Study design of Dal-GenE, a pharmacogenetic trial targeting reduction of cardiovascular events with dalcetrapib
Bibliographic record
Abstract
The objectives of precision medicine are to better match patient characteristics with the therapeutic intervention to optimize the chances of beneficial actions while reducing the exposure to unneeded adverse drug experiences. In a retrospective genome-wide association study of the overall neutral placebo-controlled dal-Outcomes trial, the effect of the cholesteryl ester transfer protein (CETP) modulator dalcetrapib on the composite of cardiovascular death, myocardial infarction or stroke was found to be influenced by a polymorphism in the adenylate cyclase type 9 (ADCY9) gene. Whereas patients with the AA genotype at position rs1967309 experienced fewer cardiovascular events with dalcetrapib, those with the GG genotype had an increased rate and the heterozygous AG genotype exhibited no difference from placebo. Measurements of cholesterol efflux and C-reactive protein (CRP) offered directionally supportive genotype-specific findings. In a separate, smaller, placebo-controlled trial, regression of ultrasonography-determined carotid intimal-medial thickness was only observed in dalcetrapib-treated patients with the AA genotype. Collectively, these observations led to the hypothesis that the cardiovascular effects of dalcetrapib may be pharmacogenetically determined, with a favorable benefit-risk ratio only for patients with this specific genotype. We describe below the design of dal-GenE, a precision medicine, placebo-controlled clinical outcome trial of dalcetrapib in patients with a recent acute myocardial infarction with the unique feature of selecting only those with the AA genotype at rs1967309 in the ADCY9 gene.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".