Nanofitin as a New Molecular-Imaging Agent for the Diagnosis of Epidermal Growth Factor Receptor Over-Expressing Tumors
Bibliographic record
Abstract
Epidermal growth-factor receptor (EGFR) is involved in cell growth and proliferation and is over-expressed in malignant tissues. Although anti-EGFR-based immunotherapy became a standard of care for patients with EGFR-positive tumors, this strategy of addressing cancer tumors by targeting EGFR with monoclonal antibodies is less-developed for patient diagnostic and monitoring. Indeed, antibodies exhibit a slow blood clearance, which is detrimental for positron emission tomography (PET) imaging. New molecular probes are proposed to overcome such limitations for patient monitoring, making use of low-molecular-weight protein scaffolds as alternatives to antibodies, such as Nanofitins with better pharmacokinetic profiles. Anti-EGFR Nanofitin B10 was reformatted by genetic engineering to exhibit a unique cysteine moiety at its C-terminus, which allows the development of a fast and site-specific radiolabeling procedure with 18 F–4-fluorobenzamido- N -ethylamino-maleimide ( 18 F–FBEM). The in vivo tumor targeting and imaging profile of the anti-EGFR Cys–B10 Nanofitin was investigated in a double-tumor xenograft model by static small-animal PET at 2 h after tail-vein injection of the radiolabeled Nanofitin 18 F–FBEM–Cys–B10. The image showed that the EGFR-positive tumor (A431) is clearly delineated in comparison to the EGFR-negative tumor (H520) with a significant tumor-to-background contrast. 18 F–FBEM–Cys–B10 demonstrated a significantly higher retention in A431 tumors than in H520 tumors at 2.5 h post-injection with a A431-to-H520 uptake ratio of 2.53 ± 0.18 and a tumor-to-blood ratio of 4.55 ± 0.63. This study provides the first report of Nanofitin scaffold used as a targeted PET radiotracer for in vivo imaging of EGFR-positive tumor, with the anti-EGFR B10 Nanofitin used as proof-of-concept. The fast generation of specific Nanofitins via a fully in vitro selection process, together with the excellent imaging features of the Nanofitin scaffold, could facilitate the development of valuable PET-based companion diagnostics.
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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 | 0.001 |
| 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 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".