Relationship Between Pharmaceutical Company User Fees and Drug Approvals in Canada and Australia: A Hypothesis-Generating Study
Bibliographic record
Abstract
BACKGROUND: Since the early- to mid-1990s, drug companies have paid fees for a variety of activities carried out by the Therapeutic Products Directorate in Canada and the Therapeutic Goods Administration in Australia. OBJECTIVE: To explore whether changes in approval times for new active substances and in the percentage of new drug submissions receiving positive decisions coincided with the level of user fees. METHODS: Data were collected from a range of Canadian and Australian government publications on the following topics: total funding for and workload of the regulatory agencies, the percentage of income that came from tax revenue and user fees, the percentage of new drug submissions that received a positive decision, and-for Canada only-the percent of submissions that were approved on first review. RESULTS: In both countries, there was a moderate-to-strong positive association between the level of industry funding and the percent of submissions that received a positive decision and a moderate-to-strong (Canada) and moderate (Australia) negative association between the level of industry funding and approval times. CONCLUSIONS: Changes observed in both countries are favorable to the pharmaceutical industry. Other than user fees leading to a pro-industry bias in the regulatory authorities, other possible explanations include a more efficient use of resources, a smaller workload (Canada), an improvement in the quality of drug submissions (Canada), and more resources (Australia). Further research strategies are needed to either confirm or refute the hypothesis that the level of industry funding affects decisions made in drug regulatory systems.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".