Design of Gut-Restricted Thiazolidine Agonists of G Protein-Coupled Bile Acid Receptor 1 (GPBAR1, TGR5).

Bile acid signaling and metabolism in the gastrointestinal tract have wide-ranging influences on systemic disease. G protein-coupled bile acid receptor 1 (GPBAR1, TGR5) is one of the major effectors in bile acid sensing, with demonstrated influence on metabolic, inflammatory, and proliferative processes. The pharmacologic utility of TGR5 agonists has been limited by systemic target-related effects such as excessive gallbladder filling and blockade of gallbladder emptying. Gut-restricted TGR5 agonists, however, have the potential to avoid these side effects and consequently be developed into drugs with acceptable safety profiles. We describe the discovery and optimization of a series of gut-restricted TGR5 agonists that elicit a potent response in mice, with minimal gallbladder-related effects. The series includes 12 (TGR5 EC50: human, 143 nM; mouse, 1.2 nM), a compound with minimal systemic availability that may have therapeutic value to patients with type 2 diabetes mellitus, nonalcoholic steatohepatitis, or inflammatory bowel disease.

Parsimonious discovery of synergistic drug combinations.

Combination therapies that enhance efficacy or permit reduced dosages to be administered have seen great success in a variety of therapeutic applications. More fundamentally, the discovery of epistatic pathway interactions not only informs pharmacologic intervention but can be used to better understand the underlying biological system. There is, however, no systematic and efficient method to identify interacting activities as candidates for combination therapy and, in particular, to identify those with synergistic activities. We devised a pooled, self-deconvoluting screening paradigm for the efficient comprehensive interrogation of all pairs of compounds in 1000-compound libraries. We demonstrate the power of the method to recover established synergistic interactions between compounds. We then applied this approach to a cell-based screen for anti-inflammatory activities using an assay for lipopolysaccharide/interferon-induced acute phase response of a monocytic cell line. The described method, which is >20 times as efficient as a naïve approach, was used to test all pairs of 1027 bioactive compounds for interleukin-6 suppression, yielding 11 pairs of compounds that show synergy. These 11 pairs all represent the same two activities: ß-adrenergic receptor agonists and phosphodiesterase-4 inhibitors. These activities both act through cyclic AMP elevation and are known to be anti-inflammatory alone and to synergize in combination. Thus we show proof of concept for a robust, efficient technique for the identification of synergistic combinations. Such a tool can enable qualitatively new scales of pharmacological research and chemical genetics.

Identification and characterization of a small molecule antagonist of human VPAC(2) receptor.

The neuropeptides vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating peptide (PACAP) and their class II G protein-coupled receptors VPAC(1), VPAC(2), and PAC(1) play important roles in human physiology. No small molecule modulator has ever been reported for the VIP/PACAP receptors, and there is a lack of specific VPAC(2) antagonists. Via high-throughput screening of 1.67 million compounds, we discovered a single small molecule antagonist of human VPAC(2), compound 1. Compound 1 inhibits VPAC(2)-mediated cAMP accumulation with an IC(50) of 3.8 microM and the ligand-activated beta-arrestin2 binding with an IC(50) of 2.3 microM. Compound 1 acts noncompetitively in Schild analysis. It is a specific VPAC(2) antagonist with no detectable agonist or antagonist activities on VPAC(1) or PAC(1). Compound 2, a close structural analog of compound 1, was also found to be weakly active. To our surprise, compound 1 is completely inactive on the closely related mouse VPAC(2). Chimera experiments indicate that compounds 1 and 2 bind to the seven transmembrane (7TM) region of the receptor as opposed to the N-terminal extracellular domain, where the natural ligand binds. Compound 1, being the first small molecular antagonist that is specific for VPAC(2), and the only VPAC(2) antagonist molecule known to date that allosterically interacts with the 7TM region, will be a valuable tool in further study of VPAC(2) and related receptors. This study also highlights the opportunities and challenges facing small molecule drug discovery for class II peptide G protein-coupled receptors.

