Image-Based Marker-Free Screening of GABAA Agonists, Antagonists, and Modulators
Bibliographic record
Abstract
The ionotropic GABA A receptors represent the main target for different groups of widely used drugs having hypnotic and anxiolytic effects. So far, most approaches used to assess GABA activity involve invasive low -throughput electrophysiological techniques or rely on fluorescent dyes, preventing the ability to conduct noninvasive and thus nonperturbing screens. To address this limitation, we have developed an automated marker-free cell imaging method, based on digital holographic microscopy (DHM). This technology allows the automatically screening of compounds in multiple plates without having to label the cells or use special plates. This methodological approach was first validated by screening the GABA A receptor expressed in HEK cells using a selection of active compounds in agonist, antagonist, and modulator modes. Then, in a second blind screen of a library of 3041 compounds (mostly composed of natural products), 5 compounds having a specific agonist action on the GABA A receptor were identified. The hits validated from this unbiased screen were the natural products muscimol, neurosteroid alphaxalone, and three compounds belonging to the avermectin family, all known for having an agonistic effect on the GABA A receptor. The results obtained were exempt from false negatives (structurally similar unassigned hits), and false-positive hits were detected and discarded without the need for performing electrophysiological measurements. The outcome of the screen demonstrates the applicability of our screening by imaging method for the discovery of new chemical structures, particularly regarding chemicals interacting with the ionotropic GABA A receptor and more generally with any ligand-gated ion channels and transporters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".