The distribution of the therapeutic monoclonal antibodies cetuximab and trastuzumab within solid tumors
Bibliographic record
Abstract
BACKGROUND: Poor distribution of some anticancer drugs in solid tumors may limit their anti-tumor activity. METHODS: Here we used immunohistochemistry to quantify the distribution of the therapeutic monoclonal antibodies cetuximab and trastuzumab in relation to blood vessels and to regions of hypoxia in human tumor xenografts. The antibodies were injected into mice implanted with human epidermoid carcinoma A431 or human breast carcinoma MDA-MB-231 transfected with ERBB2 (231-H2N) that express high levels of ErbB1 and ErbB2 respectively, or wild-type MDA-MB-231, which expresses intermediate levels of ErbB1 and low levels of ErbB2. RESULTS: The distribution of cetuximab in A431 xenografts and trastuzumab in 231-H2N xenografts was time and dose dependent. At early intervals after injection of 1 mg cetuximab into A431 xenografts, the concentration of cetuximab decreased with increasing distance from blood vessels, but became more uniformly distributed at later times; there remained however limited distribution and binding in hypoxic regions of tumors. Injection of lower doses of cetuximab led to heterogeneous distributions. Similar results were observed with trastuzumab in 231-H2N xenografts. In MDA-MB-231 xenografts, which express lower levels of ErbB1, homogeneity of distribution of cetuximab was achieved more rapidly. CONCLUSIONS: Cetuximab and trastuzumab distribute slowly, but at higher doses achieve a relatively uniform distribution after about 24 hours, most likely due to their long half-lives in the circulation. There remains poor distribution within hypoxic regions of tumors.
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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".