AGS67E, an Anti-CD37 Monomethyl Auristatin E Antibody–Drug Conjugate as a Potential Therapeutic for B/T-Cell Malignancies and AML: A New Role for CD37 in AML
Bibliographic record
Abstract
CD37 is a tetraspanin expressed on malignant B cells. Recently, CD37 has gained interest as a therapeutic target. We developed AGS67E, an antibody-drug conjugate that targets CD37 for the potential treatment of B/T-cell malignancies. It is a fully human monoclonal IgG2 antibody (AGS67C) conjugated, via a protease-cleavable linker, to the microtubule-disrupting agent monomethyl auristatin E (MMAE). AGS67E induces potent cytotoxicity, apoptosis, and cell-cycle alterations in many non-Hodgkin lymphoma (NHL) and chronic lymphocytic leukemia (CLL) cell lines and patient-derived samples in vitro. It also shows potent antitumor activity in NHL and CLL xenografts, including Rituxan-refractory models. During profiling studies to confirm the reported expression of CD37 in normal tissues and B-cell malignancies, we made the novel discovery that the CD37 protein was expressed in T-cell lymphomas and in AML. AGS67E bound to >80% of NHL and T-cell lymphomas, 100% of CLL and 100% of AML patient-derived samples, including CD34(+)CD38(-) leukemic stem cells. It also induced cytotoxicity, apoptosis, and cell-cycle alterations in AML cell lines and antitumor efficacy in orthotopic AML xenografts. Taken together, this study shows not only that AGS67E may serve as a potential therapeutic for B/T-cell malignancies, but it also demonstrates, for the first time, that CD37 is well expressed and a potential drug target in AML.
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) | 0.001 | 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".