Drug-Induced Seizures: Considerations for Underlying Molecular Mechanisms
Bibliographic record
Abstract
A broad spectrum of chemical entities have been associated with drug-induced seizure (DIS), emphasizing the importance of this potential liability across various drug classes (e.g., antidepressants, antipsychotics, antibiotics, and analgesics among others). Despite its importance within drug safety testing, an understanding of the molecular mechanisms associated with DIS is often lacking. The etiology of DIS is understood to be a result of either a deficit in inhibitory (e.g., gamma aminobutyric acid) or an elevated excitatory (e.g., glutamate) signaling, leading to synchronous neuronal depolarization affecting various brain regions and impairing normal neurological functions. How this altered neuronal signaling occurs and how these changes interact with other non-brain receptor driven DIS-associated changes such as metabolic disturbances, electrolyte imbalances, altered drug metabolism, and withdrawal effects are poorly understood. Herein, we discuss important molecular mechanisms identified in DIS for several drugs and/or drug classes. With a better understanding of the molecular mechanisms associated with DIS, in vivo or in vitro models may be applied to characterize and mitigate DIS risk during drug development. Susceptibility stratification for DIS presents species differences in the following order beagle dogs > rodents and cynomolgus monkeys > Göttingen minipigs with a more than 2-fold difference between canines and minipigs, which is important to consider during non-clinical species selection. While clinical signs such as myoclonus, severe muscle jerks, or convulsions are often associated with abnormal epileptiform EEG activity, tremors are most of the time physiological and rarely observed with concurrent epileptiform EEG activity which need to be considered during DIS risk evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".