Abstract 6028: Preferential tumor-to-normal tissue biodistribution and single-dose efficacy with ABD147, a DLL3-targeted engineered antibody-based radiotherapeutic, in preclinical small cell lung cancer models
Bibliographic record
Abstract
Abstract Targeted radiotherapies represent an emerging treatment modality for aggressive cancers with limited treatment options, such as small cell lung cancer (SCLC). ABD147 is a Delta Ligand 3 (DLL3)-targeted antibody conjugate designed to carry and deliver cytotoxic radioactive isotopes to DLL3-expressing tumor cells. DLL3 is commonly expressed on the cell surface of neuroendocrine cancer cells, including SCLC, but has limited and predominantly intracellular expression in non-malignant tissues. The ABD147 antibody specifically binds human DLL3 with high affinity and is internalized by DLL3-expressing cancer cells. To minimize radiation exposure to normal tissue, ABD147 has been engineered to clear quickly from the blood compartment. Using indium-111 to determine ABD147 pharmacokinetics and biodistribution in mice, we demonstrate rapid blood and normal tissue clearance of 111In-ABD147, as designed, following a single intravenous dose administration. However, 111In-ABD147 retains high tumor accumulation (activity concentration of up to 30% ID/g) in xenograft mouse models of SCLC despite low DLL3 surface antigen expression on tumor cells (≤ 3000 copies). 111In-ABD147 shows a favorable tumor-to-normal tissue distribution with predominant liver clearance. Following ABD147 therapeutic radioisotope delivery of actinium-225 or lutetium-177 (225Ac-ABD147, 177Lu-ABD147), we demonstrate single-dose tumor regression and dose-dependent sustained anti-tumor efficacy, corresponding to an extension of survival out to 84 days in multiple SCLC xenograft models. In summary, ABD147 can preferentially deliver radionuclides to DLL3-expressing tumor tissue while substantially reducing the systemic radioactive exposure typical of conventional IgG radioconjugates. Following promising results in preclinical models, including potent anti-tumor activity, 225Ac-ABD147 is now progressing into clinical development. Citation Format: Iva Kulic, Etienne S. Melese, Emma Cummins, Alex Mandel, Raja Viswas, Michael Abrams, Adam Judge. Preferential tumor-to-normal tissue biodistribution and single-dose efficacy with ABD147, a DLL3-targeted engineered antibody-based radiotherapeutic, in preclinical small cell lung cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6028.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".