Antidepressant and Pharmacokinetic Evaluation of Self-Nanoemulsifying Drug Delivery Systems (SNEDDS) of Escitalopram
Bibliographic record
Abstract
Abstract Escitalopram (ETP) has poor oral bioavailability due to its low water solubility, hence the goal of this work was to design and optimize a self-nano-emulsifying drug delivery system (SNEDDS). Using the results of the investigations on solubility and emulsification, a pseudo-ternary phase diagram was produced. The three main ingredients chosen for the formulation were polyethylene glycol 400 (co-surfactant), tween 80 (surfactant), and geranium oil (lipid). ETP-SNEDDS was evaluated for the size of particles and surface charge. Fourier transforms infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to evaluate the chemical compatibility and thermal stability. Ex-vivo permeability, in vitro digestion, and in vitro dissolution investigations were carried out and compared with reference tablets. The bioavailability of ETP-loaded SNEDDS was evaluated in comparison to the control in Wistar rats (n = 6). With a droplet size of 145 nm, a polydispersity index of 0.120, and an emulsification period of almost one minute, the synthesized SNEDDS were thermodynamically stable. The ETP-loaded SNEDDS displayed 96% dissolution in FSSIF. The permeation investigation revealed that, in comparison to the ETP powder and reference tablet, respectively, the SNEDDS increased drug penetration by 4.2 and 3.1-folds. The enhancement of in vitro dissolution, in vitro digestion, and ex-vivo permeability was found significant (p < 0.05). In comparison to the reference, SNEDDS had C max and AUC increases of 5.34 and 4.71 fold, respectively. These findings suggested that the SNEDDS formulation would be a promising method for increasing the oral bioavailability and absorption of ETP.
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 | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".