Population pharmacokinetics of topiramate in adult patients with epilepsy using nonlinear mixed effects modelling

UGT1A polymorphism rs4148324 associated with topiramate plasma concentration to dose ratio in children with epilepsy

PURPOSE

The objective of this study is to evaluate the association between genetic polymorphisms and the concentration to dose ratio of topiramate in children with epilepsy.

METHODS

A cohort of 163 pediatric patients with epilepsy receiving topiramate therapy were enrolled. The ultra-performance liquid chromatography-tandem mass spectrometry method was employed to measure the trough plasma concentration of topiramate at steady-state. These concentrations were normalized by dividing them by the ratio of total daily dose to body weight, yielding the concentration to dose ratio (CDR) of topiramate. MassArray system identified 30 single nucleotide polymorphisms associated with the pharmacokinetics and pharmacodynamics of topiramate. The CDR values were logarithmic transformed (lnCDR) for normal distribution. The association between the identified genetic polymorphisms and lnCDR was assessed using the PLINK software, employing linear regression analysis with adjustments by epilepsy types, estimated glomerular filtration rate, alanine aminotransferase, valproic acid, phenobarbital, and oxcarbazepine.

RESULTS

Variant rs4148324 (UGT1A1/3/4/5/6/7/8/9/10, BETA = 0.182, P = 0.010) was significantly associated with lnCDR of topiramate. Patients carrying the G allele exhibited higher normalized topiramate plasma concentrations. No other significant associations were found.

CONCLUSIONS

In pediatric patients receiving topiramate therapy, rs4148324 was associated with normalized topiramate plasma concentration. Further studies are warranted to validate and confirm the findings.

Population Pharmacokinetics of Topiramate in Patients with Epilepsy Using Nonparametric Modeling

BACKGROUND

Topiramate (TPM) is used for the treatment of various epileptic seizures and the prevention of migraine. This study aimed to develop a population pharmacokinetic model and identify covariates that influence TPM behavior in patients with epilepsy in Kuwait.

METHODS

Data were collected retrospectively from 108 patients (2 years old and above) with epilepsy who were treated with oral TPM and 174 TPM blood samples from 3 hospitals in Kuwait from 2009 to 2016. Data were randomly divided into 2 groups for model development and validation. The population pharmacokinetic model was built using the nonparametric modeling algorithm (Pmetrics). The model was evaluated internally through the visual predictive check method and externally using a new data set.

RESULTS

A 1-compartment model with first-order elimination fitted the data well. Covariates showing a significant effect on the elimination rate constant were renal function and coadministration of carbamazepine (CBZ). The mean estimated clearance was 2.11 L/h; this was 50% higher for patients coadministered with CBZ. Age and sex were essential covariates for the volume of distribution (V). The visual predictive check of the final model could predict the measured concentrations. External validation further confirmed the favorable predictive performance of the model with low bias and imprecision for predicting the concentration in a particular population.

CONCLUSIONS

TPM elimination was increased with CBZ coadministration and was affected by renal function. Meanwhile, age and sex were the main predictors for V. The predictive performance of the final model proved to be valid internally and externally.

Population pharmacokinetics of topiramate in Chinese children with epilepsy

OBJECTIVE

Topiramate, a broad-spectrum antiepileptic drug, exhibits substantial inter-individual variability in both its pharmacokinetics and therapeutic response. The aim of this study was to investigate the influence of patient characteristics and genetic variants on topiramate clearance using population pharmacokinetic (PPK) models in a cohort of Chinese pediatric patients with epilepsy.

METHOD

The PPK model was constructed using a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 236 plasma concentrations of topiramate obtained from 181 pediatric patients with epilepsy. A one-compartment model combined with a proportional residual model was employed to characterize the pharmacokinetics of topiramate. Covariate analysis was performed using forward addition and backward elimination to assess the influence of covariates on the model parameters. The model was thoroughly evaluated through goodness-of-fit analysis, bootstrap, visual predictive checks, and normalized prediction distribution errors. Monte Carlo simulations were utilized to devise topiramate dosing strategies.

RESULT

In the final PPK models of topiramate, body weight, co-administration with oxcarbazepine, and a combined genotype of GKIR1-UGT (GRIK1 rs2832407, UGT2B7 rs7439366, and UGT1A1 rs4148324) were identified as significant covariates affecting the clearance (CL). The clearance was estimated using the formulas CL (L/h) = 0.44 × (BW⁄11.7)0.82 × eOXC for the model without genetic variants and CL (L/h) = 0.49 × (BW⁄11.7)0.81 × eOXC × eGRIK1-UGT for the model incorporating genetic variants. The volume of distribution (Vd) was estimated using the formulas Vd (L) = 6.6 × (BW⁄11.7). The precision of all estimated parameters was acceptable. Furthermore, the model demonstrated good predictability, exhibiting stability and effectiveness in describing the pharmacokinetics of topiramate.

