DZ-2384 has a superior preclinical profile to taxanes for the treatment of triple-negative breast cancer and is synergistic with anti-CTLA-4 immunotherapy
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is typically aggressive, difficult to treat, and commonly metastasizes to the visceral organs and soft tissues, including the lungs and the brain. Taxanes represent the most effective and widely used therapeutic class in metastatic TNBC but possess limiting adverse effects that often result in a delay, reduction, or cessation of their use. DZ-2384 is a candidate microtubule-targeting agent with a distinct mechanism of action and strong activity in several preclinical cancer models, with reduced toxicities. DZ-2384 is highly effective in patient-derived taxane-sensitive and taxane-resistant xenograft models of TNBC at lower doses and over a wider range relative to paclitaxel. When comparing compound exposure at minimum effective doses relative to safe exposure levels, the therapeutic window for DZ-2384 is 14-32 compared with 2.0 and less than 2.8 for paclitaxel and docetaxel, respectively. DZ-2384 is effective at reducing brain metastatic lesions when used at maximum tolerated doses and is equivalent to paclitaxel. Drug distribution experiments indicate that DZ-2384 is taken up more efficiently by tumor tissue but at equivalent levels in the brain compared with paclitaxel. Selective DZ-2384 uptake by tumor tissue may in part account for its wider therapeutic window compared with taxanes. In view of the current clinical efforts to combine chemotherapy with immune checkpoint inhibitors, we demonstrate that DZ-2384 acts synergistically with anti-CTLA-4 immunotherapy in a syngeneic murine model. These results demonstrate that DZ-2384 has a superior pharmacologic profile over currently used taxanes and is a promising therapeutic agent for the treatment of metastatic TNBC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.005 | 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".