T cell engaging nanoparticles towards anti-cancer therapies
Bibliographic record
Abstract
Immunotherapy aims to make use of the body’s natural anti-tumoral response against cancer cells by enhancing or redirecting immune cells. Bi-specific T cell engagers (BiTEs) redirect the cytolytic activity of T cells by simultaneously binding the CD3 T cell receptor and a tumor associated antigen (TAA) on cancer cells. BiTEs have shown limited or poor efficacy against solid tumors in part because they were designed for systemic delivery, have a short plasma half-life as low as two hours, and the dependence on the expression of a single TAA, failing to address tumor heterogeneity. T cell engaging NPs are BiTE-like therapeutics being developed and optimized for local delivery to target multiple antigens simultaneously and avoid systemic clearance and toxicity. The design utilizes NPs modified with TAA binding antibodies, CD3 binding antibodies, and any additional antibodies to help enhance anti-tumor activity. It may also encapsulate small molecule drugs as an additional combinatorial approach. Parameters that may influence anti-cancer effects such as antibody grafting density and nanoparticle sizes were screened using polystyrene NPs (PS NPs). The targeted killing of the T cell engaging NPs were evaluated using co-culture cytotoxicity assays using HER2+ human breast cancer cells (SK-BR-3, positive control), HER2- immortalized human embryonic kidney cells (HEK-293, negative control), and HER2- B lymphocyte cells from Burkitt’s lymphoma (Raji, negative control). T cell engaging NPs modified with anti-HER2 and anti-CD3 resulted in the selective killing of the HER2+ SK-BR-3 cancer cells and not HER2- HEK-293 and Raji cells. Comparisons between 100 and 500 nm PS NPs suggest cytotoxicity is independent of particle sizes. Antibody density was found to be most important for anti-CD3, with cytotoxicity being observed at antibody grafting rates as low as 1 μg per 3 mg of nanoparticle, which is calculated to be approximately 2 antibodies per nanoparticle. Next, the degradable poly(lactic-co-glycolic acid) NPs (PLGA NPs) and non-degradable gold NPs (Au NPs) as alternative scaffolds with greater translation potential were evaluated. PLGA NPs showed insignificant cytotoxicity, likely due to particle instability upon degradation, whereas Au NPs still retained the ability to specifically kill HER2+ SK-BR-3 and without the neutralization of HER2- HEK-293. Interestingly, the incorporation of PD-1 targeting antibodies significantly increased the killing of HER2+ SK-BR-3. Thus, the targeted killing of HER2+ cancer cells were achieved in nanoparticle T cell engagers modified with HER2 targeting antibodies and CD3 targeting antibodies with improved killing upon the incorporation of PD-1 targeting antibodies. Nanoparticle T cell engagers will be further explored as combination therapies in local anti-cancer therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".