugs. For many drugs, the TI is unknown. A systematic approach was developed to characterize the TI of a drug using therapeutic drug monitoring and electronic health record (EHR) data with pharmacokinetic (PK) modeling. This approach was first tested on phenytoin, which has a known TI, and then applied to lamotrigine, which lacks a defined TI.

METHODS

Retrospective EHR data from patients in a tertiary hospital were used to develop phenytoin and lamotrigine population PK models and to identify adverse events (anemia, thrombocytopenia, and leukopenia) and efficacy outcomes (seizure-free). Phenytoin and lamotrigine concentrations were simulated for each day with an adverse event or seizure. Relationships between simulated concentrations and adverse events and efficacy outcomes were used to calculate the TI for phenytoin and lamotrigine.

RESULTS

For phenytoin, 93 patients with 270 total and 174 free concentrations were identified. A de novo 1-compartment PK model with Michaelis-Menten kinetics described the data well. Simulated average total and free concentrations of 10-15 and 1.0-1.5 mcg/mL were associated with both adverse events and efficacy in 50% of patients, resulting in a TI of 0.7-1.5. For lamotrigine, 45 patients with 53 concentrations were identified. A published 1-compartment model was adapted to characterize the PK data. No relationships between simulated lamotrigine concentrations and safety or efficacy endpoints were seen; therefore, the TI could not be calculated.

CONCLUSIONS

This approach correctly determined the TI of phenytoin but was unable to determine the TI of lamotrigine due to a limited sample size. The use of therapeutic drug monitoring and EHR data to aid in narrow TI drug classification is promising, but it requires an adequate sample size and accurate characterization of concentration-response relationships.

Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection - a long way to go

INTRODUCTION

Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in pediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size.

AREAS COVERED

In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation.

EXPERT OPINION

The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modeling and simulation concepts to prevent further delays in the development of personalised treatments for pediatric patients.

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 protein binding profile. A population pharmacokinetic approach was used to analyze paired protein-unbound and total phenytoin plasma concentrations (n = 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.

Antiepileptic drug utilization in Bangladesh: experience from Dhaka Medical College Hospital

BACKGROUND

Epilepsy is a common health problem which carries a huge medical social psychological and economic impact for a developing country. The aim of this hospital-based study was to get an insight into the effectiveness and tolerability of low cost antiepileptic drugs (AEDs) in Bangladeshi people with epilepsy.

METHODS

This retrospective chart review was done from hospital records in weekly Epilepsy outdoor clinic of Department of Neurology, Dhaka Medical College Hospital (DMCH) from October 1998 to February 2013. A total of 854 epilepsy patients met the eligibility criteria (had a complete record of two years of follow up data) from hospital database. A checklist was used to take demographics (age and gender), epilepsy treatment and adverse event related data. At least two years of follow up data were considered for analysis.

RESULTS

Out of 854 patients selected, majority of the patients attending outdoor clinic were >11-30 years age group (55.2%) with a mean age of 20.3 ± 9 years and with a male (53%) predominance. Focal epilepsy were more common (53%), among whom secondary generalized epilepsy was the most frequent diagnosis (67%) followed by complex partial seizure (21%). Among those with Idiopathic Generalized Epilepsy (46%), generalized tonic clonic seizure was encountered in 74% and absence seizure was observed in 13%. The number of patients on monotherapy and dual AED therapy were 67% and 24% respectively and polytherapy (i.e. >3 AEDs) was used only in 9%. CBZ (67%) was the most frequently prescribed AED, followed by VPA (43%), PHB (17%), and PHT (8%). CBZ was prescribed in 37% patients as monotherapy followed by VPA in 21% and PHB in 8% patients. Newer generation drugs eg lemotrigine and topiramate were used only as add on therapy in combination with CBZ and VPA in only 2% patients. The treatment retention rates over the follow up period for the AEDs in monotherapy varied between 86 and 91% and were highest for CBZ, followed by VPA. Most of the combination regimens had a treatment retention rate of 100%. The effectiveness of AED in terms of reduction of seizure frequency was highest for PHT (100%) and PHB (98%) followed by CBZ (96%) and VPA (95%). PHB and PHT were the cheapest of all AEDs (42 I$ and 56 I$/ year respectively). The costs of VPA and CBZ were two times and LTG and TOP were six to eight times higher. Adverse drug reaction (ADR) were observed among 140 (24.5%) of those with monotherapy. PHT (64%) was the most common drug to cause ADR, CBZ was at the bottom of the list to cause adverse effect (11.6%). VPA and PHB caused weight gain commonly. Adjustment of drug dose or withdrawal due to ADRs was necessary in 39% with PHT and 26% with PHB.

CONCLUSION

Though PHT and PHB are cheapest and efficacious among all, CBZ and VPA are less costly, effective and well tolerated drug for seizure control in context of Bangladesh.

The 'apparent clearance' of free phenytoin in elderly vs. younger adults

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The clearance of many drugs is reduced in the elderly, but the data regarding phenytoin are conflicting. Most studies have estimated phenytoin metabolic clearance using total drug concentrations (bound plus unbound), which may be confounded by protein binding effects. Free phenytoin concentrations are independent of protein binding and should more accurately reflect true metabolic clearance changes in elderly patients.

WHAT THIS STUDY ADDS

The two studies reported in this paper suggest a trend towards reduced free phenytoin 'apparent clearance' in the elderly, although statistically significant results were not found. Other published studies have largely found similar trends, suggesting an age effect.

