Local delivery of accutox® synergises with immune-checkpoint inhibitors at disrupting tumor growth
Bibliographic record
Abstract
Abstract Background The Accum ® platform was initially designed to accumulate biomedicines in target cells by inducing endosomal-to-cytosol escape. Interestingly however, the use of unconjugated Accum ® was observed to trigger cell death in a variety of cancer cell lines; a property further exploited in the development of Accum ® -based anti-cancer therapies. Despite the impressive pro-killing abilities of the parent molecule, some cancer cell lines exhibited resistance. This prompted us to test additional Accum ® variants, which led to the identification of the AccuTOX ® molecule. Methods A series of flow-cytometry and cell-based assays were used to assess the pro-killing properties of AccuTOX ® along with its ability to trigger the production of reactive oxygen species (ROS), endosomal breaks and antigen presentation. RNA-seq was also conducted to pinpoint the most prominent processes modulated by AccuTOX ® treatment in EL4 T-cell lymphoma. Finally, the therapeutic potency of intratumorally-injected AccuTOX ® was evaluated in three different murine solid tumor models (EL4, E0771 and B16) both as a monotherapy or in combination with three immune-checkpoint inhibitors (ICI). Results In total, 7 Accum ® variants were screened for their ability to induce complete cell death in 3 murine (EL4, B16 and E0771) and 3 human (MBA-MD-468, A549, and H460) cancer cell lines of different origins. The selected compound (hereafter refereed to as AccuTOX ® ) displayed an improved killing efficiency (~ 5.5 fold compared to the parental Accum ® ), while retaining its ability to trigger immunogenic cell death, ROS production, and endosomal breaks. Moreover, transcriptomic analysis revealed that low dose AccuTOX ® enhances H2-K b cell surface expression as well as antigen presentation in cancer cells. The net outcome culminates in impaired T-cell lymphoma, breast cancer and melanoma growth in vivo especially when combined with anti-CD47, anti-CTLA-4 or anti-PD-1 depending on the animal model. Conclusions AccuTOX ® exhibits enhanced cancer killing properties, retains all the innate characteristics displayed by the parental Accum ® molecule, and synergizes with various ICI in controlling tumor growth. These observations will certainly pave the path to continue the clinical development of this lead compound against multiple solid tumor indications. Graphical Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".