Search

Searching for bioactive compounds from natural resources such as plant materials has become a focus for study. Several models, such as animal (biofluid, organ and tissue) and cellular (several kinds of cell lines), have traditionally been used for this purpose. As a fast, economic and effective way to identify or predict bioactive compounds in complex matrices, biochromatography has developed rapidly during the past years. Combing the properties of traditional chromatography and biomaterials, biochromatographic analysis possesses features of simultaneous screening, separation and structural identification for active compounds in a complex matrix. According to the process, biochromatography can be divided into offline and online approaches. For offline bioextraction, the biomaterials are used as the extraction phase and followed by routine chromatographic analysis. For online biochromatography, the biomaterials are directly used as the stationary phase for chromatographic analysis. This paper reviews the applications of offline bioextraction followed by chromatographic analysis and online biochromatography, including molecular, cell membrane and cell, and artificial biomembrane chromatography in the screening or predicting active compounds from natural sources. [ABSTRACT FROM AUTHOR]

Although hundreds of papers related to preparative gas chromatography (pGC) have been published since the late 1950s, the success of the GC technique has largely been associated with analytical instead of preparative purposes. Actually, pGC is an ideal alternative technique for the preparation of pure substances, especially volatile compounds. This paper reviews the papers (written in English) associated with pGC published over the period from the 1950s to the 2010s. For large scale preparation, large sample injection and vaporization, a high loading capacity column, a gas splitter at the end of the column and a special collecting device are fundamentally important for a pGC system. The primary components of pGC system, including injector, column, splitter, detector and collection traps, are briefly introduced. Furthermore, the applications of pGC in the separation and purification of volatile compounds from natural essential oils, in addition to the purification of isotopes, isomers and enantiomers are summarized. [ABSTRACT FROM AUTHOR]

Volatile chemicals are a group of very important compounds in natural products. Curcuma rhizome, which contains many bioactive volatile compounds, is a traditional Chinese medicine that has long been used for the treatment of several diseases. In the present study, a microemulsion electrokinetic chromatography (MEEKC) method was developed for the analysis of four volatile components in Curcuma rhizome, including germacrone, furanodiene, curcumenol and curdione. Experimental parameters, including the pH, type and concentrations of background electrolyte, and microemulsion compositions (type and concentrations of surfactant, co-surfactant and oil phase) were intensively investigated. Finally, the primary compounds in the methanol extract of Curcuma rhizome were separated within 30 min using a running buffer composed of 2.31% w/v (80 mmol/L) sodium dodecyl sulfate (SDS), 0.91% w/v (80 mmol/L) 1-octane, 6.95% w/v (937.5 mmol/L) 1-butanol and 1.88% w/v (312.5 mmol/L) propanol in a 5-mM borate buffer (pH 8.1). The contents of the four investigated compounds were determined in the rhizome from C. phaeocaulis. The results showed that the developed MEEKC method provided an alternative tool for the analysis of volatile components, especially those of heat-sensitive compounds from natural products. [ABSTRACT FROM AUTHOR]

In drug discovery, one of the most important tasks is to find novel and biologically active molecules. Given that only a tip of iceberg of drugs was founded in nearly one-century's experimental exploration, it shows great significance to use in silico methods to expand chemical database and profile drug-target linkages. In this study, a web server named ChemGenerator was proposed to generate novel activates for specific targets based on users' input. The ChemGenerator relies on an autoencoder-based algorithm of Recurrent Neural Networks with Long Short-Term Memory by training of 7 million of molecular Simplified Molecular-Input Line-Entry System as the basic model, and further develops target guided generation by transfer learning. As results, ChemGenerator gains lower loss (<0.01) than existing reference model (0.2~0.4) and shows good performance in the case of Epidermal Growth Factor Receptor. Meanwhile, ChemGenerator is now freely accessible to the public by http://smiles.tcmobile.org. In proportion to endless molecular enumeration and time-consuming expensive experiments, this work demonstrates an efficient alternative way for the first virtual screening in drug discovery. [ABSTRACT FROM AUTHOR]