Validation of a Novel Optogenetic Drug Screening Platform for the Discovery of Neuroactive Compounds
Bibliographic record
Abstract
Zebrafish provide a powerful system for behavior-based drug discovery aimed at treating disorders of the nervous system. However, current approaches cannot target screening for the identification of drugs that alter specific neuronal cell-types. The incorporation of optogenetics, a technique for the modulation of neural activity using light, with drug screening has the potential to provide targeted screening. Here, we present research aimed at validating this novel screening approach using transgenic zebrafish that express the excitatory optogenetic protein channelrhodopsin-2 in glutamatergic or GABAergic neurons. Using a collection of compounds with known central nervous system targets, we found that optogenetics can enable targeted screening, but in unpredicted ways. We also discovered that optogenetics can amplify the effects of drugs, providing an advantage when screening for new compounds. Our results support further investigation of this drug screening strategy using more compounds with known targets as well as transgenic lines for the optogenetic stimulation of additional cell-types.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".