[ <sup>225</sup> Ac]Ac/[ <sup>89</sup> Zr]Zr-labeled N4MU01 radioimmunoconjugates as theranostics against nectin-4 positive triple negative breast cancer
Bibliographic record
Abstract
Abstract Purpose Nectin-4 is an overexpressed biomarker in 60-70% of triple-negative breast cancer (TNBC), and an ideal target for radiotherapy and PET imaging. In this study, we have developed theranostic radioimmunoconjugates (RICs) based on a fully-human anti-nectin-4 antibody, N4MU01. We characterized and evaluated the efficacy of these RICs for applications in molecular imaging and radiotherapy of aggressive TNBC models. Methods An anti-nectin-4 antibody (N4MU01) was radiolabeled with 89 Zr and 225 Ac, for imaging and radiotherapy, respectively, using TNBC xenograft and syngeneic models. Biodistribution & PET imaging of [ 89 Zr]Zr-DFO-N4MU01 RIC was studied in mice bearing nectin-4 positive xenografts. Dosimetry of [ 225 Ac]Ac-Macropa-N4MU01 was studied in healthy mice, and therapeutic efficacy was evaluated in mice bearing human TNBC MDA-MB-468 xenograft and in a syngeneic xenograft model using murine 4T1 breast cancer cells transfected with human nectin-4 (4T1. nectin-4 ). Mice received 2 doses of 13 kBq or 18.6 kBq 10 days apart. Results The pharmacokinetic profile of [ 89 Zr]Zr-DFO-N4MU01 RIC showed biphasic distribution with a moderate elimination half-life of 63 h. PET imaging and biodistribution of [ 89 Zr]Zr-DFO-N4MU01 in mice bearing MDA-MB-468 xenograft showed high tumor uptake of 13.2 ± 1.12 %IA/g at 120 h. [ 225 Ac]Ac-Macropa-N4MU01 was effectively internalized in MDA-MB-468 and was cytotoxic to the cells with IC 50 of 1.2 kBq/mL. Mice bearing MDA-MB-468 xenograft treated with [ 225 Ac]Ac-Macropa-N4MU01 (13 kBq or 18.6 kBq) had a remarkable tumor growth inhibition that was dose dependent. For the syngeneic 4T1. nectin-4 model, treatment with [ 225 Ac]Ac-Macropa-N4MU01 (13 kBq) led to complete tumor remission in 83.3% (5/6) of mice. Conclusion The specific tumor uptake and remarkable effectiveness at shrinking aggressive TNBC tumors is very promising towards clinical development of N4MU01 RICs as theranostics against TNBC.
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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) | 0.001 | 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 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".