Intratumoral administration of unconjugated Accum™ impairs the growth of pre‐established solid lymphoma tumors
Bibliographic record
Abstract
The Accum™ technology was initially designed to enhance the bioaccumulation of a given molecule in target cells. It does so by triggering endosomal membrane damages allowing endocytosed products to enter the cytosol, escaping the harsh environmental cues of the endosomal lumen. In an attempt to minimize manufacturing hurdles associated with Accum™ conjugation, we tested whether free Accum™ admixed with antigens could lead to outcomes similar to those obtained with conjugated products. Surprisingly, unconjugated Accum™ was found to promote cell death in vitro, an observation further confirmed on various murine tumor cell lines (EL4, CT-26, B16, and 4 T1). At the molecular level, unconjugated Accum™ triggers the production of reactive oxygen species and elicits immunogenic cell death while retaining its innate ability to cause endosomal damages. When administered as a monotherapy to animals with pre-established EL4 T-cell lymphoma, Accum™ controlled tumor growth in a dose-dependent manner, and its therapeutic effect relies on CD4 and CD8 T cells. Although unconjugated Accum™ synergizes with various immune checkpoint inhibitors (anti-CTLA4, anti-PD-1, or anti-CD47) at controlling tumor growth, its therapeutic potency could not be further enhanced when combined with all three tested immune checkpoint inhibitors at once due to its dependency on a specific dosing regimen. In sum, we report in this study an unprecedented new function for unconjugated Accum™ as a novel anticancer molecule. These results could pave the path for a new line of investigation aimed at exploring the pro-killing properties of additional Accum™ variants as a mean to develop second-generation anticancer therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".