Evolution of Choice of Solubility and Dissolution Media After Two Decades of Biopharmaceutical Classification System
Bibliographic record
Abstract
The introduction of the biopharmaceutics drug classification system (Biopharmaceutics Classification System (BCS)), in 1995, provided a simple way to describe the biopharmaceutics behavior of a drug. Solubility and permeability are among the major parameters, which determine the fraction dose absorbed of a drug substance and consequently its chances to be bioavailable. The purpose of this review is to summarize the evolution of the media used for determining solubility and dissolution and how this can be used in modern drug development. Over the years, physiologically adapted media and buffers were introduced with the intention to better predict the in vivo solubility and dissolution of drug substances. Water, buffer solutions, compendial media, micellar solubilization media, and biorelevant media are reviewed. At this time point, there is no universal medium available which can be used to predict every drug substance's solubility or a drug product's in vivo dissolution behavior. However, there have been many improvements and additions made to media to optimize their in vivo predictability; for example, the current phosphate concentrations in buffers seem to be too high to correlate with the carbonate buffer concentrations in vivo. Biorelevant media were updated to correlate them better with the composition of human intestinal fluids. The BCS was introduced into regulatory sciences as a scientific risk management tool to waive bioequivalence studies under certain conditions. Today's different guidance documents define the dose-solubility ratio differently. As shown for amoxicillin, this can cause more confusion than certainty for globally operating companies. Harmonization of BCS guidelines is highly desirable.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".