Evidence map›Paper›PMID 42284421›Full record

ArticleScience advances2026

Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.

Kai V Slaughter, Mickael Dang, Eric N Donders, Austin H Cheng, Gary Tom, Xiang Olivia Li, Sangwoo Han, Eric S Y Chiu, Olivia Roland, Alán Aspuru-Guzik and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 authors.

Kai V SlaughterInstitute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada.ORCID 0000-0002-5834-3351
Mickael DangDonnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
Eric N DondersInstitute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada.ORCID 0000-0002-2445-4619
Austin H ChengVector Institute for Artificial Intelligence, 108 College Street, Toronto, ON M5G 0C6, Canada.ORCID 0000-0002-9497-3932
Gary TomVector Institute for Artificial Intelligence, 108 College Street, Toronto, ON M5G 0C6, Canada.ORCID 0000-0002-8470-6515
Xiang Olivia LiDonnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
Sangwoo HanDonnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
Eric S Y ChiuDonnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.ORCID 0009-0005-2333-4403
Olivia RolandDonnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.ORCID 0009-0001-2610-6423
Alán Aspuru-GuzikDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON M5S 3E5, Canada.ORCID 0000-0002-8277-4434
Molly S ShoichetInstitute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada.ORCID 0000-0003-1830-3475

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ionizable lipid in lipid nanoparticles can be replaced with ionizable drugs to encapsulate small interfering RNA (siRNA) and allow intracellular codelivery. We wondered whether we could develop a predictive model to aid in formulation design. A small-scale screening assay was designed to evaluate siRNA complexation by ionizable drugs at low pH and validated experimentally with ionizable drug nanoparticle (IDNP) formulations. We found that siRNA complexation could be predicted by drug hydrophobicity, aromaticity, proximity of nitrogen and oxygen atoms to aromatic rings, and a machine learning model using five molecular descriptors encoding pharmacophore and structural information. For complexing drugs, siRNA encapsulation efficiency in IDNPs was related to hydrophobicity, molar refractivity, chiral centers, hydrogen bond donors, and topological charge. Netarsudil was predicted to encapsulate siRNA at high efficiency and was thus tested experimentally with siRNA targeting connective tissue growth factor (

Indexed as

NanoparticlesRNA, Small InterferingConnective Tissue Growth FactorHumansHydrophobic and Hydrophilic InteractionsConnective Tissue Growth FactorRNA, Small Interfering

Identifiers

PMID42284421
PMCPMC13262629

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.