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

Patricia D Maglalang; Jiali Wen; Christoph P Hornik; Daniel Gonzalez

doi:10.1111/cts.13793

. 2024 Apr 15;17(4):e13793. doi: 10.1111/cts.13793

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

Patricia D Maglalang ¹, Jiali Wen ¹, Christoph P Hornik ^2,³, Daniel Gonzalez ^2,^4,^✉

PMCID: PMC11017206 PMID: 38618871

Abstract

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.

INTRODUCTION

In the United States, epilepsy is the most common neurological condition in children, which affects more than 470,000 children and adolescents under the age of 18 years. ¹ While antiseizure medications (ASMs) are the first‐line treatment for epilepsy in children, ² up to 40% of children newly diagnosed with epilepsy fail the first antiseizure medication (ASM) prescribed to them. ³ In neonates and infants, phenobarbital and phenytoin are the first line of treatment for seizure disorders, which affect 1–5 per 1000 live births per year. ⁴ , ⁵ However, more than 50% of cases continue to have uncontrolled seizures despite the initiation of treatment. ⁵ When these treatments fail, clinicians choose second‐line ASMs, many of which do not have U.S. Food and Drug Administration (FDA) approval for this age group and are used off‐label. Treatment failures may be attributed to suboptimal dosing of ASMs, which can be due to various sources of pharmacokinetic/pharmacodynamic (PK/PD) variability.

Several intrinsic and extrinsic factors affect patient response to ASM treatment, such as age, seizure type, comorbidities, adverse effects, drug formulation, and drug–drug interactions (DDIs) (Figure 1). Various sources of PK/PD variability in children may be difficult to characterize due to challenges in conducting clinical pharmacology studies in this patient population. Barriers include the low number of participants, ethical concerns, low parental consent rate, and limitations in collecting blood volume and monitoring. ⁶ Further, some seizures are serious medical emergencies and require immediate treatment, which may leave little time to obtain parental informed consent. Novel trial designs in the pediatric population coupled with modeling and simulation can help address limited pediatric data available to optimize the efficacy and safety of ASMs in children. Trial designs such as opportunistic PK studies can help increase the availability of pediatric data, and the use of modeling and simulation are tools that can be used to investigate various sources of PK/PD variability. Together, these can help optimize the efficacy and safety of ASMs in children.

Potential extrinsic and intrinsic sources of pharmacokinetic and pharmacodynamic variability of antiseizure medications in the pediatric population. Figure created in Biorender.com.

The Pediatric Epilepsy Academic Consortium for Extrapolation (PEACE) reviewed biological and clinical evidence to support that the pathophysiology and manifestation of focal onset seizures (FOS) are similar between adults and children, and extrapolating the efficacy of ASMs from adults to children 2 years and older is appropriate. ⁷ In 2019, the FDA published guidance that supported this finding and concluded that efficacy for FOS can be extrapolated to pediatric patients 2 years of age and older without the need for additional efficacy trials. ⁸ Then, in 2020, the Research Roundtable for Epilepsy (RRE) convened to determine the appropriateness of extrapolation of ASMs to children aged 1 month to <2 years for FOS, and they concluded that efficacy extrapolation is appropriate to this younger age group due to similarities in pathophysiology and response to treatment. ⁹ This breakthrough in ASM treatment makes it feasible to reduce lag time in regulatory approval in children, leading to broader treatment options.

In the literature, the extrapolation of efficacy for ASMs via exposure‐response (i.e., seizure frequency reduction) analysis is made possible through modeling and simulation techniques such as population pharmacokinetic (popPK) modeling or model‐informed meta‐analysis. ¹⁰ , ¹¹ , ¹² , ¹³ , ¹⁴ To support the extrapolation of efficacy, clinical data from an adequately designed PK study in the pediatric population is still needed. ⁸ Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or quantitative systems pharmacology (QSP) models could play a major role in integrating both PK and PD to support dose selection and study design, which could help limit the number of participants needed in pediatric clinical trials. ¹⁵ Furthermore, PBPK/PD modeling can effectively characterize various sources of PK/PD variability, offering a refined approach to optimize dosing strategies specifically tailored for children.

A literature review on clinical studies of ASMs in the pediatric population published up until December 1, 2023, was performed using the PubMed database and the World Wide Web. MeSH terms for interventions (e.g., levetiracetam, phenobarbital, phenytoin) and the Advanced Search tool in PubMed with key terms (e.g., “pharmacokinetic,” “pharmacodynamic,” “antiseizure medication,” “antiepileptic drugs,” “pediatrics,” “physiologically based pharmacokinetic modeling,” “opportunistic studies”) were used. This comprehensive review highlights various physiological and developmental factors impacting the PK/PD variability of ASMs in the pediatric population and provides a detailed examination of key medications and some of their associated sources of variabilities. By delving into several methodologies and opportunities to conduct clinical pharmacology research of ASMs in children, this review serves as a resource to better understand therapeutic strategies in optimizing ASM dosing in the pediatric population.

SOURCES OF PK VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

It is evident that PK variability among ASMs exists, especially in children, potentially warranting the use of therapeutic drug monitoring for drugs with a narrow therapeutic index to ensure their efficacy and safety. Children are predisposed to unique changes in PK processes due to maturation in age and changes in body composition. It is important to characterize the impact of PK variability, such as the effects of obesity, developmental changes in elimination processes, and DDIs to optimize ASM dosing in children since suboptimal dosing of ASMs can lead to loss of seizure control or significant adverse effects.

Body weight

The incidence of obesity in children is rising, and obesity is a potential source of PK variability for ASMs that has not been extensively studied. In a retrospective cohort study performed in the U.S., authors found approximately 20% of children newly diagnosed with epilepsy have obesity, ¹⁶ and data to inform ASM dosing in this population is still lacking. There is evidence that children with obesity display obesity‐related physiological changes that affect drug disposition, such as increased body size, organ volume and blood flow, and glomerular filtration rate (GFR). ¹⁷ Children with obesity may have increased kidney volume, which could lead to increased GFR and a potential increase in absolute clearance. ¹⁷ Using a virtual population of children with obesity that accounts for key physiologically‐related changes in this patient population with clindamycin and sulfamethoxazole/trimethoprim as model drugs, the authors found children with obesity have decreased weight‐normalized clearance of the drug compared to children without obesity. ¹⁷

Various dosing considerations in children, such as weight‐based versus fixed dosing, body size metric, and dose capping (i.e., administering a maximum total dose), may complicate dosing in children with obesity. ¹⁸ Weight‐based dosing is commonly used, and for many drugs, it is not generally known whether the same FDA‐dosing label recommendations can be used for children with obesity. Further, it is recommended not to exceed the recommended maximum adult dose for this patient population. ¹⁹ For most drugs, including ASMs, dosing recommendations are categorized by age group and then further grouped into “weight bands.” It is currently unknown whether the same weight bands can also be applied to children with obesity.

