Efficient Synthesis of Glaziovianin A Isoflavone Series from Dill and Parsley Extracts and Their in Vitro/in Vivo Antimitotic Activity.

A concise six-step protocol for the synthesis of isoflavone glaziovianin A (GVA) and its alkoxyphenyl derivatives 9 starting with readily available plant metabolites from dill and parsley seeds was developed. The reaction sequence involved an efficient conversion of the key intermediate epoxides 7 into the respective ß-ketoaldehydes 8 followed by their Cu(I)-mediated cyclization into the target series 9. The biological activity of GVA and its derivatives was evaluated using a panel of seven human cancer cell lines and an in vivo sea urchin embryo assay. Both screening platforms confirmed the antimitotic effect of the parent GVA (9cg) and its alkoxy derivatives. Structure-activity relationship studies suggested that compounds 9cd and 9cf substituted with trimethoxy- and dillapiol-derived B-rings, respectively, were less active than the parent 9cg. Of the evaluated human cancer cell lines, the A375 melanoma cell line was the most sensitive to the tested molecules. Notably, the target compounds were not cytotoxic against human peripheral blood mononuclear cells up to 10 µM concentration. Phenotypic readouts from the sea urchin assay unequivocally suggest a direct microtubule-destabilizing effect of isoflavones 9cg, 9cd, and 9cf.

Computational insight into the chemical space of plant growth regulators.

An enormous technological progress has resulted in an explosive growth in the amount of biological and chemical data that is typically multivariate and tangled in structure. Therefore, several computational approaches have mainly focused on dimensionality reduction and convenient representation of high-dimensional datasets to elucidate the relationships between the observed activity (or effect) and calculated parameters commonly expressed in terms of molecular descriptors. We have collected the experimental data available in patent and scientific publications as well as specific databases for various agrochemicals. The resulting dataset was then thoroughly analyzed using Kohonen-based self-organizing technique. The overall aim of the presented study is to investigate whether the developed in silico model can be applied to predict the agrochemical activity of small molecule compounds and, at the same time, to offer further insights into the distinctive features of different agrochemical categories. The preliminary external validation with several plant growth regulators demonstrated a relatively high prediction power (67%) of the constructed model. This study is, actually, the first example of a large-scale modeling in the field of agrochemistry.