Data from High-Throughput Screening Identifies Two Classes of Antibiotics as Radioprotectors: Tetracyclines and Fluoroquinolones
Bibliographic record
Abstract
Abstract Purpose: Discovery of agents that protect or mitigate normal tissue from radiation injury during radiotherapy, accidents, or terrorist attacks is of importance. Specifically, bone marrow insufficiency, with possible infection due to immunosuppression, can occur after total body irradiation (TBI) or regional irradiation and is a major component of the acute radiation syndrome. The purpose of this study was to identify novel radioprotectors and mitigators of the hematopoietic system. Experimental Design: High-throughput screening of small-molecule libraries was done using viability of a murine lymphocyte line as a readout with further validation in human lymphoblastoid cells. The selected compounds were then tested for their ability to counter TBI lethality in mice. Results: All of two major classes of antibiotics, tetracyclines and fluoroquinolones, which share a common planar ring moiety, were radioprotective. Furthermore, tetracycline protected murine hematopoietic stem/progenitor cell populations from radiation damage and allowed 87.5% of mice to survive when given before and 35% when given 24 h after lethal TBI. Interestingly, tetracycline did not alter the radiosensitivity of Lewis lung cancer cells. Tetracycline and ciprofloxacine also protected human lymphoblastoid cells, reducing radiation-induced DNA double-strand breaks by 33% and 21%, respectively. The effects of these agents on radiation lethality are not due to the classic mechanism of free radical scavenging but potentially through activation of the Tip60 histone acetyltransferase and altered chromatin structure. Conclusions: Tetracyclines and fluoroquinolones can be robust radioprotectors and mitigators of the hematopoietic system with potential utility in anticancer radiotherapy and radiation emergencies. (Clin Cancer Res 2009;15(23):7238–45)
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".