Peptide-Chlorambucil Conjugates Combat Pgp-Dependent Drug Efflux
Bibliographic record
Abstract
Cancer drugs, such as the ovarian cancer drug adriamycin, are effective at slowing disease progression and improving remission rates in patients. However, drug resistance often arises, limiting the activity of these agents in some patients. In particular, efflux pumps, which export drugs out of cells, limit the efficacy of a variety of anticancer agents. While inhibitors to block these pumps currently exist, they are usually not used clinically because they alter other drug properties. Here, we report a novel inhibitor of drug efflux that only reduces pump activity temporarily. This decreases the risk that it will alter drug function and cause nonspecific toxicity. P-glycoprotein efflux pumps are commonly overexpressed by malignant cells and are a major contributing factor to the development of drug resistance. Many therapeutics containing basic nitrogens, hydrophobic character, or aromaticity are efficiently eliminated from cells, and Pgp inhibitors must often be coadministered to limit this process. However, currently available inhibitors often alter the pharmacokinetic profiles of therapeutics or increase off-target toxicity, limiting their clinical utility. Here, we report the development of a novel panel of peptide-chlorambucil conjugates capable of efficiently decreasing efflux of Pgp substrates. These conjugates selectively improve adriamycin toxicity and uptake for short, but not prolonged, periods reducing the risk of altered pharmacokinetics and off-target effects.
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".