Pretargeted Radioimmunotherapy Using Anti-CD45 Monoclonal Antibodies to Deliver Radiation to Murine Hematolymphoid Tissues and Human Myeloid Leukemia
Bibliographic record
Abstract
Radioimmunotherapy (RIT) for treatment of hematologic malignancies frequently fails because of disease recurrence. We therefore conducted pretargeted (P)RIT studies to augment the efficacy in mice of therapy using a pretargeted anti-human (h)CD45 antibody (Ab)-streptavidin (SA) conjugate followed by a biotinylated clearing agent and radiolabeled 1,4,7,10-tetraazacylodode cane N,N',N",N'''-tetraacetic (DOTA)-biotin. Tumor-to-blood ratios at 24 hours were 20:1 using pretargeted anti-hCD45 RIT and <1:1 with conventional RIT. In vivo imaging studies confirmed that the PRIT approach provided high-contrast tumor images with minimal blood-pool activity, whereas directly labeled anti-hCD45 Ab produced distinct tumor images but the blood pool retained a large amount of labeled Ab for a prolonged time. Therapy experiments showed that (90)Y-DOTA-biotin significantly prolonged survival of mice treated with pretargeted anti-hCD45 Ab-SA compared with mice treated with conventional RIT using (90)Y-labeled anti-hCD45 Ab at 200 muCi. Because human CD45 antigens are confined to xenograft tumor cells in this model, and all murine tissues are devoid of hCD45 and will not bind anti-hCD45 Ab, we also compared one-step and PRIT using an anti-murine (m)CD45 Ab where the target antigen is present on normal hematopoietic tissues. After 24 h, 27.3% +/- 2.8% of the injected dose of activity was delivered per gram (% ID/g) of lymph node using (131)I-A20-Ab compared with 40.0 +/- 5.4% ID/g for pretargeted (111)In-DOTA-biotin. These data suggest that pretargeted methods for delivering RIT may be superior to conventional RIT when targeting CD45 for the treatment of leukemia and may allow for the intensification of therapy, while minimizing toxicities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".