Liquid Chromatography Combinatorial chemistry

The first monolithic materials initially emerged in the 1960s, but it is during the last 20 years that monoliths have been intensively developed in a variety of fields and particularly in analytical chemistry for separation techniques. Nowadays, these macroporous materials are widely used and have found numerous applications in different chromatographic modes such as liquid chromatography (LC) or CEC, as indicated by several reviews [150, 151]. Less commonly, monolithic materials can also be applied, for example, to solid-phase extraction, combinatorial synthesis and for enzyme immobilisation. 

Undoubtedly, NMR is the most informative method for characterization of organic compounds. However, it has limited application in combinatorial chemistry due to several factors. NMR is a relatively insensitive and slow method, requires homogeneous samples, and consumes quite expensive deuterated solvents. Here we will discuss the most recent developments of this method that overcome the major limitations and make NMR one of the promising techniques in combinatorial chemistry. It relates to the application of NMR, not only for analyzing compounds attached to polymer support and for monitoring reactions on a solid phase, but also as a detector for liquid chromatography (LC/NMR). For the most recent review, see [10]. 

As in other organic synthesis laboratories, high-performance liquid chromatography (HPLC) is also intensively used for separations in combinatorial chemistry laboratories. Using standard detectors such as ultraviolet (UV),... 

The purity control and the structure verification of compound collections from automated synthesis and combinatorial chemistry play an essential role in the success of medicinal chemistry programs. High performance liquid chromatography (HPLC), mass spectrometry (MS), and liquid chromatography-mass spectrometry (LC-MS) techniques are generally accepted as the most appropriate means of characterization (1,2). While these analytical methods are fast and easy to automate, they do not provide sufficient structural and quantitative data about the desired products. 

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