Only two published studies have investigated the disposition of ASMs in children with obesity to date. ²⁰ , ²¹ In a popPK study of children with and without obesity taking levetiracetam per standard of care, fat‐free mass and estimated GFR were identified as significant covariates for clearance. ²⁰ In this analysis, children with obesity also had lower weight‐normalized clearance than children without obesity. For children with obesity, dosing simulations suggested weight‐based dosing for levetiracetam oral solution in children 2 to <4 years and weight‐tiered dosing for levetiracetam oral solution and tablets in children 4 to <16 years. ²⁰ This analysis of levetiracetam in children with obesity prompted an FDA label change to include dosing recommendations for this population, setting a positive precedent that could encourage similar analysis and further recommendations for other ASMs.

Maturation

Neonates and infants undergo rapid development, leading to physiological changes that affect the PK of drugs. A more comprehensive review of developmental changes in newborns and their effect on PK is provided elsewhere. ²² , ²³ Factors affecting age‐dependent changes in clearance include protein concentration differences and the maturation of renal function and drug‐metabolizing enzymes. ²⁴ Changes in albumin and alpha1‐acid glycoprotein (AAG) concentrations during the neonatal period may lead to protein binding changes that affect drug distribution and clearance for low‐extraction drugs such as valproic acid (95% bound to albumin) and carbamazepine (70–90% bound to AAG). Plasma protein binding levels differ in newborns relative to adults, with albumin concentrations closer to adult levels at birth (up to 80%) and AAG concentrations are half of an adult, and these differences may affect the concentration of unbound drug that exerts pharmacological effect. ²⁵

Several studies aim to characterize the PK of majorly renally excreted ASMs, such as levetiracetam and topiramate in neonates and infants. In a popPK analysis characterizing levetiracetam PK in neonates, Sharpe et al. found that weight‐normalized apparent clearance (CL/F) doubles in the first week of postnatal life, the authors suggest the increased efficiency of renal excretion and hydrolytic pathways during this period. ²⁴ , ²⁶ In a study that included subjects ages 2–46 months, the weight‐normalized clearance was slowest in infants under 6 months old and the fastest in children 6 months and older. ²⁷ For topiramate, a similar trend is observed where a twofold increase in weight‐normalized CL/F is observed in infants (1 month to 2 years of age) than in older children and weight‐normalized CL/F is further increased in children when the drug was concomitantly taken with enzyme inducers. ²⁸ , ²⁹ , ³⁰

Phase I drug‐metabolizing enzymes show different expression levels during maturation that affect the drug metabolism of key ASMs. Cytochrome P450 2C9 (CYP2C9) and CYP2C19 metabolize phenobarbital and phenytoin, while CYP3A4 metabolizes carbamazepine to its active metabolite. The developmental expression patterns for CYP2C9 and CYP2C19 were studied using fetal and pediatric liver samples and this study revealed that CYP2C9 protein values varied by 35‐fold from birth to 5 months while CYP2C19 expression increased linearly over the first 5 postnatal months and varied by 21‐fold from 5 months to 10 years. ³¹ In a study by Pitlick et al. investigating phenobarbital PK in neonates, eight neonates with seizures (30‐to‐40‐week gestational age) experienced a decrease in elimination half‐life (t _1/2z) from week 1 (115 h) to week 4 (67 h). The authors attributed changes in metabolic processes in the first weeks of neonatal life as the factor responsible for the decreased drug concentrations and increased clearance. ³² The variation in the ontogeny of these enzymes should be considered when dosing to optimize the efficacy and minimize adverse effects in young children.

Drug–drug interactions

In some cases, children require 2 or more ASMs for seizure control. The co‐administration of 2 or more ASMs may increase the risk of DDIs since some ASMs induce or inhibit drug‐metabolizing enzymes (e.g., CYP enzymes) while some ASMs are substrates of the CYP enzyme family. Valproic acid, phenobarbital, and phenytoin have a high potential for DDIs due to their complex PK and metabolism pathways. In adults, the co‐administration of phenobarbital with a CYP2C9/2C19 inhibitor, valproic acid, results in a 25–50% decrease in clearance. ³³ , ³⁴ The authors suggest decreasing the phenobarbital dose when valproic acid is initiated. ³³ A popPK analysis performed using data from Japanese pediatric and adult patients reported that valproic acid reduced phenobarbital's clearance by 32%. ³⁵ A reduction in phenobarbital clearance by 15% with co‐administration of phenytoin, another CYP2C9/2C19 inhibitor, was also observed. ³⁵ An increase in phenobarbital concentrations following phenytoin administration was observed in children with epilepsy, but the age‐dependency of this interaction was not characterized. ³⁶ A gap remains in characterizing these ASM DDIs, especially in neonates and infants. In a study analyzing the prevalence of potential DDIs among hospitalized children taking ASMs, authors found a potential DDI was identified in 42% of hospitalizations associated with the use of ASMs. The most common perpetrators are phenobarbital, phenytoin, and valproic acid. These DDIs are associated with increased length of hospital stay, greater number of medications, and intensive care unit admissions. ³⁷ Further, this could lead to receiving other concomitant medications from other drug classes that may interact with ASMs.

Other sources of PK variability of ASMs

Pediatric formulations are unique from their adult counterparts and the choice of dosage forms becomes crucial, with considerations for palatability, ease of swallowing, and age‐appropriate formulations. Drug absorption and metabolism vary in children, requiring careful consideration in formulating pediatric medications. The success of pediatric medication adherence is linked to these factors, as a child's willingness to take medication is influenced by taste, ease of administration, and dosing frequency. Ivanovska et al. discuss the need for pediatric formulations, the clinical advantages and disadvantages of different pediatric formulations, and how formulation acceptability impacts adherence. ³⁸ Further, they discuss novel frameworks and future directions for developing new formulations and drug delivery for children. Different formulations of ASMs have an impact on the PK of these drugs which affect medication management for epilepsy. The drug's half‐life typically dictates the frequency of dosing of immediate‐release (IR) ASM formulations. For example, levetiracetam is dosed twice daily due to its short half‐life of 6–8 h and reaches steady‐state in 2 days. On the contrary, IR formulations with longer half‐lives, such as perampanel (105 h) and zonisamide (63 h in plasma), can take 2–3 weeks to reach a steady state. ³⁹ For extended‐release (ER) formulations, its duration of effect may not be attributed to its half‐life and instead, the duration of effect is prolonged due to the increase in time to achieve maximum concentration (t _max) and decreasing maximum concentration (C _max). ⁴⁰ There are more than 10 ER formulations of ASMs available, and the advantages of this formulation include lower dosing frequency, improved adherence, and lower fluctuations in peak‐to‐trough serum concentrations. ⁴¹ It should be noted that although most ER formulations are once‐daily dosing, twice‐daily dosing of ER formulations (i.e., carbamazepine) is still possible. With this, regimen simplification via IR formulations with longer half‐lives and ER formulations may improve medication adherence and improve drug efficacy. ³⁹ When a clinician switches a patient from IR to an ER formulation, it is important to note that these two formulations may not always be bioequivalent and may require dosing adjustments. ³⁹ For divalproex sodium (Depakote®), the dose of Depakote® ER should be 8–20% higher than the IR formulation to maintain similar serum concentrations. ⁴² , ⁴³ In the case of topiramate, the same total daily dose can be administered when switching between the IR and ER formulations since they are bioequivalent. ⁴⁴

