Regulatory approval for new pharmacogenomic tests: a comparative overview.
Bibliographic record
Abstract
Pharmacogenomics is the study of how genetic variants affect the way in which an individual or subgroup responds to drugs. This developing field aims to inform individual drug therapy and to minimize adverse drug reactions (ADRs). It also promises great benefits in the drug development process. Innovation in pharmacogenomics and its translation into clinical practice is desirable, but appropriate regulation of the safety and effectiveness of pharmacogenomics testing is necessary. This article will describe the current regulatory framework applicable to pharmacogenomic tests in Canada, the United States and Europe. In particular, it will examine the different regulatory pathways for pharmacogenomic tests marketed as test kits and for laboratory-developed tests (LDTs). Recent and upcoming changes to the regulation of pharmacogenomic tests will also be discussed. For example, FDA's proposal to regulate LDTs could have a major impact on the development and availability of pharmacogenomic tests. This review will lead to an evaluation of the issues raised by the regulatory framework and the impact of regulatory changes in relation to meeting the goals of ensuring public safety and promoting the advancement of pharmacogenomics. Regulatory policies which successfully achieve the dual objectives of ensuring public safety and promoting innovation in health technology are imperative in order to reap the benefits of this emerging field.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".