The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey’s relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

In recent years, the significance of oxidative stress in the pathophysiology of Neurodegenerative/developmental disorders like Attention Deficit Hyperactivity Disorder, Parkinson's and Alzheimer's is being studied at an accelerating pace. Nrf2 activation via Keap1 inhibition is an established strategy for improving the activity of the cellular antioxidant mechanism. In this study, pharmacophore modeling was employed to design efficient Keap1 inhibitors from well-known polypharmacological phytochemicals after extensive structural modifications to improve their pharmacodynamic, pharmacokinetic and drug-likeness qualities (BBB > 0.9, HIA > 0.85). Density functional theory-based quantum chemical calculations at the B3LYP/6-31G (d, p) level of theory were performed for the geometry optimization of the novel ligands and for computing their electronic properties. Resveratrol-4 was found to be the most desirable candidate with an ΔE = 4.24497 eV. HOMO and LUMO distribution of the Resveratrol-4 was found to be very favourable for keap1 binding. Molecular docking studies and comparative interaction analysis also ranked the Resveratrol-4 derivative as the best multi-domain antagonist of the Keap1 protein with a binding affinity of -8 kcal/mole. The following study presents the application of Resveratrol-4 a novel, modified, phytochemical derivative, as an efficient antagonist of the Keap1 protein for enhancing nrf2 mediated neuroprotection from redox insults.

In recent years, the significance of oxidative stress in the pathophysiology of Neurodegenerative/developmental diseases like Attention Deficit Hyperactivity Disorder, Parkinson’s and Alzheimer’s is being studied at an accelerating pace. Nrf2 activation via Keap1 inhibition is an established strategy for enhancing the activity of the cellular antioxidant mechanism. In this study pharmacophore modeling was employed to design efficient Keap1 inhibitors from well-known polypharmacological phytochemicals after extensive structural modifications to improve their pharmacodynamic, pharmacokinetic and drug-likeness qualities. Quantum chemical calculations at the B3LYP/6-31G (d, p) level of theory were performed for geometry optimization of the novel ligands and for computing their electronic properties. Molecular docking studies and comparative interaction analysis ranked the Resveratrol-4 derivative as the best multi-domain antagonist of the Keap1 protein. The following study presents the application of novel, modified, phytochemical derivatives, as efficient antagonists of the Keap1 protein for enhancing neuroprotection from redox insults.

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.