Synthesis and Receptor Binding Studies of α5 GABAAR Selective Novel Imidazodiazepines Targeted for Psychiatric and Cognitive Disorders
Bibliographic record
Abstract
GABA mediates inhibitory actions through various GABAA receptor subtypes, including 19 subunits in human GABAAR. Dysregulation of GABAergic neurotransmission is associated with several psychiatric disorders, including depression, anxiety, and schizophrenia. Selective targeting of α2/3 GABAARs can treat mood and anxiety, while α5 GABAA-Rs can treat anxiety, depression, and cognitive performance. GL-II-73 and MP-III-022, α5-positive allosteric modulators have shown promising results in animal models of chronic stress, aging, and cognitive disorders, including MDD, schizophrenia, autism, and Alzheimer’s disease. Described in this article is how small changes in the structure of imidazodiazepine substituents can greatly impact the subtype selectivity of benzodiazepine GABAAR. To investigate alternate and potentially more effective therapeutic compounds, modifications were made to the structure of imidazodiazepine 1 to synthesize different amide analogs. The novel ligands were screened at the NIMH PDSP against a panel of 47 receptors, ion channels, including hERG, and transporters to identify on- and off-target interactions. Any ligands with significant inhibition in primary binding were subjected to secondary binding assays to determine their Ki values. The newly synthesized imidazodiazepines were found to have variable affinities for the benzodiazepine site and negligible or no binding to any off-target profile receptors that could cause other physiological problems.
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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) | 0.003 | 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".