In vitro and in vivo studies of temozolomide loading in zeolite structures as drug delivery systems for glioblastoma

Zeolites Y (faujasite) and MOR (mordonite) were used as hosts for temozolomide (TMZ), a current good-standard chemotherapeutic agent used in the treatment of glioblastoma brain tumors. TMZ was loaded into zeolites by liquid-phase adsorption at controlled pH. FTIR, 1H NMR, MS, SEM, UV/vis and chemical analysis demonstrated the successful loading of TMZ into zeolite hosts. The hydrolysis of TMZ in MTIC (TMZ metabolite) after the preparation of drug delivery systems (DDS) was observed in simulated body fluid. The effect of zeolites and DDS were evaluated on the viability of glioblastoma cell lines. Unloaded Y zeolite presented toxicity to cancer cells in contrast to MOR. In accordance, the best results in potentiation of the TMZ effect was obtained with MOR. We found that mordonite loaded with 0.026 mmol of TMZ was able to decrease the half maximal inhibitory concentrations (IC50) at least 3-fold in comparison to free temozolomide both in vitro and in vivo.

Development of a poly(L-Lactic acid) based drug release system

Dissertação de mestrado em Biofísica e Bionanossistemas

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.