Tissue-engineered Cancer Models in Drug Screening
Bibliographic record
Abstract
Novel anticancer therapeutics are urgently required to meet the increasing global cancer burden associated with aging populations. The development of new drugs is hindered by high failure rates at clinical stages, which are partly attributable to inadequate screening strategies which rely heavily on the use of cancer cell lines cultured in 2D and animal models. Although each of these models has certain advantages, they generally fail to accurately represent the human pathophysiology of malignant tumors. Emerging tissue-engineered 3D cancer models designed to better mimic in vivo tumors have the potential to provide additional tools to complement those currently available to address this limitation and improve drug discovery and translation in the long run. To successfully develop and implement a 3D cancer model for drug screening, several key steps are necessary: selection of the tumor type and concept to be modeled, identification of the essential components and set up of the model, model validation, establishment of a scalable manufacturing and analysis pipeline, and selection of a drug library to perform the screen. In this chapter, we elaborate on and evaluate each of these decision steps, highlight the challenges associated with each step, and discuss opportunities for future research.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".