Synthesis and pre-clinical evaluation of an (18)F-labeled single-chain antibody fragment for PET imaging of epithelial ovarian cancer.
Bibliographic record
Abstract
Anti-CA125 antibodies have been used in immunoassays to quantify levels of shed antigen in the serum of patients who are under surveillance for epithelial ovarian cancer (EOC). However, there is currently no molecular imaging probe in the clinic for the assessment of CA125 expression in vivo. The present study describes the development of an (18)F-labeled single-chain variable fragment (scFv) for PET imaging of CA125 in preclinical EOC models. Anti-CA125 scFv was derived from MAb-B43.13 by recombinant expression of the fragment in E.coli. Fragment scFv-B43.13 was purified via immobilized metal affinity chromatography and characterized for antigen binding via immuno-staining and flow cytometry. Prosthetic group N-succinimidyl 4-[(18)F]fluorobenzoate ([(18)F]SFB) was used for radiolabeling of scFv-B43.13. Preclinical ovarian cancer models were developed based on ovarian cancer cell lines OVCAR3 (CA125-positive) and SKOV3 (CA125-negative) in NIH-III mice. The radiopharmacological profile of (18)F-labeled scFv-B43.13 ([(18)F]FBz-scFv-B43.13) was studied with PET. [(18)F]FBz-scFv-B43.13 was prepared in radiochemical yields of 3.7 ± 1.8% (n = 5) at an effective specific activity of 3.88 ± 0.76 GBq/µmol (n = 5). The radiotracer demonstrated selective uptake in CA125-positive OVCAR3 cells and virtually no uptake in CA125-negative SKOV3 cells. Standardized uptake values (SUV) of radioactivity uptake in OVCAR3 tumors was 0.5 (n = 3) and 0.3 (n = 2) in SKOV3 tumors after 60 min post injection (p.i.).
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".