Image-Based Dosimetry in Dogs and Cross-Reactivity with Human Tissues of IGF2R-Targeting Human Antibody
Bibliographic record
Abstract
Background: Osteosarcoma (OS) represents the most common primary bone tumor in humans and in companion dogs, being practically phenotypically identical. There is a need for effective treatments to extend the survival of patients with OS. Here, we examine the dosimetry in beagle dogs and cross-reactivity with human tissues of a novel human antibody, IF3, that targets the insulin growth factor receptor type 2 (IGF2R), which is overexpressed on OS cells, making it a candidate for radioimmunotherapy of OS. Methods: [89Zr]Zr-DFO-IF3 was injected into three healthy beagle dogs. PET/CT was conducted at 4, 24, 48, and 72 h. RAPID analysis was used to determine the dosimetry of [177Lu]Lu-CHXA”-IF3 for a clinical trial in companion dogs with OS. IF3 antibody was biotinylated, and a multitude of human tissues were assessed with immunohistochemistry. Results: PET/CT revealed that only the liver, bone marrow, and adrenal glands had high uptake. Clearance was initially through renal and hepatobiliary excretion in the first 72 h followed by primarily physical decay. RAPID analysis showed bone marrow to be the dose-limiting organ with a therapeutic range for 177Lu calculated to be 0.487–0.583 GBq. Immunohistochemistry demonstrated the absence of IGF2R expression on the surface of healthy human cells, thus suggesting that radioimmunotherapy with [177Lu]Lu-CHXA”-IF3 will be well tolerated. Conclusions: Image-based dosimetry has defined a safe therapeutic range for canine clinical trials, while immunohistochemistry has suggested that the antibody will not cross-react with healthy human tissues.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".