[An editorial to] Recent Advances on Knowledge Discovery and Business Intelligence

Special issue guest editorial, June, 2015.

Synthesis, antiangiogenesis evaluation and molecular docking studies of 1-Aryl-3-[(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas: Discovery of a new substitution pattern for type II VEGFR-2 Tyr kinase inhibitors

The synthesis and biological evaluation of novel 1-aryl-3-[2-, 3- or 4-(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas 3, 4 and 5 as VEGFR-2 tyrosine kinase inhibitors, are reported. The 1-aryl-3-[3-(thieno[3,2-b]pyridin-7-ylthio)phenyl]ureas 4a-4h, with the arylurea in the meta position to the thioether, showed the lowest IC50 values in enzymatic assays (10-206 nM), the most potent compounds 4d-4h (IC50 10-28 nM) bearing hydrophobic groups (Me, F, CF3 and Cl) in the terminal phenyl ring. A convincing rationalization was achieved for the highest potent compounds 4 as type II VEGFR-2 inhibitors, based on the simultaneous presence of: (1) the thioether linker and (2) the arylurea moiety in the meta position. For compounds 4, significant inhibition of Human Umbilical Vein Endothelial Cells (HUVECs) proliferation (BrdU assay), migration (wound-healing assay) and tube formation were observed at low concentrations. These compounds have also shown to increase apoptosis using the TUNEL assay. Immunostaining for total and phosphorylated (active) VEGFR-2 was performed by Western blotting. The phosphorylation of the receptor was significantly inhibited at 1.0 and 2.5 microM for the most promising compounds. Altogether, these findings point to an antiangiogenic effect in HUVECs.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.