Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

Improving and measuring the solubility of favipiravir and montelukast in SC-CO2 with ethanol projecting their nanonization

Supercritical carbon dioxide (SC-CO2)-based approaches have become more popular in recent years as alternative methods for creating micro- or nanosized medicines. Particularly, high drug solubility is required in those techniques using SC-CO2 as a solvent. During the most recent pandemic years, favipiravir and montelukast were two of the most often prescribed medications for the treatment of COVID-19. In this study, ethanol at 1 and 3 mol% was utilized as a cosolvent to increase the solubility of both medicines in SC-CO2 by a static approach using a range of temperatures (308 to 338 K) and pressure (12 to 30 MPa) values. The experimentally determined solubilities of favipiravir and montelukast in SC-CO2 + 3 mol% ethanol showed solubility values up to 33.3 and 24.5 times higher than that obtained for these drugs with only SC-CO2. The highest values were achieved in the pressure of 12 MPa and temperature of 338 K. Last but not least, six density-based semi-empirical models with various adjustable parameters were used to perform the modeling of the solubility of favipiravir and montelukast.

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO₂) for particle engineering. SCCO₂ has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO₂ systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10⁻⁸, 2.173 × 10⁻⁹, and 1.372 × 10⁻⁸, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10⁻³ as an output.

A critical review on the particle generation and other applications of rapid expansion of supercritical solution

The novel particle generation processes of Active Pharmaceutical Ingredient (API)/drug have been extensively explored in recent decades due to their wide-range applications in the pharmaceutical industry. The Rapid Expansion of Supercritical Solutions (RESS) is one of the promising techniques to obtain the fine particles (micro to nano-size) of APIs with narrow particle size distribution (PSD). In RESS, supercritical carbon dioxide (SC CO₂) and API are used as solvent and solute respectively. In this literature survey, the application of RESS in the formation of fine particles is critically reviewed. Solubility of API in SC CO₂ and supersaturation are the key factors in tuning the particle size. The different approaches to model and predict the solubility of API in SC CO₂ are discussed. Then, the effect of process parameters on mean particle size and the particle size distribution are interpreted in the context of solubility and supersaturation. Furthermore, the less-explored applications of RESS in preparation of solid-lipid nanoparticles, liposome, polymorphic conversion, cocrystallization and inclusion complexation are compared with traditional processes. The solubility enhancement of API in SC CO₂ using co-solvent and its applications in particle generation are explored in published literature. The development and modifications in the conventional RESS process to overcome the limitations of RESS are presented. Finally, the perspective on RESS with special attention to its commercial operation is highlighted.

Improving Solubility and Bioavailability of Breviscapine with Mesoporous Silica Nanoparticles Prepared Using Ultrasound-Assisted Solution-Enhanced Dispersion by Supercritical Fluids Method

Background

Breviscapine (BRE) has significant efficacy in cardiovascular disease, but the poor water solubility of breviscapine affects its oral absorption and limits its clinical application. In this study, supercritical carbon dioxide (SCF-CO₂) technology was used to improve the solubility and bioavailability of BRE loaded into mesoporous silica nanoparticles (MSNs).

Methods

The solubility of BRE in SCF-CO₂ was measured under various conditions to investigate the feasibility of preparing drug-loaded MSNs by using ultrasound-assisted solution-enhanced dispersion by supercritical fluids (USEDS). The preparation process of drug-loaded MSNs was optimized using the central composite design (CCD), and the optimized preparation was comprehensively characterized. Furthermore, the drug-loaded MSNs prepared by the conventional method were compared. Finally, the dissolution and bioavailability of the preparations were evaluated by in vitro release and pharmacokinetics study.

Results

The solubility of BRE in SCF-CO₂ was extremely low which was suitable to prepare BRE-loaded MSNs by USEDS technology. The particle size of the preparation was 177.24 nm, the drug loading was 8.63%, and the specific surface area was 456.3m²/g. As compared to the conventional preparation method of solution impregnation-evaporation (SIV), the formulation prepared by USEDS technology has smaller particle size, higher drug loading, less residual solvent and better stability. The results of the in vitro release study showed that drug-loaded MSNs could significantly improve drug dissolution. The results of pharmacokinetics showed that the bioavailability of drug-loaded MSNs was increased 1.96 times compared to that of the BRE powder.

Conclusion

Drug-loaded MSNs can significantly improve the solubility and bioavailability of BRE, indicating a good application prospect for MSNs in improving the oral absorption of drugs. In addition, as a solid dispersion preparation technology, USEDS technology has incomparable advantages.