Co-delivery of Doxorubicin and PSC 833 (Valspodar) by Stealth Nanoliposomes for Efficient Overcoming of Multidrug Resistance
Bibliographic record
Abstract
PURPOSE: This study was aimed at developing co-encapsulated stealth nanoliposomes containing PSC 833, an efficient MDR modulator, and doxorubicin (DOX) in order to increase the effectiveness and decrease adverse effects of the anticancer drug. METHODS: In attempt to increase the encapsulation efficiency of drugs, different methods for liposome preparation were tested and the effect of different parameters such as drug to lipid molar ratio, cholesterol mole percent and lipid compositions, were investigated. The final product with a lipid composition of EPC:DSPE-PEG2000:Chol (60:5:30 %mol) was prepared by thin layer film hydration method. After preparation of empty liposomes, DOX and PSC 833 were loaded using ammonium sulfate gradient and remote film loading methods, respectively. Physical characteristics of optimized liposomes (DOX/PSC-L) such as particle size, zeta potential, encapsulation efficiency, in-vitro drugs release and stability were evaluated. Furthermore, in vitro cytotoxicity study of various liposomal formulations as well as drugs, solutions against resistant human breast cancer cell line, T47D/TAMR-6, was evaluated using MTT assay. RESULTS: The best formulation showed a narrow size distribution with average diameter of 91.3 ± 0.2 nm with zeta potential of -6 ± 1.2, the encapsulation efficiency for DOX and PSC 833 were more than 95% and 65.5%, respectively. In DOX-resistant T47D/TAMR-6 cells, dual-agent stealth liposomes showed significantly greater cytotoxicity (P < 0.05) than free DOX and liposomal DOX plus free PSC 833 treatments. CONCLUSIONS: Co-encapsulation of DOX and PSC 833 presents a promising anticancer formulation, capable of effective reversal of drug resistance, and should be explored further in therapeutic studies with animal tumor xenograft models. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.000 | 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".