Desarrollo de un modelo predictivo mediante fingerprint cromatográfico para evaluar el potencial antibacteriano para el género Hypericum

Abstract

The genus Hypericum includes 469 species, which possess diverse bioactivities, including antibacterial activity. The present study intend to establish a predictive model of antibacterial activity for species of the genus Hypericum from the chromatographic fingerprint of H. laricifolium and H. quitense and use data of IC50 against MRSA strains, using multivariate analysis tools. The tests were carried out on 29 extracts of H. laricifolium and 20 extracts of H. quitense. The predictive model was tested on 10 extracts of H. decandrum. Genetic identification of H. quitense and H. decandrum was performed using the genetic barcode technique. The loci rbcL and ITS1 were sequenced. Extracts of the aerial parts of the species under study were obtained by means of supercritical fluids using as CO2 solvent with 10% of ethanol. The evaluation of antibacterial activity was carried out using the microdilution technique in broth against MRSA (strain ATCC BAA 44). The HPLC-DAD method was developed to separate the components present in the species under study and to obtain their chromatographic fingerprint using optimized chromatographic conditions. Samples were analyzed at four wavelengths, resulting at 254 nm the best for analysis. To create the multivariate regression model we used Partial least squares. Using the regression coefficients of the model, the predictor of antibacterial activity was established. The model developed did not have good predictive properties, which can be attributed to the different chemical composition among the species under study.