The aim of this research was to evaluate the fractions obtained from the leaf, stem and roots of Allamanda schottii Pohl, Apocynaceae, responsible for the cytotoxicity, using several cell lines. Cytotoxicity was correlated with the season the part of the plant, and the major compounds were assessed. The ethanol extracts of leaves, stems and roots obtained at different seasons were evaluated in the human erythromyeloblastoid leukemia cell line (K562). Subsequently the ethanol extracts and dichloromethane fractions collected in winter were evaluated in mouse fibroblast cell line (Mus musculus) (L929), cervix adenocarcinoma (HeLa), human pre-B leukemia (Nalm6), as well as K562 cell line. The compounds plumericin, plumieride and ursolic acid isolated from ethanol extracts of the stems were evaluated in the same cell lines, as well as on breast adenocarcinoma cell line (MCF-7), and Mus musculus skin melanoma cell line (B16F10). The chromatographic profiles of the dichloromethane fractions were obtained by high performance liquid chromatography. The results revealed that the season during which A. schottii was collected, and the part of the plant analyzed, influence the cytotoxicity on the K562 cells tested. On the other hand the dichloromethane fractions, mainly from the stems and roots, are responsible for the cytoxicity on the cells tested. These results may be associated with the seasonal variation of plumericin in these parts of the plant. This information is in accordance with the HPLC analysis. The results clearly show the potential for the phytotherapeutic use of this species, and suggest that the cytotoxic activity observed may be due to the presence of plumericin, or to minor compounds not yet identified. The seasonal influence on the production of secondary metabolites was verified.

Application of artificial neural network (ANN) in chemical engineering with special reference to drying process is discussed in the paper. Two types of networks: RBF and MLP, which are important for the description of a process dynamics, are presented. As an example drying and degradation of ascorbic acid in agricultural products are considered. The final conclusion supported with experimental data states that the type of ANN should be carefully selected because the real capability of the ANN model for a given dynamic problem is expressed in recurrent working mode.