Inclusion complexes of atorvastatin calcium ( <scp>ATV‐Ca</scp> ) and rosuvastatin calcium ( <scp>ROV‐Ca</scp> ) drugs with <scp> <i>α</i> ‐CD </scp> , <scp> <i>β</i> ‐CD </scp> , <scp> <i>γ</i> ‐CD </scp> , <scp> HP‐ <i>β</i> ‐CD </scp> , <scp> M‐ <i>β</i> ‐CD </scp> , and maltodextrin along with their characterizations through experimental and computational methods
Bibliographic record
Abstract
Abstract The aim of this research is a comparison of the efficiency of six commercially available cyclodextrins (CDs) to improve the solubility and oral bioavailability of atorvastatin calcium (ATV‐Ca) and rosuvastatin calcium (ROV‐Ca) drugs in aqueous media. Inclusion complexes of both drugs with non‐toxic α ‐CD, β ‐CD, γ ‐CD, HP‐ β ‐CD, M‐ β ‐CD, and maltodextrin were prepared in a 1:1 stoichiometry via the kneading method. To reach the best CD, various experimental and computational analyses were performed including phase solubility, dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FT‐IR), X‐ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), atomic force microscopy (AFM), hydrogen‐1 nuclear magnetic resonance ( 1 HNMR), carbon‐13 nuclear magnetic resonance ( 13 CNMR), and molecular docking calculations. The M‐ β ‐CD turned out to be the best substrate for the micro‐encapsulation of both drugs. Also, ATV showed a higher tendency than ROV to form inclusion complexes with CDs. Molecular docking studies showed that HP– β –CD and M‐ β ‐CD are the most suitable substrates for the formation of inclusion complexes, respectively. Our research showed that the β ‐CD is not necessarily the most efficient substrate for increasing solubility based on previous reports in the literature; meanwhile, the other employed substrates in this study can show acceptable performances in this regard. According to our results, M‐ β ‐CD is the best substrate for the micro‐encapsulation of both drugs, which increases their solubility in water.
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How this classification was reachedexpand
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".