Patient-specific dosimetry of pretargeted radioimmunotherapy using CC49 fusion protein in patients with gastrointestinal malignancies.
Bibliographic record
Abstract
UNLABELLED: Pretargeted radioimmunotherapy (RIT) using CC49 fusion protein, comprised of CC49-(scFv)4 and streptavidin, in conjunction with 90Y/111In-DOTA-biotin (DOTA = dodecanetetraacetic acid) provides a new opportunity to improve efficacy by increasing the tumor-to-normal tissue dose ratio. To our knowledge, the patient-specific dosimetry of pretargeted 90Y/111In-DOTA-biotin after CC49 fusion protein in patients has not been reported previously. METHODS: Nine patients received 3-step pretargeted RIT: (a) 160 mg/m2 of CC49 fusion protein, (b) synthetic clearing agent (sCA) at 48 or 72 h later, and (c) 90Y/111In-DOTA-biotin 24 h after the sCA administration. Sequential whole-body 111In images were acquired immediately and at 2-144 h after injection of 90Y/111In-DOTA-biotin. Geometric-mean quantification with background and attenuation correction was used for liver and lung dosimetry. Effective point source quantification was used for spleen, kidneys, and tumors. Organ and tumor 90Y doses were calculated based on 111In imaging data and the MIRD formalism using patient-specific organ masses determined from CT images. Patient-specific marrow doses were determined based on radioactivity concentration in the blood. RESULTS: The 90Y/111In-DOTA-biotin had a rapid plasma clearance, which was biphasic with <10% residual at 8 h. Organ masses ranged from 1,263 to 3,855 g for liver, 95 to 1,009 g for spleen, and 309 to 578 g for kidneys. The patient-specific mean 90Y dose (cGy/37 MBq, or rad/mCi) was 0.53 (0.32-0.78) to whole body, 3.75 (0.63-6.89) to liver, 2.32 (0.58-4.46) to spleen, 7.02 (3.36-11.2) to kidneys, 0.30 (0.09-0.44) to lungs, 0.22 (0.12-0.34) to marrow, and 28.9 (4.18-121.6) to tumors. CONCLUSION: Radiation dose to normal organs from circulating radionuclide is substantially reduced using pretargeted RIT. Tumor-to-normal organ dose ratios were increased about 8- to 11-fold compared with reported patient-specific mean dose to liver, spleen, marrow, and tumors from 90Y-CC49.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".