Solubility of Lacosamide in supercritical carbon Dioxide: An experimental analysis and thermodynamic modeling

Preparation of Erlotinib hydrochloride nanoparticles (anti-cancer drug) by RESS-C method and investigating the effective parameters

The size of the drug particles is one of the essential factors for the proper absorption of the drug compared to the dose of the drug. When particle size is decreased, drug uptake into the body increases. Recent studies have revealed that the rapid expansion of supercritical solution with cosolvent plays a significant role in preparing micron and submicron particles. This paper examines the preparation of Erlotinib hydrochloride nanoparticles using a supercritical solution through the cosolvent method for the first time. An examination of the parameters of temperature (318-338 K), pressures (15-25 MPa) and nozzle diameter (300-700 μm) was investigated by Box-Behnken design, and their respective effects on particle size revealed that the nozzle diameter has a more significant impact on particle size than the other parameters. The smallest particles were produced at temperature 338 K, pressure 20 MPa, and nozzle diameter 700 μm. Besides, the ERL nanoparticles were characterized using SEM, DLS, XRD, FTIR, and DSC analyses. Finally, the results showed that the average size of the ERL particles decreased from 31.6 μm to 200-1100 nm.

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.

Solubility measurement of verapamil for the preparation of developed nanomedicines using supercritical fluid

A static method is employed to determine the solubilities of verapamil in supercritical carbon dioxide (SC-CO2) at temperatures between 308 and 338 K and pressures between 12 and 30 MPa. The solubility of verapamil in SC-CO2 expressed as mole fraction are in the range of 3.6 × 10-6 to 7.14 × 10-5. Using four semi-empirical density-based models, the solubility data are correlated: Chrastil, Bartle, Kumar-Johnston (K-J), and Mendez-Santiago and Teja (MST), two equations of state (SRK and PC-SAFT EoS), expanded liquid models (modified Wilson's models), and regular solution model. The obtained results indicated that the regular solution and PC-SAFT models showed the most noteworthy exactness with AARD% of 1.68 and 7.45, respectively. The total heat, vaporization heat, and solvation heat of verapamil are calculated at 39.62, 60.03, and - 20.41 kJ/mol, respectively. Regarding the poor solubility of verapamil in SC-CO2, supercritical anti-solvent methods can be an appropriate choice to produce fine particles of this drug.

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.

Effect of the Processing Conditions on the Supercritical Extraction and Impregnation of Rosemary Essential Oil in Linear Low-Density Polyethylene Films

The supercritical fluid extraction of essential oil from rosemary leaves and its subsequent impregnation in linear low-density polyethylene (LLDPE) films were studied. The effects of temperature (318 and 338 K), pressure (15 and 25 MPa) and rosemary particle size (0.9 and 0.15 mm) on the extraction yield were investigated. Impregnation assays were developed at two different values of pressure (12 and 20 MPa), temperature (308 and 328 K), and impregnation time (1 and 5 h). The extraction yield of rosemary essential oil was increased by increasing pressure and decreasing particle size and temperature. ANOVA results showed that temperature, pressure, and time significantly impacted the essential oil impregnation yield in LLDPE films. The maximum impregnation yield (1.87 wt. %) was obtained at 12 MPa, 328 K, and 5 h. The antioxidant activity and the physical-mechanical properties of impregnated films were analyzed. The IC50 values for all the impregnated LLDPE samples were close to the IC50 value of the extract showing that the impregnated films have a significant antioxidant activity.

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO₂ was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R²-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).