Pharmacogenomic Challenges in Cardiovascular Diseases: Examples of Drugs and Considerations for Future Integration in Clinical Practice
Bibliographic record
Abstract
INTRODUCTION: Even if cardiovascular disease (CVD) drugs are supported by high level proofs, the results of CVD treatment present great disparities: there are still patients dying with supposed optimal treatment, patients facing adverse events and CVD remains the primary cause of death in the world. Pharmacogenomics is the basis of personalisation of the treatment able to allow higher medication success rates. In this review, we will present detailed examples of CVD drugs to highlight the complexity of this challenging field and we will discuss novel concepts that should be considered for a fastest integration of pharmacogenomics in clinical practice of CVD. Areas Covered: The complexity of pharmacogenetics and pharmacogenomics of CVD drugs are presented though examples of medications such as statins, with a focus on their effectiveness and adverse effects. Expert Opinion: The application of personalised medicine in the CVD medical practice requires the study of human genome with regard to drugs pharmacokinetics, pharmacodynamics, interactions and tolerance profile. The existing state -of-the-art of CVD drugs gives hopes for a future revolution in the drug development that will maximise cardiovascular patients benefit while decreasing their risks for adverse effects. Article Highlights Box: • Coronary heart disease (CHD) remains the first cause of death worldwide. • Cardiovascular treatment has a significant percentage of insufficient efficacy, poor tolerance and compliance. • Predicting the response to therapy while diminishing the side effects is the basis of personalised medicine; pharmacogenomics is leading towards this direction. • The response to CVD therapy and side effects are in the heart of CVD pharmacogenomics and significant progress has been noted. • The application of pharmacogenomics in the CVD medical practice is facing many methodological, technical, ethical, behavioral and financial issues, while cost-effectiveness is the main prerequisite. • The consideration of gene × gene × environment interactions and the inclusion of "omics" data in pharmacogenomic studies of CVD drugs will facilitate the generation of reliable results and will promote tailored treatments and new strategies of drug research and development.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".