CONCLUSION

The clearance of topiramate in pediatric patients with epilepsy may be subject to the influence of factors such as body weight, co-administration with oxcarbazepine, and genetic polymorphism. In this study, PPK models were developed to better understand and account for these factors, thereby improving the precision and individualization of topiramate therapy in children with epilepsy.

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

Severity of Topiramate-Related Working Memory Impairment Is Modulated by Plasma Concentration and Working Memory Capacity

Drug side effects that impair cognition can lead to diminished quality of life and discontinuation of therapy. Topiramate is an antiepileptic drug that elicits cognitive deficits more frequently than other antiepileptic drugs, impairing multiple cognitive domains including language, attention, and memory. Although up to 40% of individuals taking topiramate may experience cognitive deficits, we are currently unable to predict which individuals will be most severely affected before administration. The objective of this study was to show the contributions of plasma concentration and working memory capacity in determining the severity of an individual's topiramate-related cognitive impairment. Subjects were enrolled in a double-blind, placebo-controlled crossover study during which they received a single dose of either 100, 150, or 200 mg topiramate. Working memory function was assessed using a modified Sternberg working memory task with 3 memory loads administered 4 hours after dosing. After adjustment for differences in working memory capacity, each 1 μg/mL of topiramate plasma concentration was associated with a 3.6% decrease in accuracy for all memory loads. Placebo effects occurred as a function of working memory capacity, with individuals with high working memory capacity experiencing less severe placebo-related impairment compared with those with low working memory capacity. Our results demonstrate that severity of topiramate-related cognitive deficits occurs as a function of both drug exposure and baseline cognitive function. By identifying patient- and exposure-related characteristics that modulate the severity of cognitive side effects, topiramate dosing strategies may be individually tailored in the future to prevent unwanted cognitive impairment.

Pharmacokinetic and pharmacodynamic modeling of levetiracetam: investigation of factors affecting the clinical outcome

This study aimed to evaluate the pharmacokinetics and pharmacodynamics of oral levetiracetam therapy in drug refractory adult epileptic outpatients, as well as factors affecting them. Concentration-time data were collected at steady state, while seizure recurrence was monitored for 13 months. Non-linear mixed effects modeling was applied, and covariates assessed included weight, height, age, daily dose and creatinine clearance.Plasma concentrations of levetiracetam were best described by a one-compartment pharmacokinetic model (V/F = 34.7 L) with first-order absorption (ka = 0.616 h^-1) and clearance (CL/F = 3.26 L/h). Patient's CrCL was found to significantly affect levetiracetam clearance (beta = 0.795). Time to seizure occurrence followed an exponential distribution and the mean time to seizure occurrence was estimated Te = 22.08 days. Seizure rate per month followed a Poisson distribution, while mean seizure rate per month was estimated λ = 1.33. Daily dose significantly affected the mean estimated time to seizure (beta = -2.2) and the mean monthly seizure rate (beta = 2.27) in a reverse way. Using discrete time Markov chains, it was shown that the transition probability from focal seizures to focal to bilateral tonic-clonic is significantly altered in relation to patient's CrCL.Simulations showed that dose should be adjusted in relation to CrCL, while low doses of levetiracetam are more effective for seizure control. Modeling and simulation in every-day clinical practice may provide significant information for the optimization of seizure control using well-known agents.

Topiramate pharmacokinetics in neonates undergoing therapeutic hypothermia and proposal of an optimised dosing schedule

AIM

The adequate dosing of topiramate in neonates undergoing therapeutic hypothermia has not been established. The aim of this study was to design a dosing schedule capable of providing topiramate serum concentrations within the accepted therapeutic range.

METHODS

Neonates (n = 52) with hypoxic ischaemic encephalopathy and subjected to therapeutic hypothermia were dosed with topiramate, 5 mg/kg on day one and 3 mg/kg on days two to five, to decrease seizure events. A total of 451 topiramate serum concentrations obtained in the patients were used to develop a population pharmacokinetic model using a non-linear mixed-effects modelling approach.

RESULTS

A one-compartment model with first-order absorption and two different clearance terms, one for the cooling period and another for the post-warming period, were used to describe the concentration-time topiramate data. The probability of no-seizure events could not be related to topiramate concentrations, which was attributed to excessively low topiramate concentrations. A modified dosage schedule was designed with the aim of obtaining more than 90% of patients with topiramate concentrations within the therapeutic range after the first dose.

CONCLUSION

The dosage schedule of topiramate in these patients should be modified with the aim of decreasing the frequency of seizure events.

