Repurposing a Histamine Detection Platform for High-Throughput Screening of Histidine Decarboxylase
Bibliographic record
Abstract
Histidine decarboxylase (HDC) is the primary enzyme that catalyzes the conversion of histidine to histamine. HDC contributes to many physiological responses as histamine plays important roles in allergic reaction, neurological response, gastric acid secretion, and cell proliferation and differentiation. Small-molecule modulation of HDC represents a potential therapeutic strategy for a range of histamine-associated diseases, including inflammatory disease, neurological disorders, gastric ulcers, and select cancers. High-throughput screening (HTS) methods for measuring HDC activity are currently limited. Here, we report the development of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay for monitoring HDC activity. The assay is based on competition between HDC-generated histamine and fluorophore-labeled histamine for binding to a Europium cryptate (EuK)-labeled anti-histamine antibody. We demonstrated that the assay is highly sensitive and simple to develop. Assay validation experiments were performed using low-volume 384-well plates and resulted in good statistical parameters. A pilot HTS screen gave a Z' score > 0.5 and a hit rate of 1.1%, and led to the identification of a validated hit series. Overall, the presented assay should facilitate the discovery of therapeutic HDC inhibitors by acting as a novel tool suitable for large-scale HTS and subsequent interrogation of compound structure-activity relationships.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".