Designing, Development and Formulation of Mouth Disintegrating Telmisartan Tablet with Extended Release Profile Using Response Surface Methodology
Bibliographic record
Abstract
The Mouth Disintegrating Tablets (MDT’s) is continuously growing in clinical practice. The pharmaceuticals researchers are exploring the formulations for extended release dosage form with optimal therapeutic benefits. Hence, we aimed this study to develop the mouth disintegrating telmisartan tablet with extended release profile using response surface methodology. Mouth Disintegrating Extended Release Tablets (MDERT’s) was prepared with the intention of obtaining immediate as well as sustain therapeutic effect. The MDERT’s characterized with different determinants. The results were tabulated to illustrate the drug release curves of all 6 formulations upto 12 hrs, DSC spectra of Telmisartan, Kyron T134134, Primogel, Telmisartan + Kyron T134134 + Primogel, Chitosan, CMC and different excepients. The response surface models were developed for disintegration time, wetting time, water absorption ratio and cumulative % drug release (10 min) to determine the significant variance. The cumulative release %age of Telmisartan formulation F2 was comparatively important because of having ≥80% cumulative release. Moreover, a least volume of media 17ml was used by F2 formulation. Whereas, Fourier Transform Infrared Spectroscopy (FT-IR) and DSC studies were executed for any possible chemical interaction or incompatibility in drugs, polymers and excipients used in intended formulations. Additionally, the wetting and dispersion time for F2 were also in targeted rang of 52 and 44 seconds respectively, indicating the rapid disintegration of Telmisartan to produce desired therapeutic effects. Hence, in conclusion, the determinants of MDERTS are adjustable within acceptable range to enhance the efficiency. An extended release profile using response surface methodology may also be designed to formulate the MDT’s to render the dose, regimen, protocol and frequency of hypertensive patient in clinical practice.
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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".