Evidence map›Paper›PMID 42211693›Full record

ArticleMolecular therapy. Nucleic acids2026

Evaluation and forecasting of siRNA delivery technologies: An analysis of hierarchical decision model based on patent data.

Shiyun Chen, Yixuan Peng, Dilei Yan, Liyang Lyu, Thomas Scherngell, Yuanjia Hu

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shiyun ChenState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macao SAR 999078, China.
Yixuan PengState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macao SAR 999078, China.
Dilei YanState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macao SAR 999078, China.
Liyang LyuState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macao SAR 999078, China.
Thomas ScherngellCenter for Innovation Systems & Policy, Austrian Institute of Technology, Vienna 1210, Austria.
Yuanjia HuState Key Laboratory of Mechanism and Quality of Chinese Medicine, University of Macau, Macao SAR 999078, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small interfering RNA (siRNA) represents a transformative modality in next-generation therapeutics, yet challenges in delivery efficiency still limit its clinical translation. Systematic evaluation tools are needed to reveal technological progress in this area. Given the innovation momentum inherent in patents, this study develops a patent-based evaluation model for siRNA delivery technologies from a technology management perspective. By integrating the Hierarchical Decision Model (HDM) with patent landscape analysis, the model incorporates 15 multidimensional criteria across technological, commercial, and legal perspectives, with the commercial perspective assigned the greatest weight (41%), followed by technical (32%) and legal (27%) perspectives. A large-scale dataset comprising 20,319 siRNA delivery-related patent documents was quantitatively assessed using this model. The results reveal that high-value patents are primarily concentrated in lipid-based carriers and ligand-siRNA conjugates, with firms such as Alnylam and Arbutus emerging as dominant innovation leaders. Furthermore, the analysis highlights that therapeutic indication, assignee's technological accumulation, technological impact, and drug delivery method are key drivers of patent value. Overall, the proposed model supports strategic decision-making in patent portfolio management and technology forecasting by identifying high-value innovations.

Indexed as

drug delivery technologyhierarchical decision modelhigh-value patentsMT: Delivery Strategiespatent evaluationsiRNA

Identifiers

PMID42211693
PMCPMC13213759

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.