Preclinical models for pediatric solid tumor drug discovery: current trends, challenges and the scopes for improvement
Bibliographic record
Abstract
INTRODUCTION: The enhancement in pediatric cancer survival achieved in the past few decades has been confined to low- and moderate-risk cancers, whereas no notable improvement in survival was observed in high-risk and advanced-stage childhood cancers. High attrition rate of candidate drugs in clinical trials is a major hurdle in the development of effective therapies for pediatric solid tumors. In order to reduce the failure rate of candidate drugs in clinical trials, more effective strategies are needed to enhance the predictability of preclinical testing. AREAS COVERED: The authors have described the current trends in preclinical drug development for treating pediatric solid tumors. Furthermore, the authors review their limitations and the available remedies, with regards to choice of models, pharmacokinetic considerations and the criteria for assessing the long-term efficacy of a candidate drug. EXPERT OPINION: In many solid tumors, common differences between pediatric and adult cancers have been observed, and therefore, clinical trials for pediatric solid tumors must be conducted on the basis of preclinical observations in pediatric solid tumor models. There is a need to invest in extensive preclinical testing on pediatric solid tumor models. None of the preclinical models can fully recapitulate the human cancers. Therefore, these limitations must be considered while conducting a preclinical trial. The dose and schedule of drugs used for preclinical testing must be clinically relevant. While testing the efficacy of drugs, the markers of apoptosis, drug resistance, hypoxia and tumor-initiating cells can inform us about the long-term therapeutic response of a cancer.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".