Modular Lipid Nanoparticle Platform Technology for siRNA and Lipophilic Prodrug Delivery
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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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) | 0.001 | 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 | 0.001 |
| 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".