Comparison of Tumor Penetration of Podophyllotoxin–Carboxymethylcellulose Conjugates with Various Chemical Compositions in Tumor Spheroid Culture and In Vivo Solid Tumor
Bibliographic record
Abstract
Polymer conjugation is an attractive approach for delivering insoluble and highly toxic drugs to tumors. However, most reports in the literature only disclose the optimal composition without emphasizing rational design or composition optimization to achieve maximized biological effects. In this study, we aimed to demonstrate that composition of a polymer conjugate would determine its physiochemical characteristics, tumor penetration, and, ultimately, the in vivo efficacy. We also aimed to examine whether the tumor spheroid model could generate comparable results with the in vivo tumor model in terms of tumor penetration and efficacy of the various polymer conjugates. We have designed a polymer conjugate delivery system for a chemotherapeutic drug podophyllotoxin (PPT) by covalently conjugating PPT and polyethylene glycol (PEG) with acetylated carboxymethyl cellulose to yield conjugates containing various amounts of PPT and PEG. Depending on the composition, these conjugates self-assembled into nanoparticles (NPs) with different physicochemical properties. Conjugates with an increased PPT content formed particles with an increased diameter. In the present study, we selected three conjugates representing compositions containing high, medium, and low drug content, and compared their particle formation, drug release kinetics, their ability to penetrate tumor spheroid and in vivo s.c. tumor, and finally their antitumor efficacy in spheroid culture and an in vivo s.c. tumor model. We found that the low drug content conjugate formed smaller NPs (20 nm) compared to the high drug content conjugates (30-120 nm), and displayed faster drug release kinetics (5%/day vs 1-3%/day), improved tumor penetration, and enhanced antitumor efficacy in both the spheroid model and s.c. tumor model. In particular, the low drug content conjugate preferentially accumulated in the hypovascular region within the tumor, inducing complete regression of s.c. tumors and the metastasis to the lungs. Our data indicate composition optimization is needed to select the optimal conjugate, and tumor spheroid culture is a robust screening tool to help select the optimal formulation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".