Global drug development in cancer: A cross-sectional study of clinical trial registries
Bibliographic record
Abstract
2520 Background: Clinical trials are increasingly funded by industry. High costs of drug development may lead to attempts to develop new drugs in more ‘profitable’ (i.e., more prevalent) as compared to ‘less profitable’ (i.e., more deadly) cancers. Here we determine the focus of current global drug development. Methods: We determined characteristics of phase II and III clinical trials evaluating new drugs in oncology, which were registered with WHO International Clinical Trial Registries between 01/2008 and 06/2008. Estimates of incidence, mortality, and prevalence in the more- and less-developed world (MDW, LDW) were obtained from GLOBOCAN 2002. Simple correlation analysis was performed between the number of clinical trials and incidence, mortality and prevalence per cancer site after log transformation of variables. Results: We identified 399 newly registered trials. Of 374 trials with information about recruitment, 322 (86.1%) and 39 (10.4%) recruited patients only from the MDW and LDW, respectively, while 13 (3.5%) had worldwide recruitment. 229 (58%) of trials were sponsored by industry and 324 trials were phase II (81%). Most trials (and most phase III trials) evaluated treatments for globally prevalent cancers: breast, lung, prostate, and colorectal cancer (Table). Prevalence of a particular cancer type in both the MDW and LDW correlated significantly with the number of clinical trials (Pearson r = 0.63 and 0.55; p = 0.01 and 0.03, respectively). In contrast, mortality in the MDW (Pearson r = 0.73; p= 0.002), but not in the LDW (Pearson r = 0.38; p= 0.17), correlated significantly with the number of clinical trials. Conclusions: Global drug development in cancer predominates in globally prevalent cancers, which are a more important cause of mortality in the MDW than in the LDW. Cancer sites that are major killers globally, and especially in the LDW (e.g., stomach, liver, and esophageal cancer) should receive priority for clinical research. [Table: see text] No significant financial relationships to disclose.
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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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".