DOTA-based Ga(III) and Gd(III) chelates for medical imaging (PET, SPECT and MRI)

PhD Thesis in Sciences Specialization in Chemistry

Poly(vinylidene fluoride-trifluoroethylene)/NAY zeolite hybrid membranes as a drug release platform applied to ibuprofen release

Poly(vinylidene fluoride-trifluoroethylene)/NaY zeolite composite membranes were prepared by solvent casting and evaluated as a suitable drug release platform through the evaluation of loading and release of ibuprofen. The membranes were characterized at the morphological, structural and mechanical levels. The 1H-NMR spectra indicate that only the membranes with 16 and 32 % of NaY were useful for IBU encapsulation and the drug release was followed by UV-Vis spectroscopy. The release profile is independent of the zeolite content and can be described by the Korsmeyer-Peppas model. The membrane with 32 % zeolite content releases more than double IBU amount when compared with the membrane with 16 % showing that zeolite content allows tailoring membrane drug release content for specific applications. The drug release platform developed in this work is suitable for other drugs and applications.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.