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Pharmacogenomic Challenges in Cardiovascular Diseases: Examples of Drugs and Considerations for Future Integration in Clinical Practice

2017· review· en· W2580519227 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCurrent Pharmaceutical Biotechnology · 2017
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
FundersHealth Canada
KeywordsPharmacogenomicsMedicineDiseasePrecision medicinePersonalized medicineIntensive care medicineClinical PracticeBioinformaticsPharmacologyInternal medicineBiologyPathologyFamily medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.385
GPT teacher head0.531
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it