Pretargeted Radioimmunotherapy (RIT) with a Novel Anti-TAG-72 Fusion Protein
Bibliographic record
Abstract
Pretargeted radioimmunotherapy (RIT) increases the dose of radionuclide delivered to tumor sites while limiting radiation to normal tissues. The three components in Pretarget include a streptavidin-containing targeting molecule, a synthetic clearing agent (sCA), and (90)Y and/or (111)In-DOTA-biotin. This trial determined the feasibility and safety of using a genetically engineered fusion protein directed to TAG-72 as the targeting agent. Nine (9) patients with metastatic colorectal cancer (TAG-72+) received 160 mg/m(2) of CC49Fusion protein intravenously (i.v.), followed by the sCA, 45 mg/m(2) i.v. Twenty-four (24) hours later, patients received radiolabeled DOTA-biotin (either 0.65 or 1.3 mg/m(2)). All patients received 5 mCi of (111)In-DOTA-biotin for imaging and dosimetry purposes and patients 4-9 received 10 mCi/m2 of (90)Y-DOTA-biotin as well. The mean plasma T1/2 of CC49Fusion protein was 23 +/- 6 hours. Greater than 95% of the circulating CC49Fusion protein was eliminated from the circulation within 6 hours of sCA administration. The radiolabeled DOTA-biotin rapidly localized to tumor sites while the unbound fraction was rapidly excreted. The mean tumor-to-marrow radiation dose ratio was 139:1 and mean tumor: whole body was 56:1. No infusion-related, renal, hepatic, or hematologic toxicities were noted. CC49Fusion protein performs well in a pretargeted RIT schema, and further study with escalating doses of (90)Y should be pursued. This strategy has the potential to deliver effective radiation tumor doses to TAG- 72+ tumors.
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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) | 0.001 | 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".