Anti-Cancer Effects of α-Cubebenoate Derived from Schisandra chinensis in CT26 Colon Cancer Cells

α-Cubebenoate derived from Schisandra chinensis has been reported to possess anti-allergic, anti-obesity, and anti-inflammatory effects and to exhibit anti-septic activity, but its anti-cancer effects have not been investigated. To examine the anti-cancer activity of α-cubebenoate, we investigated its effects on the proliferation, apoptosis, and metastasis of CT26 cells. The viabilities of CT26 cells (a murine colorectal carcinoma cell line) and HCT116 cells (a human colon cancer cell line) were remarkably and dose-dependently diminished by α-cubebenoate, whereas the viability of CCD-18Co cells (a normal human fibroblast cell line) were unaffected. Furthermore, α-cubebenoate treatment increased the number of apoptotic CT26 cells as compared with Vehicle-treated cells and increased Bax, Bcl-2, Cas-3, and Cleaved Cas-3 protein levels by activating the MAP kinase signaling pathway. α-Cubebenoate also suppressed CT26 migration by regulating the PI3K/AKT signaling pathway. Furthermore, similar reductions were observed in the expression levels of some migration-related proteins including VEGFA, MMP2, and MMP9. Furthermore, reduced VEGFA expression was found to be accompanied by the phosphorylations of FAK and MLC in the downstream signaling pathway of adhesion protein. The results of the present study provide novel evidence that α-cubebenoate can stimulate apoptosis and inhibit metastasis by regulating the MAPK, PI3K/AKT, and FAK/MLC signaling pathways.     

PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions

Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models'' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom–atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein–ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.

PIGNet, a deep neural network-based drug–target interaction model guided by physics and extensive data augmentation, shows significantly improved generalization ability and model performance.     

Binary mixing of micelles using Pluronics for a nano-sized drug delivery system

Pluronics with different structural compositions and properties are used for several applications, including drug delivery systems. We developed a binary mixing system with two Pluronics, L121/P123, as a nano-sized drug delivery carrier. The lamellar-forming Pluronic L121 (0.1 wt%) was incorporated with Pluronic P123 to produce nano-sized dispersions (in case of 0.1 and 0.5 wt% P123) with high stability due to Pluronic P123 and high solubilization capacity due to Pluronic L121. The binary systems were spherical and less than 200-nm diameter, with high thermodynamic stability (at least 2 weeks) in aqueous solution. The CMC of the binary system was located in the middle of the CMC of each polymer. In particular, the solubilization capacity of the binary system (0.1/0.1 wt%) was higher than mono-systems of P123. The main advantage of binary systems is overcoming limitations of mono systems to allow tailored mixing of block copolymers with different physicochemical characteristics. These nano-sized systems may have potential as anticancer drug delivery systems with simple preparation method, high stability, and high loading capacity.     

Block Copolymer Micelles with Near Infrared Metal Phthalocyanine Dyes for Laser Induced Writing

A route has been developed to disperse metal‐containing phthalocyanine dyes in a non‐polar medium based on amphiphilic block copolymer micelles of poly[styrene‐block‐(4‐vinylpyridine)] (PS‐b‐P4VP) and poly[styrene‐block‐(acrylic acid)] (PS‐b‐PAA) copolymers. Polar P4VP and PAA efficiently encapsulate cobalt(II ), manganese(II ), and nickel(II ) phthalocyanine dyes by axial coordination of nitrogen and µ‐oxo bridged dimerization with the transition metals, respectively. Good dispersion of the dyes is confirmed by the linear enhancement of Q‐bands in UV–vis absorption spectra with dye concentration. A thin monolayered PS‐b‐P4VP micelle film that contained a nickel(II ) phthalocyanine dye which efficiently adsorbs a laser beam on a localized area to generate a local heat higher than the glass transition temperatures of both blocks. One‐dimensional laser writing on the dye‐containing film allows the fabrication of a few submicrometer wide line patterns in which the self‐assembled nanostructure of the block copolymer is modified by the directional heat arising from laser scanning.

     

Monitoring Transformations of Catalytic Active States in Photocathodes Based on MoSx Layers on CuInS2 Using In Operando Raman Spectroscopy

The electrochemical activation of CuInS₂/MoS_x for photoelectrochemical (PEC) H₂ production was revealed for the first time through in operando Raman spectroscopy. During the activation process, the initial metallic MoS_x phase was transformed to semiconducting MoS_x, which facilitates charge carrier transfer between CuInS₂ and MoS_x. Ex situ X-ray photoelectron spectroscopy and Raman spectroscopy suggest the existence of MoO₃ after the activation process. However, apart from contradicting these results, in operando Raman spectroscopy revealed some of the intermediate steps of the activation process.