Bioinspired Nanocomplexes Comprising Phenolic Acid Derivative and Human Serum Albumin for Cancer Therapy
Bibliographic record
Abstract
Inspired by the natural phenomenon of phenolic-protein interactions, we translate this "naturally evolved interaction" to a "phenolic acid derivative based albumin bound" technology, through the synthesis of phenolic acid derivatives comprising a therapeutic cargo linked to a phenolic motif. Phenolic acid derivatives can bind to albumin and form nanocomplexes after microfluidic mixing. This strategy has been successfully applied to different types of anticancer drugs, including taxanes, anthraquinones, etoposides, and terpenoids. Paclitaxel was selected as a model drug for an in-depth study. Three novel paclitaxel-phenolic acid conjugates have been synthesized. Molecular dynamics simulations provide insights into the self-assembled mechanisms of phenolic-protein nanocomplexes. The nanocomplexes show improved pharmacokinetics, elevated tolerability, decreased neurotoxicity, and enhanced anticancer efficacies in multiple murine xenograft models of breast cancer, in comparison with two clinically approved formulations, Taxol (polyoxyethylated castor oil-formulated paclitaxel) and Abraxane (nab-paclitaxel). Such a robust system provides a broadly applicable platform for the development of albumin-based nanomedicines and has great potential for clinical translation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".