Comparison between multi-linear- and radial-basis-function-neural-network-based QSPR Models for the prediction of the critical temperature, critical pressure and acentric factor of organic compounds

Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance

Solubility of Cortisone and Hydrocortisone in Supercritical Carbon Dioxide and Ethanol

Cortisone and hydrocortisone are poorly water-soluble corticosteroids widely used to treat many inflammatory and immune diseases. In the prospect of employing them in pharmaceutical applications or analytical techniques involving cosolvent-modified supercritical fluids, their solubility has been determined, for the first time, in a mixture of supercritical CO2 and 2, 3, or 4 mol % of ethanol, at 318.15, 328.15, 348.15, and 373.15 K, and in the pressure range of 13-27 MPa. Tests were conducted in a semi-flow laboratory apparatus, and results were fitted with the most popular density-based correlations reported in the literature for ternary systems. Even though the two drugs exhibit similar molecular structures, the solubility of cortisone in the ethanol-scCO2 mixtures is on average 3.5 times higher than that of hydrocortisone. This is congruent with the fact that the solubility of cortisone in pure ethanol is higher. The seven-parameter Reddy-Madras model (adjusted absolute average percent deviation (AARD%) = 8.8%) proved to be the best correlating one for cortisone while the six-parameter Garlapati-Madras approach (AARD% = 11.6%) provided the best fitting for hydrocortisone. The reliability of the experimental results was also confirmed by the positive response to the Mendez-Santiago-Teja self-consistency test

Characterization of Ketoprofen/Methyl-β-Cyclodextrin Complexes Prepared Using Supercritical Carbon Dioxide

Complexes of methyl-β-cyclodextrin and ketoprofen, a crystalline anti-inflammatory drug with poor water solubility, have been prepared for the first time in the presence of supercritical CO2at 40°C and 20 MPa. The supercritical treatment allows these pharmaceutical formulations to be prepared without the use of any auxiliary agents or organic solvents. The treated samples were characterized through differential scanning calorimetry, X-ray diffractometry, and the Fourier transform infrared spectroscopy to exclude the presence of crystalline drug and check the formation of the complexes. The increase of the drug dissolution rate was investigated performing in vitro release tests in aqueous solutions. The results showed that the supercritical treatment can be an efficient method to obtain inclusion complexes with enhanced release kinetics. The operating methods of the release tests, that is, the "tablet method" or the "dispersed amount method," affected both the dissolution rate and its dependence on the drug amount in the samples. On the contrary, the variation of the pH of the dissolution medium did not show any effect on the release rate of the supercritical complexes

Silica–Cyclodextrin Hybrid Materials: Two Possible Synthesis Processes

: Both cyclodextrin (CD) and porous silica possess interesting properties of adsorption and release. A silica-CD hybrid, therefore, could synergically merge the properties of the two components, giving rise to a material with appealing properties for both environmental and pharmaceutical applications. With this aim, in the present study, a first hybrid is obtained through one-pot sol-gel synthesis starting from CD and tetramethyl orthosilicate (TMOS) as a silica precursor. In particular, methyl-β-cyclodextrin (bMCD) is selected for this purpose. The obtained bMCD-silica hybrid is a dense material containing a considerable amount of bMCD (45 wt.%) in amorphous form and therefore represents a promising support. However, since a high specific surface area is desirable to increase the release/adsorption properties, an attempt is made to produce the hybrid material in the form of an aerogel. Both the synthesis of the gel and its drying in supercritical CO2 are optimized in order to reach this goal. All the obtained samples are characterized in terms of their physico-chemical properties (infra-red spectroscopy, thermogravimetry) and structure (X-ray diffraction, electron microscopy) in order to investigate their composition and the interaction between the organic component (bMCD) and the inorganic one (silica)

A mesostructured hybrid CTA–silica carrier for curcumin delivery

Curcumin is a natural active principle with antioxidant, antibacterial and anti-inflammatory properties. Its use is limited by a low water solubility and fast degradation rate, which hinder its bioavailability. To overcome this problem, curcumin can be delivered through a carrier, which protects the drug molecule and enhances its pharmacological effects. The present work proposes a simple one-pot sol–gel synthesis to obtain a hybrid carrier for curcumin delivery. The hybrid consists of a mesostructured matrix of amorphous silica, which stabilizes the carrier, and hexadecyltrimethylammonium (CTA), a surfactant where curcumin is dissolved to increase its water solubility. The carrier was characterized in terms of morphology (FESEM), physicochemical properties (XRD, FTIR, UV spectroscopy) and release capability in pseudo-physiological solutions. Results show that curcumin molecules were entrapped, for the first time, in a silica-surfactant mesostructured hybrid carrier. The hybrid carrier successfully released curcumin in artificial sweat and in a phosphate buffer saline solution, so confirming its efficacy in increasing curcumin water solubility. The proposed drug release mechanism relies on the degradation of the carrier, which involves the concurrent release of silicon. This suggests strong potentialities for topical administration applications, since curcumin is effective against many dermal diseases while silicon is beneficial to the skin

