No abstract

ATP-sensitive potassium channels (K(ATP) channels) are heteromeric complexes of pore-forming inwardly rectifying potassium channel subunits and regulatory sulfonylurea receptor subunits. K(ATP) channels were identified in a variety of tissues including muscle cells, pancreatic beta-cells, and various neurons. They are regulated by the intracellular ATP/ADP ratio; ATP induces channel inhibition and MgADP induces channel opening. Functionally, K(ATP) channels provide a means of linking the electrical activity of a cell to its metabolic state. Shortening of the cardiac action potential, smooth muscle relaxation, inhibition of both insulin secretion, and neurotransmitter release are mediated via K(ATP) channels. Given their many physiological functions, K(ATP) channels represent promising drug targets. Sulfonylureas like glibenclamide block K(ATP) channels; they are used in the therapy of type 2 diabetes. Openers of K(ATP) channels (KCOs), for example, relax smooth muscle and induce hypotension. KCOs are chemically heterogeneous and include as different classes as the benzopyrans, cyanoguanidines, thioformamides, thiadiazines, and pyridyl nitrates. Examples for new chemical entities more recently developed as KCOs include cyclobutenediones, dihydropyridine related structures, and tertiary carbinols.

Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H(3) receptor (H(3)R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H(3)R binders from inactive molecules were successfully used for the identification of new H(3)R ligands. Prospective virtual screening of 156,090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H(3)R ligands that offer new potential starting points for the design of H(3)R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.

Lipophilicity is a major determinant of pharmacokinetic and pharmacodynamic properties of drug molecules. Correspondingly, there is great interest in medicinal chemistry in developing methods of deriving the quantitative descriptor of lipophilicity, the partition coefficient P, from molecular structure. Roughly, methods for calculating log P can be divided into two major classes: Substructure approaches have in common that molecules are cut into atoms (atom contribution methods) or groups (fragmental methods); summing the single-atom or fragmental contributions (supplemented by applying correction rules in the latter case) results in the final log P. Whole molecule approaches inspect the entire molecule; they use for instance molecular lipophilicity potentials (MLP), topological indices or molecular properties to quantify log P. In this review, representative members of substructure and whole molecule approaches for calculating log P are described; their advantages and shortcomings are discussed. Finally, the predictive power of some calculation methods is compared and a scheme for classifying calculation methods is proposed.

The predictive power of 14 calculation procedures for molecular lipophilicity is checked by comparing with reliable experimental logP values from the literature. The database of 138 test compounds comprises 90 simple organic structures and 48 chemically heterogeneous drug molecules (P-blockers, class I antiarrhythmics and neuroleptics).The present investigations lead us to conclude that the predictive power of the calculation procedures is significantly better for simple organic molecules than for chemically heterogeneous drug structures. The calculation procedures should be arranged in three groups with significantly differing predictive power: fragmental > atom-based > conformation-dependent approaches.