P.036 Targeted molecular therapy with quinidine for seizures in a neonate with KCNT1 mutation leads to poor response
Bibliographic record
Abstract
Background: The KCNT1 gene encodes subunits of the Na+-activated K+ channel, widely expressed in the CNS. Mutations of this gene have been implicated in Malignant Migrating Partial Seizures of Infancy (MMPSI). This early-onset epileptic encephalopathy represents a challenge due to pharmacoresistance. The channel-specific mutation represents the potential for targeted pharmacotherapy. Quinidine is a partial antagonist of the KCNT1 encoded channel; patients with MMPSI have been reported to have responded to doses ranging 34.4/kg/d - 60mg/kg/d. We present a case of MMPSI with a KCNTI mutation (c.G1283A:p.R428Q) trialled on quinidine. Methods: Following ineffective trials of 6 anti-seizure medications, this patient was trialled on oral quinidine. This patient was titrated up to a dose of 52mg/kg/d. Twenty-four hour EEG monitoring prior to quinidine therapy, and at target dose were compared. Results: Prior to initiation of quinidine, this patient experienced 22 electrographic seizures over 24 hours. At target dose, this patient experienced greater than 70 seizures over 24 hours. Conclusions: Quinidine has previously been reported to be effective in patients with MMPSI with the same and different mutations. We report the second case of a patient with MMPSI and KCNT1 mutation R428Q with poor clinical response to quinidine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".