AMG 757, a Half-Life Extended, DLL3-Targeted Bispecific T-Cell Engager, Shows High Potency and Sensitivity in Preclinical Models of Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Small-cell lung cancer (SCLC) is an aggressive neuroendocrine tumor with a high relapse rate, limited therapeutic options, and poor prognosis. We investigated the antitumor activity of AMG 757, a half-life extended bispecific T-cell engager molecule targeting delta-like ligand 3 (DLL3)-a target that is selectively expressed in SCLC tumors, but with minimal normal tissue expression. EXPERIMENTAL DESIGN: AMG 757 efficacy was evaluated in SCLC cell lines and in orthotopic and patient-derived xenograft (PDX) mouse SCLC models. Following AMG 757 administration, changes in tumor volume, pharmacodynamic changes in tumor-infiltrating T cells (TILs), and the spatial relationship between the appearance of TILs and tumor histology were examined. Tolerability was assessed in nonhuman primates (NHPs). RESULTS: AMG 757 showed potent and specific killing of even those SCLC cell lines with very low DLL3 expression (<1,000 molecules per cell). AMG 757 effectively engaged systemically administered human T cells, induced T-cell activation, and redirected T cells to lyse tumor cells to promote significant tumor regression and complete responses in PDX models of SCLC and in orthotopic models of established primary lung SCLC and metastatic liver lesions. AMG 757 was well tolerated with no AMG 757-related adverse findings up to the highest tested dose (4.5 mg/kg weekly) in NHP. AMG 757 exhibits an extended half-life in NHP, which is projected to enable intermittent administration in patients. CONCLUSIONS: AMG 757 has a compelling safety and efficacy profile in preclinical studies making it a viable option for targeting DLL3-expressing SCLC tumors in the clinical setting.
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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 | 0.001 |
| 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".