Formulation and Evaluation of Lurasidone Hydrochloride Fast Dissolving Tablets
Bibliographic record
Abstract
Antipsychotic medication lurasidone is used to treat schizophrenia and bipolar disorder. In this study, a new effort was made to improve the solubility, rate of dissolution, and oral bioavailability of the poorly soluble drug lurasidone by manufacturing it as solid dispersions utilising a variety of methods and a carrier called polyethylene glycol (PEG) 6000. The prepared solid dispersions were used to prepare fast dissolving tablets by using super disintegrants. A novel super disintegrant called Microcrystalline Cellulose-Polyethylene Glycol (MCC-PEG) Conjugate was also created as part of the project. PEGylation of Microcrystalline Cellulose (MCC) was done since PEG has the ability to increase the water absorption capacity and MCC acts as a disintegrant. Super disintegrants such as sodium starch glycolate, croscarmellose sodium, crospovidone, and microcrystalline cellulose (MCC)-polyethylene glycol (PEG) conjugate were used to create Lurasidone tablets that dissolve quickly. Tablets were tested for physical parameters and drug release by in vitro dissolution studies. Through SEM examination, DSC, and XRD tests, optimised solid dispersions surface properties, drug-excipient interactions, and crystal morphology have all been assessed. All the tablet preparations containing superdisintegrants were formed to release the drug in the order MCC-PEG Conjugate>Crospovidone> Croscarmellose sodium > Sodium starch glycolate. The dissolution rate of such tablet formulations were found to release the drug at a faster rate than the tablets prepared with plain drug
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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".