Studies on Risperidone Loaded β-Cyclodextrin Nanosponges for Managing Altered Mental Status and Delirium in Cancer Patients
Bibliographic record
Abstract
This study explores the prospective of risperidone-loaded β-cyclodextrin nanosponges as a therapeutic strategy for managing altered mental status (AMS) and delirium in cancer patients. Almost 87% of patients with advanced cancer experience AMS or delirium, significantly impacting prognosis and quality of life. The present study aims to enhance the solubility, bioavailability, and therapeutic effectiveness of second-generation antipsychotic medication risperidone (RSP), with poor aqueous solubility, it was encapsulated in β-cyclodextrin nanosponges. The nanosponges prepared by fusion technique using different β-CD: DPC molar ratios, were tested for their ability to encapsulation efficiency, drug loading, and dissolutions kinetics. Batch 1, (1:1 molar ratio) exhibits RSP loading capacity (454.2 µg/mg) and encapsulation efficiency (90.84%) along with DSC and FTIR also confirmed that the RSP was successfully encapsulated and without any chemical interactions. In vitro dissolution studies demonstrated a biphasic release profile, with an initial burst followed by sustained release, governed by Fickian diffusion as confirmed by release kinetics modeling. The improved solubility and dissolution profile of the nanosponges will be significant to improve risperidone delivery, ensuring better symptom management in a vulnerable population. These findings highlight the potential of β-cyclodextrin nanosponges as an innovative and adaptable platform for enhancing antipsychotic drug delivery.
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 | 0.001 |
| 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".