Time-resolved fluorescence resonance energy transfer and surface plasmon resonance-based assays for retinoid and transthyretin binding to retinol-binding protein 4.

Retinol-binding protein-4 (RBP4) is an emerging candidate drug target for type 2 diabetes and lipofuscin-mediated macular degeneration. The retinoic acid derivative fenretinide (N-(4-hydroxyphenyl) retinamide; HPR) exerts therapeutic effects in mouse models of obesity, diabetes, and Stargardt's disease by targeting RBP4. Fenretinide competes with retinoids for RBP4 binding, disrupts RBP4-transthyretin (TTR) complexes, and results in urinary secretion of RBP4 and systemic depletion of retinol. To enable the search for nonretinoid molecules with fenretinide-like activities we developed a HTS-compatible homogeneous TR-FRET assay monitoring the displacement of retinoic acid derivatives from RBP4 in high-density 384-well and 1536-well microtiter plate formats. The retinoid displacement assay proved to be highly sensitive and robust after miniaturization with IC(50)s for fenretinide and retinol ranging around 50 and 100 nM, respectively, and Z'-factors around 0.7. In addition, a surface plasmon resonance (SPR)-based secondary assay was developed to interrogate small molecule RBP4 binders for their ability to modulate the RBP4-TTR interaction. Finally, a 1.6 x 10(6) compound library was screened against the retinoid displacement assay. Several potent retinoid competitors were identified that also appeared to disrupt RBP4-TTR complexes. Some of these compounds could potentially serve as valuable tools to further probe RBP4 biology in the future.

In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen.

The growing resistance to current first-line antimalarial drugs represents a major health challenge. To facilitate the discovery of new antimalarials, we have implemented an efficient and robust high-throughput cell-based screen (1,536-well format) based on proliferation of Plasmodium falciparum (Pf) in erythrocytes. From a screen of approximately 1.7 million compounds, we identified a diverse collection of approximately 6,000 small molecules comprised of >530 distinct scaffolds, all of which show potent antimalarial activity (<1.25 microM). Most known antimalarials were identified in this screen, thus validating our approach. In addition, we identified many novel chemical scaffolds, which likely act through both known and novel pathways. We further show that in some cases the mechanism of action of these antimalarials can be determined by in silico compound activity profiling. This method uses large datasets from unrelated cellular and biochemical screens and the guilt-by-association principle to predict which cellular pathway and/or protein target is being inhibited by select compounds. In addition, the screening method has the potential to provide the malaria community with many new starting points for the development of biological probes and drugs with novel antiparasitic activities.

Learning from the data: mining of large high-throughput screening databases.

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.

An efficient rapid system for profiling the cellular activities of molecular libraries.

Rapid quantitative methods for characterizing small molecules, peptides, proteins, or RNAs in a broad array of cellular assays would allow one to discover new biological activities associated with these molecules and also provide a more comprehensive profile of drug candidates early in the drug development process. Here we describe a robotic system, termed the automated compound profiler, capable of both propagating a large number of cell lines in parallel and assaying large collections of molecules simultaneously against a matrix of cellular assays in a highly reproducible manner. To illustrate its utility, we have characterized a set of 1,400 kinase inhibitors in a panel of 35 activated tyrosine-kinase-dependent cellular assays in dose-response format in a single experiment. Analysis of the resulting multidimensional dataset revealed subclusters of both inhibitors and kinases with closely correlated activities. The approach also identified activities for the p38 inhibitor BIRB796 and the dual src/abl inhibitor BMS-354825 and exposed the expected side activities for Glivec/STI571, including cellular inhibition of c-kit and platelet-derived growth factor receptor. This methodology provides a powerful tool for unraveling the cellular biology and molecular pharmacology of both naturally occurring and synthetic chemical diversity.

Ontology recapitulates physiology.

High-content information experiments in the post-genomic era hold the promise of deciphering age-old questions in biology and new ones in the biomedical arena. In response, researchers are devising computationally intensive and novel strategies to extract answers from multidimensional data sets.