Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors

Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work assist in finding selective subspaces in chemical space occupied by particular groups of SphK inhibitors. A combination of a genetic algorithm (GA) and a counter propagation artificial neural network (CPANN) was utilized to select the most efficient subsets of the molecular descriptors. The optimized models in this work reasonably separate active inhibitors of SphK1 from active SphK2 inhibitors. Generally, the CPANN models in this work were used to classify the compounds according to their therapeutic targets and activities. The simplicity of the chosen descriptors and their relative importance sheds some light on the structural features necessary to induce selective inhibitory activity to the studied molecules. The areas under the receiver operating characteristic (ROC) curves for the GA-CPANN models in this work were 0.934 and 0.922 for active SphK1 and SphK2 inhibitors, respectively. Generally, the results in this work suggest some important molecular features and pharmacophores that could help medicinal chemists develop selective and potent SphK inhibitors.

Therapeutic Strategies and Pharmacological Tools Influencing S1P Signaling and Metabolism

During the last two decades the study of the sphingolipid anabolic, catabolic, and signaling pathways has attracted enormous interest. Especially the introduction of fingolimod into market as first p.o. therapeutic for the treatment of multiple sclerosis has boosted this effect. Although the complex regulation of sphingosine-1-phosphate (S1P) and other catabolic and anabolic sphingosine-related compounds is not fully understood, the influence on different (patho)physiological states from inflammation to cytotoxicity as well as the availability of versatile pharmacological tools that represent new approaches to study these states are described. Here, we have summarized various aspects concerning the many faces of sphingolipid function modulation by different pharmacological tools up to clinical candidates. Due to the immense heterogeneity of physiological or pharmacological actions and complex cross regulations, it is difficult to predict their role in upcoming therapeutic approaches. Currently, inflammatory, immunological, and/or antitumor aspects are discussed.

Assessing the ligand selectivity of sphingosine kinases using molecular dynamics and MM-PBSA binding free energy calculations

The dynamic balance of sphingolipids plays a crucial role in diverse biological processes such as mitogenesis, cell migration and angiogenesis. Sphingosine kinases (SKs) including SK1 and SK2 phosphorylate sphingosine to sphingosine 1-phosphate (S1P), and control the critical balance. SK1 overexpression was reported to increase cell survival and proliferation. Although several SK1 selective inhibitors have been reported, detailed analysis toward their selectivity to understand the molecular mechanism has not been performed to our knowledge. Herein, the crystal structure of SK1 and a homology model of SK2 were used to dock five inhibitors (1, 2, 3, 4 and 5). Protein-ligand complexes were then subjected to a molecular dynamics study and MM-PBSA binding free energy calculations. By analyzing the binding model of these inhibitors, we found that residues ILE170, PHE188 and THR192 in SK1 significantly contribute a favorable binding energy to the selectivity.