Drug Resistance in Epilepsy: Clinical Impact, Potential Mechanisms, and New Innovative Treatment Options

Abstract

Epilepsy is a chronic neurologic disorder that affects over 70 million people worldwide. Despite the availability of over 20 antiseizure drugs (ASDs) for symptomatic treatment of epileptic seizures, about one-third of patients with epilepsy have seizures refractory to pharmacotherapy. Patients with such drug-resistant epilepsy (DRE) have increased risks of premature death, injuries, psychosocial dysfunction, and a reduced quality of life, so development of more effective therapies is an urgent clinical need. However, the various types of epilepsy and seizures and the complex temporal patterns of refractoriness complicate the issue. Furthermore, the underlying mechanisms of DRE are not fully understood, though recent work has begun to shape our understanding more clearly. Experimental models of DRE offer opportunities to discover, characterize, and challenge putative mechanisms of drug resistance. Furthermore, such preclinical models are important in developing therapies that may overcome drug resistance. Here, we will review the current understanding of the molecular, genetic, and structural mechanisms of ASD resistance and discuss how to overcome this problem. Encouragingly, better elucidation of the pathophysiological mechanisms underpinning epilepsies and drug resistance by concerted preclinical and clinical efforts have recently enabled a revised approach to the development of more promising therapies, including numerous potential etiology-specific drugs ("precision medicine") for severe pediatric (monogenetic) epilepsies and novel multitargeted ASDs for acquired partial epilepsies, suggesting that the long hoped-for breakthrough in therapy for as-yet ASD-resistant patients is a feasible goal. SIGNIFICANCE STATEMENT: Drug resistance provides a major challenge in epilepsy management. Here, we will review the current understanding of the molecular, genetic, and structural mechanisms of drug resistance in epilepsy and discuss how the problem might be overcome.

Copyright © 2020 by The Author(s).

Figures

Fig. 1.

Introduction of antiseizure drugs (ASDs) to the market from 1853 to 2019. Licensing varied from country to country. We give here the year of first licensing or the first mention of clinical use in a country of Europe, the United States, or Japan. We have not included all derivatives of listed ASDs nor ASDs used solely for treatment of status epilepticus. The first generation of ASDs, entering the market from 1857 to 1958, includes potassium bromide, phenobarbital, and a variety of drugs that were mainly derived by modification of the barbiturate structure, including phenytoin, primidone, trimethadione, and ethosuximide. The second-generation ASDs, including carbamazepine, valproate, and the benzodiazepines, which were introduced between 1960 and 1975, differed chemically from the barbiturates. The era of the third-generation ASDs started in the 1980s with “rational” (target-based) developments such as progabide, vigabatrin, and tiagabine, i.e., drugs that were designed to selectively target a mechanism that was thought to be critical for the occurrence of epileptic seizures. The figure also illustrates the impact of preclinical seizure models on ASD development. The use of seizure models for drug screening started with the experiments performed by Merritt and Putnam in the 1930s, who used an electroshock seizure model in cats, leading to the discovery of phenytoin. Subsequently, the electroshock model was adapted to rodents and, together with chemical seizure models, used for drug screening in diverse laboratories, leading to discovery of various additional ASDs. In 1975, the NIH/NINDS ASP was established in the United States as part of a larger Antiepileptic Drug Development program to promote industry interest in ASD development. Since its start, the seizure tests have been performed at a contract facility based at the University of Utah, using three rodent models, i.e., the maximal electroshock seizure (MES) test, the pentylenetetrazole (PTZ) seizure test, and the rotarod test for assessing neurotoxicity. Later, other seizure models were added. The seizure tests were performed on a blinded and confidential basis and at no cost to the ASP participants, thus providing opportunities for researchers from academia and industry in the United States and abroad to submit compounds for screening in a battery of well established rodent seizure models. Approximately 32,000 compounds from more than 600 participants from 38 countries have been screened by this program, and the ASP has contributed to bringing nine currently available ASDs to market since 1990 (Kehne et al., 2017). More recently (2016), the ASP has been renamed Epilepsy Therapy Screening Program (ETSP) with the refocused mission to identify novel agents that will help address the considerable remaining unmet medical needs in epilepsy, particularly ASD-resistant seizures (Kehne et al., 2017). Figure modified from Löscher and Schmidt (2011). For further details, see text and Löscher et al. (2013a).

