Solubility measurement of a chemotherapeutic agent (Imatinib mesylate) in supercritical carbon dioxide: Assessment of new empirical model

Solubility of lumiracoxib in supercritical carbon dioxide

This study aims to use a static-based solubility method for measuring the solubility of lumiracoxib at a temperature of 308-338 K and pressure of 120-400 bar for the first time. The obtained solubility data for lumiracoxib is between 4.74 × 10-5 and 3.46 × 10-4 (mole fraction) for the studied ranges of pressure and temperature. The solubility values reveal that the lumiracoxib experiences a crossover pressure of about 160 bar. Moreover, the measured solubility data of these two drugs are correlated with density-based semi-empirical correlations namely Bartle et al., Mendez-Santiago-Teja, Kumar and Johnstone, Chrastil and modified Chrastil models with an average absolute relative deviation of 10.7%, 9.5%, 9.8%, 7.8%, and 8.7% respectively for lumiracoxib. According to these findings, it is obvious that all of the examined models are rather accurate and there is no superiority between these models for both examined drugs although the Chrastil model is slightly better in the overall view.

Determination of Regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO2: Experimental and thermodynamic modeling

In this study, the solubilities of Regorafenib monohydrate (REG), a widely used as a colorectal anticancer drug, in supercritical carbon dioxide (ScCO2) were measured under various pressures and temperature conditions, for the first time. The minimum value of REG in mole fraction was determined to be 3.06×10-7, while the maximum value was found to be 6.44×10-6 at 338 K and 27 MPa. The experimental data for REG were correlated through the utilization of two types of models: (1) a set of 25 existing empirical and semi-empirical models that incorporated 3-8 parameters according to functional dependencies, (2) a model that relied on solid-liquid equilibrium (SLE) and the newly improved association models. All of the evaluated models were capable of generating suitable fits to the solubility data of REG, however, the average absolute relative deviation (AARD) of Gordillo et al. model (AARD=13.2%) and Reddy et al. model (AARD=13.5%) indicated their superiority based on AARD%. Furthermore, solvation and sublimation enthalpies of REG drug were estimated for the first time.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors (TKIs) have emerged as the first-line small molecule drugs in many cancer therapies, exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways. However, there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites, which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments, alongside other potential side effects or adverse reactions. Therefore, an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods, clinical pharmacokinetics, and therapeutic drug monitoring of different TKIs. This paper provides a comprehensive overview of the advancements in pretreatment methods, such as protein precipitation (PPT), liquid-liquid extraction (LLE), solid-phase extraction (SPE), micro-SPE (μ-SPE), magnetic SPE (MSPE), and vortex-assisted dispersive SPE (VA-DSPE) achieved since 2017. It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography (HPLC) and high-resolution mass spectrometry (HRMS) methods, capillary electrophoresis (CE), gas chromatography (GC), supercritical fluid chromatography (SFC) procedures, surface plasmon resonance (SPR) assays as well as novel nanoprobes-based biosensing techniques. In addition, a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors

The objective of this study was to model the solubility of active pharmaceutical ingredients (APIs) in different ionic liquids (ILs) based on the σ-moments of cations, anions, and APIs that were used as molecular descriptors calculated using the σ-profiles of three categories of descriptors based on conductor-like screening model for real solvents. The database of 83 API-ILs systems composed of 14 APIs, 12 cations, and 7 anions (25 ILs combinations) was collected as 850 data points at different temperature ranges. A hybrid Improved Grey Wolf Support vector regression, abbreviated as I-GWO-SVR(r), algorithm was selected as the learning method. Based on a comprehensive comparison with 11 different models, various statistical factors, and graphical analyses, including an external validation test, analysis of variance (ANOVA), and sensitivity analysis, the capability and validity of the proposed approach have been assessed and verified. The overall study confirmed that the proposed new model provided the best results in terms of predicting the solubility of APIs in ILs.

Extraction Process Optimization of Curcumin from Curcuma xanthorrhiza Roxb. with Supercritical Carbon Dioxide Using Ethanol as a Cosolvent

Curcuma xanthorrhiza Roxb., known as temulawak, Javanese ginger, or Javanese turmeric, is a plant species belonging to the ginger family. This plant originated in Indonesia, more specifically on Java Island, and is usually used as medicine. It contains a high amount of a phenolic compound, namely, curcumin. A supercritical carbon dioxide extraction technique was employed to extract curcumin from C. xanthorrhiza. The objective of this work was to investigate the effects of temperature, pressure, and CO2 flow rate on the extraction yield and curcumin recovery from C. xanthorrhiza, which was extracted using supercritical carbon dioxide and ethanol as a cosolvent. The Box-Behnken design (BBD) experimental design and response surface methodology were used to optimize the extraction yield and curcumin recovery. The extraction conditions at a temperature of 40 °C, a pressure of 25 MPa, and a CO2 flow rate of 5.34 mL/min produced the optimum extraction yield of 10.4% and curcumin recovery of 3.2%. From Fourier transform infrared analysis, although the physical-chemical structure in the residue of the starting material was almost similar, the quantity of all functional groups in the residue decreased from the starting material. From scanning electron microscopy analysis, it was confirmed that the cell was broken due to the high-pressure effect, so that the extraction process runs easily.