Population Pharmacokinetics of Topiramate in Japanese Pediatric and Adult Patients With Epilepsy Using Routinely Monitored Data

BACKGROUND

Topiramate is a second-generation antiepileptic drug used as monotherapy and adjunctive therapy in adults and children with partial seizures. A population pharmacokinetic (PPK) analysis was performed to improve the topiramate dosage adjustment for individualized treatment.

METHODS

Patients whose steady-state serum concentration of topiramate was routinely monitored at Kyoto University Hospital from April 2012 to March 2013 were included in the model-building data. A nonlinear mixed effects modeling program was used to evaluate the influence of covariates on topiramate pharmacokinetics. The obtained PPK model was evaluated by internal model validations, including goodness-of-fit plots and prediction-corrected visual predictive checks, and was externally confirmed using the validation data from January 2015 to December 2015.

RESULTS

A total of 177 steady-state serum concentrations from 93 patients were used for the model-building analysis. The patients' age ranged from 2 to 68 years, and body weight ranged from 8.6 to 105 kg. The median serum concentration of topiramate was 1.7 mcg/mL, and half of the patients received carbamazepine coadministration. Based on a one-compartment model with first order absorption and elimination, the apparent volume of distribution was 105 L/70 kg, and the apparent clearance was allometrically related to the body weight as 2.25 L·h·70 kg without carbamazepine or phenytoin. Combination treatment with carbamazepine or phenytoin increased the apparent clearance to 3.51 L·h·70 kg. Goodness-of-fit plots, prediction-corrected visual predictive check, and external validation using the validation data from 43 patients confirmed an appropriateness of the final model. Simulations based on the final model showed that dosage adjustments allometrically scaling to body weight can equalize the serum concentrations in children of various ages and adults.

CONCLUSIONS

The PPK model, using the power scaling of body weight, effectively elucidated the topiramate serum concentration profile ranging from pediatric to adult patients. Dosage adjustments based on body weight and concomitant antiepileptic drug help obtain the dosage of topiramate necessary to reach an effective concentration in each individual.

Pharmacokinetic interactions and dosing rationale for antiepileptic drugs in adults and children

AIMS

Population pharmacokinetic modelling has been widely used across many therapeutic areas to identify sources of variability, which are incorporated into models as covariate factors. Despite numerous publications on pharmacokinetic drug-drug interactions (DDIs) between antiepileptic drugs (AEDs), such data are not used to support the dose rationale for polytherapy in the treatment of epileptic seizures. Here we assess the impact of DDIs on plasma concentrations and evaluate the need for AED dose adjustment.

METHODS

Models describing the pharmacokinetics of carbamazepine, clobazam, clonazepam, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, topiramate, valproic acid and zonisamide in adult and paediatric patients were collected from the published literature and implemented in NONMEM v7.2. Taking current clinical practice into account, we explore simulation scenarios to characterize AED exposure in virtual patients receiving mono- and polytherapy. Steady-state, maximum and minimum concentrations were selected as parameters of interest for this analysis.

RESULTS

Our simulations show that DDIs can cause major changes in AED concentrations both in adults and children. When more than one AED is used, even larger changes are observed in the concentrations of the primary drug, leading to significant differences in steady-state concentration between mono- and polytherapy for most AEDs. These results suggest that currently recommended dosing algorithms and titration procedures do not ensure attainment of appropriate therapeutic concentrations.

CONCLUSIONS

The effect of DDIs on AED exposure cannot be overlooked. Clinical guidelines must consider such covariate effects and ensure appropriate dosing recommendations for adult and paediatric patients who require combination therapy.

Nonlinear mixed effects modelling approach in investigating phenobarbital pharmacokinetic interactions in epileptic patients

PURPOSE

The present study aimed to establish population pharmacokinetic model for phenobarbital (PB), examining and quantifying the magnitude of PB interactions with other antiepileptic drugs concomitantly used and to demonstrate its use for individualization of PB dosing regimen in adult epileptic patients.

METHODS

In total 205 PB concentrations were obtained during routine clinical monitoring of 136 adult epilepsy patients. PB steady state concentrations were measured by homogeneous enzyme immunoassay. Nonlinear mixed effects modelling (NONMEM) was applied for data analyses and evaluation of the final model.

RESULTS

According to the final population model, significant determinant of apparent PB clearance (CL/F) was daily dose of concomitantly given valproic acid (VPA). Typical value of PB CL/F for final model was estimated at 0.314 l/h. Based on the final model, co-therapy with usual VPA dose of 1000 mg/day, resulted in PB CL/F average decrease of about 25 %, while 2000 mg/day leads to an average 50 % decrease in PB CL/F.

CONCLUSIONS

Developed population PB model may be used in estimating individual CL/F for adult epileptic patients and could be applied for individualizing dosing regimen taking into account dose-dependent effect of concomitantly given VPA.