RNA-targeted therapeutics in arterial hypertension
Bibliographic record
Abstract
Hypertension is a common and serious medical condition affecting millions of people worldwide. While existing treatments are effective for many hypertensive patients, up to one-third fail to achieve adequate blood pressure control-often due to poor adherence, complex polypharmacy, or true pharmacological resistance. In this context, novel precision medicine approaches such as RNA-targeted therapeutics may represent tailored, long-acting alternatives particularly beneficial for patients with resistant hypertension, poor compliance, or multiple comorbidities. Small interfering RNAs (siRNAs) and antisense oligonucleotides (ASOs) have shown promising results as potential treatments for hypertension. On the one hand, zilebesiran (formerly ALN-AGT01) is currently in phase 2 clinical trials and targets the hepatic synthesis of angiotensinogen through a novel mechanism of action. On the other hand, IONIS-AGT-LRX is a hepatocyte-directed antisense oligonucleotide designed to target AGT mRNA in hepatocytes, thereby reducing angiotensinogen synthesis and circulating plasma levels. Furthermore, literature show sparse trials on RNA-targeted nucleic acid therapeutics as potential hypertension treatment, in preclinical and clinical phases, in animal and human targets, analyzed in this review in their safety and efficacy. RNA-based drugs, administered as subcutaneous injections, offer several advantages over traditional antihypertensive agents, including greater target specificity and prolonged duration of action, potentially improving adherence and long-term blood pressure control. However, these therapies are not suitable for the acute management of hypertensive emergencies or urgencies, and evidence regarding their effects on cardiovascular outcomes, target organ protection, and mortality is still lacking. Further studies are warranted to define their role in clinical practice.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".