Docetaxel-Loaded Methoxy poly(ethylene glycol)-poly (L-lactic Acid) Nanoparticles for Breast Cancer: Synthesis, Characterization, Method Validation, and Cytotoxicity
Bibliographic record
Abstract
This study aimed to synthesize and characterize DTX-mPEG-PLA-NPs along with the development and validation of a simple, accurate, and reproducible method for the determination and quantification of DTX in mPEG-PLA-NPs. The prepared NPs were characterized using AFM, DLS, zetasizer, and drug release kinetic profiling. The RP-HPLC assay was developed for DTX detection. The cytotoxicity and anti-clonogenic effects were estimated using MTT and clonogenic assays, respectively, using both MCF-7 and MDA-MB-231 cell lines in a 2D and 3D culture system. The developed method showed a linear response, high precision, accuracy, RSD values of ≤2%, and a tailing factor ≤2, per ICH guidelines. The DTX-mPEG-PLA-NPs exhibited an average particle size of 264.3 nm with an encapsulation efficiency of 62.22%. The in vitro drug kinetic profile, as per the Krosmeyers-Peppas model, demonstrated Fickian diffusion, with initial biphasic release and a multistep sustained release over 190 h. The MTT assay revealed improved in vitro cytotoxicity against MCF-7 and MDA-MB-231 in the 2D cultures and MCF-7 3D mammosphere cultures. Significant inhibitions of the clonogenic potential of MDA-MB-231 were observed for all concentrations of DTX-mPEG-PLA-NPs. Our results highlight the feasibility of detecting DTX via the robust RP-HPLC method and using DTX-mPEG-PLA-NPs as a perceptible and biocompatible delivery vehicle with greater cytotoxic and anti-clonogenic potential, supporting improved outcomes in BC.
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".