Genetic polymorphisms that may affect drug disposition and response to treatment is another important source of PK variability. Since many ASMs undergo hepatic metabolism via CYP or Uridine 5′‐diphospho‐glucuronosyltransferase (UGT) enzymes, genetic polymorphisms among these enzymes have been studied. Genetic polymorphisms in drug‐metabolizing enzymes, such as CYP450 or UGT enzymes, can lead to substantial variability in the rate at which ASMs are metabolized in the liver. This variability may result in differences in drug clearance, bioavailability, overall efficacy, and adverse drug reactions. Furthermore, genetic variations in drug transporters and receptors may affect drug distribution and response at the target sites in the brain. The Clinical Pharmacogenetics Implementation Consortium (CPIC) released a guideline for phenytoin dosing regarding CYP2C9 and HLA‐B genotypes. CYP2C9 is highly polymorphic, while the variant allele HLA‐B*15:02 is associated with an increased risk of Stevens‐Johnson syndrome and toxic epidermal necrolysis. ⁴⁵ In a large retrospective analysis in adult patients aiming to assess the impact of CYP2C9 variation in patient response to phenytoin, patients with low‐intermediate/poor metabolizer genotype had increased dose‐adjusted phenytoin blood concentration compared to patients with normal metabolizer genotype (p < 0.01). The higher phenytoin blood concentrations for patients with low‐intermediate/poor metabolizer genotype resulted in an increased risk of neurologic side effects and patients had a higher likelihood of receiving a lower dose at the end of their first year of treatment. Further, authors associated pharmacogenetic outcomes from medication dispensing records and found that patients with decreased CYP2C9 function were associated with lower adherence and were more likely to be switched to a different ASM. ⁴⁶ Understanding an individual's pharmacogenetic profile can help tailor ASM regimens to optimize therapeutic outcomes, minimize side effects, and reduce the risk of treatment resistance.

SOURCES OF PD VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

Currently, there is limited information on how maturation affects PD, and multiple barriers exist to studying PD endpoints of ASMs in neonates and infants due to the heterogeneity of the physiology and maturation of this patient population. One well‐documented example of PD and age‐dependent differences is the higher incidence of hepatotoxicity manifesting as drug‐induced acute liver failure in children less than 2 years from valproic acid. To better understand the risk factors for valproate hepatotoxicity, the metabolism of valproic acid was investigated in patients with epilepsy receiving valproic acid alone and valproic acid with other ASMs (e.g., phenobarbital, phenytoin, benzodiazepines). The authors found that younger patients and patients taking valproic acid with concomitant ASMs may be more vulnerable to hepatotoxicity. ⁴⁷ Additionally, enzyme‐inducing ASMs may enhance the production of 4‐ene‐valproic acid which supports the increased incidence of hepatotoxicity observed in patients taking the drug with other ASMs. Prescribing information for valproic acid contains a boxed warning for increased risk of hepatotoxicity in children under 2 years and warns that this product should be used as the sole drug in this patient group. ⁴⁸

The maturation of neurotransmitters and receptors in neonates and infants may also play a role in ASM response. Gamma‐aminobutyric acid type A (GABA_A) receptor activation in the mature brain inhibits signals, while in early postnatal development, it induces excitability due to low expression of transporters responsible for maintaining low intracellular chloride concentrations, leading to nerve cell depolarization and excitation. ⁴⁹ In animal studies, GABA switches from an excitatory to an inhibitory neurotransmitter at 2 weeks postnatal, but the time frame is unknown in humans. ⁵⁰ Changes in GABA receptor density and receptor subunit composition during maturation may impact the efficacy of ASMs. ⁵¹

To characterize the drug action and PD, measuring the drug effect at the site of action (i.e., brain) is important, and this poses a challenge for ASMs. In prior clinical studies, the reduction in seizure frequency from baseline and the percentage of patients achieving a 50% or greater reduction in seizure frequency from baseline are the most common efficacy measures in clinical trials in adults and children with FOS. ⁵² Scientists have explored biomarkers to investigate PD and evaluate drug effects in the pediatric population. ²² Electroencephalography (EEG) is used to confirm epilepsy diagnosis and evaluate the efficacy of ASMs, but patients are not routinely monitored throughout disease and treatment duration via EEG, and instead, medication and dosage adjustments occur through medical history. Further, capturing seizure activity on EEGs is time‐dependent, and recordings that may not coincide with a seizure event may lead to missing important diagnostic information or delays in adjusting treatment. ⁵³ In contrast, EEG is a physiological biomarker that can be used to understand age‐dependent PD. Epilepsy experts have described the similarity of electrographic patterns seen in EEG and clinical characteristics among infants aged 1 month to <2 years, children 2 years and older, and adults with FOS. ⁷ , ⁹ A comprehensive review of the role of diagnostic and prognostic biomarkers for epilepsy was conducted by Pitkanen et al. ⁵⁴ They address the heterogeneity of epilepsies as one of the key challenges in designing epilepsy biomarker studies and how these studies may not account for variability in age since most biomarker identification studies are performed in adults. Appropriate specimen collection and accessibility to biomarkers are other challenges they identified. Overall, there are various sources of PD variability in the pediatric population taking ASMs, and more research is needed to understand and elucidate better PD endpoints across the age spectrum to optimize the efficacy and safety of these drugs.

SELECT COMMONLY USED ASM IN CHILDREN

Over 30 ASMs are FDA‐approved and used for the treatment of seizures in the pediatric population. ⁵⁵ The first generation entered the market in 1857 with bromide, followed by phenobarbital in 1910. ⁵⁵ The discovery and development of phenytoin were pivotal for current ASM therapy, as it led to the creation of other first‐generation ASMs, including phenobarbital and ethosuximide, which were derived from the barbiturate structure. ⁵⁵ Second‐generation ASMs (e.g., diazepam, carbamazepine, valproate) have different chemical structures from first‐generation drugs. Third‐generation ASMs (e.g., levetiracetam, topiramate, lamotrigine, lacosamide) entered the market in the 1990s, and these drugs show fewer DDIs and better tolerability over the older generations. ⁵⁵ This section will highlight several commonly used ASMs, their indication and PK properties and associated sources of PK variability (Table 1).

TABLE 1.

Examples of key PK/PD variability of select ASMs in the pediatric population.

