Pharmacogenomic approaches to lipid-regulating trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: Randomized clinical outcome trials are costly, long, and often yield neutral or modestly positive results, and these issues have impeded cardiovascular drug development in the past decade. Despite the significant reduction of cardiovascular morbidity and mortality with statins, substantial residual risk of major cardiovascular events remains. This could be because of the difficulty of demonstrating benefits of new drugs in addition to the current standard of care in unselected populations as well as the interindividual variability in drug response. Pharmacogenomics is a promising avenue for the development of novel or failed drugs and for the repurposing of other medications. RECENT FINDINGS: Several variants were identified in genes that were associated with the effects of statins on plasma lipids. Genomic studies of mutations in genes that encode drug targets have the potential to inform on the link between drug therapy acting on those targets and clinical outcomes. Recently, ADCY9 gene variants were shown to be significantly associated with responses to dalcetrapib in terms of clinical outcomes, atherosclerosis imaging, cholesterol efflux, and inflammation, which provided support for the conduct of a new prospective clinical trial in a genetically determined population. SUMMARY: Pharmacogenomics hold great potential in future lipid trials to decrease failure rates in drug development and to identify patients who will respond with greater benefits and smaller risk.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".