Development of a Discriminating Dissolution Method for Immediate-Release Soft Gelatin Capsules Containing a BCS Class II Compound
Bibliographic record
Abstract
The purpose of this study was to develop a robust dissolution procedure for liquid-filled, soft gelatin capsules (SGCs) that can distinguish small but real changes in drug product formulation. SGCs were manufactured using different ratios of fill formulations. The formulations were evaluated by performing dissolution testing on fresh capsules and capsules that were aged at different time points and conditions. USP Apparatus 1 (basket) and 2 (paddle) at rotating speeds of 50, 75, or 100 rpm were used to evaluate the release characteristics of the formulations. The model-independent method using difference factor (f1) and similarity factor (f2) was employed to compare dissolution profiles. Dissolution data showed that fill material characteristics played a major role in controlling the rate of dissolution of our drug products. The basket increased the discriminatory power of the dissolution process regardless of speed. We observed that the paddle rotation speed may impact discrimination power but not the ability of the method to discriminate. The basket was successful for the quality assessment and characterization of formulation changes of our drug products. Through understanding of drug product characteristics and evaluating parameters of dissolution testing, a methodology can be established to enable batch-to-batch evaluation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".