Enhancement of the Tumor Penetration of Monoclonal Antibody by Fusion of a Neuropilin-Targeting Peptide Improves the Antitumor Efficacy
Bibliographic record
Abstract
The limited localization and penetration of monoclonal antibodies (mAb) into solid tumors restricts their antitumor efficacy. Here, we describe a solid tumor-targeting antibody with enhanced tumor penetration activity. We designed a 22-residue peptide (A22p), which was extracted from the C-terminal basic region of semaphorin 3A (Sema3A) but modified to have higher affinity with neuropilin receptors (NRP), and genetically fused it to the C-terminus of Fc of human immunoglobulin G1 via a 15-residue (G4S)3 linker, generating Fc-A22p, for the bivalent binding to NRPs. In contrast to Fc or the monovalent A22p peptide alone, Fc-A22p homed to tumor vessels and induced vascular permeability through VE-cadherin downregulation and penetrated tumor tissues by interacting with NRPs in mice bearing human tumor xenografts. We extended the Fc-A22p platform by generating mAb-A22p antibodies of two clinically approved solid tumor-targeting mAbs, the anti-EGF receptor mAb cetuximab (erbitux), and the anti-Her2 mAb trastuzumab (herceptin). The mAb-A22p antibodies retained the intrinsic antigen binding, natural Fc-like biophysical properties, and productivity in mammalian cell cultures, comparable with those of the parent mAbs. In mouse xenograft tumor models, the mAb-A22p antibodies more efficiently homed to tumor vessels and spread into the extravascular tumor parenchyma, which significantly enhanced antitumor efficacy compared with the parent mAbs. Our results suggest that mAb-A22p is a superior format for solid tumor-targeting antibodies due to its enhanced tumor tissue penetration and greater antitumor efficacy compared with conventional mAbs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".