New NMR Structure Determination Methods for Prenylated Phenols

Abstract

Recently, the occurrences of isoprenoid-substituted phenols from natural sources have become fairly familiar. Over the past 20 years, there have been increasing reports of isoprenylated compounds with structural, biological, and pharmacological interest.1 Some of them have interesting bioactivities, e.g., antitumor promoting activity,2 hypotensive effect,3 antagonism for bombesin receptor,4 inhibitory effects for some enzymes,5,6 because they have both hydrophilic and hydrophobic groups in the molecule. It would not seem that use of modern NMR techniques (e.g., 2D NMR measurements) could result in the proposing of an incorrect structure. On the other hand, many structures of isoprenoid-substituted phenols had been reported without unambiguous evidence before 2D NMR spectrometry became routine work. Some structures were revised in reinvestigation of the plant source or in conflicting of a structure for other compounds. Nevertheless, most compounds have not been thoroughly described when a significant bioactivity is not found in the sources.7 We wanted to get new techniques for the inspection of proposed structure by using earlier reported data. In the course of our study on phenolic compounds from medicinal plants, we found two new NMR methods for structure determination of 3-methyl-2-butenyl (prenyl) or (E)-3,7-dimethyl-2,6-octadienyl (geranyl) phenols. These methods also suit our objective of reinspecting previously reported structures. The first method involves classification with variation in the chemical shift of methylene carbon of the prenyl group and the second method is based on variation in the chemical shift of the 5-hydroxy proton of prenylated flavonoids. In this chapter, we describe these methods and a third method of classification of isoprenoid-substituted flavones.KeywordsChemical ShiftDownfield ShiftMethylene CarbonRoot BarkMulberry TreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.