Modular lipid nanoparticle platform technology for siRNA and lipophilic prodrug delivery
Bibliographic record
Abstract
ABSTRACT Successfully employing therapeutic nucleic acids, such as small interfering RNA (siRNA), requires chemical modifications or the use of nanocarrier technology to prevent their degradation in the circulation and to facilitate intracellular delivery. Lipid nanoparticles (LNP) are among the most advanced nanocarriers culminating in the first siRNA therapeutic’s clinical translation and approval. At the same time, their applicability as modular platform technology due to the interchangeable building blocks and siRNA payload hallmarks one of LNPs’ major advantages. In addition, drug derivatization approaches to synthesize lipophilic small molecule prodrugs enable stable incorporation in LNPs. This provides ample opportunities to develop combination therapies by co-encapsulating multiple therapeutic agents in a single formulation. Here, we describe how the modular LNP platform can be applied for combined gene silencing and chemotherapy to induce additive anti-cancer effects. We show 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. In addition, we demonstrate that prodrug incorporation does not affect LNPs’ gene silencing ability and that the combination therapy induces additive therapeutic effects in vitro . Using a double-radiolabeling approach, we quantitively determined the LNPs’ and prodrugs’ pharmacokinetic properties and biodistribution following systemic administration in tumor-bearing mice. Our results indicate that co-encapsulation of siRNA and lipophilic prodrugs into LNPs is an attractive and straightforward approach for combination therapy development. GRAPHICAL ABSTRACT
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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".