Studies on solubility measurement of codeine phosphate (pain reliever drug) in supercritical carbon dioxide and modeling

In this study, the solubilities of codeine phosphate, a widely used pain reliever, in supercritical carbon dioxide (SC-CO2) were measured under various pressures and temperature conditions. The lowest determined mole fraction of codeine phosphate in SC-CO2 was 1.297 × 10-5 at 308 K and 12 MPa, while the highest was 6.502 × 10-5 at 338 K and 27 MPa. These measured solubilities were then modeled using the equation of state model, specifically the Peng-Robinson model. A selection of density models, including the Chrastil model, Mendez-Santiago and Teja model, Bartle et al. model, Sodeifian et al. model, and Reddy-Garlapati model, were also employed. Additionally, three forms of solid-liquid equilibrium models, commonly called expanded liquid models (ELMs), were used. The average solvation enthalpy associated with the solubility of codeine phosphate in SC-CO2 was calculated to be - 16.97 kJ/mol. The three forms of the ELMs provided a satisfactory correlation to the solubility data, with the corresponding average absolute relative deviation percent (AARD%) under 12.63%. The most accurate ELM model recorded AARD% and AICc values of 8.89% and - 589.79, respectively.

Improving and measuring the solubility of favipiravir and montelukast in SC-CO2 with ethanol projecting their nanonization

Supercritical carbon dioxide (SC-CO2)-based approaches have become more popular in recent years as alternative methods for creating micro- or nanosized medicines. Particularly, high drug solubility is required in those techniques using SC-CO2 as a solvent. During the most recent pandemic years, favipiravir and montelukast were two of the most often prescribed medications for the treatment of COVID-19. In this study, ethanol at 1 and 3 mol% was utilized as a cosolvent to increase the solubility of both medicines in SC-CO2 by a static approach using a range of temperatures (308 to 338 K) and pressure (12 to 30 MPa) values. The experimentally determined solubilities of favipiravir and montelukast in SC-CO2 + 3 mol% ethanol showed solubility values up to 33.3 and 24.5 times higher than that obtained for these drugs with only SC-CO2. The highest values were achieved in the pressure of 12 MPa and temperature of 338 K. Last but not least, six density-based semi-empirical models with various adjustable parameters were used to perform the modeling of the solubility of favipiravir and montelukast.

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.

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

Nilotinib hydrochloride monohydrate (NHM) is an anti-cancer drug whose solubility was statically determined in supercritical carbon dioxide (SC-CO2) for the first time at various temperatures (308-338 K) and pressures (120-270 bar). The mole fraction of the drug dissolved in SC-CO2 ranged from 0.1 × 10-5 to 0.59 × 10-5, corresponding to the solubility range of 0.016-0.094 g/L. Four sets of models were employed to evaluate the correlation of experimental data; (1) ten empirical and semi-empirical models with three to six adjustable parameters, such as Chrastil, Bartle, Sparks, Sodeifian, Mendez-Santiago and Teja (MST), Bian, Jouyban, Garlapati-Madras, Gordillo, and Jafari-Nejad; (2) Peng-Robinson equation of state (Van der Waals mixing rule, had an AARD% of 10.73); (3) expanded liquid theory (modified Wilson model, on average, the AARD of this model was 11.28%); and (4) machine learning (ML) algorithms (random forest, decision trees, multilayer perceptron, and deep neural network with respective R2 values of 0.9933, 0.9799, 0.9724 and 0.9701). All the models showed an acceptable agreement with the experimental data, among them, the Bian model exhibited excellent performance with an AARD% of 8.11. Finally, the vaporization (73.49 kJ/mol) and solvation (- 21.14 kJ/mol) enthalpies were also calculated for the first time.

