Investigation of the <scp>candesartan cilexetil</scp> antihypertensive drug microencapsulation by <scp>PLA‐PVP K30</scp> biodegradable polymers: Experimental optimization and release kinetics modelling
Bibliographic record
Abstract
Abstract Candesartan is an angiotensin receptor blocker (ARB) used to treat hypertension. However, its poor aqueous solubility and oral bioavailability have limited its therapeutic applications. In order to increase bioavailability and control the release of candesartan condensation, microspheres containing biodegradable polymers (polyvinylpyrrolidone [PVP K30] and polylactic acid [PLA]) in different ratios were prepared by the o/o solvent evaporation method using Span 80 as a surfactant. In addition, the impact of encapsulation parameters (i.e., PVP K30 and PLA concentrations) on the encapsulation ratio and release percentage was investigated by the mixed factorial design method. The release kinetics of the microspheres was simulated by combining two methods, the Dragonfly algorithm and a support vector machine (DA‐SVM). The experimental data were in good agreement with the predicted data, with a coefficient of determination close to unity and a mean square error close to zero. Fourier‐transfer infrared spectrometry (FTIR) analysis revealed the presence of condensation in all formulations without reporting distortion in the spectra. Scanning electron microscopy (SEM) confirmed the successful synthesis of microspheres, whose sizes were between 12 and 26 μm. Formulations with a PLA‐drug ratio of 6:1 (N15, N17, and N18) showed the highest encapsulation efficiency (68%, 71%, and 70%, respectively), while formulations that do contain PVP K30, such as N5, N4, and N3, showed a higher release (83%, 84%, and 89%, respectively), indicating that the agent (PVP K30) enhanced the bioavailability and release of candesartan. Overall, N3 showed a higher drug release rate at 12 h and important encapsulation efficiency, making it the optimal formulation obtained in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".