Solubilities of four macrolide antibiotics in supercritical carbon dioxide and their correlations using semi-empirical models

Experimental solubility of aripiprazole in supercritical carbon dioxide and modeling

The solubility of compounds in supercritical carbon dioxide (SC-[Formula: see text]) has found crucial significance in the fabrication of micro/nano-scaled drugs. In this research, the solubility of Aripiprazole was measured in SC-[Formula: see text] at various temperatures (308-338 K) and pressures (12-30 MPa). Moreover, the experimental solubility results were correlated with several semi-empirical models (Chrastil, Bartle et al., Kumar & Johnston, Menden-Santiago & Teja, Sodeifian et al., and Jouyban et al.) as well as the modified Wilson model. The molar fraction of the drug in SC-[Formula: see text] varied in the range of [Formula: see text] to [Formula: see text]. The solubility highly depended on the operating pressure and temperature. The Chrastil (0.994), Jouyban et al. (0.993) and Sodeifian et al. (0.992) models showed the highest consistency with the obtained values. Furthermore, self-consistency tests were performed on the solubility of Aripiprazole in SC-[Formula: see text]. The approximate total enthalpy ([Formula: see text]), vaporization enthalpy ([Formula: see text]), and solubility enthalpy ([Formula: see text]) were also calculated.

Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model

Synthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO₂ (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug’s solubility in supercritical CO₂ is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO₂. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO₂ as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 × 10⁻⁶), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 10⁻¹⁰), and regression coefficient (R² = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO₂.

Development and validation of a green and sustainable procedure for the preparation of Perilla frutescens extracts

A procedure combining supercritical CO₂ and ultrasound-assisted (USC-CO₂) extraction was developed to obtain rosmarinic acid (RA)-rich extracts from Perilla frutescens. Based on extraction yields and efficiencies, USC-CO₂ was considered the best extraction method among the methods studied for obtaining RA from P. frutescens. The constant extraction rate period and the falling extraction rate period for USC-CO₂ extraction of P. frutescens were 45 and 96 min long, respectively, and they were significantly shorter than those of traditional SC-CO₂ (TSC-CO₂) extraction. Furthermore, mass transfer coefficients were derived using the Sovová model for the fluid and solid phases from USC-CO₂ extraction, with values of 9.752 × 10^-3 and 4.203 × 10^-3 min^-1, respectively, which were obviously higher than those for TSC-CO₂ extraction. Consequently, the theoretical solubilities of RA in the supercritical solvents used in dynamic USC-CO₂ and TSC-CO₂ extractions were estimated and found to be well correlated using three density-based models.

Penicillins' Solubility in Supercritical Carbon Dioxide: Modeling by Cubic Equations of States Revisited

Development of processes using green solvents as supercritical fluids (SCFs) depends on the accuracy of modeling and predicting phase equilibrium which is of considerable importance to exploit the use of SCF process at the level of pharmaceutical industries. Solid-Fluid equilibrium modeling is associated to many drawbacks when compressed gas-based models as cubic equations of states (cEoSs) are used. The unavailability of experimental values of solute's sublimation pressure presents one of the major obstacles to the solubility modeling with this type of models, and thus, its estimation is essential and inevitable. This work is an attempt to address a question regarding "accurate estimated value" of sublimation pressure of two antibiotics Penicillin G (benzyl penicillin) and Penicillin V (phenoxymethyl penicillin). Toward that, first, cEoSs are provided as the thermodynamics modeling framework and fundamental approach. Second, a discussion and a review of some literature results are given. Third, results are invoked to present a criticism analysis that comes from the use of modified form of Peng-Robinson (PR) equation of states. Finally, considerable improvement of modeling results by using a new sublimation pressure is shown.

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

The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin (SM) in both pure supercritical carbon dioxide (SCCO₂) and SCCO₂ with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks (BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation (AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO₂ and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO₂ techniques.