Trastuzumab-deBouganin Conjugate Overcomes Multiple Mechanisms of T-DM1 Drug Resistance
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.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".