pH-Dependent Release and Folate Receptor Alpha (FRα) Targeting To Allow Exclusive Targeting of Lipid-Nanoparticles (LNP)
Bibliographic record
Abstract
Introduction: Colorectal cancer rates continue to rise in Canada, placing an increasing economic burden on the healthcare system and profoundly impacting patients’ quality of life. Here, we propose a novel method for delivering anti-colorectal cancer treatment that uses a Eudragit-coated folate-LNP conjugation to allow pH-sensitive release and specificity for cancerous colon cells. Methods: GFP-tagged IL-10 mRNA will serve as a biomarker to evaluate folate receptor alpha (FR binding, transfection, and release of therapeutic contents by the novel LNP formulation in LS174 human cells, measured by flow cytometry. To confirm exclusive delivery, tissue samples from the small and large intestines of ApcMin/+ mice treated with the formulation will be analyzed using ELISA following oral gavage. MTT assay of healthy, non-cancerous CCD 841 CoN will confirm consistent cell viability across non-targeted colonic tissue. Expected Results: The folate-LNP formulation is expected to bind FR on the LS174 cells efficiently and release the encapsulated IL-10 mRNA after endosomal escape, evidenced by significantly higher fluorescence in treated cells than the negative controls. In the mouse model, the Eudragit coating is anticipated to dissolve exclusively in the colonic tissue of the mouse models, with minimal dissolution in the small intestine, allowing targeted binding to cancerous colonic enterocytes. Discussion: Our proposed folate-LNP formulation is designed to achieve targeted delivery to colonic tissue by leveraging pH-sensitive release and FR upregulation in colorectal cancer cells. Efficient binding and successful translation of IL-10 mRNA in the LS174 cells will demonstrate the formulation’s specificity and therapeutic potential. Exclusive localization to cancerous colonic tissue in the mouse model will confirm the ability to bypass non-target tissues. Conclusion: This approach represents the first known proposal for a Eudragit-coated folate-LNP molecule targeting the colon. If successful, this approach could offer a more effective, targeted treatment for colorectal cancer, minimizing systemic side effects and improving patient outcomes. Future research and clinical validation are required to confirm the safety and efficacy of this approach, as well as to determine the patients who will benefit most.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".