Intraperitoneal Pretarget Radioimmunotherapy with CC49 Fusion Protein
Bibliographic record
Abstract
PURPOSE: This study examined a pretarget radioimmunotherapy strategy for treatment of an i.p. tumor model (LS174T). EXPERIMENTAL DESIGN: The strategy used regional administration (i.p.) of a novel targeting molecule composed of four CC49 anti-tumor-associated glycoprotein 72 (TAG-72) single-chain antibodies linked to streptavidin as a fusion protein (CC49 fusion protein); 24 hours later, a synthetic clearing agent was administered i.v. to produce hepatic clearance of unbound CC49 fusion protein/synthetic clearing agent complexes. Four hours later, a low molecular weight radiolabeled reagent composed of biotin conjugated to the chelating agent 7,10-tetra-azacyclododecane-N,N',N'',N'''-tetraacetic acid (DOTA) complexed with (111)In-, (90)Y-, or (177)Lu-DOTA-biotin was injected. RESULTS: Radiolocalization to tumor sites was superior with i.p. administration of radiolabeled DOTA-biotin as compared with i.v. administration. Imaging and biodistribution studies showed excellent tumor localization of radioactivity with (111)In- or (177)Lu-DOTA-biotin. Tumor localization of (111)In-DOTA-biotin was 43% ID/g and 44% ID/g at 4 and 24 hours with the highest normal tissue localization in the kidney with 6% ID/g at 48 and 72 hours. Therapy studies with (90)Y-DOTA-biotin at doses of 400 to 600 microCi or (177)Lu-DOTA-biotin at doses of 600 to 800 microCi produced significant prolongation of survival compared with controls (P = 0.03 and P < 0.01). CONCLUSIONS: Pretarget radioimmunotherapy using regional administration of CC49 fusion protein and i.p. (90)Y- or (177)Lu-DOTA-biotin represents a successful therapeutic strategy in the LS174T i.p. tumor model and this strategy may be applicable to human trials in patients with i.p. ovarian cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".