Lipases from different sources, Pseudomonas fluorescens (AK lipase), Burkholderia cepacia (PS lipase), Penicillium camembertii (lipase G) and Porcine pancreas lipase (PPL), previously immobilized on epoxy SiO(2)-PVA, were screened for the synthesis of xylitol monoesters by esterification of the protected xylitol using oleic acid as acyl donor group. Among all immobilized derivatives, the highest esterification yield was achieved by P. camembertii lipase, showing to be attractive alternative to bulk chemical routes to satisfy increasing commercial demands. Further experiments were performed to determine the influence of fatty acids chain size on the reaction yield and the feasibility of using non-conventional heating systems (microwave and ultrasound irradiations) to enhance the reaction rate.

BACKGROUND: Fatty acid sugar esters are used as non-ionic surfactants in cosmetics, foodstuffs and pharmaceuticals. In particular, monoesters of xylitol have attracted industrial interest due to their outstanding biological activities. In this work, xylitol monoesters were obtained by chemoenzymatic synthesis, in which, first, xylitol was made soluble in organic solvent by chemo-protecting reaction, followed by enzymatic esterification reaction using different acyl donors. A commercial immobilized Candida antartica lipase was used as catalyst, and reactions with pure xylitol were carried out to generate data for comparison.

This paper describes the use of artificial neural networks as a theoretical tool in the structural determination of alkaloids from (13)C NMR chemical shift data, aiming to identify skeletal types of those compounds. For that, 162 aporphine alkaloids belonging to 12 different skeletons were codified with their respective (13)C NMR chemical shifts. Each skeleton pertaining to aporphine alkaloid type was used as output, and the (13)C NMR chemical shifts were used as input data of the net. Analyzing the obtained results, one can then affirm the skeleton to which each one of these compounds belongs with high degree of confidence (over 97%). The relation between the correlation coefficient and the number of epochs and the architecture of net (3-layer MLP or 4-layer MLP) were analyzed, too. The analysis showed that the results predicted by the 3-layer MLP networks trained with a number of the epochs higher than 900 epochs are the best ones. The artificial neural nets were shown to be a simple and efficient tool to solve structural elucidation problems making use of (13)C NMR chemical shift data, even when a similarity between the searched skeletons occurs, offering fast and accurate results to identification of skeletons of organic compounds.

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%.

Prostaglandins (PG's) are a very important class of naturally occurring physiologically active substances, which have been the subject of continuous attention by biologists, pharmacists and chemists since their discovery in the 1930s. Pharmacological studies have shown that prostaglandins exhibit an extremely broad spectrum of activity such as on the smooth muscles of various organs, their mediating role in the central and peripheral nervous systems, in inflammatory processes, and their hormone-like and antihormonal activities. Due to the diversified biological activity and rapid metabolism of the natural prostaglandins, a lot of effort has been done to synthesize prostaglandin analogs. Particularly interesting are the prostaglandin analogs containing heteroatoms in the cyclopentane ring which have received a great deal of attention in relation to their potential biological properties and diversified biological activity.This article is dedicated to Prof. Sune K. Bergstrom (in memoriam) for his milestone contributions to prostaglandin research.