2. Academic Validation
  3. Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles

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

  • Sci Adv. 2026 Jun 12;12(24):eaed2731. doi: 10.1126/sciadv.aed2731.
Kai V Slaughter 1 2,  Mickael Dang 2 3,  Eric N Donders 1 2 3,  Austin H Cheng 4 5 6,  Gary Tom 4 5 6,  Xiang Olivia Li 2 3,  Sangwoo Han 2 3,  Eric S Y Chiu 2,  Olivia Roland 2,  Alán Aspuru-Guzik 3 4 5 6 7 8,  Molly S Shoichet 1 2 3 6
Affiliations

Affiliations

  • 1 Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada.
  • 2 Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
  • 3 Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON M5S 3E5, Canada.
  • 4 Vector Institute for Artificial Intelligence, 108 College Street, Toronto, ON M5G 0C6, Canada.
  • 5 Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 3H6, Canada.
  • 6 Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada.
  • 7 Department of Materials Science and Engineering, University of Toronto, 184 College Street, Toronto, ON M5S 3E4, Canada.
  • 8 Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Avenue, Toronto, ON M5G 1M1, Canada.
PMID: 42284421 DOI: 10.1126/sciadv.aed2731
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 (CTGF) in fibrotic human trabecular meshwork cells: Reduced CTGF mRNA expression and actin network density were observed. These predictive tools may unlock combination therapies.

Figures
Products