Are regional funding decisions resticting pharmaceutical research and development in metastatic renal cell carcinoma (mRCC)?
Bibliographic record
Abstract
506 Background: Targeted therapy has revolutionized the treatment of mRCC. However, cost has lead to variable regional approval and funding. Can such variation in funding of established drugs impede approval of newer agents? Methods: Regulatory agency decisions regarding axitinib, the most recently approved mRCC therapy in the U.S., were reviewed. Reports from the US Food and Drug administration (FDA), the European Medicines Agency (EMA), the pan-Canadian Oncology Drug Review (pCODR), UK's National Institute for Health and Care Excellence (NICE), and the Therapeutic Goods Administration (TGA) of Australia were included. Data abstracted included the date of report publication, basis for determination of clinical efficacy, determination of cost-effectiveness, theme of stakeholder submissions prior to and after the regulatory decision, final decision, and rational for approval/rejection. Results: At the time of analysis, all agency decisions had been finalized. All of the agencies based their assessment of clinical efficacy of primarily on the same phase III clinical trial: the AXIS trial, demonstrating a progression-free survival benefit of axitinib compared to sorafenib (6.7 versus 4.7 months, p<0.001). Cost-effectiveness data was considered by NICE and pCODR, but not the FDA, the EMA, or the TGA. Axitinib was recommended received approval by the FDA, the EMA, and the TGA for use in patients with mRCC who have failed prior treatment with sunitinib or cytokines. pCODR recommended axitinib for funding in the 2nd line setting only in patients unable to tolerate everolimus. And NICE recommended against the funding of axitinib. Both dissenting organizations cited concerns with the pivotal trial’s control arm, sorafenib, not representing their regional standards of care as primary reasons for restricted access and rejection. Cost considerations were also cited as factors in the rejections. Conclusions: Differing regulatory approvals and funding worldwide are creating regional standards of care that impeding the research, development, and approval of newer agents. Divergent standards of care before and after clinical trials may restrict their external validity and applicability.
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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 | 0.249 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".