Abstract 562: High-Throughput Analysis of Compounds Targeting Zebrafish Angiogenesis and Vascular Development
Bibliographic record
Abstract
Introduction: Angiogenic sprouting occurs in the presence of high levels of exogenous pro-angiogenesis factors. Pathological over expression of pro-angiogenesis factors leads to excessive tumor growth. Inhibition of pro-angiogenesis factors can restrict tumor growth. But, prolonged inhibition can lead to the activation of alternate pro-angiogenesis pathways. The objective of this research is to discover compounds that inhibit multiple pro-angiogenesis factors and increase efficacy in the treatment of cancerous tumors using zebrafish vascular development as a model in a high throughput setting. Methods: The NIH Clinical Collections Libraries I and II will be screened with a fully automated high throughput drug screening platform (HTS). Compounds that cause abnormal vascular phenotypes will be identified on Tg(flk1:EGFP) embryos. The lead compounds will then be analyzed with 3-D confocal imaging, studies on cultured human umbilical vein endothelial cells (HUVECs), and animal cancer models. Results: The HTS was constructed and made operational. A protocol for anti-angiogenesis screening with Tg(flk1:EGFP) zebrafish has been developed and validated with indirubin 3’ monoxime (I3M), a known angiogenesis inhibitor. Pixel counting (to quantify the overall vascular network) and ISV counting (to count the number of ISVs and give an indication of overall vascular morphology) programs were created to provide readouts from confocal images taken within the HTS. Normative parameters, including several endothelial cell counts and various vessel length measurements, were characterised at the 1 and 2 days post fertilization (dpf) stages using 3-D confocal imaging. These values will be compared to values found in the presence of lead compounds to validate them as angiogenesis inhibitors. Conclusions & Future Work: The framework for a robust drug discovery process has been put in place. The next step is to screen the NIH Clinical Collections Libraries I and II and analyze the efficacy and mechanism of lead compounds. Based on data from previous manual screens, 5-10 lead compounds are expected to be identified. These compounds will have the potential to be developed as repurposed anti-angiogenesis compounds and improve therapeutics for cancerous tumors.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".