Nanotopography-based lymphatic delivery for improved anti-tumor responses to checkpoint blockade immunotherapy
Bibliographic record
Abstract
Rationale: Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) is a co-inhibitory checkpoint receptor that is expressed by naïve T-cells in lymph nodes (LNs) to inhibit activation against "self" antigens (Ags).In cancer, anti-CTLA-4 blocks inhibitory action, enabling robust activation of T-cells against tumor Ags presented in tumor draining LNs (TDLNs).However, anti-CTLA-4 is administered intravenously with limited exposure within TDLNs and immune related adverse events (irAEs) are associated with over-stimulation of the immune system.Methods: Herein, we first deliver anti-CTLA-4 in an orthotopic mammary carcinoma murine model using a nanotopographical microneedle-array device to compare its anti-tumor response to that from systemic administration.Additionally, to demonstrate the feasibility of lymphatic delivery in humans using the device, we use near-infrared fluorescence imaging to image delivery of ICG to LNs.Results: Our data show that lymphatic infusion results in more effective tumor growth inhibition, arrest of metastases, increased tumor infiltrating lymphocytes and complete responses when compared to conventional systemic administration.In clinical studies, we demonstrate for the first time that nanotopographic infusion can deliver ICG through the lymphatics directly to the axilla and inguinal LNs of healthy human volunteers.Conclusion: Taken together, these results suggest that regional delivery using a nanotopography-based microneedle array could revolutionize checkpoint blockade immunotherapy by reducing systemic drug exposure and maximizing drug delivery to TDLNs where tumor Ags present.Future work is needed to determine whether lymphatic delivery of anti-CTLA-4 can alleviate irAEs that occur with systemic dosing.
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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 | 0.000 |
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".