Identification and Activities of Human Carboxylesterases for the Activation of CPT-11, a Clinically Approved Anticancer Drug
Bibliographic record
Abstract
CPT-11 is a clinically approved anticancer drug used for the treatment of advanced colorectal cancer. Upon administration, the carbamate side chain of the drug is hydrolyzed, resulting in the release of SN-38, an agent that has approximately 1000-fold increased cytotoxic activity. Since only a very small percentage of the injected dose of CPT-11 is converted to SN-38, there is a significant opportunity to improve its therapeutic efficacy and to diminish its systemic toxicity by selectively activating the drug within tumor sites. We envisioned that a mAb-human enzyme conjugate for CPT-11 activation would be of interest, particularly since the conjugate would likely be minimally immunogenic, and the prodrug is clinically approved. Toward this end, it was necessary to identify the most active human enzyme that could convert CPT-11 to SN-38. We isolated enzymes from human liver microsomes based on their abilities to effect the conversion and identified human carboxylesterase 2 (hCE-2) as having the greatest specific activity. hCE-2 was 26-fold more active than human carboxylesterase 1 and was 65% as active as rabbit liver carboxylesterase, the most active CPT-11 hydrolyzing enzyme known. The anti-p97 mAb 96.5 was linked to hCE-2, forming a conjugate that could bind to antigen-positive cancer cells and convert CPT-11 to SN-38. Cytotoxicity assays established that the conjugate led to the generation of active drug, but the kinetics of prodrug activation (48 pmol x min(-1) x mg(-1) was insufficient for immunologically specific prodrug activation. These results confirm the importance of hCE-2 for CPT-11 activation and underscore the importance of enzyme kinetics for selective prodrug activation.
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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".