Hooking the big one: the potential of zebrafish xenotransplantation to reform cancer drug screening in the genomic era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current preclinical pipeline for drug discovery can be cumbersome and costly, which limits the number of compounds that can effectively be transitioned to use as therapies. Chemical screens in zebrafish have uncovered new uses for existing drugs and identified promising new compounds from large libraries. Xenotransplantation of human cancer cells into zebrafish embryos builds on this work and enables direct evaluation of patient-derived tumor specimens in vivo in a rapid and cost-effective manner. The short time frame needed for xenotransplantation studies means that the zebrafish can serve as an early preclinical drug screening tool and can also help personalize cancer therapy by providing real-time data on the response of the human cells to treatment. In this Review, we summarize the use of zebrafish embryos in drug screening and highlight the potential for xenotransplantation approaches to be adopted as a preclinical tool to identify and prioritize therapies for further clinical evaluation. We also discuss some of the limitations of using zebrafish xenografts and the benefits of using them in concert with murine xenografts in drug optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it