Genetically Enriched Clinical Trials for Precision Development of Noncancer Therapeutics: A Scoping Review
Bibliographic record
Abstract
Genetically driven clinical trial enrichment has been proposed to accelerate and reduce the cost of developing new therapeutics. Usage of this approach has not been comprehensively reviewed. We searched Ovid MEDLINE, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov, and WHO ICTRP for articles published between 2010 and 2023. Excluding absorption, distribution, metabolism, and elimination pharmacogenetic studies and anti-infectives, we found 95 completed, 4 terminated, and 22 ongoing prospective genetically enriched trials on 110 drugs for 48 nononcology, nonrare syndromic indications. Trial sizes ranged from 4 to 6,147 participants (median 72) and covered numerous disease areas, particularly neurology (30), metabolism (22), and psychiatry (17). Fifty-six completed studies (60%) met their primary end point. Overall, this scoping review demonstrates that genetically enriched trials are feasible and scalable across disease areas and provide critical information for further development, or attrition, of investigational drugs. Large, appropriately designed disease-, hospital-, or population-based biobanks will undoubtedly facilitate this type of precision drug development approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".