Abstract B049: Lipid nanoparticle-delivered toll-like receptor agonists are highly effective in cancer immunotherapy
Bibliographic record
Abstract
Abstract Background: Tumor immunotherapy has shown promising efficacy in various types of cancer. However, the effectiveness of immunotherapy is often limited by the immune-suppressive microenvironment within tumors. To address this challenge, we developed a method of intratumoral injection using lipid nanoparticles (LNPs) to deliver Toll-like receptor 7, 8, and 9 agonists. LNPs effectively enhance immune activation while reducing systemic responses by increasing drug retention within the tumor. Methods: We encapsulated 30nt-CpG and Resiquimod into LNPs (QTsomeTM). Subcutaneous inoculation of 2 × 106 MC38 cells was performed in mice. Once the average tumor volume reached approximately 100mm3, we divided 15 mice into 3 groups based on tumor volume. Intratumoral injections began on the same day of grouping, with a dosage of 1mg/kg (5 injections every 3 days). Tumor volume and body weight were measured twice a week. We conducted two parallel experiments, each with 7 mice for the MC38 model and 8 mice for the B16 melanoma model in each group, to compare the efficacy of 26nt-CpG and 30nt-CpG. Results: On the 15th day post-dosing, the tumor growth inhibition rate (TGI) was 79.9% (P<0.001), resulting in a 73.7% increase in survival rate. LNP-delivered 26nt-CpG demonstrated a TGI rate of 75.4% (P<0.001), while 30nt-CpG showed a TGI of 81.9% (P<0.001). Interestingly, the 26nt-CpG group exhibited faster weight gain, indicating better safety. In the mouse B16 melanoma model, LNP-delivered 26nt-CpG achieved a TGI rate of 75.2% (P<0.001), slightly higher than 30nt-CpG (71.0%, P<0.001), and also resulted in faster weight gain. Conclusion: In conclusion, the use of LNP-delivered Toll-like receptor agonists demonstrated excellent tumor therapeutic effects and good safety in mouse models. Activation of Toll-like receptors effectively stimulated immune cells such as CD8+ T cells and NK cells, enhancing their ability to kill tumor cells. Direct injection of LNPs into the tumor improved this effect while reducing systemic side effects. This drug holds promise as a broad-spectrum anti-tumor treatment and may have potential for expanding the indications of existing tumor immunotherapies. Citation Format: Jun Bai, Fei Su, Chen Li, Yujing Wu, Jing Li, Xiaobin Zhao, Yongsheng Yang, Robert J Lee. Lipid nanoparticle-delivered toll-like receptor agonists are highly effective in cancer immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B049.
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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) | 0.003 | 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".