Lipid-based delivery of CpG oligodeoxynucleotides for cancer immunotherapy
Bibliographic record
Abstract
The anti-tumor activity of CpG-containing oligodeoxynucleotides (ODNs) has been well established in numerous animal models and confirmed in a number of early clinical trials. While the use of chemical modifications has effectively reduced the sensitivity of ODNs to nuclease degradation and a number of human trials have yielded promising results, the clinical utility of free CpG ODN still faces several significant challenges that must be addressed to achieve optimal potency and therapeutic activity. These include unfavorable pharmacokinetic/biodistribution characteristics, lack of specificity for target cells and poor intracellular uptake. To overcome these challenges, lipid-based delivery systems have been developed to protect the CpG ODN payload, modify their circulation/distribution characteristics, enhance immune cell targeting and facilitate intracellular uptake. In preclinical cancer models, lipid-mediated delivery has demonstrated the capacity to increase the immunopotency of CpG ODNs and dramatically enhance their anti-tumor efficacy as monotherapies, vaccine adjuvants and combination therapies with monoclonal antibodies or chemotherapy. This review will focus on investigating CpG ODNs as a cancer immunotherapeutic and the promising enhancement in efficacy that can be achieved through the use of lipid nanoparticles as delivery vehicles.
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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) | 0.000 | 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".