Modular Lipid Nanoparticle Platform Technology for siRNA and Lipophilic Prodrug Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Successfully employing small interfering RNA (siRNA) therapeutics requires the use of nanotechnology for efficient intracellular delivery. Lipid nanoparticles (LNPs) have enabled the approval of various nucleic acid therapeutics. A major advantage of LNPs is the interchangeability of its building blocks and RNA payload, which allow it to be a highly modular system. In addition, drug derivatization approaches can be used to synthesize lipophilic small molecule prodrugs that stably incorporate in LNPs. This provides ample opportunities to develop combination therapies by co-encapsulating multiple therapeutic agents in a single formulation. Here, it is described how the modular LNP platform is applied for combined gene silencing and chemotherapy to induce additive anticancer effects. It is shown that various lipophilic taxane prodrug derivatives and siRNA against the androgen receptor, a prostate cancer driver, can be efficiently and stably co-encapsulated in LNPs without compromising physicochemical properties or gene-silencing ability. Moreover, it is demonstrated that the combination therapy induces additive therapeutic effects in vitro. Using a double-radiolabeling approach, the pharmacokinetic properties and biodistribution of LNPs and prodrugs following systemic administration in tumor-bearing mice are quantitatively determined. These results indicate that co-encapsulating siRNA and lipophilic prodrugs into LNPs is an attractive and straightforward plug-and-play approach for combination therapy development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 it