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Table · dataset · 2026

Supplementary file 1_Long-acting RNAi therapeutics in hypertension: limitations of single-target AGT silencing and rational multi-target synergistic strategies.pdf

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<p>Hypertension affects over 1.3 billion people worldwide, fewer than 20% achieve adequate blood pressure (BP) control.

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Poor long-term BP management accelerates irreversible damage to target organs, highlighting the urgent need for novel therapeutic approaches. Small interfering RNAs (siRNAs) are synthetic oligonucleotides that harness endogenous RNA interference machinery to silence specific disease-related genes with high sequence specificity, an approach that has opened new therapeutic avenues in cardiovascular medicine by enabling durable target modulation with infrequent dosing.

N-acetylgalactosamine (GalNAc)-conjugated long-acting siRNA agents address two major limitations of oral antihypertensives: poor patient daily adherence and short-lived BP reduction. However, nearly all clinical candidates are designed to silence hepatic angiotensinogen (AGT). Single-target inhibition cannot fully overcome the compensatory signaling networks that drive primary hypertension, limiting protection against progressive target-organ damage.

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Existing reviews have largely summarized AGT-targeted RNA interference (RNAi) therapeutics. Rather than cataloguing individual agents, we propose a phenotype-matched framework that uses disease mechanisms to guide target selection, delivery strategies, and translational development toward precision RNAi therapy for hypertension. We review current clinical progress, examine the mechanistic basis of multi-pathway blockade, and discuss emerging molecular scaffolds for dual- and multi-target siRNA therapeutics.

Finally, we discuss the major barriers to clinical translation—including extrahepatic delivery, pharmacological reversibility, and safety considerations—and identify the advances required for phenotype-guided RNAi therapies to move toward precision cardiovascular medicine. We emphasize that this phenotype-matched model is intended to generate hypotheses for target prioritization and validation, and does not represent a clinically established treatment strategy.</p>

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Disease 75% · Oncology and carcinogenesis 69%
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