Taxane-antibody conjugates afford potent cytotoxicity, enhanced solubility, and tumor target selectivity.
Bibliographic record
Abstract
Paclitaxel (Taxol) is a chemotherapeutic agent that prevents disassembly of microtubular polymers, causing a growth arrest in the G2-M phase of the cell cycle and leading to apoptotic death. Paclitaxel has remarkable efficacy against fast-growing tumors but possesses major drawbacks, such as poor solubility and lack of tumor selectivity. Conversely, monoclonal antibodies usually have low therapeutic efficacy but are highly soluble and selectively target tumor markers overexpressed in cancer cells. Therefore, to improve the therapeutic index of taxanes as chemotherapeutics, the high toxicity of paclitaxel was combined with the high selectivity and solubility of monoclonal antibodies as targeting agents. We report the chemical coupling and characterization of paclitaxel-antibody conjugates for treatment of neuroectoderm-derived tumors. Paclitaxel-antibody conjugates afforded selective toxicity toward cells expressing the target marker and were more cytotoxic in vitro than equimolar concentrations of free paclitaxel or free paclitaxel plus free antibody. In an in vivo model of xenografted tumors, systemic administration of paclitaxel-antibody conjugates prevented tumor growth and prolonged survival of mice better than free drugs. In addition, paclitaxel-antibody conjugates were highly soluble in water and stable at -20 degrees C for at least 3 months. These studies may lead to an increase or an improvement of the armamentarium and selectivity of cytotoxic agents.
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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".