Evaluation of antibody fragment properties for near-infrared fluorescence imaging of HER3-positive cancer xenografts
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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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) | 0.000 | 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 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".