Comparison between Multi-Linear- and Radial-Basis-Function-Neural-Network-Based QSPR Models for The Prediction of The Critical Temperature, Critical Pressure and Acentric Factor of Organic Compounds

Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance

Solubility of Tolbutamide and Chlorpropamide in Supercritical Carbon Dioxide

Tolbutamide and chlorpropamide are oral hypoglycemic drugs that are used to treat diabetic patients. In the context of employing these drugs in green pharmaceutical applications that make use of supercritical fluids, the solubility of tolbutamide and chlorpropamide in supercritical CO 2 has been measured at 313.15, 333.15, and 353.15 K and in the pressure range of 10−30 MPa. A semiflow apparatus equipped with a continuous solvent-dilution device of the depressurization line, which avoids the solubility data to be underestimated, was employed. The solubility of tolbutamide is in the range of 1.66 ×10 −5 to 40.5 ×10 −5 mole fraction while that of chlorpropamide is in the range of 2.29 ×10 −6 to 72.2 ×10 −6 mole fraction, which indicates that the first drug has higher solubility in the supercritical fluid than the latter, probably due to its higher hydrophobicity. The results were successfully correlated with the most popular empirical and semiempirical models reported in the literature. The Sparks model provided the best correlation for chlorpropamide with an adjusted absolute average percent deviation (AARD%) of 3.5%, while the Keshmiri model provided the best correlation for tolbutamide, with an AARD% of 4.8%. The self-consistency of the experimental data was checked through the Mé ndez-Santiago and Teja model

A Simple Pseudo-Homogeneous Reversible Kinetic Model for the Esterification of Different Fatty Acids with Methanol in the Presence of Amberlyst-15

Fatty acid esterification with alcohols is a crucial step in biodiesel synthesis. Biodiesel consists of long-chain alkyl esters that derive from the transesterification or hydro-esterification of the triglycerides that are contained in vegetable oils. In the first route, the esterification of the free fatty acids is an important pretreatment of the feed; in the second, it is the main reaction of the industrial process. Knowledge of appropriate kinetic models for the catalytic esterification of fatty acids with alcohols is critical in the design of biodiesel synthesis processes. In this work, the kinetic behavior of the reversible esterification of lauric, myristic, palmitic and stearic acid, which are the most common saturated fatty acids that are contained in triglyceride feedstocks for biodiesel, with methanol at different temperatures (70–150 °C) and molar ratios of the reactants (1:1–1:2–1:5) was investigated in a batch laboratory basket reactor both in the presence and absence of Amberlyst-15 as the catalyst. Results obtained with Amberlyst-15 were fitted through a ready-to-use pseudo-homogeneous reversible model suitable for process design. The kinetic model was compared with that obtained in a previous work with niobium oxide as the catalyst. With respect to the results that were obtained with niobium oxide, the influence of the chain length of the acid on the kinetic behavior was strongly reduced in the presence of Amberlyst-15. This phenomenon was ascribed to a different catalytic mechanism

Investigation of the piroxicam/hydroxypropyl-β-cyclodextrin inclusion complexation by means of a supercritical solvent in the presence of auxiliary agents

The complexation of piroxicam and 2-hydroxypropyl-b-cyclodextrin by means of supercritical CO2has been investigated. The experiments were carried out by varying the temperature, pressure and contact time and introducing two different auxiliary agents: polyvinyl pyrrolidone orl-lysine. Cyclodextrins, which are widely used to solubilize a large variety of poorly soluble drugs, are often used in combination with some auxiliary agents to enhance the complexation efficiency of the conventional techniques. While many recent literature works report that supercritical carbon dioxide is a clean, nontoxic alternative to organic solvents, the use of auxiliary agents in the supercritical complexation process has been scarcely examined and still needs to be investigated. The inclusion complexes obtained in this work were analysed by means of the ‘differential solubility method', differential scanning calorimetry and Fourier transform infrared spectroscopy. The results showed that the supercritical treatment could be successfully employed below 140-150°C without incurring thermal degradation of the samples. While 66% inclusion efficiency could be obtained at 140°C and 30 MPa for a mixture of piroxicam/2-hydroxypropyl-b-cyclodextrin (1:2 molar ratio), higher percentages of complexation (95% in the ternary samples with polyvinyl pyrrolidone and 89-91% in those with l-lysine) could be obtained at a lower temperature (130°C) when auxiliary agents were employe