Approval of new pharmacogenomic tests: is the Canadian regulatory process adequate?
Bibliographic record
Abstract
In the first part of our analysis, we will examine the impact which pharmacogenomics is expected to have on drug research and development, on the drug approval process and on post-marketing surveillance and clinical practice. This will allow us to show how pharmacogenomic testing could be beneficial to drug companies, regulatory bodies, and patients. The second part of our analysis will focus on the regulatory framework applicable to the approval of pharmacoge- nomic tests in Canada, although we are aware of the fact that most manufacturers decide to approve their tests outside of Canada. As mentioned, the applicable regu- lations will depend on the way the test is marketed; the federal and provincial re- quirements will both be covered in detail. It is interesting to note that very few pharmacogenomic test developers currently choose to get their tests approved by Health Canada. Most often they will commercialize their product in the United States first and go through the provincial approval route when seeking approbation in Canada.7\nThe review of the expected benefits and of the Canadian regulatory framework governing pharmacogenomic tests will then allow us to evaluate if the latter is ap- propriate considering the positive impact pharmacogenomics could have on phar- maceutical development and the health care system. We will discuss the question of whether it should be changed and simplified so that manufacturers could obtain faster (or simplified) approval for their tests in order to commercialize them more rapidly.
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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 | 0.046 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".