Cefdinir Inclusion in Mesoporous Silica as Effective Dissolution Enhancer with Improved Physical Stability
Bibliographic record
Abstract
Objective: The objective of this research was to enhance the physical stability and the dissolution rate of cefdinir, a BCS class IV drug, characterized by low and variable bioavailability due to both its low solubility and low permeability. Methods: Cefdinir was loaded into the mesoporous silica (SBA-15), by using the solvent immersion method starting from different organic solvents. And then formula (F3), which exhibited the highest loading percentage, was selected to study its drug release in media with different pH (1.2, 4.5, and 6.8), and has been fully characterized by using: Fourier Transform Infrared Spectroscopy (FT-IR) Spectroscopy, Differential Scanning Calorimetry, Powder X-ray Diffraction, and has been subjected to accelerated stability tests using different temperatures and relative humidity. Drug release kinetics were studied by using the following models: Probit, Gompertz, Weibull, and Logistic. Results: The results showed a remarkable dissolution improvement of cefdinir from the loaded silica in comparison to the crystalline drug at the different studied media. Drug release behaviors were well simulated by the Weibull model for F3 in all studied media and for pure Cefdinir in phosphate buffer only, and by the Gompertz function for pure Cefdinir in HCl buffer and Acetate buffer. FTIR results showed hydrogen bonds formed between the drug and silica, DSC and PXRD results revealed the transformation of cefdinir into an amorphous form upon adsorption. Stability studies under different conditions revealed the ability of mesoporous silica to maintain the amorphous state of the drug, which has been formed upon adsorption, and to prevent re-organization in the crystal nucleus of the drug molecules. Conclusion: Thus, loading cefdinir onto mesoporous silica can be used as a promising method to enhance drug dissolution, and maintain the physical stability of its amorphous form.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".