Development of Organoid-on-a-chip Platform for Preclinical DrugScreening
Bibliographic record
Abstract
Drug-discovery is a lengthy process causing a rapid increase in the global health care cost. In the current days, there is a growing need to implement reliable and predictable in vitro approaches into the early stages of the drug-discovery pipeline, particularly in the context of cancer research. Such effort has been focused on the development of patient-derived tumour organoids, 3D culture models that retain the structure and functions of the organs and display great potential for preclinical drug screening, prediction of patient outcomes, and guiding optimized therapy strategies at the individual level Compared to pre-existing models (tumour cell lines and patient-derived xenografts), tumour organoids can be scaled-up for high throughput testing with fewer ethical concerns. However, numerous challenges may hamper the implementation of this approach in a clinical setting, particularly due to the multifunctional and structural complex nature of organoids. Combining the ability of microfluidic platforms to precisely control fluid input and distribution with that of 3D tumour organoids models to recapitulate tumour organization and in vivo functions, the generation of an appropriate pre-clinical tumour modelorganoid-on-a-chip -can be envisaged, paving the way for personalized cancer medicine
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".