Antiseizure medication	Methods	PK/PD variability in the pediatric population
Levetiracetam	popPK modeling using data obtained from a retrospective study involving 18 neonates with seizures ⁵⁸	Postmenstrual age on clearance and weight on volume of distribution
	popPK modeling using data from a prospective studying involving 20 neonates with seizures ⁵⁹ popPK modeling using data from a prospective open trial involving 44 children between 4 and 16 years ⁶⁰	Body weight on clearance and volume of distribution
	popPK modeling using pooled data collected from 228 children with epilepsy ages from 3 months to 18 years ⁶¹	Age on absorption rate constant
		Body weight on V _d/F
		Body weight and concomitant enzyme‐inducing ASMs (CBZ, PHT, PHB, PMD) on CL/F
Oxcarbazepine	popPK modeling using data obtained from 185 Chinese children with epilepsy ⁶⁵	Age on MHD clearance
Oxcarbazepine		Genotype (ABCB‐UG‐SCN‐INSR) on clearance
Phenobarbital	Retrospective popPK analysis using data from 355 patients <19 years of age ⁶⁸	Age‐dependent changes such as maturation of renal function and CYP enzyme metabolism affecting clearance
		Age‐dependent maturation of gastric acid secretion
		Concomitant medications (e.g., enteral phenytoin, pantoprazole, midazolam) resulting in drug–drug interactions affecting clearance
Phenytoin	CPIC guideline providing interpretation of HLA‐B and/or CYP2C9 genotype test results ⁴⁵	Pharmacogenetics
	popPK modeling using retrospective data from 83 infants and neonates ⁷²	Age‐related changes due to enzyme maturation and body weight affecting clearance
		Age‐related changes due to maturation affecting rate and extent of absorption
Topiramate	popPK model using data from another PK study ³⁰ involving 22 children ²⁹	Age and body weight effect on V _d/F and enzyme‐inducing ASMs on CL/F
	popPK/PD modeling using an integrated dataset in children ages 2–<10 years ⁷⁵	Body weight, age, and concomitant enzyme‐inducing ASMs affecting PK
		Efficacy was not different among different age groups
Valproic acid	popPK modeling using data from 98 children ¹⁰⁰	Formulation effect on drug exposure
	popPK modeling using data from 313 pediatric patients in China ages 0–16 years old ⁷⁸	Drug–drug interactions with concomitant administration of lamotrigine
		Body weight affecting clearance
		Age‐based maturation of drug‐metabolizing enzymes affecting clearance
	PBPK modeling using published adult and pediatric observed PK data ⁸⁶	Age‐related hepatotoxicity in children <10 years
	popPK modeling using data from 290 children with epilepsy ⁷⁹	Genotype effect on clearance

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Abbreviations: ABCB‐UG‐SCN‐INSR, g enotype of six combined variants (rs1045642, rs2032582, rs7668282, rs2396185, rs2304016, rs1128503); ASMs, antiseizure medications; CBZ, carbamazepine; CL/F, plasma oral clearance; CPIC, Clinical Pharmacogenetics Implementation Consortium; CYP, cytochrome P450; CYP2C9, cytochrome P450 2C9; HLA‐B, human leukocyte antigen B; MHD, 10‐monohydroxy derivative; NONMEM, NONlinear Mixed Effects Modeling; PBPK, physiologically based pharmacokinetic; PHB, phenobarbital; PHT, phenytoin; PK, pharmacokinetic; PMD, primidone; popPK, population pharmacokinetic; popPK/PD, population pharmacokinetic/pharmacodynamic; V _d/F, apparent volume of distribution.

Levetiracetam

Levetiracetam has several FDA‐approved indications for adjunctive therapy in children with FOS (≥1 month of age), myoclonic seizures in patients with juvenile myoclonic epilepsy (≥12 years of age), and primary generalized tonic–clonic seizures (≥6 years of age). ⁵⁶ It is also widely used as an off‐label treatment for neonatal seizures. ⁵⁷ Levetiracetam's rapid absorption and nearly complete bioavailability make it suitable for oral and intravenous administration, ensuring flexibility in pediatric treatment regimens. The drug is primarily eliminated via renal excretion, accounting for approximately 66% of the dose. The rest is metabolized into an inactive metabolite (L057) by type B esterase expressed primarily in red blood cells (RBCs). Children may experience developmental changes in renal function, influencing levetiracetam clearance. Several popPK studies in the pediatric population have identified age, weight, and concomitant medications as significant covariates influencing drug clearance. ⁵⁸ , ⁵⁹ , ⁶⁰ , ⁶¹ , ⁶² Through PBPK modeling, Sinha et al. characterized the age‐related changes in the clearance of levetiracetam in the presence and absence of concomitant enzyme‐inducing ASM. This work revealed that the DDI impact on levetiracetam's clearance increases with age, from 20% in children 2 years of age to 30% in adolescents, which indicates an age‐dependent DDI effect. ⁶³

Oxcarbazepine

Oxcarbazepine is a second‐generation ASM approved for adjunctive therapy for FOS and primary generalized clonic–tonic seizures in children 2 years and older. ⁶⁴ The drug undergoes rapid and complete conversion to its active metabolite, 10‐monohydroxy derivative (MHD), by aldo‐keto reductase enzymes, which are responsible for its therapeutic effects. Oxcarbazepine is primarily absorbed in the gastrointestinal tract and extensively metabolized in the liver by glucuronidation in the liver via UGT1A9 and UGT2B7, with MHD exhibiting linear kinetics. Body weight, age‐dependent changes in liver enzyme activity, and maturation of drug‐metabolizing pathways can contribute to variability in MHD clearance. In a popPK study in Chinese children with epilepsy, Li et al. identified age, eGFR, and ABCB‐UGT‐SCN‐INSR genotype as significant covariates that affect MHD clearance. The presence of these 6 variants in 4 genes (ABCB‐UGT‐SCN‐INSR) in patients resulted in a 10% decrease in the MHD clearance rate. The article also discusses various sources of genetic polymorphisms that may affect the efficacy and concentrations of MHD in the body. ⁶⁵

Phenobarbital

Phenobarbital is one of the oldest yet commonly used ASMs, especially in younger pediatric patients. It is approved as monotherapy for FOS, generalized onset seizures, and neonatal seizures. ⁶⁶ Due to its narrow therapeutic index and extensive use in the neonatal population, its trough concentrations are routinely monitored with a therapeutic range of 10–40 mcg/mL. ⁶⁷ The drug is well‐absorbed orally and exhibits variable and nonlinear kinetics, which can be particularly pronounced in children due to age‐related changes in drug metabolism. In pediatric patients, the maturation of hepatic enzyme systems responsible for phenobarbital metabolism may lead to fluctuations in drug clearance. Phenobarbital's association with enzyme induction further complicates its PK, potentially affecting the metabolism of co‐administered drugs. It has a high risk for DDIs since phenobarbital is a strong CYP3A4 inducer and is primarily metabolized by CYP2C9, CYP2C19, and CYP2E1 enzymes. In a retrospective popPK analysis involving pediatric patients (N = 355), significant covariates affecting phenobarbital clearance were serum creatinine, postmenstrual age, and DDIs. Using a sigmoidal model, the authors characterized phenobarbital clearance during the neonatal and infant periods, indicating enzyme maturation extending into the postnatal period. ⁶⁸ Examining DDIs between concomitant ASMs such as midazolam and phenytoin and other commonly used medications (e.g., pantoprazole) resulted in changes in phenobarbital clearance. The concomitant use of phenobarbital and phenytoin decreased phenobarbital clearance, which could result in higher phenobarbital exposure. The potential for DDIs between phenobarbital and other concomitant medications potentially warrants the use of therapeutic drug monitoring due to changes in phenobarbital clearance identified from several PK and popPK studies. A study aimed at evaluating the effects of CYP2C19 genetic polymorphism in neonates and infants with seizures showed that the CYP2C19 genotype did not reveal differences in phenobarbital clearance. Authors suggested that in early infancy, there is low metabolic enzyme activity since CYP2C19 activity in infants is 30% of that in adults, and the differences between normal and poor metabolizers may not be distinct in infants. ⁶⁹

