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

Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population

Multiple treatment options exist for children with epilepsy, including surgery, dietary therapies, neurostimulation, and antiseizure medications (ASMs). ASMs are the first line of therapy, and more than 30 ASMs have U.S. Food and Drug Administration (FDA) approval for the treatment of various epilepsy and seizure types in children. Given the extensive FDA approval of ASMs in children, it is crucial to consider how the physiological and developmental changes throughout childhood may impact drug disposition. Various sources of pharmacokinetic (PK) variability from different extrinsic and intrinsic factors such as patients' size, age, drug-drug interactions, and drug formulation could result in suboptimal dosing of ASMs. Barriers exist to conducting clinical pharmacological studies in neonates, infants, and children due to ethical and practical reasons, limiting available data to fully characterize these drugs' disposition and better elucidate sources of PK variability. Modeling and simulation offer ways to circumvent traditional and intensive clinical pharmacology methods to address gaps in epilepsy and seizure management in children. This review discusses various physiological and developmental changes that influence the PK and pharmacodynamic (PD) variability of ASMs in children, and several key ASMs will be discussed in detail. We will also review novel trial designs in younger pediatric populations, highlight the role of extrapolation of efficacy in epilepsy, and the use of physiologically based PK modeling as a tool to investigate sources of PK/PD variability in children. Finally, we will conclude with current challenges and future directions for optimizing the efficacy and safety of these drugs across the pediatric age spectrum.

Treatment and care of women with epilepsy before, during, and after pregnancy: a practical guide

Women with epilepsy (WWE) wishing for a child represent a highly relevant subgroup of epilepsy patients. The treating epileptologist needs to delineate the epilepsy syndrome and choose the appropriate anti-seizure medication (ASM) considering the main goal of seizure freedom, teratogenic risks, changes in drug metabolism during pregnancy and postpartum, demanding for up-titration during and down-titration after pregnancy. Folic acid or vitamin K supplements and breastfeeding are also discussed in this review. Lamotrigine and levetiracetam have the lowest teratogenic potential. Data on teratogenic risks are also favorable for oxcarbazepine, whereas topiramate tends to have an unfavorable profile. Valproate needs special emphasis. It is most effective in generalized seizures but should be avoided whenever possible due to its teratogenic effects and the negative impact on neuropsychological development of in utero-exposed children. Valproate still has its justification in patients not achieving seizure freedom with other ASMs or if a woman decides to or cannot become pregnant for any reason. When valproate is the most appropriate treatment option, the patient and caregiver must be fully informed of the risks associated with its use during pregnancies. Folate supplementation is recommended to reduce the risk of major congenital malformations. However, there is insufficient information to address the optimal dose and it is unclear whether higher doses offer greater protection. There is currently no general recommendation for a peripartum vitamin K prophylaxis. During pregnancy most ASMs (e.g. lamotrigine, oxcarbazepine, and levetiracetam) need to be increased to compensate for the decline in serum levels; exceptions are valproate and carbamazepine. Postpartum, baseline levels are reached relatively fast, and down-titration is performed empirically. Many ASMs in monotherapy are (moderately) safe for breastfeeding and women should be encouraged to do so. This review provides a practically oriented overview of the complex management of WWE before, during, and after pregnancy.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates

AIM

Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models.

METHODS

Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement.

RESULTS

A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

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.

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis

AIMS AND BACKGROUND

Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters.

METHOD

A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020.

RESULTS

Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104 L/h/kg and 0.37 to 1.21 L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach.

CONCLUSION

Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Population pharmacokinetics of phenobarbital in Caucasian patients with epilepsy

The present study aimed to establish a population pharmacokinetic (PopPK) model of Phenobarbital (PB) in Caucasian patients with epilepsy included in a Therapeutic Drug Monitoring program. In total, 855 PB serum concentrations (steady-state trough concentrations) were retrospectively collected during routine clinical monitoring of 395 patients over 15 years of age with epilepsy. The PopPK analysis was performed with NONMEM using a non-linear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment, and comedication variables on the apparent clearance (CL/F) of PB were analysed. Goodness of fit plots and the bootstrap method were performed to evaluate the final model. External validation was carried out using an independent group of patients (107 patients, 178 blood samples). A one-compartment model with first-order absorption and elimination successfully described the data. In the final model, CL/F included body surface area (BSA) and comedication with phenytoin (PHT) and valproic acid (VPA), resulting in the following equation: CL/F[L/h]=(0.236+(0.115×(BSA-1.7)))×(0.822^PHT)×(0.711^VPA) The model presents acceptable estimation errors in the parameters of fixed (<12%) and random effects (<13%), and of the shrinkage values (<21%). Internal and external validations demonstrated the good predictability of the final model. A PopPK model of PB in Caucasian patients over 15 years of age was successfully established, which can be used to estimate phenobarbital CL/F. BSA and drug-drug interactions with PHT and VPA should be incorporated into dosing decisions. This PopPK, using Bayesian algorithms, could help establish an optimal dosage regimen in routine patient care.

Reduced Clearance of Phenobarbital in Advanced Cancer Patients near the End of Life

BACKGROUND AND OBJECTIVES

Little is known about the pharmacokinetics of phenobarbital in terminally ill cancer patients. We investigated whether phenobarbital clearance alters depending on the length of survival.

METHODS

We retrospectively reviewed the clinical, laboratory, and therapeutic drug monitoring (TDM) records of patients who received parenteral or oral phenobarbital for 21 consecutive days or longer between 2000 and 2016. Patients were divided into non-cancer and cancer groups. Cancer patients were further stratified according to the survival interval after TDM: those who survived > 3 months were classified as long-surviving and the remainders short-surviving cancer patients. Phenobarbital clearance (CL_PB) was calculated at steady state. Multiple comparisons of median CL_PB were conducted among the three groups.

RESULTS

Data were collected from 44 non-cancer patients and 34 cancer patients comprising 24 long-surviving and 10 short-surviving cancer patients. Among 10 short-surviving cancer patients, 4 had hepatic metastasis. Median CL_PB (range) in short-surviving cancer patients [0.076 (0.057‒0.114) L/kg/day] was significantly (p < 0.05) lower than that in non-cancer patients [0.105 (0.060‒0.226) L/kg/day] and in long-surviving cancer patients [0.100 (0.082‒0.149) L/kg/day].

CONCLUSION

Terminally ill patients with advanced cancer may have reduced CL_PB, thereby TDM is recommended for these patients particularly near the end of life.

Effect of Phenobarbital on Nitric Oxide Level in Term Newborn Infants with Perinatal Asphyxia

OBJECTIVES

Perinatal asphyxia (PA) is very significant in perinatal medicine due to the involvement of the central nervous system. This study was conducted to investigate the biochemical, clinical, and paraclinical changes associated with phenobarbital administration in neonates with PA.

METHODS

In this prospective, case-control study, 30 neonates with PA in two groups of 15 each (case and control) were investigated. The case group received 20 mg/kg intravenous phenobarbital within six hours of birth, and the control group did not receive phenobarbital. Serum concentrations of nitric oxide (NO) were measured at enrollment and one week after birth in the two groups. Clinical, electroencephalography, and magnetic resonance imaging findings of the two groups were compared.

RESULTS

At enrollment, the two groups did not differ in clinical severity, seizure incidence, or NO concentration. After one week, NO concentration was significantly lower in the case group (p < 0.050), but there was no significant difference in other variables between the two groups.

CONCLUSIONS

Early administration of phenobarbital in term neonates with PA could protect them against encephalopathy.