Multicellular Tumor Spheroids for Evaluation of Cytotoxicity and Tumor Growth Inhibitory Effects of Nanomedicines In Vitro: A Comparison of Docetaxel-Loaded Block Copolymer Micelles and Taxotere®
Bibliographic record
Abstract
While 3-D tissue models have received increasing attention over the past several decades in the development of traditional anti-cancer therapies, their potential application for the evaluation of advanced drug delivery systems such as nanomedicines has been largely overlooked. In particular, new insight into drug resistance associated with the 3-D tumor microenvironment has called into question the validity of 2-D models for prediction of in vivo anti-tumor activity. In this work, a series of complementary assays was established for evaluating the in vitro efficacy of docetaxel (DTX) -loaded block copolymer micelles (BCM+DTX) and Taxotere® in 3-D multicellular tumor spheroid (MCTS) cultures. Spheroids were found to be significantly more resistant to treatment than monolayer cultures in a cell line dependent manner. Limitations in treatment efficacy were attributed to mechanisms of resistance associated with properties of the spheroid microenvironment. DTX-loaded micelles demonstrated greater therapeutic effect in both monolayer and spheroid cultures in comparison to Taxotere®. Overall, this work demonstrates the use of spheroids as a viable platform for the evaluation of nanomedicines in conditions which more closely reflect the in vivo tumor microenvironment relative to traditional monolayer cultures. By adaptation of traditional cell-based assays, spheroids have the potential to serve as intermediaries between traditional in vitro and in vivo models for high-throughput assessment of therapeutic candidates.
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) | 0.001 | 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 | 0.001 |
| 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".