DeBouganin Diabody Fusion Protein Overcomes Drug Resistance to ADCs Comprised of Anti-Microtubule Agents
Bibliographic record
Abstract
Antibody drug conjugates (ADC), comprised of highly potent small molecule payloads chemically conjugated to a full-length antibody, represent a growing class of therapeutic agents. The targeting of cytotoxic payloads via the specificity and selectivity of the antibody has led to substantial clinical benefits. However, ADC potency can be altered by mechanisms of resistance such as overexpression of efflux pumps or anti-apoptotic proteins. DeBouganin is a de-immunized variant of bouganin, a ribosome-inactivating protein (RIP) that blocks protein synthesis, thereby leading to apoptosis. When conjugated to trastuzumab (T-deB), deBouganin was more potent than ado-trastuzumab-emtansine (T-DM1) and unaffected by resistance mechanisms to which DM1 is susceptible. To further highlight the differentiating mechanism of action of deBouganin, HCC1419 and BT-474 tumor cells that survived T-DM1 or trastuzumab-MMAE (T-MMAE) treatment were treated with an anti-HER2 C6.5 diabody-deBouganin fusion protein or T-deB. C6.5 diabody-deBouganin and T-deB were potent against HCC1419 and BT-474 cells that were resistant to T-DM1 or T-MMAE killing. The resistant phenotype involved MDR pumps, Bcl-2 family members, and the presence of additional unknown pathways. Overall, the data suggest that deBouganin is effective against tumor cell resistance mechanisms selected in response to ADCs composed of anti-microtubule payloads.
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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".