Characterization of novel soybean‐oil‐based thermosensitive amphiphilic polymers for drug delivery applications
Bibliographic record
Abstract
Abstract Hydrolyzed polymers of soybean oil (HPSO) and of epoxidized soybean oil (HPESO) were developed previously. Owing to their natural food origin and biocompatibility, we exploited further their potential as a drug delivery system and pharmaceutical excipients. This work aimed to investigate self‐assembly, thermal transition, interaction with various drugs and surface activity of these novel amphiphilic polymers. The critical micelle concentration of HPSO and HPESO was determined by the surface tension method. The molecular interaction between HPESO and anticancer drug doxorubicin HCl was examined. The effect of the polymers on the solution contact angle and surface energy of compressed tablets of hydrophobic drugs ibuprofen and nifedipine was measured. The thermal transition temperatures Ttr (cloud points) of the polymers in aqueous solutions increased with increasing polymer concentration. HPSO exhibited lower Ttr than HPESO. The critical micelle concentration was found to be 0.05 mg mL−1 for HPSO and 0.08 mg mL−1 for HPESO. Strong molecular interactions between HPESO and doxorubicin were observed. Both polymers reduced the interfacial energy and contact angles of drug tablets with more effect on ibuprofen tablets with the use of HPSO. These results suggest that the novel soybean‐oil‐based amphiphilic polymers have great potential for drug delivery and pharmaceutical formulations. Copyright © 2012 Society of Chemical Industry
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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) | 0.001 | 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".