Evaluation of antibody fragment properties for near-infrared fluorescence imaging of HER3-positive cancer xenografts
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Bibliographic record
Abstract
In vivo imaging is influenced by the half-life, tissue penetration, biodistribution, and affinity of the imaging probe. Immunoglobulin G (IgG) is composed of discrete domains with known functions, providing a template for engineering antibody fragments with desired imaging properties. Here, we engineered antibody-based imaging probes, consisting of different combinations of antibody domains, labeled them with the near-infrared fluorescent dye IRDye800CW, and evaluated their in vivo imaging properties. Antibody-based imaging probes were based on an anti-HER3 antigen binding fragment (Fab) isolated using phage display. Methods: We constructed six anti-HER3 antibody-based imaging probes: a single chain variable fragment (scFv), Fab, diabody, scFv-CH3, scFv-Fc, and IgG. IRDye800CW-labeled, antibody-based probes were injected into nude mice bearing FaDu xenografts and their distribution to the xenograft, liver, and kidneys was evaluated. Results: These imaging probes bound to recombinant HER3 and to the HER3-positive cell line, FaDu. Small antibody fragments with molecular weight <60 kDa (scFv, diabody, and Fab) accumulated rapidly in the xenograft (maximum accumulation between 2-4 h post injection (hpi)) and cleared primarily through the kidneys. scFv-CH3 (80 kDa) had fast clearance and peaked in the xenograft between 2-3 hpi and cleared from xenograft in a rate comparable to Fab and diabody. IgG and scFv-Fc persisted in the xenografts for up to 72 hpi and distributed mainly to the xenograft and liver. The highest xenograft fluorescence signals were observed with IgG and scFv-Fc imaging probes and persisted for 2-3 days. Conclusion: These results highlight the utility of using antibody fragments to optimize clearance, tumor labeling, and biodistribution properties for developing anti-HER3 probes for image-guided surgery or PET imaging.
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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 | 0.001 | 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) | 0.000 | 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 it