Exploring structure-activity relationships of tricyclic farnesyltransferase inhibitors using ECLiPS libraries

The development of structure-activity relationships (SARs) relating to the function of a biological protein is often a long and protracted undertaking when using an iterative medicinal chemistry approach. High throughput screening of ECLiPS (Encoded Combinatorial Libraries on Polymeric Support) libraries can be used to simplify this process. In this paper, we illustrate how a large ECLiPS library of 26,908 compounds, based on a tricyclic core structure, was used to define a multitude of SARs for the oncogenic target, farnesyltransferase (FTase). This library, FT-2, was prepared using a split-and-pool approach in which small molecules are constructed on resin that contains tag/linker constructs to track the synthetic process [1-5] Highly defined SARs were produced from this screen that enhanced our understanding of FTase binding site interactions. The pivotal compounds culled from this library were potent in both cell-free and cell-based FTase assays, selective over the closely related enzyme, geranylgeranyltransferase I (GGTase I), and inhibited the adherent-independent growth of a transformed cell line.     

Preparation of compounds in the new dipyrrolo[3,4-b:3′,4′-e]-pyridine series from 1-benzylidene-2,3-dioxopyrrolidines. A variation of the hantzsch synthesis

Treatment of easily prepared l-substituted-4-benzylidene-2,3-dioxopyrrolidines with ammonium formate produces 1,2,4,6,7,8-hexahydro-2,6-disubstituted-8-aryldipyrrolo [3,4-b:3′,4′-e]pyridine-3,5-diones (II), usually in yields of 50 to 60%. Aromatization of the dihydropyridine ring of the hexahydroderivatives II yields corresponding 1,2,6,7-tetrahydro-2,6-disubstituted-8-aryldipyrrolo[3,4-b:3′,4′-e]pyridine-3,5-diones (III). These compounds appear to be the first to incorporate the dipyrrolo[3,4-b:3′,4′-e]pyridine ring system.     

Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

Background  

Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5–4 Hz, 4–8 Hz, 8–10 Hz, 10–13 Hz, 13–30 Hz and 30–45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks.