Tatjana L Djaković-Sekulić1, Adam Smoliński. 1. Department of Chemistry, Biochemistry and Environmental Protection, Faculty of Sciences, University of Novi Sad, Novi Sad, Republic of Serbia. tatjana.djakovic-sekulic@dh.uns.ac.rs
Abstract
OBJECTIVE: In this study 14 newly synthesized s-triazine derivatives were investigated by means of reversed-phase thin-layer chromatography (TLC) on C-18 stationary and five different mobile phases: acetone-water, acetonitrile-water, methanol-water, 2-propanol-water, and tetrahydrofuran-water. METHODS: Principal component analysis (PCA) was performed to explore and visualize similarities and differences among the compounds and among the mobile phases. Observations from the PCA were supported using hierarchical cluster analysis (HCA). RESULTS: Physicochemical parameters that are significant for activity, that is, absorption, distribution, and bonding for different receptors in target tissues were calculated. CONCLUSION: Highly predictive models, describing quantitative relationships between chromatographic retention and parameters that influence activity, were obtained using partial least squares (PLS) method.
OBJECTIVE: In this study 14 newly synthesized s-triazine derivatives were investigated by means of reversed-phase thin-layer chromatography (TLC) on C-18 stationary and five different mobile phases: acetone-water, acetonitrile-water, methanol-water, 2-propanol-water, and tetrahydrofuran-water. METHODS: Principal component analysis (PCA) was performed to explore and visualize similarities and differences among the compounds and among the mobile phases. Observations from the PCA were supported using hierarchical cluster analysis (HCA). RESULTS: Physicochemical parameters that are significant for activity, that is, absorption, distribution, and bonding for different receptors in target tissues were calculated. CONCLUSION: Highly predictive models, describing quantitative relationships between chromatographic retention and parameters that influence activity, were obtained using partial least squares (PLS) method.