Abstract 1808: Engineering lipid-polymer nanoparticles for siRNA delivery to breast cancer cells
Bibliographic record
Abstract
Abstract RNA interference (RNAi) is a powerful tool that can specifically target the expression of virtually any protein without the expensive and time-consuming drug development studies. Despite the initial excitement and extensive efforts, the potential impact of RNAi approaches is yet to be materialized fully in clinical settings. This is mainly due to the challenges in delivering RNA molecules. Lipid nanoparticles (LNPs) have been the leading delivery system for nucleic acids, an achievement established by introducing the first FDA-approved small interfering RNA (siRNA) drug and COVID-19 vaccine to clinics. However, targeted delivery to a solid tumor still eludes the developed LNPs. On the other hand, polymers are among the oldest delivery systems for nucleic acids, and polyethyleneimine (PEI) was once considered the gold standard in nucleic acid delivery. In this study, we introduce a novel lipid-polymer nanoparticle (LPNP) platform, meticulously engineered for targeted delivery of siRNA to cells implicated in breast cancer. We hypothesized that specially designed low molecular weight PEIs can partially or completely replace the ionizable lipids for a more accommodating structure for additional moieties, which could lead to a safer and more efficient nucleic acid delivery. We first optimized the LNP formulations as a point of reference for cellular uptake, cytotoxicity, and protein silencing efficiency, employing sophisticated designs facilitated by the Design-Expert software. Leveraging the optimal LNP formulation, we integrated specifically designed cationic polymers as partial or complete replacements for the ionizable lipid. This methodological approach, incorporating optimal combined designs and response surface methodologies, refined the LPNPs to an optimal efficiency. Our results indicate that these refined LPNPs enhance the delivery of siRNA, leading to efficient gene silencing in targeted cancer cells. The improved delivery efficiency not only underscores the potential for specific therapeutic applications, but also suggests a broader utility for this platform in various cancer treatments. Citation Format: Abdulelah Alhazza, Arthur Manda, Hamidreza Montazeri Aliabadi, Hasan Uludag. Engineering lipid-polymer nanoparticles for siRNA delivery to breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1808.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".