Development, stability and in vitro delivery profile of new loratadine-loaded nanoparticles
Bibliographic record
Abstract
Purpose: Loratadine is used as antihistaminic without side effects in nervous systems. This drug is a weak base and it is absorbed from the intestine. The nitrogen of the pyridine ring is protonated in the stomach affecting the oral bioavailability. The aim of this paper was obtaining, characterize and evaluate the release profiles and the stability of a gastroresistant loratadine nanosuspension. Methods: The nanosuspension was prepared by the solvent displacement evaporation method, using three different polymers (Eudragit® L 100 55, Kollicoat® MAE 100P and PEG 4000) and Polysorbate 80. Dynamic Light Scattering was used for evaluating the particle size (PS), zeta potential, and conductivity of the nanosuspension. Loratadine release profiles were evaluated in simulated gastrointestinal fluids. The shelf and accelerated stability were assessed during three months. Results: Nanosuspension particle size was 45.94 ± 0.50 nm, with a low polydispersion index (PdI, 0.300). Kollicoat® MAE 100P produced a hard and flexible coating layer. In simulated intestinal fluids, the 100 percent of loratadine was released in 40 min, while in simulated stomach fluids the release was lesser than 5%. Nanosuspension presented a good physicochemical stability showing a reduction in PS and PdI after three months (43.29 ± 0.16 and 0.250; respectively). Conclusions: A promissory loratadine nanosuspension for loratadine intestinal delivery was obtained, by using a low energy method, which is an advantage for a possible scale up for practical purpose.
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 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) | 0.000 | 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 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".