Phenytoin

Phenytoin is used for several seizure types, including tonic–clonic seizures, status epilepticus (SE), and FOS across the age spectrum from neonates to adolescents. The drug is highly protein‐bound to albumin (90%), and 98% of the drug is hepatically metabolized through CYP2C9 (major) and CYP2C19 (minor). ⁷⁰ , ⁷¹ Factors such as developmental changes in hepatic enzyme activity and the impact of concomitant medications contribute to the variability in phenytoin PK in children. From a popPK study in neonates and infants administered phenytoin, developmental changes in infants significantly influenced phenytoin clearance independent of body size, and authors suggest the role of CYP2C9 and CYP2C19 maturation and its influence on the drug's clearance at early stages of life. Further, their results show that three‐quarters of the enteral dose reaches the systemic circulation, the authors suggest this may be attributed to changes in gastric pH and intestinal motility early in life. ⁷² This may be important to consider when switching patients from intravenous to oral formulations of the drug.

Topiramate

Topiramate is a broad‐spectrum ASM approved for initial monotherapy for patients ≥2 years old with FOS or primary generalized tonic–clonic seizures and as adjunctive therapy in children ≥2 years old with FOS, primary generalized tonic–clonic seizures, or seizures associated with Lennox–Gastaut syndrome. ⁷³ The area under the concentration vs. time curve increases linearly with doses over the 100 to 1200 mg range, and approximately 50% of the dose is excreted renally. ⁷⁴ Suboptimal dosing of topiramate can result from PK variability due to patient factors like body size and age. PopPK studies in children demonstrate that sources of variability such as body weight, age, and DDIs with other ASMs influence topiramate clearance. ²⁹ , ⁷⁵ Using topiramate as an example, Gidal et al. aimed to assess the clinical relevance of effective half‐life (t _1/2eff), or rate of drug loss during the entire dosing interval, compared to t_1/2z for ER drugs with multi‐compartment kinetics. The investigation, based on a phase I study of USL255 (Qudexy® XR) and immediate‐release topiramate (TPM‐IR), revealed similar t _1/2z values but a 1.5‐fold longer t _1/2eff for USL255 (Qudexy® XR). Their study revealed that t_1/2z was insensitive to formulation differences while t _1/2eff was greater for the ER over the IR formulation (55.7 vs. 37.1 h), suggesting that t_1/2eff may be a more meaningful parameter than t_1/2z for determining dosing recommendations, highlighting potential discrepancies, and emphasizing the importance of considering sampling duration in PK assessments. ⁷⁶

Valproic acid

Valproic acid has FDA approval for use in both adults and children for mono‐ and adjunctive therapy to treat seizures and bipolar disorder. It is FDA‐approved for children 10 years and older and is not recommended for children <2 years of age due to the risk of drug‐induced acute liver injury. ⁴⁸ Valproic acid undergoes therapeutic drug monitoring due to its narrow therapeutic index, and the recommended therapeutic range for total valproic acid is 50–100 mcg/mL. ⁴³ The drug is highly protein‐bound to albumin (>90% bound) and displays saturable, concentration‐dependent protein binding; variations in protein levels in pediatric patients may affect the free drug concentration, influencing both efficacy and toxicity. Further, it can displace certain protein‐bound drugs such as phenytoin. The drug is well‐absorbed after oral administration, and valproic acid is almost entirely metabolized by the liver (>95%), involving hepatic glucuronidation and mitochondrial β‐oxidation. ⁴³ In children ages 2 years and above, age‐related changes in hepatic enzyme activity can significantly influence valproic acid clearance, leading to variations in drug metabolism. ⁷⁷ Additionally, the drug is susceptible to DDIs with other ASMs, such as carbamazepine, phenytoin, and phenobarbital, necessitating careful monitoring when co‐administered with other medications.

A maturation model in a popPK study was utilized by Gu et al., ⁷⁸ which indicated that age and body weight play a role in the maturation of children under 2 years, with body weight being the primary factor for those over 2 years of age. The early age‐dependent PK may reflect the maturation of liver drug enzymes, specifically UGT enzymes involved in valproic acid elimination, which reach adult levels at different stages during the early years of life. Since the metabolism of valproic acid occurs primarily in the liver by UGT and CYP enzymes, there is interest in identifying and characterizing genetic polymorphisms involved in valproic acid metabolism. ⁷⁸ Mei et al. aimed to determine the effect of CYP2C19, UGT1A8, and UGT2B7 on valproic acid clearance in children and found four combined genotypes significantly influencing valproic acid clearance. Further, they concluded that CYP450 metabolism is more important than UGT metabolism in children than in adults due to the higher CYP450 activity in children even though CYP metabolism only accounts for 15–20% of VPA metabolism. ⁷⁹

OPPORTUNITIES TO STUDY CLINICAL PHARMACOLOGY IN CHILDREN

Novel trial designs in younger pediatric populations

Traditional PK studies in children pose major ethical and logistical concerns. These concerns include the limited blood volume that can be safely drawn, the need to collect data across the pediatric age groups to study potential age‐related changes in PK, and low parental consent rates. ⁸⁰ On the contrary, opportunistic PK studies utilize blood samples collected from children as a part of routine care. Sparse opportunistic sampling is obtaining additional blood samples from patients while clinical samples are ordered, which minimizes additional punctures, while scavenged sampling utilizes leftover residual blood from clinical samples. ⁸⁰ The multicenter study, “Pharmacokinetics, Pharmacodynamics, and Safety Profile of Understudied Drugs Administered to Children Per Standard of Care” trial (ClinicalTrials.gov #NCT04278404) under the Pediatric Trials Network utilizes an opportunistic PK study approach to evaluate the PK of >50 drugs. These opportunistic PK studies provide real‐world data in children that can often be hard to obtain to help inform PK models and ASM dosing in understudied pediatric populations. The multicenter, prospective, open‐label, PK and safety study, “Pharmacokinetics of Anti‐epileptic Drugs in Obese Children and Adolescents” (AED01, NICHD‐2015‐AED01, clinicaltrials.gov #NCT02993861), investigates 4 ASMs in children and adolescents with obesity (≥2 years of age). This study provides an excellent opportunity to leverage real‐world data in this understudied pediatric population to inform dosing for children with obesity.

