In Silico Simulation of Dissolution Profiles for Development of Extended-Release Doxazosin Tablets
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Bibliographic record
Abstract
Developing extended-release (ER) formulations with appropriate release characteristics can be challenging for formulation scientists. The aim of this study was to demonstrate the use of computer-simulated dissolution profiles associated with statistical experimental design in the development of doxazosin ER tablet formulations. Experimental doxazosin ER tablets were prepared and tested using USP Apparatus 2 with 900 mL of simulated gastric fluid without enzyme at 37 0.5 C and 75 rpm for 960 minutes. The results were used to optimize calibration constants for the ingredients in the simulation software, DDDPlus. Design Expert software was used to obtain different mixtures between lactose and HPMC K100M, creating seven formulations with dissolution profiles simulated in DDDPlus. After statistical analysis, an optimized doxazosin ER formulation was identified, manufactured, and tested for comparison with the predicted profile. A correlation coefficient of 0.99 was obtained for observed and predicted dissolution profiles of the optimized doxazosin ER formulation. The use of test simulations led to a 66.67% reduction in analyst working hours and 77.78% reduction in both equipment usage time and dissolution medium volume. Computer simulations associated with design of experiments can save time and reduce costs in the development of ER formulations.
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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 | 0.001 | 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 | 0.001 |
| 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 it