Solubility measurement and modeling of hydroxychloroquine sulfate (antimalarial medication) in supercritical carbon dioxide

A supercritical fluid, such as supercritical carbon dioxide (scCO₂) is increasingly used for the micronization of pharmaceuticals in the recent past. The role of scCO₂ as a green solvent in supercritical fluid (SCF) process is decided by the solubility information of the pharmaceutical compound in scCO₂. The commonly used SCF processes are the rapid expansion of supercritical solution (RESS) and supercritical antisolvent precipitation (SAS). To implement micronization process, solubility of pharmaceuticals in scCO₂ is required. Present study is aimed at both measuring and modeling of solubilities of hydroxychloroquine sulfate (HCQS) in scCO₂. Experiments were conducted at various conditions (P = 12 to 27 MPa and T = 308 to 338 K), for the first time. The measured solubilities were found to be ranging between (0.0304 × 10^-4 and 0.1459 × 10^-4) at 308 K, (0.0627 × 10^-4 and 0.3158 × 10^-4) at 318 K, (0.0982 × 10^-4 and 0.4351 × 10^-4) at 328 K, (0.1398 × 10^-4 and 0.5515 × 10^-4) at 338 K. To expand the usage of the data, various models were tested. For the modelling task existing models (Chrastil, reformulated Chrastil, Méndez-Santiago and Teja (MST), Bartle et al., Reddy-Garlapati, Sodeifian et al., models) and new set of solvate complex models were considered. Among the all models investigated Reddy-Garlapati and new solvate complex models are able to fit the data with the least error. Finally, the total and solvation enthalpies of HCQS in scCO₂ were calculated with the help of model constants obtained from Chrastil, reformulated Chrastil and Bartle et al., models.

Solubility of palbociclib in supercritical carbon dioxide from experimental measurement and Peng-Robinson equation of state

Palbociclib is a poorly water-soluble medicine which acts against metastatic breast cancer cells. Among various techniques to improve the solubility of this medicine, applying supercritical technologies to produce micro- and nano-sized particles is a possible option. For this purpose, extraction of solubility data is required. In this research, the solubility of palbociclib in supercritical carbon dioxide (ScCO₂) at different equilibrium conditions was measured at temperatures between 308 and 338 K and pressures within 12-27 MPa, for the first time. The minimum and maximum solubility data were found to be 8.1 × 10^-7 (at 338 K and 12 MPa) and 2.03 × 10^-5 (at 338 K and 27 MPa), respectively. Thereafter, two sets of models, including ten semi-empirical equations and three Peng-Robinson (PR) based integrated models were used to correlate the experimental solubility data. Bian's model and PR equation of state using van der Waals mixing rules (PR + vdW) showed better accuracy among the examined semi-empirical and integrated models, respectively. Furthermore, the self-consistency of the obtained data was confirmed using two distinct semi-empirical models. At last, the total and vaporization enthalpies of palbociclib solubility in ScCO₂ were calculated from correlation results of semi-empirical equations and estimated to be 40.41 and 52.67 kJ/mol, respectively.

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).

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.

Experimental solubility and thermodynamic modeling of empagliflozin in supercritical carbon dioxide

The solubility of empagliflozin in supercritical carbon dioxide was measured at temperatures (308 to 338 K) and pressures (12 to 27 MPa), for the first time. The measured solubility in terms of mole faction ranged from 5.14 × 10^–6 to 25.9 × 10^–6. The cross over region was observed at 16.5 MPa. A new solubility model was derived to correlate the solubility data using solid–liquid equilibrium criteria combined with Wilson activity coefficient model at infinite dilution for the activity coefficient. The proposed model correlated the data with average absolute relative deviation (AARD) and Akaike’s information criterion (AIC_c), 7.22% and − 637.24, respectively. Further, the measured data was also correlated with 11 existing (three, five and six parameters empirical and semi-empirical) models and also with Redlich-Kwong equation of state (RKEoS) along with Kwak-Mansoori mixing rules (KMmr) model. Among density-based models, Bian et al., model was the best and corresponding AARD% was calculated 5.1. The RKEoS + KMmr was observed to correlate the data with 8.07% (correspond AIC_c is − 635.79). Finally, total, sublimation and solvation enthalpies of empagliflozin were calculated.

Solubility measurement and thermodynamic modeling of pantoprazole sodium sesquihydrate in supercritical carbon dioxide

Knowing the solubility data of pharmaceutical compounds in supercritical carbon dioxide (ScCO₂) is essential for nanoparticles formation by using supercritical technology. In this work, solubility of solid pantoprazole sodium sesquihydrate in ScCO₂ is determined and reported at 308, 318, 328 and 338 K and at pressures between 12 and 27 MPa. The solubilities are ranged between 0.0301 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10^–4 and 0.463 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 10^–4 in mole fraction. The determined solubilities are modelled with a new model using solid–liquid equilibrium criteria and the required activity coefficient is developed using regular solution theory. The measured solubilities data are also modelled with three recent and four conventional empirical models. The recent models used are, Alwi-Garlapati (AARD = 13.1%), Sodeifian et al. (14.7%), and Tippana-Garlapati (15.5%) models and the conventional models used are Chrastil (17.54%), reformulated Chrastil (16.30%), Bartle (14.1%) and Mendenz Santiago and Teja (MT) (14.9%) models. The proposed model is correlating the data with less than 14.9% and 16.23% in terms of AARD for temperature dependent and independent cases. Among exiting models, Mendez Santiago and Teja (MT) and Alwi-Garlapati models correlate the data better than other models (corresponding AARD% and AIC_c are 14.9, 13.1 and −518.89, −504.14, respectively). The correlation effectiveness of the models is evaluated in terms of Corrected Akaike’s Information Criterion (AIC_c). Finally, enthalpy of solvation and vaporization of pantoprazole sodium sesquihydrate are calculated and reported. The new model proposed in this study can be used for the combination of any complex compound with any supercritical fluid.

