Pharmaceutical organogels prepared from aromatic amino acid derivatives
Bibliographic record
Abstract
Organogels are semi-solid systems in which an organic liquid phase is immobilized by a 3-dimensional network composed of self-assembled gelator molecules. Although there is a large variety of organogel systems, relatively few have been investigated in the field of drug delivery, owing mostly to the lack of information on their biocompatibility and toxicity. In this work, organogelator-biocompatible structures based on aromatic amino acids, namely, tyrosine, tryptophan, and phenylalanine were synthesized by derivatization with aliphatic chains. Their ability to gel an injectable vegetable oil (i.e. safflower oil) and to sustain the release of a model anti-Alzheimer drug (i.e.rivastigmine) was then evaluated. Organogels and molecular packing were characterized by differential scanning calorimetry, rheology analysis, Fourier-transform infrared spectroscopy and X-ray crystallography. The amino acid derivatives were able to gel safflower oil through van der Waals interactions and H-bonds. Tyrosine-derivatives produced the strongest gels while tryptophan was associated with poor gelling properties. The superior gelling ability of tyrosine derivatives could be explained by their well-structured 2-dimensional packing in the network. The addition of an optimal N-methyl-2-pyrrolidone amount to tyrosine gels fluidized the network and allowed their injection through conventional needles. Upon contact with an aqueous medium, the gels formed in situ and released entrapped rivastigmine in a sustained fashion.
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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".