Development of a Simple and Effective Lipid-A Antagonist Based on Computational Prediction.

Sepsis is a serious medical condition characterized by bacterial infection and a subsequent massive systemic inflammatory response. In an effort to identify compounds that block lipopolysaccharide (LPS)-induced inflammation reported herein is the development of simple Lipid-A analogues that lack a disaccharide core yet still possess potent antagonistic activity against LPS. The structure of the new lead compound was developed based on predictive computational experiments. LPS antagonism by the lead compound was not straightforward, and a biphasic effect was observed suggesting a possibility of more than one binding site. An IC50 value of 13 nM for the new compound was determined for the possible high affinity site. The combination of computational, synthetic, and biological studies revealed new structural determinants of these simplified analogues. It is expected that the acquired information will aid future design of LPS targeting glycopharmaceuticals.

Synthesis and structure-activity relationships of 3,4,5-trisubstituted-1,2,4-triazoles: high affinity and selective somatostatin receptor-4 agonists for Alzheimer's disease treatment.

Somatostatin receptor-4 (SST4) is highly expressed in brain regions affiliated with learning and memory. SST4 agonist treatment may act to mitigate Alzheimer's disease (AD) pathology. An integrated approach to SST4 agonist lead optimization is presented herein. High affinity and selective agonists with biological efficacy were identified through iterative cycles of a structure-based design strategy encompassing computational methods, chemistry, and preclinical pharmacology. 1,2,4-Triazole derivatives of our previously reported hit (4) showed enhanced SST4 binding affinity, activity, and selectivity. Thirty-five compounds showed low nanomolar range SST4 binding affinity, 12 having a K i < 1 nM. These compounds showed >500-fold affinity for SST4 as compared to SST2A. SST4 activities were consistent with the respective SST4 binding affinities (EC50 < 10 nM for 34 compounds). Compound 208 (SST4 K i = 0.7 nM; EC50 = 2.5 nM; >600-fold selectivity over SST2A) display a favorable physiochemical profile, and was advanced to learning and memory behavior evaluations in the senescence accelerated mouse-prone 8 model of AD-related cognitive decline. Chronic administration enhanced learning with i.p. dosing (1 mg kg-1) compared to vehicle. Chronic administration enhanced memory with both i.p. (0.01, 0.1, 1 mg kg-1) and oral (0.01, 10 mg kg-1) dosing compared to vehicle. This study identified a novel series of SST4 agonists with high affinity, selectivity, and biological activity that may be useful in the treatment of AD.

Decoding Protein-protein Interactions: An Overview.

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

The compromise of virtual screening and its impact on drug discovery.

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.