Various novel study designs, such as minimal‐risk studies, opportunistic drug protocols, and the use of non‐plasma biological fluids, have been proposed to evaluate drug disposition to reduce the frequency of blood sampling in infants. ⁸¹ These strategies could be applied to inform optimal ASM dosing for infants with seizures since drug choices for this patient population are limited due to inherent difficulties in performing studies. The International Neonatal Consortium (INC), which comprises experts from academia, industry, regulatory, nursing, and patient advocacy groups, developed recommendations for designing trials for treating neonatal seizures in 2018. Their guideline includes a seizure treatment paradigm and recommendations for inclusion/exclusion criteria, treatment arms, and trial design. This group largely advocates for the need that neonates deserve evidence‐based therapies and proposes guidelines to conduct efficient and successful drug trials to improve treatment and outcomes in this patient population. ⁸² Discussion among epilepsy experts revealed that traditional designs involving patients with epilepsy or seizures have high demands and are outdated, such as requiring high baseline seizure frequency, two hospitalizations for EEG monitoring, and willingness to potentially receive placebo treatment. ⁸³ In 2019, ILAE and Pediatric Epilepsy Research Consortium (PERC) presented a novel clinical trial design to study new ASMs in children ages 1 month to 4 years old using “time to Nth seizure” as the primary outcome. Their proposed trial design has the potential to include more patients, reduced risks from placebo exposure, and seizure counting by caregivers, all of which could reflect more real‐world treatment of infants with FOS. ⁸³

Role of extrapolation of efficacy in epilepsy and seizure treatment

Epilepsy experts have determined that extrapolation of efficacy of ASMs from adults to children is appropriate and the FDA supports this recommendation as they have published a guidance and analysis for this work. The approval of eslicarbazepine acetate, lacosamide, and brivaracetam has been approved in children 4 years and older for FOS based on extrapolation. More recently, the U.S. FDA concluded the efficacy of drugs approved for FOS can be extrapolated to pediatric patients 1 month of age and older based on an analysis by Mehrotra et al. where they conducted an exposure‐response relationship analysis of 8 drugs (levetiracetam, oxcarbazepine, topiramate, lamotrigine, gabapentin, perampanel, tiagabine, and vigabatrin) approved for FOS in adults and pediatric patients. ¹²

Several examples of extrapolation of efficacy using popPK modeling of ASMs to the pediatric population exist in the literature (Table 2). Schoemaker et al. used model‐informed meta‐analysis where they used an existing PK/PD model for brivaracetam, and this was applied to an adult‐pediatric dataset of levetiracetam since these two drugs belong to the same drug class. They conducted dosing simulations using the adult PK/PD model in combination with the popPK model in pediatrics to assess exposure‐response and predict efficacious doses of brivaracetam in children with FOS ages 4–16 years old. ¹⁰ The U.S. FDA utilized a PK/PD bridging approach to approve topiramate for initial monotherapy in pediatric patients ages 2–9 years old with FOS and generalized tonic–clonic seizures. This bridging approach was possible under specific conditions, requiring available PK data from (1) pediatric patients, (2) previous placebo‐controlled adjunctive trials in adults and pediatric patients demonstrating therapeutic efficacy, and (3) adult monotherapy trials demonstrating efficacy. The authors utilized a popPK model for exposure‐response analysis in monotherapy trials using a Cox proportional hazard model to correlate steady‐state trough concentrations to time‐to‐first seizure and dosing simulations to determine monotherapy dosing in pediatric patients. A PK/PD bridging method was also successfully employed in the approval of oxcarbazepine as monotherapy for pediatric subjects with epilepsy; both scenarios eliminate the need for a new clinical trial in pediatric subjects. ¹⁴ , ⁸⁴ A model‐informed precision dosing guidance for ethosuximide for childhood absence epilepsy (CAE) utilizing popPK modeling and exposure‐response analysis using seizure status as a PD marker was developed by Mizuno et al. Drug exposure measures and clinical seizure status were used for the exposure‐response analysis, and a logistic regression equation was used to predict the probability of seizure freedom. Through these approaches, the study proposed optimal dosing guidance for ethosuximide in newly diagnosed patients with CAE, identifying target exposure estimates and corresponding daily doses associated with a 50% and 75% probability of achieving a seizure‐free response. ⁸⁵

TABLE 2.

Examples of extrapolation of efficacy studies for focal onset seizures and other seizure types in the pediatric population.

Antiseizure medication	Data analysis approach	Key findings
Brivaracetam	Model‐based meta‐analysis by using a combined brivaracetam adult and pediatric population PK/PD model and application of the model to a combined adult and pediatric PK/PD dataset of levetiracetam ¹⁰	No difference was found in PK/PD drug‐related parameters between adults and children with FOS Supports the use of adult brivaracetam PK/PD model to predict efficacious doses in children ≥4 years with FOS
Eslicarbazepine Acetate	Model‐based exposure matching in pediatric subjects (4–17 years) and adults via popPK modeling and simulation ¹¹	Supported the extrapolation of efficacy from adults to derive pediatric doses Supports submission to obtain U.S. FDA approval for adjunctive and monotherapy in pediatric patients 4–17 years
Gabapentin, Lamotrigine, Levetiracetam, Oxcarbazepine, Perampanel, and Topiramate	popPK modeling and use of observed data to compare of exposures at approved doses in adults and pediatric patients, comparison of placebo response, and comparison of exposure‐response in adults and pediatric patients using subject‐level data ¹²	U.S. FDA concluded the efficacy of drugs approved for FOS can be extrapolated to pediatric patients 1 month of age and older
Oxcarbazepine	PK/PD bridging approach by leveraging existing PK/PD data in adults and pediatric patients using oxcarbazepine for adjunctive therapy ⁸⁴	Supports the indication of oxcarbazepine as monotherapy in pediatric subjects with epilepsy
Pregabalin	popPK modeling and exposure‐response analysis to confirm pediatric dosage recommendations in children ages 4 to 16 years with FOS ¹³	Successfully demonstrated similar exposure‐response relationship between children and adults with FOS Supports pediatric dosing recommendations for FOS in the U.S. FDA package label
Tiagabine and Vigabatrin	Comparison of range of approved doses, dosing regimen, and response at approved doses in adults and pediatric patients; model‐based analysis of exposure‐response analysis for vigabatrin ¹²	U.S. FDA concluded the efficacy of drugs approved for FOS can be extrapolated to pediatric patients 1 month of age and older
Topiramate	PK/PD bridging approach using popPK modeling and a Cox proportional hazard model to correlate steady‐state trough concentrations to time‐to‐first seizure and dosing simulations to determine monotherapy dosing in pediatric patients ¹⁴	Led to the U.S. FDA approval for initial monotherapy in pediatric patients ages 2–9 years old with FOS and generalized tonic–clonic seizures

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Abbreviations: FOS, focal onset seizures; PK/PD, pharmacokinetic/pharmacodynamic; popPK, population pharmacokinetics; U.S. FDA, United States Food and Drug Administration.

Adopting model‐informed drug development, specifically using extrapolation, can be an effective strategy to expedite the approval process for pediatric use. In the case of pregabalin, a popPK and exposure‐response analysis was utilized to confirm pediatric dosage recommendations of this drug in children ages 4–16 years with FOS. Pregabalin is currently approved for children aged 1 month and older. However, this was only approved after Phase I, Phase III safety and efficacy and long‐term safety trials and 15 years after approval for FOS in adults. Chan et al. ¹³ suggest that if extrapolation had been employed, regulatory approval could be obtained much earlier. The authors advocate for using innovative and quantitative approaches, such as popPK analysis and PBPK modeling, to support efficacy and PK extrapolation, potentially reducing the reliance on extensive clinical trials, especially in the pediatric phase I studies for renally eliminated drugs. This approach can potentially streamline drug development, ensuring earlier access to novel treatments for pediatric patients.

