Adverse Event Profiles of Antiseizure Medications and the Impact of Coadministration on Drug Tolerability in Adults with Epilepsy

Abstract

Background: Antiseizure medication (ASM) as monotherapy or in combination is the treatment of choice for most patients with epilepsy. Therefore, knowledge about the typical adverse events (AEs) for ASMs and other coadministered drugs (CDs) is essential for practitioners and patients. Due to frequent polypharmacy, it is often difficult to clinically assess the AE profiles of ASMs and differentiate the influence of CDs.

Objective: This retrospective analysis aimed to determine typical AE profiles for ASMs and assess the impact of CDs on AEs in clinical practice.

Methods: The Liverpool AE Profile (LAEP) and its domains were used to identify the AE profiles of ASMs based on data from a large German multicenter study (Epi2020). Following established classifications, drugs were grouped according to their mode of action (ASMs) or clinical indication (CDs). Bivariate correlation, multivariate ordinal regression (MORA), and artificial neural network (ANNA) analyses were performed. Bivariate correlation with Fisher's z-transformation was used to compare the correlation strength of LAEP with the Hospital Anxiety and Depression Scale (HADS) and Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) to avoid LAEP bias in the context of antidepressant therapy.

Results: Data from 486 patients were analyzed. The AE profiles of ASM categories and single ASMs matched those reported in the literature. Synaptic vesicle glycoprotein 2A (SV2A) and voltage-gated sodium channel (VGSC) modulators had favorable AE profiles, while brivaracetam was superior to levetiracetam regarding psychobehavioral AEs. MORA revealed that, in addition to seizure frequency, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) modulators and antidepressants were the only independent predictors of high LAEP values. After Fisher's z-transformation, correlations were significantly lower between LAEP and antidepressants than between LAEP and HADS or NDDI-E. Therefore, a bias in the results toward over interpreting the impact of antidepressants on LAEP was presumed. In the ANNA, perampanel, zonisamide, topiramate, and valproic acid were important nodes in the network, while VGSC and SV2A modulators had low relevance for predicting relevant AEs. Similarly, cardiovascular agents, analgesics, and antipsychotics were important CDs in the ANNA model.

Conclusion: ASMs have characteristic AE profiles that are highly reproducible and must be considered in therapeutic decision-making. Therapy using perampanel as an AMPA modulator should be considered cautiously due to its relatively high AE profile. Drugs acting via VGSCs and SV2A receptors are significantly better tolerated than other ASM categories or substances (e.g., topiramate, zonisamide, and valproate). Switching to brivaracetam is advisable in patients with psychobehavioral AEs who take levetiracetam. Because CDs frequently pharmacokinetically interact with ASMs, the cumulative AE profile must be considered.

Trial registration: DRKS00022024, U1111-1252-5331.

Fig. 1

Tolerability of ASMs as mono and dual therapies: the tolerability of ASM categories measured by the Liverpool Adverse Events Profile (LAEP) in a mono and b dual therapies is shown based on Pearson’s r for bivariate correlations with total LAEP scores (for color coding of the correlation coefficient, see bar chart on the right). Significant correlations are highlighted with an asterisk. Missing values are displayed in gray, and unusual combinations of ASMs of the same category are crossed out. ASM antiseizure medication, VGSC voltage-gated sodium channel, SV2A synaptic vesicle glycoprotein 2A, GABA gamma-aminobutyric acid, AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, VPA valproic acid, ESM ethosuximide, TPM topiramate, ZNS zonisamide

Fig. 2

AE profiles of ASMs: a the individual AE profiles of different ASM categories measured by the Liverpool Adverse Events Profile (LAEP) are shown as a heatmap of Pearson’s r values for bivariate correlations with LAEP total and subdomain scores (for color coding of the correlation coefficient, see bar chart on the right). Individual AE profiles are shown for the frequently used ASM categories: b VGSC and c SV2A modulators. CBZ, OXC, and ESL are collectively referred to as DBZs. Significant correlations are highlighted with an asterisk. ASM antiseizure medication, VGSC voltage-gated sodium channel, SV2A synaptic vesicle glycoprotein 2A, GABA gamma-aminobutyric acid, AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, VPA valproic acid, ESM ethosuximide, TPM topiramate, ZNS zonisamide, DBZ dibenzazepine, LTG lamotrigine, LCM lacosamide, LEV levetiracetam, BRV brivaracetam

Fig. 3

MORA: A forest plot of the multivariate analysis results identifying independent predictors for increased AE profiles. In addition to seizure frequency as a clinical epilepsy severity marker, only AMPA receptor modulators and antidepressant therapies remained statistically relevant predictors. ASM antiseizure medication, VGSC voltage-gated sodium channel, SV2A synaptic vesicle glycoprotein 2A, GABA gamma-aminobutyric acid, AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, VPA valproic acid, ESM ethosuximide, TPM topiramate, ZNS zonisamide, CI confidence interval, LAEP Liverpool Adverse Events Profile

Fig. 4

ANNA: concentric polar plots of the normalized importance of nodes within the ANNA network for a ASM categories, comparing relevant AE profiles for VGSC and SV2A modulators to ZNS, TPM, VPA, and AMPA modulators, and b CD categories. ASM antiseizure medication, VGSC voltage-gated sodium channel, SV2A synaptic vesicle glycoprotein 2A, GABA gamma-aminobutyric acid, AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, VPA valproic acid, TPM topiramate, ZNS zonisamide