Enhanced water solubility, antioxidant activity, and oral absorption of hesperetin by D-α-tocopheryl polyethylene glycol 1000 succinate and phosphatidylcholine

Hesperetin,an abundant bioactive component of citrus fruits,is poorly water-soluble,resulting in low oral bioavailability.We developed new formulations to improve the water solubility,antioxidant activity,and oral absorption of hesperetin.Two nano-based formulations were developed,namely hesperetin-TPGS(D-α-tocopheryl polyethylene glycol 1000 succinate)micelles and hesperetin-phosphatidylcholine(PC)complexes.These two formulations were prepared by a simple technique called solvent dispersion,using US Food and Drug Administration(FDA)-approved excipients for drugs.Differential scanning calorimetry(DSC)and dynamic light scattering(DLS)were used to characterize the formulations’physical properties.Cytotoxicity analysis,cellular antioxidant activity assay,and a pharmacokinetic study were performed to evaluate the biological properties of these two formulations.The final weight ratios of both hesperetin to TPGS and hesperetin to PC were 1:12 based on their water solubility,which increased to 21.5-and 20.7-fold,respectively.The hesperetin-TPGS micelles had a small particle size of 26.19 nm,whereas the hesperetin-PC complexes exhibited a larger particle size of 219.15 nm.In addition,the cellular antioxidant activity assay indicated that both hesperetin-TPGS micelles and hesperetin-PC complexes increased the antioxidant activity of hesperetin to 4.2-and 3.9-fold,respectively.Importantly,the in vivo oral absorption study on rats indicated that the micelles and complexes significantly increased the peak plasma concentration(Cmax)from 2.64μg/mL to 20.67 and 33.09μg/mL and also increased the area under the concentration–time curve of hesperetin after oral administration to 16.2-and 18.0-fold,respectively.The micelles and complexes increased the solubility and remarkably improved the in vitro antioxidant activity and in vivo oral absorption of hesperetin,indicating these formulations’potential applications in drugs and healthcare products.

Nonparallel Support Vector Machine Based on One Optimization Problem for Pattern Recognition

In this paper,we present a novel nonparallel support vector machine based on one optimization problem(NSVMOOP)for binary classification.Our NSVMOOP is formulated aiming to separate classes from the largest possible angle between the normal vectors and the decision hyperplanes in the feature space,at the same time implementing the structural risk minimization principle.Different from other nonparallel classifiers,such as the representative twin support vector machine,it constructs two nonparallel hyperplanes simultaneously by solving a single quadratic programming problem,on which a modified sequential minimization optimization algorithm is explored.The NSVMOOP is analyzed theoretically and implemented experimentally.Experimental results on both artificial and publicly available benchmark datasets show its feasibility and effectiveness.