The Promises of Proteomics and Metabolomics for Unravelling the Mechanism and Side Effect Landscape of Beta-Adrenoceptor Antagonists in Cardiovascular Therapeutics
Bibliographic record
Abstract
Cardiovascular medicine witnessed notable advances for the past decade. Multiomics research offers a new lens for precision/personalized medicine for existing and emerging drugs used in the cardiovascular clinic. Beta-blockers are vital in treating hypertension and chronic heart failure. However, clinical use of beta-blockers is also associated with side effects and person-to-person variations in their pharmacokinetics and pharmacodynamics. A comprehensive understanding of the mechanisms that underpin the side effect landscape of beta-blockers is imperative to optimize their therapeutic value. In addition, current research emphasizes the circadian clock's vital roles in regulating pharmacological parameters. Administration of the beta-blockers at specific dosing times could potentially improve their effectiveness and reduce their toxic effects. The rapid development of mass spectrometry technologies with chemical proteomics and thermal proteome profiling methods has also substantially advanced our understanding of underlying side effects mechanisms by unbiased deconvolution of drug targets and off-targets. Metabolomics is steadily demonstrating its utility for conducting mechanistic and toxicological analyses of pharmacological compounds. This article discusses the promises of cutting-edge proteomics and metabolomics approaches to investigate the molecular targets, mechanism of action, adverse effects, and dosing time dependency of beta-blockers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".