Abstract 760: Conjugation of daratumumab with 225actinium greatly increases its antitumor activity against multiple myeloma tumors
Bibliographic record
Abstract
Abstract Daratumumab is a human cytolytic antibody specific for CD38 that is used clinically for treatment of patients with multiple myeloma (MM). Current therapeutic regimens require multiple injections over months of treatment. Increasing the potency of daratumumab to shorten the length of treatment would be beneficial. 225Ac is an alpha-particle emitting radionuclide that has potent cytotoxic activities over short distances, allowing for precise targeting of a lethal dose of radiation. Previously we have established that labeling daratumumab with 225Ac increased more than 10-fold its ability to kill MM cell lines in vitro (ASH 2017, Poster Number 4427). In this study we evaluated the ability of 225Ac-daratumumab to kill established tumors in mice. Mice deficient in T- and B-cells were injected subcutaneously with human MM tumor cells and once tumors reached an average volume of ~200 mm3, mice were treated with the 225Ac-daratumumab. To determine the localization of daratumumab within the tumor-bearing mice it was labeled with 111In, an γ-emitting radioisotope. 111In-daratumumab can be easily imaged and is used as a surrogate to estimate the localization of 225Ac-daratumumab. The distribution of the 111In-daratumumab was then followed for 10 days using a microSPECT/CT scanner. To evaluate the antitumor ability of the 225Ac-daratumumab, tumor-bearing mice were injected with 225Ac-daratumumab at a dose of of 400 nCi/0.3 μg of antibody, and 200 nCi/0.3 μg of antibody. As a control, mice were injected with either saline or an equivalent amount of unlabeled daratumumab. In addition, a group of mice was also treated with 30 times greater dose of unlabeled daratumumab (10 μg) - a dose which was previously shown to be effective against established tumors. 111In-daratumumab begun to accumulate in the tumor 24 hours after intraperitoneal injection and by 7 days was exclusively present in the tumor. The growth of the tumors in mice treated with 400 nCi/0.3 μg was significantly retarded compared to mice treated with equal concentration of unlabeled daratumumab or saline. Tumor growth was similar between mice treated with 400 nCi/0.3 μg of 225Ac-daratumumab and mice treated with 10 μg of unlabeled daratumumab. In conclusion, this study shows that labeling daratumumab with 225Ac increases its antitumor activity ~30-fold. This study suggests that approach to increase the potency of daratumumab via 225Ac labeling could greatly reduce the amount of daratumumab treatment needed in the clinic. This study also highlights the potential of targeting α-emitters to tumors as a viable therapeutic approach. Citation Format: Wojciech Dawicki, Kevin Allen, Rubin Jiao, Mackenzie Malo, Keisha Thomas, Mark S. Berger, Ekaterina Dadachova. Conjugation of daratumumab with 225actinium greatly increases its antitumor activity against multiple myeloma tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 760.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".