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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".