Use of PBPK modeling

PBPK models account for physiological changes and DDI mechanisms to accurately predict drug disposition in pediatrics. This method integrates physiological information and drug‐specific properties to predict drug disposition throughout the body. PBPK models can predict a priori drug exposure in pediatric patients based on a validated adult model incorporating in‐vitro data, physiochemical properties, and clinical data. Since specific physiological parameters vary as a function of maturation, PBPK can implement these changes that may alter drug disposition. PBPK modeling can predict DDI potential in pediatric patients based on adult DDI data and experimental parameters, potentially circumventing the need to conduct prospective pediatric DDI studies.

The PBPK modeling technique could be applied to investigate ASM disposition about obesity, maturation, and DDIs (Figure 2). This method can be used to assess optimal dosing in children with epilepsy and obesity since only 1 ASM (i.e., levetiracetam) has dosing information in its package label related to obesity. PBPK models offer advantages over popPK models by describing developmental and physiological changes in children to capture the effect of age and body size on disposition. Since PBPK modeling offers the advantage of integrating physiological parameters (i.e., increased organ size and blood flow), drug parameters (i.e., physicochemical properties and metabolism), and known efficacy targets, it makes it feasible to study the effects of obesity on drug disposition. Due to the difficulty of conducting appropriate PK studies in neonates and infants, PBPK modeling can inform dosing in this patient population by incorporating maturation changes in metabolic and renal clearance pathways. PBPK modeling is widely used to predict adult DDI potential, but limited examples exist in pediatrics. Since ASMs are used across the pediatric age continuum, there is value in evaluating DDIs among ASMs and other commonly used medications via PBPK modeling.

Application of physiologically based pharmacokinetic (PBPK) modeling to optimize the use of antiseizure medications (ASMs) in the pediatric population. PBPK model development involves (1) creating building blocks such as gathering drug‐specific and system‐specific information, (2) leverage and digitize literature PK data in adults and children, and develop an adult PBPK model, (3) scale the adult model to pediatrics, and (4) perform dosing simulations. Applications of PBPK modeling in children include investigating the effects of obesity‐related physiological changes in children with obesity, studying the effect of developmental changes, and characterizing drug–drug interactions. Figure created in Biorender.com. AAG, alpha1‐acid glycoprotein; CYP, Cytochrome P450; DDI, drug–drug interaction; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetics; UGT, Uridine 5′‐diphospho‐glucuronosyltransferase.

A growing number of PBPK models exist for ASMs, with most of the studies leveraging published adult and pediatric literature data (Table 3). To better understand valproic acid toxicity in children <10 years old, Huang et al. leveraged PBPK modeling to investigate the impact of enzyme ontogeny on hepatotoxic risk and proposed dosing regimens that minimize this serious adverse effect in this age group. ⁸⁶ A PBPK model investigated cannabidiol exposure in healthy and hepatically impaired adults and children. ⁸⁷ This supports the applicability of PBPK modeling to successfully predict drug exposures when sparse clinical data is available, and future applications include assessing cannabidiol‐drug or cannabidiol‐disease interactions in special populations. PBPK modeling can be applied to determine DDIs in children, as performed by Zhao et al. to examine DDI between tacrolimus and phenobarbital and Conner et al. to examine DDI between lamotrigine and enzyme‐inducing and enzyme‐inhibiting drugs. ⁸⁸ , ⁸⁹ Further, Sinha, et al. constructed PBPK models for levetiracetam and oxcarbazepine to delineate the effect of age and DDI potential of enzyme‐inducing ASMs in children 2 years and above. ⁶³ PBPK can also be leveraged to investigate drug absorption and dissolution to optimize formulations in the pediatric population, as conducted by Pawar et al. and Kohlmann et al., with carbamazepine as the drug of interest. ⁹⁰ , ⁹¹

TABLE 3.

Representative PBPK studies of ASMs in the pediatric population discussed in this review.

Antiseizure medication	Pediatric application
Cannabidiol ⁸⁷	Development of PBPK model to explore cannabidiol exposure in healthy and hepatically impaired adults, and children
Carbamazepine ⁹⁰ , ⁹¹	Investigate age‐dependent changes in oral absorption and dissolution profile in the pediatric population
Lamotrigine ⁸⁹	Examine DDI between lamotrigine and enzyme‐inducing and enzyme‐inhibiting drugs via PBPK modeling
Levetiracetam ⁶³ Oxcarbazepine ⁶³	Delineate the effect of age and DDI potential of enzyme‐inducing ASMs in children 2 years and above
Phenobarbital ⁸⁸	Development of PBPK models to examine DDI between tacrolimus and phenobarbital, and different formulations of phenobarbital
Valproic Acid ⁸⁶	Use of PBPK modeling to understand valproic acid toxicity in children <10 years old and investigate the impact of enzyme ontogeny on hepatotoxicity risk

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Abbreviations: ASMs, antiseizure medications; DDI, drug–drug interaction; PBPK, physiologically based pharmacokinetic.

CURRENT CHALLENGES AND FUTURE DIRECTIONS

Clinical pharmacology studies of ASMs in the pediatric population face several challenges and hold promising future directions. One of the primary challenges is the limited number of pediatric patients available for research, particularly in certain age groups. This scarcity can make it difficult to conduct comprehensive studies and generate sufficient data to establish optimal dosing regimens, safety profiles, and efficacy in children. Ethical considerations and parental consent pose additional challenges when recruiting pediatric participants. Using opportunistic studies can circumvent this challenge since this methodology minimizes the need for additional clinical visits or punctures, and patients are already taking the drug per standard of care.

Another challenge is evaluating the efficacy and characterizing exposure‐response relationships for ASMs in the pediatric population. Firstly, the heterogeneity in the pediatric population, including differences in age, weight, and developmental stages, complicates the extrapolation of findings from adult studies. Children may metabolize drugs differently, and their response to treatment can vary widely. Second, seizure types and etiologies in children are diverse, making it challenging to establish standardized endpoints for efficacy assessments. Epilepsy experts discussed how advancements and scientific discoveries in the field of epilepsy warranted the revision of seizure and epilepsy classification since classification drives ASM selection and management. With this, the ILAE approved and updated the classification of seizure types and epilepsy syndromes in 2017. ⁹² , ⁹³ With the update on seizure and epilepsy classification, more research is needed to determine the role of extrapolation in other seizure types and epilepsy syndromes. This is demonstrated by Arzimanoglou et al. where they concluded that there is sufficient literature and data on extrapolating lacosamide dosing to pediatric populations with FOS but the feasibility of extrapolation was difficult in the setting of other epilepsy types. ⁹⁴ Third, ASMs are generally titrated to effect in clinical practice and it is often difficult to collect this information with sparse sampling and opportunistic studies. Unless a patient is taking a narrow therapeutic index drug such as phenobarbital or phenytoin, where drug levels are associated with efficacy and toxicity, most ASMs may have a limited correlation between plasma concentrations and efficacy or toxicity. This poses a challenge when identifying optimal dosing regimens via dosing simulations, and researchers are left to utilize target attainment metrics that may not translate clinically. Despite these obstacles, advancements in PK/PD modeling and innovative trial designs are essential to address these challenges and ensure the safe and effective use of ASMs in pediatric patients.