Ni-Based Catalyst for Carbon Dioxide Methanation: A Review on Performance and Progress

Catalytic conversion of CO2 into methane is an attractive method because it can alleviate global warming and provide a solution for the energy depletion crisis. Nickel-based catalysts were commonly employed in such conversions due to their high performance over cost ratio. However, the major challenges are that Ni tends to agglomerate and cause carbon deposition during the high-temperature reaction. In the past decades, extensive works have been carried out to design and synthesize more active nickel-based catalysts to achieve high CO2 conversion and CH4 selectivity. This review critically discusses the recent application of Ni-based catalyst for CO2 methanation, including the progress on the effect of supporting material, promoters, and catalyst composition. The thermodynamics, kinetics, and mechanism of CO2 methanation are also briefly addressed.

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.

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.

Fabrication of Imatinib Mesylate-Loaded Lactoferrin-Modified PEGylated Liquid Crystalline Nanoparticles for Mitochondrial-Dependent Apoptosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major cause of concern as it has substantial morbidity associated with it. Previous reports have ascertained the antiproliferative activity of imatinib mesylate (IMS) against diverse types of carcinomas, but limited bioavailability has also been reported. The present study envisaged optimized IMS-loaded lactoferrin (LF)-modified PEGylated liquid crystalline nanoparticles (IMS-LF-LCNPs) for effective therapy of IMS to HCC via asialoglycoprotein receptor (ASGPR) targeting. Results displayed that IMS-LF-LCNPs presented an optimum particle size of 120.40 ± 2.75 nm, a zeta potential of +12.5 ± 0.23 mV, and 73.94 ± 2.69% release. High-resolution transmission electron microscopy and atomic force microscopy were used to confirm the surface architecture of IMS-LF-LCNPs. The results of cytotoxicity and 4,6-diamidino-2-phenylindole revealed that IMS-LF-LCNPs had the highest growth inhibition and significant apoptotic effects. Pharmacokinetics and biodistribution studies showed that IMS-LF-LCNPs have superior pharmacokinetic performance and targeted delivery compared to IMS-LCNPs and plain IMS, which was attributed to the targeting action of LF that targets the ASGPR in hepatic cells. Next, our in vivo experiment established that the HCC environment existed due to suppression of BAX, cyt c, BAD, e-NOS, and caspase (3 and 9) genes, which thus owed upstream expression of Bcl-xl, iNOS, and Bcl-2 genes. The excellent therapeutic potential of IMS-LF-LCNPs began the significant stimulation of caspase-mediated apoptotic signals accountable for its anti-HCC prospect. ¹H nuclear magnetic resonance (serum) metabolomics revealed that IMS-LF-LCNPs are capable of regulating the disturbed levels of metabolites linked to HCC triggered through N-nitrosodiethylamine. Therefore, IMS-LF-LCNPs are a potentially effective formulation against HCC.

Using static method to measure tolmetin solubility at different pressures and temperatures in supercritical carbon dioxide

Tolmetin is a non-steroidal anti-inflammatory drug being used to decrease the level of hormones which are the reasons for pain, swelling, tiredness, and stiffness for osteoarthritis and rheumatoid arthritis cases. We evaluated its solubility in supercritical carbon dioxide (SC-CO₂) with the aim of drug nanonization, considering temperature and pressure variations between 120 and 400 bar and 308-338 K, in the experiments. In this way, a PVT solubility cell based on static solubility approach coupled with a simple gravimetric procedure was utilized to evaluate the solubility of tolmetin. The solubility values between 5.00 × 10^-5 and 2.59 × 10^-3 mol fraction were obtained for tolmetin depending on the pressure and temperature of the cell. The measured data demonstrated a direct correlation between pressure and solubility of tolmetin, while the effect of temperature was a dual effect depending on the crossover pressure (160 bar). The calculated solubility data were modeled using several semi-empirical correlations, and the fitting parameters were calculated using the experimental data via appropriate optimization method. The correlated solubility data revealed that the KJ model was the most accurate one with an average absolute relative deviation percent (AARD%) of 6.9. Moreover, the carried out self-consistency analysis utilizing these correlations illustrated great potential of these models to extrapolate the solubility of tolmetin beyond the measured conditions.