Report on the use of non‐clinical studies in the regulatory evaluation of oncology drugs
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
Non-clinical studies are necessary at each stage of the development of oncology drugs. Many experimental cancer models have been developed to investigate carcinogenesis, cancer progression, metastasis, and other aspects in cancer biology and these models turned out to be useful in the efficacy evaluation and the safety prediction of oncology drugs. While the diversity and the degree of engagement in genetic changes in the initiation of cancer cell growth and progression are widely accepted, it has become increasingly clear that the roles of host cells, tissue microenvironment, and the immune system also play important roles in cancer. Therefore, the methods used to develop oncology drugs should continuously be revised based on the advances in our understanding of cancer. In this review, we extensively summarize the effective use of those models, their advantages and disadvantages, ranges to be evaluated and limitations of the models currently used for the development and for the evaluation of oncology drugs.
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 | 0.035 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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