Dual effects of gabapentin and pregabalin on glutamate release at rat entorhinal synapses <i>in vitro</i>
Bibliographic record
Abstract
We have recently shown that the anticonvulsant drugs phenytoin, lamotrigine and sodium valproate all reduce the release of glutamate at synapses in the entorhinal cortex in vitro. In the present investigation we determined whether this property was shared by gabapentin and pregabalin, using whole-cell patch-clamp recordings of excitatory postsynaptic currents (EPSCs) in layer V neurons in slices of rat entorhinal cortex. Both drugs reduced the amplitude and increased the paired-pulse ratio of EPSCs evoked by electrical stimulation of afferent inputs, suggesting a presynaptic effect to reduce glutamate release. The frequency of spontaneous EPSCs (sEPSCs) was concurrently reduced by GBP, further supporting a presynaptic action. There was no significant change in amplitude although a slight reduction was seen, particularly with gabapentin, which may reflect a reduction in the number of larger amplitude sEPSCs. When activity-independent miniature EPSCs were recorded in the presence of tetrodotoxin, both drugs continued to reduce the frequency of events with no change in amplitude. The reduction in frequency induced by gabapentin or pregabalin was blocked by application of the l-amino acid transporter substrate l-isoleucine. The results show that gabapentin and pregabalin, like other anticonvulsants, reduce glutamate release at cortical synapses. It is possible that this reduction is a combination of two effects: a reduction of activity-dependent release possibly via interaction with P/Q-type voltage-gated Ca channels, and a second action, as yet unidentified, occurring downstream of Ca influx into the presynaptic terminals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".