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 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) | 0.001 | 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 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".