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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".