New Oncologic Drugs from 2008 to 2023—Differences in Approval and Access between the United States, Europe and Brazil
Bibliographic record
Abstract
Introduction: Advancements in oncology have revolutionized cancer treatment, with new drugs being approved at different rates worldwide. Our objective was to evaluate the approval of new oncological drugs for solid tumors by the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Brazilian Health Regulatory Agency (ANVISA) since 2008. Methods: Data were collected from public and online databases by searching for the date of submission, the date of the procedure, the date of approval, clinical indication, and drug characteristics. The distribution was tested using the Shapiro–Wilk, test and comparisons were made using the Mann–Whitney U test; the data are reported using median days and interquartile range (IQR1–IQR3). Results: In total, 104 new oncologic drugs for the treatment of solid tumors were approved by the three agencies: 98 by the FDA, 90 by the EMA, and 68 by ANVISA. The cancer types with the highest number of first indications were lung cancer (n = 24), breast cancer (n = 15), and melanoma (n = 15). Most approvals were for oral medications (n = 63) and tyrosine–kinase inhibitors or other small-molecule inhibitors (n = 54). Time to approval after submission was as follows: the FDA—224 days (167–285); the EMA—364 days (330–418); and ANVISA—403 days (276–636) (p < 0.00001 for the FDA to the EMA and the FDA to ANVISA). The difference between submission dates among the agencies was as follows: EMA–FDA: 24 days (0–85); ANVISA–FDA: 255 (114–632); and ANVISA–EMA: 260 (109–645). The difference in approval dates between the agencies was as follows: EMA–FDA: 185 days (59–319); ANVISA–FDA: 558 (278–957); and ANVISA–EMA: 435 days (158–918). Conclusions: New oncologic drugs are submitted to the FDA and EMA for approval on similar dates; however, the longer appraisal period by the EMA pushes the approval date for Europe to approximately 6 months later. The same steps at ANVISA delay the approval by 1.5 years. Such procedures cause a significant difference in available medications between these regions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".