G-protein-coupled receptors (GPCRs) comprise a large protein family of significant past and current interest of pharmaceutical research. X-ray crystallography and molecular modeling combined with site-directed mutagenesis studies suggest that most family A GPCRs share a small-molecule binding site located in the outer part of the seven-transmembrane (7TM) bundle. Here we describe an automated method to derive sequence-derived three-dimensional (3D) pharmacophore models capturing the key elements for addressing this binding site by a small-molecule ligand. We have generated structure-based pharmacophore models from 10 homology models and 3 X-ray structures of receptor-ligand complexes. These 13 pharmacophores have been dissected into 35 different single-feature pharmacophore elements, each associated with a sequence motif or chemoprint, describing its molecular interaction partner(s) in the receptor. Subsequently, the protein sequences of 270 GPCRs have been searched for the presence of chemoprints and the appropriate single-feature pharmacophores have been assembled into three- to seven-feature 3D-pharmacophore models for each human family A GPCR. These models can be applied for virtual screening and for the design of subfamily directed libraries. A case study demonstrates the successful application of this approach for the identification of potent agonists for the complement component 3a receptor 1 (C3AR1) by virtual screening.

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Background: Technosphere® insulin (TI), an inhaled human insulin with a fast onset of action, provides a novel option for the control of prandial glucose. We used the University of Virginia (UVA)/Padova simulator to explore in-silico the potential benefit of different dosing regimens on postprandial glucose (PPG) control to support the design of further clinical trials. Tested dosing regimens included at-meal or postmeal dosing, or dosing before and after a meal (split dosing).Methods: Various dosing regimens of TI were compared among one another and to insulin lispro in 100 virtual type-1 patients. Individual doses were identified for each regimen following different titration rules. The resulting postprandial glucose profiles were analyzed to quantify efficacy and the risk for hypoglycemic events.Results: This approach allowed us to assess the benefit/risk for each TI dosing regimen and to compare results with simulations of insulin lispro. We identified a new titration rule for TI that could significantly improve the efficacy of treatment with TI.Conclusion: In-silico clinical trials comparing the treatment effect of different dosing regimens with TI and of insulin lispro suggest that postmeal dosing or split dosing of TI, in combination with an appropriate titration rule, can achieve a superior postprandial glucose control while providing a lower risk for hypoglycemic events than conventional treatment with subcutaneously administered rapid-acting insulin products.

The three-dimensional (3D) superimposition of molecules of one biological target reflecting their relative bioactive orientation is key for several ligand-based drug design studies (e.g., QSAR studies, pharmacophore modeling). However, with the lack of sufficient ligand-protein complex structures, an experimental alignment is difficult or often impossible to obtain. Several computational 3D alignment tools have been developed by academic or commercial groups to address this challenge. Here, we present a new approach, MARS (Multiple Alignments by ROCS-based Similarity), that is based on the pairwise alignment of all molecules within the data set using the tool ROCS (Rapid Overlay of Chemical Structures). Each pairwise alignment is scored, and the results are captured in a score matrix. The ideal superimposition of the compounds in the set is then identified by the analysis of the score matrix building stepwise a superimposition of all molecules. The algorithm exploits similarities among all molecules in the data set to compute an optimal 3D alignment. This alignment tool presented here can be used for several applications, including pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification of structural outliers, and addition of compounds to an already existing alignment. Case studies are shown, validating the 3D alignments for six different data sets.

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