Experimental evaluation and thermodynamic analysis of Febuxostat solubility in supercritical solvent

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine

In recent years, nanomedicines prepared using supercritical technology have garnered widespread research attention due to their inherent attributes, including structural stability, high bioavailability, and commendable safety profiles. The preparation of these nanomedicines relies upon drug solubility and mixing efficiency within supercritical fluids (SCFs). Solubility is closely intertwined with operational parameters such as temperature and pressure while mixing efficiency is influenced not only by operational conditions but also by the shape and dimensions of the nozzle. Due to the special conditions of supercriticality, these parameters are difficult to measure directly, thus presenting significant challenges for the preparation and optimization of nanomedicines. Mathematical models can, to a certain extent, prognosticate solubility, while simulation models can visualize mixing efficiency during experimental procedures, offering novel avenues for advancing supercritical nanomedicines. Consequently, within the framework of this endeavor, we embark on an extensive review encompassing the application of mathematical models, artificial intelligence (AI) methodologies, and computational fluid dynamics (CFD) techniques within the medical domain of supercritical technology. We undertake the synthesis and discourse of methodologies for calculating drug solubility in SCFs, as well as the influence of operational conditions and experimental apparatus upon the outcomes of nanomedicine preparation using supercritical technology. Through this comprehensive review, we elucidate the implementation procedures and commonly employed models of diverse methodologies, juxtaposing the merits and demerits of these models. Furthermore, we assert the dependability of employing models to compute drug solubility in SCFs and simulate the experimental processes, with the capability to serve as valuable tools for aiding and optimizing experiments, as well as providing guidance in the selection of appropriate operational conditions. This, in turn, fosters innovative avenues for the development of supercritical pharmaceuticals.

Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models

This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R², MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R² values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.

Solubility and Thermodynamic Properties of Febuxostat in Various (PEG 400 + Water) Mixtures

The solubility of the poorly soluble medicine febuxostat (FXT) (3) in various {polyethylene glycol 400 (PEG 400) (1) + water (H₂O) (2)} mixtures has been examined at 298.2-318.2 K and 101.1 kPa. FXT solubility was measured using an isothermal method and correlated with "van't Hoff, Apelblat, Buchowski-Ksiazczak λh, Yalkowsky-Roseman, Jouyban-Acree, and Jouyban-Acree-van't Hoff models". FXT mole fraction solubility was enhanced via an increase in temperature and PEG 400 mass fraction in {(PEG 400 (1) + H₂O (2)} mixtures. Neat PEG 400 showed the highest mole fraction solubility of FXT (3.11 × 10^-2 at 318.2 K), while neat H₂O had the lowest (1.91 × 10^-7 at 298.2 K). The overall error value was less than 6.0% for each computational model, indicating good correlations. Based on the positive values of apparent standard enthalpies (46.72-70.30 kJ mol^-1) and apparent standard entropies (106.4-118.5 J mol^-1 K^-1), the dissolution of FXT was "endothermic and entropy-driven" in all {PEG 400 (1) + H₂O (2)} mixtures examined. The main mechanism for FXT solvation in {PEG 400 (1) + H₂O (2)} mixtures was discovered to be an enthalpy-driven process. In comparison to FXT-H₂O, FXT-PEG 400 showed the strongest molecular interactions. In conclusion, these results suggested that PEG 400 has considerable potential for solubilizing a poorly soluble FXT in H₂O.