Experimental Models of Epilepsy: A Modern Classification Framework

Shu Cedric^*
*Correspondence: Shu Cedric, Department of Neurology, Guangxi Medical University, Nanning, China, Email:

Introduction

Epilepsy, a chronic neurological disorder characterized by spontaneous, recurrent seizures, affects millions of individuals worldwide and remains a significant clinical and scientific challenge. Despite advances in diagnostics and therapeutics, approximately one-third of patients continue to experience drug-resistant seizures, underscoring the need for a deeper understanding of the underlying mechanisms and the development of novel treatment strategies. Experimental models of epilepsy have played a foundational role in advancing our knowledge of seizure generation (ictogenesis), epileptogenesis (the process by which a normal brain becomes epileptic), and treatment response. However, as the field evolves, so too has the landscape of experimental modelling. From early electrical stimulation and chemoconvulsant models to genetically engineered animals and organoids, researchers now have a wide and complex arsenal of tools to mimic various forms of epilepsy. The challenge lies in categorizing these models effectively to ensure translational relevance, reproducibility, and targeted application. This review proposes a modern classification framework for experimental models of epilepsy, aiming to contextualize their strengths, limitations, and suitability for studying different aspects of the disorder. By re-evaluating how we classify and utilize these models, we can enhance their contribution to preclinical research and move closer to precision therapies [1].

Description

Experimental models of epilepsy can be broadly classified based on several key criteria: (1) Induction method (e.g., chemical, electrical, and genetic), (2) Seizure type or epilepsy syndrome mimicked, (3) Chronicity (acute vs. chronic), (4) Species and developmental stage, and (5) Pathophysiological relevance to human epilepsy. These overlapping dimensions provide the basis for a modern classification that is not limited to a single axis, but rather acknowledges the complexity and multi-layered nature of epilepsy as a disorder. One of the most widely used classification strategies is based on the method of seizure induction, dividing models into acute seizure models and chronic epilepsy models. Acute seizure models, such as those induced by Pentylenetetrazol (PTZ), kainic acid (KA), or Maximal Electroshock (MES), are used primarily for rapid screening of antiepileptic drugs (AEDs). These models do not typically reflect the long-term changes in brain structure or function associated with human epilepsy, but they are invaluable for understanding basic mechanisms of excitability and pharmacological response. In contrast, chronic models such as the kainate- or pilocarpine-induced status epilepticus models lead to the development of Spontaneous Recurrent Seizures (SRS), thereby more closely mirroring the natural course of epilepsy, including latent periods, neuronal loss, gliosis, and altered network dynamics [2].

Another critical category includes genetic models of epilepsy, which have become increasingly important with the identification of epilepsy-related gene mutations in human populations. These models are particularly relevant for studying childhood-onset epileptic encephalopathies and rare monogenic forms of epilepsy. Examples include the Scn1a knockout mouse model of Dravet syndrome, or mutations in Kcnq2 and Kcnq3 associated with benign familial neonatal seizures. Genetic models offer the advantage of recapitulating human genetic mutations and their pathophysiological consequences, but often lack the broader systemic complexity or environmental modulation seen in acquired epilepsy. An emerging and innovative area involves in vitro and ex vivo models, including organotypic hippocampal slice cultures, brain organoids, and Induced Pluripotent Stem Cell (iPSC)-derived neurons. These models allow precise manipulation of genetic and molecular variables and are increasingly used to study human-specific disease mechanisms, screen therapeutics, and investigate developmental epileptogenesis. However, their utility is often limited by the lack of intact circuitry, long-term seizure dynamics, and systemic factors like immune responses or vascular contributions [3].

In addition, non-rodent models including zebrafish, Drosophila, and non-human primates provide unique insights into epilepsy mechanisms across evolutionary scales. Zebrafish larvae, for instance, offer a high-throughput, transparent system with conserved neurotransmitter systems, suitable for genetic manipulation and drug screening. Non-human primates, while more ethically and economically challenging, offer a closer approximation to human brain complexity, particularly useful for testing neuromodulation or surgical interventions. When considering the utility of these models, it is critical to examine not just seizure type (e.g., generalized tonic-clonic, focal, absence), but also the epilepsy syndrome being modeled. Models of Temporal Lobe Epilepsy (TLE), the most common and pharmaco-resistant form in adults, have been extensively developed using chemoconvulsants like kainic acid and pilocarpine. Models of absence seizures, such as the WAG/Rij and GAERS rat strains, offer spontaneous, generalized spike-and-wave discharges that mimic childhood absence epilepsy. Yet, many experimental models fall short of replicating the full spectrum of comorbidities, such as cognitive impairment, psychiatric symptoms, or Sudden Unexpected Death in Epilepsy (SUDEP), limiting translational impact [4].

Thus, a modern classification must account not only for seizure induction and phenotypic output, but also for relevance to specific research goals. Is the model intended to explore mechanisms of epileptogenesis? To test anti-seizure drug efficacy? To model genetic contributions? To study brain networks and plasticity? Only by aligning the choice of model with clearly defined scientific aims can the field avoid misinterpretation and maximize translational value. When considering the utility of these models, it is critical to examine not just seizure type (e.g., generalized tonic-clonic, focal, absence), but also the epilepsy syndrome being modeled. Models of Temporal Lobe Epilepsy (TLE), the most common and pharmaco-resistant form in adults, have been extensively developed using chemoconvulsants like kainic acid and pilocarpine. Models of absence seizures, such as the WAG/Rij and GAERS rat strains, offer spontaneous, generalized spike-and-wave discharges that mimic childhood absence epilepsy meaningful comparisons across studies. Ultimately, the thoughtful categorization and utilization of experimental models will be central to unlocking new therapies and improving outcomes for people living with epilepsy [5].

Conclusion

The growing diversity of experimental epilepsy models reflects the increasing recognition that epilepsy is not a singular disease, but a complex, heterogeneous group of disorders with distinct etiologies, manifestations, and treatment responses. A modern classification framework must therefore be flexible, multidimensional, and purpose-driven categorizing models not only by how seizures are induced, but by the biological, genetic, and functional characteristics they replicate. As the field moves toward precision medicine, it becomes ever more important to match experimental tools with clinical realities, ensuring that preclinical findings are both mechanistically informative and translationally relevant. Future progress in epilepsy research will depend on our ability to refine existing models, develop more human-relevant systems, and adopt integrative classification schemes that guide.

Acknowledgement

None.

Conflict of Interest

None.

References

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