AMPA receptors as drug targets in neurological disease – advantages, caveats, and future outlook
Bibliographic record
Abstract
Most excitatory transmission in the brain is mediated by the AMPA receptor subtype of the ionotropic glutamate receptors. In many neurological diseases, synapse structure and AMPA receptor function are altered, thus making AMPA receptors potential therapeutic targets for clinical intervention. The work summarized in this review suggests a link between AMPA receptor function and debilitating neuropathologies, and discusses the current state of therapies targeting AMPA receptors in four diseases. In amyotrophic lateral sclerosis, AMPA receptors allow cytotoxic levels of calcium into neurons, leading to motor neuron death. Likewise, in some epilepsies, overactivation of AMPA receptors leads to neuron damage. The same is true for ischemia, where oxygen deprivation leads to excitotoxicity. Conversely, Alzheimer's disease is characterized by decreased AMPA activation and synapse loss. Unfortunately, many clinical studies have had limited success by directly targeting AMPA receptors in these diseases. We also discuss how the use of AMPA receptor modulators, commonly known as ampakines, in neurological diseases initially seemed promising in animal studies, but mostly ineffective in clinical trials. We propose that indirectly affecting AMPA receptors, such as by modulating transmembrane AMPA receptor regulatory proteins or, more generally, by regulating glutamatergic transmission, may provide new therapeutic potential for neurological disorders.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".