Guidance for securing approvals for new biomarkers: from discovery to clinical implementation
Bibliographic record
Abstract
The journey of translating a molecular discovery into the clinic involves multiple steps and requires planning, time, effort, and money. In this review, we provide a quick guide on the technical and clinical validation parameters that are necessary for successful commercialization of molecular and other markers. We also briefly address the different options for regulatory approvals. Successful clinical implantation depends on rigorous technical and clinical validation, and the ability to develop clear guidelines for the indications for testing (i.e. which patients are eligible to have this test), the frequency of testing, and also a clear interpretation of test results. Successful implementation requires providing evidence that the results of this test can be used to improve patient care. There are currently multiple routes for implementation of clinical molecular tests, which include regulatory agency- approved companion diagnostics, laboratory developed tests, or direct-to-consumer testing. Regulatory approval is considered the gold-standard, but it requires time and resources. There is an ongoing debate about the need for regulatory approval of laboratory developed testing. Ongoing oversight is maintained through lab accreditation and proficiency testing programs, which provide a common approach to ensuring high standards and consistent performance in clinical molecular labs. Before moving into the clinic, confirmation of both the clinical and analytic validity of a new molecular test is essential.
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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 | 0.027 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".