Pharmacogenomics of Cardiovascular Drugs and Adverse Effects in Pediatrics
Bibliographic record
Abstract
Individual response to medication is highly variable. For many drugs, a substantial proportion of patients show suboptimal response at standard doses, whereas others experience adverse drug reactions (ADRs). Pharmacogenomics aims to identify genetic factors underlying this variability in drug response, providing solutions to improve drug efficacy and safety. We review recent advances in pharmacogenomics of cardiovascular drugs and cardiovascular ADRs, including warfarin, clopidogrel, β-blockers, renin-angiotensin-aldosterone system inhibitors, drug-induced long QT syndrome, and anthracycline-induced cardiotoxicity. We particularly focus on the applicability of pharmacogenomic findings to pediatric patients in whom developmental changes in body size and organ function may affect drug pharmacokinetics and pharmacodynamics. Solid evidence supports the importance of gene variants in CYP2C9 and VKORC1 for warfarin dosing and in CYP2C19 for clopidogrel response in adult patients. For the other cardiovascular drugs or cardiovascular ADRs, further studies are needed to replicate or clarify genetic associations before considering uptake of pharmacogenetic testing in clinical practice. With the exception of warfarin and anthracycline-induced cardiotoxicity, there is lack of pharmacogenomic studies on cardiovascular drug response or ADRs aimed specifically at children or adolescents. The first pediatric warfarin pharmacogenomic study indeed indicates differences from adults, pointing out the importance and need for pediatric-focused pharmacogenomic studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".