Benefit, Risk, and Outcomes in Drug Development: A Systematic Review of Sunitinib
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
BACKGROUND: Little is known about the total patient burden associated with clinical development and where burdens fall most heavily during a drug development program. Our goal was to quantify the total patient burden/benefit in developing a new drug. METHODS: We measured risk using drug-related adverse events that were grade 3 or higher, benefit by objective response rate, and trial outcomes by whether studies met their primary endpoint with acceptable safety. The differences in risk (death rate) and benefit (overall response rate) between industry and nonindustry trials were analyzed with an inverse-variance weighted fixed effects meta-analysis implemented as a weighted regression analysis. All statistical tests were two-sided. RESULTS: We identified 103 primary publications of sunitinib monotherapy, representing 9092 patients and 3991 patient-years of involvement over 10 years and 32 different malignancies. In total, 1052 patients receiving sunitinib monotherapy experienced objective tumor response (15.7% of intent-to-treat population, 95% confidence interval [CI] = 15.3% to 16.0%), 98 died from drug-related toxicities (1.08%, 95% CI = 1.02% to 1.14%), and at least 1245 experienced grade 3-4 drug-related toxicities (13.7%, 95% CI = 13.3% to 14.1%). Risk/benefit worsened as the development program matured, with several instances of replicated negative studies and almost no positive trials after the first responding malignancies were discovered. CONCLUSIONS: Even for a successful drug, the risk/benefit balance of trials was similar to phase I cancer trials in general. Sunitinib monotherapy development showed worsening risk/benefit, and the testing of new indications responded slowly to evidence that sunitinib monotherapy would not extend to new malignancies. Research decision-making should draw on evidence from whole research programs rather than a narrow band of studies in the same indication.
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 | 0.017 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it