Bibliographic record
Abstract
evaluated for the treatment of hematologic malignancies are listed in the table.The ADC used in this study by Deckert et al comprises a humanized anti-CD37 antibody, K7153A, linked via a SMCC [N-succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate] linker to the maytansinoid DM1 payload.DM1 exerts its antimitotic effect by depolymerizing microtubules and arresting the cells in prometaphase/metaphase.The authors go on to dissect in detail the antitumor mechanism of the DM1-conjugated antibody (IMGN529), demonstrating that the conjugated compound retains the same strong immune effector activities as the unconjugated K7153A.When the naked K7153A and IMGN529 were compared in cytotoxicity assays, the cytotoxic potency induced by IGMN529 was far superior compared with the naked antibody, with induction of cell death in a dose-dependent manner in the picomolar range.Mice inoculated with a human B-cell lymphoma cell line receiving single doses of IMGN529 had a better tumor-free survival time than mice treated with the unconjugated antibody or with rituximab, confirming the additive effect of DM1 delivery.B-cell depletion was observed in the treated animal, and it was more profound than that induced by rituximab treatment.Given these results, IMGN529 seems to be a promising drug, and certainly the planned first-in-man clinical trial is warranted.Overall, the success of trastuzumab-emtansine, as well as that of brentuximab vedotin, suggests that ADCs might take a spotlight in the current landscape of anticancer drugs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".