Longitudinal studies that span the developmental spectrum are necessary to capture the evolving nature of epilepsy in childhood and to assess the long‐term safety of ASMs. Still, these types of studies have their own challenges. The evolving nature of neurological and cognitive functions in children necessitates prolonged observation to capture potential adverse effects that may manifest over time. Long‐term safety assessments must also account for the diverse spectrum of pediatric epilepsy diseases, each with unique characteristics, making it challenging to generalize findings. As described earlier, phenobarbital and phenytoin are first‐line treatments for neonatal seizures, while levetiracetam is increasingly used as an off‐label treatment for this indication. There are concerns over the long‐term neurodevelopmental impact of phenobarbital, but this has not been fully characterized in humans. There is conflicting evidence over the long‐term neurodevelopment outcomes in newborns who were given levetiracetam versus phenobarbital. One meta‐analysis concludes levetiracetam to be safer than phenobarbital in terms of short‐term adverse effects and found no difference in the risk of neurodevelopmental impairment between the two groups. ⁹⁵ While another concludes patients taking levetiracetam have better neurodevelopmental outcomes over patients taking phenobarbital. ⁹⁶

The increasing application of artificial intelligence (AI) and machine learning (ML) can significantly impact the diagnosis and treatment paradigm for children with epilepsy. AI and ML algorithms can efficiently analyze large datasets to identify patterns, correlations, and factors contributing to variability in drug response. The use of AI in pediatric epilepsy research has been reviewed by Kim et al. where they provided examples of AI application in various areas of epilepsy care such as the use of electronic health records (EHR) to predict treatment response and adverse drug reactions. ⁹⁷ Huang et al. demonstrated the accuracy of an AI neural network they developed in the early prediction of refractory epilepsy in children by leveraging EEG dataset and signal data. ⁹⁸ Further, the area of seizure forecasting with AI to tailor ASM dosing and frequency is a growing area of interest since this could lead to anticipatory administration of an ASM which could alleviate maintenance therapy and reduce long‐term adverse effects. ⁹⁹

The utilization of modeling and simulation in studying the clinical pharmacology of ASMs in the pediatric population has significantly advanced over the last several years. The ability to extrapolate efficacy from adults to pediatric patients until the age of 1 month for FOS has allowed the approval of some ASMs in younger age groups, expanding treatment options. However, it is important to note that safety and PK data cannot be appropriately extrapolated from adults to children, and these types of studies would need to be conducted. PBPK modeling provides an excellent platform to investigate sources of PK/PD variability of ASMs in the pediatric population due to its ability to integrate physiological changes and drug properties to predict drug disposition without the need for robust clinical data. Integrating PBPK modeling into regulatory decision‐making processes may streamline drug approvals for pediatric populations, ensuring that children receive medications tailored to their specific needs while minimizing the risk of adverse effects. Overall, the future utilization of AI/ML and PBPK/PD modeling in pediatric drug development can significantly improve childrens' healthcare quality, fostering a safer and more efficient approach to therapeutic interventions.

CONCLUSIONS

ASMs display unique and complex PK characteristics, and incorporating this information via modeling and simulation could lead to drug optimization in the pediatric population, especially neonates and infants. The ability to extrapolate efficacy data from adults to children 1 month and older for FOS approved by the FDA will continue to expand treatment options. The use of PBPK modeling to incorporate developmental and physiological changes to elucidate the disposition of drugs in understudied pediatric populations is a powerful and novel technique to optimize the efficacy and safety of these medications. By investigating sources of PK and PD variability through modeling and simulation and leveraging real‐world data, these methodologies can transform how ASMs are dosed in the pediatric population, leading to optimal dosing, better health outcomes, and increased quality of life for children with epilepsy and seizures. Ultimately, these approaches could be extended to existing and ASMs under development to broaden treatment options for this patient population and achieve seizure control.

FUNDING INFORMATION

This research was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under awards R01HD096435, R01HD102949, and K24AI143971. P.D.M. receives support from the Eunice Kennedy Shriver NICHD of the NIH under Award Number T32HD104576. The content is solely the authors' responsibility and does not necessarily represent the official views of the NIH.

CONFLICT OF INTEREST STATEMENT

The authors do not have relevant conflicts of interest.

Maglalang PD, Wen J, Hornik CP, Gonzalez D. Sources of pharmacokinetic and pharmacodynamic variability and clinical pharmacology studies of antiseizure medications in the pediatric population. Clin Transl Sci. 2024;17:e13793. doi: 10.1111/cts.13793

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PERMALINK

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

Patricia D Maglalang

Jiali Wen

Christoph P Hornik

Daniel Gonzalez

Abstract

INTRODUCTION

FIGURE 1.

SOURCES OF PK VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

Body weight

Maturation

Drug–drug interactions

Other sources of PK variability of ASMs

SOURCES OF PD VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

SELECT COMMONLY USED ASM IN CHILDREN

TABLE 1.

Levetiracetam

Oxcarbazepine

Phenobarbital

Phenytoin

Topiramate

Valproic acid

OPPORTUNITIES TO STUDY CLINICAL PHARMACOLOGY IN CHILDREN

Novel trial designs in younger pediatric populations

Role of extrapolation of efficacy in epilepsy and seizure treatment

TABLE 2.

Use of PBPK modeling

FIGURE 2.

TABLE 3.

CURRENT CHALLENGES AND FUTURE DIRECTIONS

CONCLUSIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

REFERENCES

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

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

Patricia D Maglalang

Jiali Wen

Christoph P Hornik

Daniel Gonzalez

Abstract

INTRODUCTION

FIGURE 1.

SOURCES OF PK VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

Body weight

Maturation

Drug–drug interactions

Other sources of PK variability of ASMs

SOURCES OF PD VARIABILITY AND EFFECT ON PATIENT RESPONSE TO ASM TREATMENT

SELECT COMMONLY USED ASM IN CHILDREN

TABLE 1.

Levetiracetam

Oxcarbazepine

Phenobarbital

Phenytoin

Topiramate

Valproic acid

OPPORTUNITIES TO STUDY CLINICAL PHARMACOLOGY IN CHILDREN

Novel trial designs in younger pediatric populations

Role of extrapolation of efficacy in epilepsy and seizure treatment

TABLE 2.

Use of PBPK modeling

FIGURE 2.

TABLE 3.

CURRENT CHALLENGES AND FUTURE DIRECTIONS

CONCLUSIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

REFERENCES

ACTIONS

PERMALINK

RESOURCES

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