Neural simulation and experimental investigation of Chloroquine solubility in supercritical solvent

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.

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

This study introduces a universal correlation based on the modified version of the Arrhenius equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO₂). A combination of an Arrhenius-shape term and a departure function was proposed to estimate the solubility of anti-cancer drugs in supercritical CO₂. This modified Arrhenius correlation predicts the solubility of anti-cancer drugs in supercritical CO₂ from pressure, temperature, and carbon dioxide density. The pre-exponential of the Arrhenius linearly relates to the temperature and carbon dioxide density, and its exponential term is an inverse function of pressure. Moreover, the departure function linearly correlates with the natural logarithm of the ratio of carbon dioxide density to the temperature. The reliability of the proposed correlation is validated using all literature data for solubility of anti-cancer drugs in supercritical CO₂. Furthermore, the predictive performance of the modified Arrhenius correlation is compared with ten available empirical correlations in the literature. Our developed correlation presents the absolute average relative deviation (AARD) of 9.54% for predicting 316 experimental measurements. On the other hand, the most accurate correlation in the literature presents the AARD = 14.90% over the same database. Indeed, 56.2% accuracy improvement in the solubility prediction of the anti-cancer drugs in supercritical CO₂ is the primary outcome of the current study.

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.