Preformulation Characterization of a Novel Delta-9-Tetrahydrocannabinol Amino Acid Prodrug
Bibliographic record
Abstract
The current study evaluates the preformulation characteristics of THC-Serine, a novel prodrug of the poorly water soluble compound Delta-9-Tetrahydrocannabinol (THC). Aqueous solubility and stability and solubility in different surfactants and 2-hydroxypropyl-β-cyclodextrin (HPβCD) were studied. The LogP and pKa were calculated using computer modeling. Chemical, thermal and enzymatic stability of the prodrug was assessed at different pH (25°C), elevated temperature (120°C) and in human saliva, respectively. THC-Serine demonstrated pH dependent solubility. Highest solubility was observed at pH 2.0 (92-fold greater than THC). Solubility of the prodrug in Tween® 80 was 320-fold higher (256.65 ± 20.52 µg/ml) than THC. With increasing concentrations of HPβCD solubility of THC-Serine was also observed to increase. Log P and pKa of THC-Serine were 3.18 and 7.05, respectively. Prodrug was most stable at pH 2.0, with a degradation rate constant of 3.17 × 10 −4 h −1 . Almost 80% of the prodrug remained intact after heating at 120°C for 8 minutes. The degradation rate constant in saliva was found to be 11.52 × 10 −2 h −1 . The above results indicate that THC-Serine is a lead candidate for transmucosal THC delivery and warrants further investigation. Acknowledgements: This work was supported by Grant Number P20RR021929 from the National Center for Research Resources (NIH/NCRR).
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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) | 0.002 | 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".