The aim of this study was to evaluate (+)-catechin and (−)-epigallocatechin gallate (EGCG) cellular uptake and transport across human intestinal Caco-2 cell monolayer in both the absence and presence of niosomal carrier in variable conditions. The effect of free drugs and drug-loaded niosomes on the growth of Caco-2 cells was studied. The effects of time, temperature, and concentration on drug cellular uptake in the absence or presence of its niosomal delivery systems were investigated. The intestinal epithelial membrane transport of the drug-loaded niosomes was examined using the monolayer of the human Caco-2 cells. The kinetics of transport, and the effect of temperature, adenosine triphosphate inhibitor, permeability glycoprotein inhibitor, multidrug resistance-associated protein 2 inhibitor, and the absorption enhancer on transport mechanism were investigated. It was found that the uptake of catechin, EGCG, and their niosomes by Caco-2 cells was 1.22±0.16, 0.90±0.14, 3.25±0.37, and 1.92±0.22 μg/mg protein, respectively (n=3). The apparent permeability coefficient values of catechin, EGCG, and their niosomes were 1.68±0.16, 0.88±0.09, 2.39±0.31, and 1.42±0.24 cm/second (n=3) at 37°C, respectively. The transport was temperature- and energy-dependent. The inhibitors of permeability glycoprotein and multidrug resistance-associated protein 2 and the absorption enhancer significantly enhanced the uptake amount. Compared with the free drugs, niosomal formulation significantly enhanced drug absorption. Additionally, drug-loaded niosomes exhibited stronger stability and lower toxicity. These findings showed that the oral absorption of tea flavonoids could be improved by using the novel drug delivery systems.

DNA walkers have shown superior performance in biosensing due to their programmability to design molecular walking behaviors with specific responses to different biological targets. However, it is still challenging to make DNA walkers capable of distinguishing DNA targets with single-base differences, so that DNA walkers that can be used for circulating tumor DNA sensing are rarely reported. Herein, a flap endonuclease 1 (FEN 1)-assisted DNA walker has been proposed to achieve mutant biosensing. The target DNA is captured on a gold nanoparticle (AuNP) as a walking strand to walk by hybridizing to the track strands on the surface of the AuNP. FEN 1 is employed to report the walking events by cleaving the track strands that must form a three-base overlapping structure recognized by FEN 1 after hybridizing with the captured target DNA. Owing to the high specificity of FEN 1 for structure recognition, the one-base mutant DNA target can be discriminated from wild-type DNA. By constructing a sensitivity-enhanced DNA walker system, as low as 1 fM DNA targets and 0.1% mutation abundance can be sensed, and the theoretical detection limits for detecting the DNA target and mutation abundance achieve 0.22 fM and 0.01%, respectively. The results of epidermal growth factor receptor (EGFR) L858R mutation detection on cell-free DNA samples from 15 patients with nonsmall cell lung cancer were completely consistent with that of next-generation sequencing, indicating that our DNA walker has potential for liquid biopsy.

Genetic polymorphism and environment each influence individual variability in drug metabolism and disposition. It is preferable to predict such variability, which may affect drug efficacy and toxicity, before drug administration. We examined individual differences in the pharmacokinetics of atorvastatin by applying gas chromatography-mass spectrometry-based metabolic profiling to predose plasma samples from 48 healthy volunteers. We determined the level of atorvastatin in plasma using liquid chromatography-tandem mass spectrometry. With the endogenous molecules, which showed a good correlation with pharmacokinetic parameters, a refined partial least-squares model was calculated based on predose data from a training set of 36 individuals and exhibited good predictive capability for the other 12 individuals in the prediction set. In addition, the model was successfully used to predictively classify individual pharmacokinetic responses into subgroups. Metabolites such as tryptophan, alanine, arachidonic acid, 2-hydroxybutyric acid, cholesterol, and isoleucine were indicated as candidate markers for predicting by showing better predictive capability for explaining individual differences than a conventional physiological index. These results suggest that a pharmacometabonomic approach offers the potential to predict individual differences in pharmacokinetics and therefore to facilitate individualized drug therapy.

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