Understanding the Role of Sodium Lauryl Sulfate on the Biorelevant Solubility of a Combination of Poorly Water-Soluble Drugs Using High Throughput Experimentation and Mechanistic Absorption Modeling
Bibliographic record
Abstract
This study investigates the influence of surfactant sodium lauryl sulfate (SLS) on the solubility of poorly-water soluble drug substances, model Compound X and Compound Y, used in a fixed dose combination oral solid dosage form. To determine the impact of SLS concentration on the solubility of compounds X and Y, we experimentally determined the critical micelle concentration (CMC) of SLS in water, simulated gastric fluid (SGF), and fed state simulated intestinal fluid (FeSSIF) in the presence of Compound X and Compound Y using UV/Visible spectrophotometry at 25°C. The aggregation of SLS was characterized by calculating the standard Gibbs free energy of micellization in all the media investigated. To enhance the understanding of SLS aggregation, high throughput experiments and in-vivo mechanistic modelling were used to determine the effect of increasing levels of SLS on the solubility of compounds X and Y as both single agent and combination products to be formulated into a suitable oral solid dosage form. Micellar formation of SLS is a spontaneous process as shown by the negative values of the standard free energy of micellization. The CMC of SLS in the various media investigated in the presence of compounds X and Y decreases in the following order: water> FeSSIF> SGF. However, the aggregation of SLS in the various media is overall more spontaneous in the following order: SGF>FeSSIF>water. Using high throughput experimentation and in-vivo mechanistic modelling, it was determined that a combination oral solid product of compounds X and Y will have optimum solubility and in-vivo absorption if 2 mg of SLS was used in the oral solid dosage form. The results obtained from this study will help broaden the understanding of the micellization process involving SLS and poorly-water soluble drugs used in combination oral solid dosage forms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".