Trastuzumab-deBouganin Conjugate Overcomes Multiple Mechanisms of T-DM1 Drug Resistance
Why this work is in the frame
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Bibliographic record
Abstract
The development of antibody drug conjugates has provided enhanced potency to tumor-targeting antibodies by the addition of highly potent payloads. In the case of trastuzumab-DM1 (T-DM1), approved for the treatment of metastatic breast cancer, the addition of mertansine (DM1) to trastuzumab substantially increased progression-free survival. Despite these improvements, most patients eventually relapse due to complex mechanisms of resistance often associated with small molecule chemotherapeutics. Therefore, identifying payloads with different mechanisms of action (MOA) is critical for increasing the efficacy of targeted therapeutics and ultimately improving patient outcomes. To evaluate payloads with different MOA, deBouganin, a deimmunized plant toxin that inhibits protein synthesis, was conjugated to trastuzumab and compared with T-DM1 both in vitro and in vivo. The trastuzumab-deBouganin conjugate (T-deB) demonstrated greater potency in vitro against most cells lines with high levels of Her2 expression. In addition, T-deB, unlike T-DM1, was unaffected by inhibitors of multidrug resistance, Bcl-2-mediated resistance, or Her2-Her3 dimerization. Contrary to T-DM1 that showed only minimal cytotoxicity, T-deB was highly potent in vitro against tumor cells with cancer stem cell properties. Overall, the results demonstrate the potency and efficacy of deBouganin and emphasize the importance of using payloads with different MOAs. The data suggest that deBouganin could be a highly effective against tumor cell phenotypes not being addressed by current antibody drug conjugate formats and thereby provide prolonged clinical benefit.
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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 | 0.000 | 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 it