Biphasic Dissolution as an Exploratory Method during Early Drug Product Development
Bibliographic record
Abstract
Dissolution testing is a major tool used to assess a drug product's performance and as a quality control test for solid oral dosage forms. However, compendial equipment and methods may lack discriminatory power and the ability to simulate aspects of in vivo dissolution. Using low buffer capacity media combined with an absorptive phase (biphasic dissolution) increases the physiologic relevance of in vitro testing. The purpose of this study was to use non-compendial and compendial dissolution test conditions to evaluate the in vitro performance of different formulations. The United States Pharmacopeia (USP)-recommended dissolution method greatly lacked discriminatory power, whereas low buffer capacity media discriminated between manufacturing methods. The use of an absorptive phase in the biphasic dissolution test assisted in controlling the medium pH due to the drug removal from the aqueous medium. Hence, the applied non-compendial methods were more discriminative to drug formulation differences and manufacturing methods than conventional dissolution conditions. In this study, it was demonstrated how biphasic dissolution and a low buffer capacity can be used to assess in vitro drug product performance differences. This can be a valuable approach during the early stages of drug product development for investigating in vitro drug release with improved physiological relevance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".