A novel metabolism-based phenotypic drug discovery platform in zebrafish uncovers HDACs 1 and 3 as a potential combined anti-seizure drug target
Bibliographic record
Abstract
Despite the development of newer anti-seizure medications over the past 50 years, 30-40% of patients with epilepsy remain refractory to treatment. One explanation for this lack of progress is that the current screening process is largely biased towards transmembrane channels and receptors, and ignores intracellular proteins and enzymes that might serve as efficacious molecular targets. Here, we report the development of a novel drug screening platform that harnesses the power of zebrafish genetics and combines it with in vivo bioenergetics screening assays to uncover therapeutic agents that improve mitochondrial health in diseased animals. By screening commercially available chemical libraries of approved drugs, for which the molecular targets and pathways are well characterized, we were able to reverse-identify the proteins targeted by efficacious compounds and confirm the physiological roles that they play by utilizing other pharmacological ligands. Indeed, using an 870-compound screen in kcna1-morpholino epileptic zebrafish larvae, we uncovered vorinostat (Zolinza™; suberanilohydroxamic acid, SAHA) as a potent anti-seizure agent. We further demonstrated that vorinostat decreased average daily seizures by ∼60% in epileptic Kcna1-null mice using video-EEG recordings. Given that vorinostat is a broad histone deacetylase (HDAC) inhibitor, we then delineated a specific subset of HDACs, namely HDACs 1 and 3, as potential drug targets for future screening. In summary, we have developed a novel phenotypic, metabolism-based experimental therapeutics platform that can be used to identify new molecular targets for future drug discovery in epilepsy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".