TMS as a pharmacodynamic indicator of cortical activity of a novel anti‐epileptic drug, XEN1101
Bibliographic record
Abstract
OBJECTIVE: Transcranial magnetic stimulation (TMS) produces characteristic deflections in the EEG signal named TMS-evoked EEG potentials (TEPs), which can be used to assess drug effects on cortical excitability. TMS can also be used to determine the resting motor threshold (RMT) for eliciting a minimal muscle response, as a biomarker of corticospinal excitability. XEN1101 is a novel potassium channel opener undergoing clinical development for treatment of epilepsy. We used TEPs and RMT to measure the effects of XEN1101 in the human brain, to provide evidence that XEN1101 alters cortical excitability at doses that might be used in future clinical trials. METHODS: TMS measurements were incorporated in this Phase I clinical trial to evaluate the extent to which XEN1101 modulates TMS parameters of cortical and corticospinal excitability. TEPs and RMT were collected before and at 2-, 4-, and 6-hours post drug intake in a double-blind, placebo-controlled, randomized, two-period crossover study of 20 healthy male volunteers. RESULTS: Consistent with previous TMS investigations of antiepileptic drugs (AEDs) targeting ion channels, the amplitude of TEPs occurring at early (15-55 msec after TMS) and at late (150-250 msec after TMS) latencies were significantly suppressed from baseline by 20 mg of XEN1101. Furthermore, the RMT showed a significant time-dependent increase that correlated with the XEN1101 plasma concentration. INTERPRETATION: Changes from baseline in TMS measures provided evidence that 20 mg of XEN1101 suppressed cortical and corticospinal excitability, consistent with the effects of other AEDs. These results support the implementation of TMS as a tool to inform early-stage clinical trials.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".