Evidence map›Paper›PMID 41902701›Full record

ArticleEpilepsia2026

Ranking of antiseizure medications in a panel of focal seizure models predicts their comparative efficacy in clinical add-on trials in drug-resistant focal epilepsy.

Wolfgang Löscher, Pavel Klein

Abstract readComparative Study
In one paragraph

Article in Epilepsia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wolfgang LöscherTranslational Epilepsy Research Group, NIFE, Department of Experimental Otology of the ENT Clinics, Hannover Medical School, Hannover, Germany.ORCID https://orcid.org/0000-0002-9648-8973
Pavel KleinMid-Atlantic Epilepsy and Sleep Center, Bethesda, Maryland, USA.ORCID https://orcid.org/0000-0001-7244-3722

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMost antiseizure medications (ASMs) have been discovered by testing in animal models, which are generally thought to predict antiseizure activity in patients. However, it is not known whether any of these models (or a combination of models) can predict whether a novel ASM exhibits higher clinical efficacy in focal drug-resistant epilepsy (DRE) than benchmark ASMs such as carbamazepine or levetiracetam. This important question has plagued epilepsy drug discovery for over three decades and serves as the basis of the present analysis.

methodsThe present analysis includes 23 ASMs that the U.S. Food and Drug Administration (FDA) approved for the treatment of focal-onset seizures. For assessing comparative preclinical activity, we used the median effective dose (ED

resultsExcept for the pilocarpine model, none of the seizure models predicted the comparative efficacy of add-on treatment with ASMs in focal DRE. However, when we combined the ED SIGNIFICANCE: The present study shows that ranking of ASMs across a panel of mechanistically complementary but agnostic models of focal-onset seizures yields greater translational accuracy and clinical predictivity for patients with focal DRE than rankings based on any single model. This novel finding can be used when defining a path forward that best identifies the most promising investigational compounds to advance to more expensive and time-consuming clinical investigations.

Indexed as

AnticonvulsantsDrug Resistant EpilepsyEpilepsies, PartialSeizuresAnimalsDisease Models, AnimalHumansPilocarpineRatsTreatment OutcomeAnticonvulsantsPilocarpineanimal modelsdrug resistancefocal‐onset seizureskindlingpilocarpinepredictive validity

Identifiers

PMID41902701
PMCPMC13360943

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.