Nanoparticle–Peptide–Drug Bioconjugates for Unassisted Defeat of Multidrug Resistance in a Model Cancer Cell Line
Bibliographic record
Abstract
Multidrug resistance (MDR) is a significant challenge in the treatment of many types of cancers as membrane-associated transporters actively pump drugs out of the cell, limiting therapeutic efficacy. While nanoparticle (NP)-based therapeutics have emerged as a mechanism for overcoming MDR, they often rely on the delivery of multiple anticancer drugs, nucleic acid hybrids, or MDR pump inhibitors. The effectiveness of these strategies, however, can be limited by their off-target toxicity or the need for genetic transfection. In this paper, we describe a NP-peptide-drug bioconjugate that achieves significant cell killing in MDR-positive cancer cells without the need for additional drugs. We use a quantum dot (QD) as a central scaffold to append two species of peptide, a cell-uptake peptide to facilitate endocytic internalization and a peptide-drug conjugate that is susceptible to cleavage by esterases found within the endocytic pathway. This approach relies on spatiotemporal control over drug release, where endosomes traffic drug away from membrane-resident pumps and release it closer to the nucleus. Cellular internalization studies showed high uptake of the NP-drug complex and nuclear localization of the drug after 48 h in MDR-positive cells. Additionally, cellular proliferation assays demonstrated a 40% decrease in cell viability for the NP-drug bioconjugate compared to free drug, confirming the utility of this system in overcoming MDR in cancer cells.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".