Functionalized Lipid Nanocarriers for Simultaneous Delivery of Docetaxel and Tariquidar to Chemoresistant Cancer Cells
Bibliographic record
Abstract
The simultaneous drug delivery efficiency of a co-loaded single-carrier system of docetaxel (DTX)- and tariquidar (TRQ)-loaded nanostructured lipid carrier (NLC) functionalized with PEG and RIPL peptide (PRN) (D^T-PRN) was compared with that of a physically mixed dual-carrier system of DTX-loaded PRN (D-PRN) and TRQ-loaded PRN (T-PRN) to overcome DTX mono-administration-induced multidrug resistance. NLC samples were prepared using the solvent emulsification evaporation technique and showed homogeneous spherical morphology, with nano-sized dispersion (<220 nm) and zeta potential values of −15 to −7 mV. DTX and/or TRQ was successfully encapsulated in NLC samples (>95% encapsulation efficiency and 73–78 µg/mg drug loading). In vitro cytotoxicity was concentration-dependent; D^T-PRN exhibited the highest MDR reversal efficiency, with the lowest combination index value, and increased the cytotoxicity and apoptosis in MCF7/ADR cells by inducing cell-cycle arrest in the G2/M phase. A competitive cellular uptake assay using fluorescent probes showed that, compared to the dual nanocarrier system, the single nanocarrier system exhibited better intracellular delivery efficiency of multiple probes to target cells. In the MCF7/ADR-xenografted mouse models, simultaneous DTX and TRQ delivery using D^T-PRN significantly suppressed tumor growth as compared to other treatments. A single co-loaded system for PRN-based co-delivery of DTX/TRQ (1:1, w/w) constitutes a promising therapeutic strategy for drug-resistant breast 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 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".