Hypoxia‐activated ADCC‐enhanced humanized anti‐CD147 antibody for liver cancer imaging and targeted therapy with improved selectivity
Bibliographic record
Abstract
Abstract Therapeutic antibodies (Abs) improve the clinical outcome of cancer patients. However, on‐target off‐tumor toxicity limits Ab‐based therapeutics. Cluster of differentiation 147 (CD147) is a tumor‐associated membrane antigen overexpressed in cancer cells. Ab‐based drugs targeting CD147 have achieved inadequate clinical benefits for liver cancer due to side effects. Here, by using glycoengineering and hypoxia‐activation strategies, we developed a conditional Ab‐dependent cellular cytotoxicity (ADCC)‐enhanced humanized anti‐CD147 Ab, HcHAb18‐azo‐PEG 5000 (HAP18). Afucosylated ADCC‐enhanced HcHAb18 Ab was produced by a fed‐batch cell culture system. Azobenzene (Azo)‐linked PEG 5000 conjugation endowed HAP18 Ab with features of hypoxia‐responsive delivery and selective targeting. HAP18 Ab potently inhibits the migration, invasion, and matrix metalloproteinase secretion, triggers the cytotoxicity and apoptosis of cancer cells, and induces ADCC, complement‐dependent cytotoxicity, and Ab‐dependent cellular phagocytosis under hypoxia. In xenograft mouse models, HAP18 Ab selectively targets hypoxic liver cancer tissues but not normal organs or tissues, and has potent tumor‐inhibiting effects. HAP18 Ab caused negligible side effects and exhibited superior pharmacokinetics compared to those of parent HcHAb18 Ab. The hypoxia‐activated ADCC‐enhanced humanized HAP18 Ab safely confers therapeutic efficacy against liver cancer with improved selectivity. This study highlights that hypoxia activation is a promising strategy for improving the tumor targeting potential of anti‐CD147 Ab drugs.
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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".