A Modular Antibody‐Oligomer T Cell Engager for Applications in Local Therapies
Bibliographic record
Abstract
Abstract Immunotherapeutics, such as bispecific T cell engagers (BiTEs), have shown promise in cancer therapies, however their efficacy against solid tumors is hindered by transport barriers. Local therapies are being investigated to improve solid tumor immunotherapies and minimize systemic toxicity. Because local therapies bypass the circulatory system, drug properties can be optimized to further enhance local efficacy. Herein, the use of a larger BiTE‐like antibody‐oligomer conjugate is investigated, modular T cell engagers (MoTEs), to extend the duration of activity within local tissue mimics. Specifically, an anti‐CD3 antibody is modified with heterobifunctional ethylene oxide ((EO)4‐12) linkers, which are subsequently modified with cancer targeting ligands (CTLs). The (EO)x molecular weight and CTL grafting densities are optimized to achieve targeted cytotoxicity within in vitro co‐cultures against prostate‐specific membrane antigen (PSMA) positive and human epidermal growth factor receptor 2 (HER2) positive cancer cells. In local tissue models comprised of embedded PSMA positive spheroids in collagen‐hyaluronic acid hydrogels with T cells, it is demonstrated that MoTEs resulted in ≈2.5‐fold greater cytotoxicity toward cancer spheroids than a PSMA targeting BiTE at longer 12‐day timepoints. MoTEsmay therefore prove beneficial for local therapies by extending the duration of action after single‐dose administration and establishing simple synthetic protocols to target various cancer antigens.
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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".