Leveraging Cisplatin-Derived Prodrugs as Helper Lipids in LNPs to Boost the Efficacy of Cancer Chemoimmunotherapy
Bibliographic record
Abstract
Cisplatin's clinical utility is limited by its toxicity, drug resistance, and promotion of immune suppression due to phosphatidylserine (PS) exposure during tumor cell apoptosis. This study introduces a novel approach that integrates cisplatin-derived lipid prodrugs into lipid nanoparticles (PtLNPs) to overcome these challenges. By converting cisplatin to lipid derivatives, we facilitate its incorporation into LNPs, enhancing both the delivery and protection of functional siRNA/mRNA molecules. The synthesized lipid-Pt prodrugs significantly improved the in vitro delivery efficiency and stability of these nucleic acids. Additionally, a codelivery system combining Xkr8 siRNA and annexin A5 (ANX5) mRNA within PtLNPs effectively mitigated cisplatin-induced PS exposure by inhibiting Xkr8 upregulation and promoting ANX5 expression to bind PS. In vivo studies demonstrated that the PtLNP system substantially enhanced the efficacy of combined chemotherapy and immunotherapy, offering a promising strategy to refine cisplatin-based cancer therapy by integrating nucleic acid delivery technologies.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".