Simultaneous delivery of docetaxel and tariquidar to chemoresistance cancer cells using functionalized lipid nanocarriers
Bibliographic record
Abstract
Abstract Background The simultaneous administration of tariquidar (TRQ) with docetaxel (DTX) using a nanostructured lipid carrier (NLC) functionalized with PEG (5mol%) and RIPL peptide (PRN) was constructed to overcome the multidrug resistance caused by DTX administration alone. Results Either DTX or TRQ loaded PRN (D-PRN or T-PRN) and both DTX and TRQ loaded PRN (D/T-PRN) were prepared using the solvent emulsification evaporation technique. NLC samples showed homogeneous spherical morphology with nano-sized dispersion (< 220 nm) and ZP values from − 15 mV to − 7 mV. DTX and/or TRQ was successfully encapsulated in NLC samples (> 95% EE and 73–78 µg/mg DL. In vitro cytotoxicity was concentration-dependent, and D/T-PRN exhibited the highest MDR reversal efficiency with the lowest combination index value. D/T-PRN increased the apoptotic cell death in MCF7/ADR cells by inducing cell cycle arrest in the G2/M phase. In a competitive cellular uptake assay, the single nanocarrier system exhibited better intracellular delivery efficiency of multiple probes to target cells compared to dual nanocarrier system. In the MCF7/ADR-xenografted mouse models, co-delivery with D/T-PRN significantly suppressed tumor growth compared to the other treatments. Conclusions Co-delivery of DTX/TRQ (1:1, w/w) using PRN will be a promising strategy against drug-resistant breast cancer cells.
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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".