Experimental measurements and correlation of the solubility of N,N-dialkylamides in supercritical carbon dioxide

A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches

The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO2 (scCO2)-based techniques. Measuring the solubility of drugs in scCO2 at varying conditions is a crucial parameter in this context. In this research, the supercritical solubility of two pharmaceutical ingredients, namely Febuxostat and Chlorpromazine, has been assessed theoretically using various thermodynamic approaches, including PR, SRK, UNIQUAC, and Wilson models. Additionally, hybrid machine learning models of PO-GPR, and PO-KNN were applied to anticipate the supercritical solubility of these medicines. Verification of the accuracy of each model for each pharmaceutical substance is conducted against previously reported experimental solubility data. In the comparison between the SRK and PR models, it is observed that the SRK model displays greater precision in correlating the solubility of both drugs. It consistently achieves a mean Radj value of 0.995 across all cases and mean AARD% values of 14.47 and 9.30 for Febuxostat and Chlorpromazine, respectively. Furthermore, the findings indicate that the UNIQUAC model surpasses the Wilson model in precisely representing the solubility of both medicines. It consistently achieves a mean Radj value higher than 0.985 across both cases and mean AARD% values of 11.39 and 7.08 for Febuxostat and Chlorpromazine, respectively. Additionally, the performance of both hybrid machine learning models proved to be excellent in anticipating the supercritical solubility of both compounds.

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.

Measurement and modeling of clemastine fumarate (antihistamine drug) solubility in supercritical carbon dioxide

The solubilities of clemastine fumarate in supercritical carbon dioxide (ScCO₂) were measured for the first time at temperature (308 to 338 K) and pressure (12 to 27 MPa). The measured solubilities were reported in terms of mole faction (mol/mol total) and it had a range from 1.61 × 10^–6 to 9.41 × 10^–6. Various models were used to correlate the data. The efficacy of the models was quantified with corrected Akaike’s information criterion (AICc). A new cluster salvation model was derived to correlate the solubility data. The new model was able to correlate the data and deviation was 10.3% in terms of average absolute relative deviation (AARD). Furthermore, the measured solubilities were also correlated with existing K.-W. Chen et al., model, equation of state model and a few other density models. Among density models, Reddy and Garlapati model was observed to be the best model and corresponding AARD was 7.57% (corresponding AIC_c was − 678.88). The temperature independent Peng–Robinson equation of state was able to correlate the data and AARD was 8.25% (corresponding AIC_c was − 674.88). Thermodynamic parameters like heats of reaction, sublimation and solvation of clemastine fumarate were calculated and reported.

Supercritical carbon dioxide extraction of plant phytochemicals for biological and environmental applications - A review

Recently, supercritical fluid CO₂ extraction (SFE) has emerged as a promising and pervasive technology over conventional extraction techniques for various applications, especially for bioactive compounds extraction and environmental pollutants removal. In this context, temperature and pressure regulate the solvent density and thereby effects the yield, selectivity, and biological/therapeutic properties of the extracted components. However, the nature of plant matrices primarily determines the extraction mechanism based on either density or vapor pressure. The present review aims to cover the recent research and developments of SFE technique in the extraction of bioactive plant phytochemicals with high antioxidant, antibacterial, antimalarial, and anti-inflammatory activities, influencing parameters, process conditions, the investigations for improving the yield and selectivity. In another portion of this review focuses on the ecotoxicology and toxic metal recovery applications. Nonpolar properties of Sc-CO₂ create strong solvent strength via distinct intermolecular interaction forces with micro-pollutants and toxic metal complexes. This results in efficient removal of these contaminants and makes SFE technology as a superior alternative for conventional solvent-based treatment methods. Moreover, a compelling assessment on the therapeutic, functional, and solvent properties of SFE is rarely focused, and hence this review would add significant value to the SFE based research studies. Furthermore, we mention the limitations and potential of future perspectives related to SFE applications.

Solubility of Ketoconazole (antifungal drug) in SC-CO2 for binary and ternary systems: measurements and empirical correlations

One of the main steps in choosing the drug nanoparticle production processes by supercritical carbon dioxide (SC-CO₂) is determining the solubility of the solid solute. For this purpose, the solubility of Ketoconazole (KTZ) in the SC-CO₂, binary system, as well as in the SC-CO₂-menthol (cosolvent), ternary system, was measured at 308–338 K and 12–30 MPa using the static analysis method. The KTZ solubility in the SC-CO₂ ranged between 0.20 × 10^–6 and 8.02 × 10^–5, while drug solubility in the SC-CO₂ with cosolvent varied from 1.2 × 10^–5 to 1.96 × 10^–4. This difference indicated the significant effect of menthol cosolvent on KTZ solubility in the SC-CO₂. Moreover, KTZ solubilities in the two systems were correlated by several empirical and semiempirical models. Among them, Sodeifian et al., Bian et al., MST, and Bartle et al. models can more accurately correlate experimental data for the binary system than other used models. Also, the Sodeifian and Sajadian model well fitted the solubility data of the ternary system with AARD% = 6.45, R_adj = 0.995.

Preparation of phthalocyanine green nano pigment using supercritical CO2 gas antisolvent (GAS): experimental and modeling

Phthalocyanine green nano pigment was prepared using supercritical gas antisolvent (GAS) process based on the SC-CO₂ method. Thermodynamic models were developed to study the volume expansion and operating conditions of the GAS process. Peng-Robinson EoS were applied for binary (CO₂ and DMSO) and ternary (CO₂, DMSO, and pigment) systems. A Box-Behnken experimental design was used to optimize the process. Influences of temperature (308, 318 and 328 K), pressure (10, 15 and 20 MPa) and solute concentration (10, 40 and 70 mg/mL) were studied on the particles size and their morphology. The fine particles produced were characterized by SEM, DLS, XRD, FTIR and DSC. Experimental results showed a great reduction in size of pigment particles in comparison to the original particles. The mean particle sizes of nanoparticles were obtained to 27.1 nm after GAS based on SC-CO₂ method.