AIMS

To test the hypothesis that the 'apparent clearance' of free phenytoin is reduced in elderly patients.

METHODS

Two separate studies were conducted comparing free phenytoin 'apparent clearance' in elderly vs. younger adults. The first study was a retrospective analysis of free phenytoin concentrations measured at Christchurch Hospital from 1997 to 2006. In the second study free phenytoin concentrations were measured prospectively in ambulatory subjects who were taking phenytoin regularly.

RESULTS

In the retrospective study (n= 29), free phenytoin 'apparent clearance' was 0.27 +/- 0.04 l kg(-1) day(-1) (95% CI 0.19, 0.34) in the elderly cohort vs. 0.37 +/- 0.06 l kg(-1) day(-1) (95% CI 0.22, 0.52) in younger adults, but the difference was not statistically significant. In the prospective study, free phenytoin 'apparent clearance' showed a non-significant trend to being reduced in the elderly patients (0.12 +/- 0.02 l kg(-1) day(-1), 95% CI 0.07, 0.17) compared with the younger cohort (0.18 +/- 0.07 l kg(-1) day(-1), 95% CI 0.09, 0.26) in those not taking interacting drugs (n= 21).

CONCLUSIONS

This research does not prove the hypothesis that the 'apparent clearance' of free phenytoin is reduced in the elderly. However, the trends found in these two studies are supported by trends in the same direction in other published studies, suggesting an age effect.

[Drugs for status epilepticus treatment]

The pharmacokinetics and pharmacodynamics of major antiepileptic agents are presented. The onset of action and the factors leading to extraction across the blood brain barrier are described as well as the mechanism and extent of metabolism, and the main interactions with other drugs. For each class, the dosing scheme and practical issues related to administration are described, based on evidence when available in the literature.

Therapeutic drug monitoring of phenytoin in critically ill patients

Therapeutic drug monitoring of phenytoin is necessary to ensure therapeutic and nontoxic levels. Hypoalbuminemia, renal failure, and interactions with other highly protein-bound drugs (e.g., valproic acid) alter protein binding of phenytoin. When these conditions are present, free serum concentrations, which represent the pharmacologically active entity, cannot be predicted from total serum concentrations. Besides general alterations in drug distribution and elimination, protein binding is often altered in critically ill patients. Case reports describe phenytoin toxicity secondary to inappropriate dosage adjustments based solely on total serum concentrations in patients with hypoalbuminemia. Free drug measurements and theoretical equations to facilitate the interpretation of total phenytoin serum levels have been introduced. However, they are not widely implemented in clinical practice because evidence of improvements in patient outcomes is limited. Knowledge of the pharmacokinetic properties of phenytoin is indispensable for correct interpretation of total serum concentrations when protein binding is altered. Free serum concentrations should be measured, or theoretically calculated if measurements are unavailable, to avoid misinterpretation of total serum levels and consequent inappropriate adjustments in the dosage of phenytoin in critically ill patients.

Usefulness of monitoring free (unbound) concentrations of therapeutic drugs in patient management

Drugs are bound to various serum proteins in different degrees and only unbound or free drug is pharmacologically active. Although free drug concentration can be estimated from total concentration, for strongly bound drugs, prediction of free level is not always possible. Conditions like uremia, liver disease and hypoalbuminemia can lead to significant increases in free drug resulting in drug toxicity even if the concentration of total drug is within therapeutic range. Drug-drug interactions may also lead to a disproportionate increase in free drug concentrations. Elderly patients may have increased free drug concentrations due to hypoalbuminemia. Elevated free phenytoin concentrations have also been reported in patients with AIDS and pregnancy. Currently free drug concentrations of anticonvulsants such as phenytoin, carbamazepine and valproic acid are widely measured in clinical laboratories. Newer drugs such as mycophenolic acid mofetil and certain protease inhibitors are also considered as candidates for monitoring free drug concentration.

Efficacy and tolerability of antiepileptic drugs in an Omani epileptic population

OBJECTIVE

The aim of this hospital-based study is to get an insight into the efficacy and tolerability of antiepileptic drugs (AED) in Omani epileptic patients.

PATIENTS AND METHODS

All Omani patients (aged 14 years and above) suffering from epileptic seizures for at least 2 years and followed-up by board-certified neurologists in Sultan Qaboos University Hospital (SQUH) were evaluated. The treatment retention rate since first visit at SQUH and over the last 2 years was used as primary efficacy measure of AED therapy. Change in seizure-frequency and side effect profiles were also assessed.

RESULTS

In this population of 203 confirmed epileptic patients, generalized tonic-clonic (40%) and partial seizures (39%) were most commonly observed, idiopathic/cryptogenic origin (81%) being the most frequent encountered origin. Sixty one percent of the patients were controlled with an AED in monotherapy and overall 34% of patients could be successfully maintained on monotherapy during the whole follow-up period at SQUH (median 6 years). The treatment retention rates for carbamazepine (CBZ) at a daily dose of 400-600 mg, sodium valproate (VPA) at a daily dose of 500-1000 mg, and phenytoin (PHT) at a daily dose of 300 mg, in monotherapy over the total follow-up period was 51, 50, and 21%, respectively. In contrast, over the last 2 years these rates were highest for VPA (91%) followed by CBZ (83%) and PHT (73%). Adverse drug reactions were recorded in 67% of patients, and were most commonly encountered with VPA.

CONCLUSIONS

Despite a higher adverse effect profile for VPA, long-term treatment with CBZ and VPA appeared to be equally effective in terms of treatment retention rates and seizure control.