High throughput screening of small molecule libraries for modifiers of radiation responses
Bibliographic record
Abstract
PURPOSE: An unbiased approach of drug discovery through high-throughput screening (HTS) of libraries of chemically defined and bioactive small molecule compounds was used to identify modulators of radiation injury with an emphasis on radioprotectors and mitigators rather than radiosensitisers. Assay system endpoints included radiation-induced genotoxicity and DNA damage in yeast and apoptosis in murine lymphocytes. Large-scale data mining of chemically diverse libraries identified agents that were effective with all endpoints. HTS of bioactive compound libraries against murine lymphocytes profiled tetracycline and fluoroquinolone antibiotics and cyclopiazonic acid as having activity, and structure-activity analysis showed a common pharmacophore. Purine nucleosides, the interferon inducer tilorone, and linoleic acid were also identified as potential mitigators of radiation damage that often were also radioprotective. Many of these compounds enhance DNA repair, have anti-inflammatory activity, and stimulate hematopoiesis. Selected compounds within these initial verified hits from both types of libraries identified potent mitigators of lethal whole body irradiation (WBI) in mice. CONCLUSION: In spite of the fact that in vitro HTS has limitations and is unable to fully recapitulate all aspects of the complex in vivo acute radiation response, it identified several classes of molecules that had activity as radioprotectors and radiomitigators of the hematopoietic system in vivo. In the future, addition of 3-dimensional (3-D) or stem cell cultures or pathway analysis, may improve the power of HTS, but our findings indicate that common, evolutionary conserved, canonical pathways can be identified that could be exploited to mitigate radiation-induced defects.
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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 | 0.001 |
| 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) | 0.003 | 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".