Optimized Carbohydrate-Based Nanogel Formulation to Sensitize Hypoxic Tumors
Bibliographic record
Abstract
Solid tumors are often poorly vascularized, which impairs oxygen supply and drug delivery to the cells. This often leads to genetic and translational adaptations that promote tumor progression, invasion, metastasis, and resistance to conventional chemo-/radiotherapy and immunotherapy. A hypoxia-directed nanosensitizer formulation of a hypoxia-activated prodrug (HAP) was developed by encapsulating iodoazomycin arabinofuranoside (IAZA), a 2-nitroimidazole nucleoside-based HAP, in a functionally modified carbohydrate-based nanogel, facilitating delivery and accrual selectively in the hypoxic head and neck and prostate cancer cells. Although IAZA has been reported as a clinically validated hypoxia diagnostic agent, recent studies have pointed to its promising hypoxia-selective anti-tumor properties, which make IAZA an excellent candidate for further exploration as a multimodal theranostic of hypoxic tumors. The nanogels are composed of a galactose-based shell with an inner core of thermoresponsive (di(ethylene glycol) methyl ethyl methacrylate) (DEGMA). Optimization of the nanogels led to high IAZA-loading capacity (≅80–88%) and a slow time-controlled release over 50 h. Furthermore, nano IAZA (encapsulated IAZA) displayed superior in vitro hypoxia-selective cytotoxicity and radiosensitization in comparison to free IAZA in the head and neck (FaDu) and prostate (PC3) cancer cell lines. The acute systemic toxicity profile of the nanogel ( NG1 ) was studied in immunocompromised mice, indicating no signs of toxicity. Additionally, growth inhibition of subcutaneous FaDu xenograft tumors was observed with nano IAZA, demonstrating that this nanoformulation offers a significant improvement in tumor regression and overall survival compared to the control.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".