Effect of Modulating FcRn Binding on Direct and Pretargeted Tumor Uptake of Full-length Antibodies
Bibliographic record
Abstract
Abstract Full-length antibodies lack ideal pharmacokinetic properties for rapid targeted imaging, prompting the pursuit of smaller peptides and fragments. Nevertheless, studying the disposition properties of antibody-based imaging agents can provide critical insight into the pharmacology of their therapeutic counterparts, particularly for those coupled with potent payloads. Here, we evaluate modulation of binding to the neonatal Fc receptor (FcRn) as a protein engineering-based pharmacologic strategy to minimize the overall blood pool background with directly labeled antibodies and undesirable systemic click reaction of radiolabeled tetrazine with circulating pretargeted trans-cyclooctene (TCO)-modified antibodies. Noninvasive SPECT imaging of mice bearing HER2-expressing xenografts was performed both directly (111In-labeled antibody) and indirectly (pretargeted TCO-modified antibody followed by 111In-labeled tetrazine). Pharmacokinetic modulation of antibodies was achieved by two distinct methods: Fc engineering to reduce binding affinity to FcRn, and delayed administration of an antibody that competes with binding to FcRn. Tumor imaging with directly labeled antibodies was feasible in the absence of FcRn binding, rapidly attaining high tumor-to-blood ratios, but accompanied by moderate liver and spleen uptake. Pretargeted imaging of tumors with non-FcRn-binding antibody was also feasible, but systemic click reaction still occurred, albeit at lower levels than with parental antibody. Our findings demonstrate that FcRn binding impairment of full-length IgG antibodies moderately lowers tumor accumulation of radioactivity, and shifts background activity from blood pool to liver and spleen. Furthermore, reduction of FcRn binding did not eliminate systemic click reaction, but yielded greater improvements in tumor-to-blood ratio when imaging with directly labeled antibodies than with pretargeting.
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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 | 0.000 |
| 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".