pH- Modified Solid Dispersions of Cefdinir for Dissolution Rate Enhancement: Formulation and Characterization
Bibliographic record
Abstract
Objective: Cefdinir is a poorly- water-soluble drug, it belongs to Biopharmaceutical Classification System class IV, which shows that it may have limited therapeutic effects due to its low solubility and poor bioavailability. The aim of the present work was to design a pH-modified solid dispersion (pHM-SD) that can improve the dissolution rate of cefdinir and subsequently its bioavailability. Materials and Methods: pHM-SDs of cefdinir were prepared at different drug-to-carrier ratios by the spray-drying technique. The solid dispersions were investigated by dissolution studies at different pH media, drug release kinetics were studied, and their solid-state characterizations were performed by FTIR spectrophotometer, Scanning electron microscopy (SEM), Differential scanning calorimetry (DSC), and Powder X-ray diffraction (PXRD). Results: PVP- based and HPMC- based pHM-SDs exhibited a marked improvement in the dissolution behavior when compared with crystalline cefdinir powder, whereas Eudragit L100-based pHM-SDs showed lower dissolution at pH 1.2 and 4.5. FTIR results may indicate a formation of a salt between cefdinir and the alkalizer. Solid-state characterization may indicate a change in crystallinity of cefdinir into an amorphous state. Mathematical modeling of in vitro dissolution data indicated the best fitting with Korsmeyer–Peppas model and the drug release kinetics primarily as Fickian diffusion. Conclusion: According to these observations, pHM-SD in the presence of an alkalizer for a poorly water-soluble acidic drug, cefdinir, appeared to be efficacious for enhancing its dissolution rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".