Fig. 2.

Pharmacoresistant epilepsy workflow for the Epilepsy Therapy Screening Program (ETSP). The figure has been provided by John Kehne and slightly modified for consistency with the text of this review. For details, see text and Kehne et al. (2017) and Wilcox et al. (2020).

Fig. 3.

Various potential mechanisms of ASD resistance or factors predicting poor outcome have been implicated in patients with epilepsy and animal models of medically resistant seizures, indicating that intrinsic or acquired resistance to ASDs is a multifactorial phenomenon. Based on these findings, a number of hypotheses of ASD resistance, including the target, transporter, network, intrinsic severity, and genetic variant hypotheses, have been suggested (see text). These hypotheses are not mutually exclusive but may be relevant for the same patient, thus complicating any strategy to counteract or reverse pharmacoresistance. Modified from Löscher et al. (2013a).

Fig. 4.

Differences between ASD responders and nonresponders in two animal models of DRE. For comparison, alterations associated with ASD resistance in patients are shown. Those alterations that occur both in the models and in patients are highlighted by the colored boxes. For details, see Löscher (2011), Löscher et al. (2013a), and Löscher (2016).

Fig. 5.

Selection and characterization of ASD responders and nonresponders by phenobarbital in a rat model of TLE in which spontaneous recurrent seizures (SRS) develop following sustained electrical stimulation of the basolateral amygdala. (A) Schematic illustration of selection of drug-resistant and drug-responsive epileptic rats by prolonged administration of phenobarbital. (B) Effect of phenobarbital (PB) on SRS. About 5 months after the electrically induced SE, SRS were recorded over a period of 2 weeks before onset of PB treatment (predrug control), followed by drug treatment of 2 weeks and then a 2-week postdrug control period. All data are shown as means ± S.E.M. The graphs in (B) show 1) average seizure data from 33 epileptic rats from three prospective experiments, 2) respective data from 20 responders, 3) data from 13 nonresponders, and 4) average plasma concentration of PB from the blood samples taken at the end of the treatment period. The shaded area indicates the therapeutic plasma concentration range of PB. In the responder group, PB significantly suppressed SRS compared with the pre- and postdrug periods (*P < 0.001). Note the higher average frequency of SRS in nonresponders versus responders. (C) Pgp expression in brain capillary endothelial cells of responders and nonresponders. Significant differences are indicated by asterisk (*P < 0.05). (D) Coadministration of PB and the Pgp inhibitor tariquidar lead to a significant (*P < 0.05) suppression of SRS in PB nonresponders. Three different doses of tariquidar (10, 15, and 20 mg/kg) were used, demonstrating a dose-dependent effect. Data are from Brandt et al. (2004), Volk and Löscher (2005), Brandt et al. (2006), Bethmann et al. (2007), and Brandt and Löscher (2014).

Fig. 6.

Schematic representation of the evidence-based pathologic links between inflammatory mediators and mechanisms of drug resistance. Inflammatory mediators (including but not limited to cytokines) may contribute to drug-resistant seizures mainly by three (nonmutually exclusive) pathways: 1) the induction of BBB dysfunction by promoting breakdown of tight junctions or inducing transocytosis, aberrant angiogenesis generating “leaky” vessels, and oxidative stress. The inflammatory phenotype of astrocytes is pivotal for these actions to take place, and reciprocally, BBB permeability changes may promote the expression of inflammatory molecules in astrocytes. This vicious cycle contributes to recurrent seizures, cell loss, and maladaptive neuronal network plasticity, therefore contributing to increase the “intrinsic severity” of the disease. Morever, BBB dysfunction will enhance albumin brain extravasation into the brain parenchyma and potentially increase the “buffering” effect of albumin binding to drugs, thus decreasing functionally relevant unbound drug levels at brain target sites. 2) Another mechanism is the induction of Pgp in endothelial cells, and likely in perivascular astrocytes, by specific inflammatory pathways involving COX2-PGE2-EP1R and the IL-1beta-IL-1R1 axis, thus contributing to the transporter hypothesis of drug resistance. 3) Inflammatory mediators can also induce post-translational modifications in voltage-gated and receptor-operated ion channels resulting in less responsive ASD targets, which may contribute to the pharmacodynamic (target) hypothesis of drug resistance. Details and references are reported in the main text.