A Systematic Approach to Identify Biased Agonists of the Apelin Receptor through High-Throughput Screening.

Biased agonists are defined by their ability to selectively activate distinct signaling pathways of a receptor, and they hold enormous promise for the development of novel drugs that specifically elicit only the desired therapeutic response and avoid potential adverse effects. Unfortunately, most high-throughput screening (HTS) assays are designed to detect signaling of G protein-coupled receptors (GPCRs) downstream of either G protein or ß-arrestin-mediated signaling but not both. A comprehensive drug discovery program seeking biased agonists must employ assays that report on the activity of each compound at multiple discrete pathways, particularly for HTS campaigns. Here, we report a systematic approach to the identification of biased agonists of human apelin receptor (APJ). We synthesized 448 modified versions of apelin and screened them against a cascade of cell-based assays, including intracellular cAMP and ß-arrestin recruitment to APJ, simultaneously. The screen yielded potent and highly selective APJ agonists. Representative hits displaying preferential signaling via either G-protein or ß-arrestin were subjected to a battery of confirmation assays. These biased agonists will be useful as tools to probe the function and pharmacology of APJ and provide proof of concept of our systematic approach to the discovery of biased ligands. This approach is likely universally applicable to the search for biased agonists of GPCRs.

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility.

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicity library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation.

Total synthesis of stemaphylline N-oxide and related C9a-epimeric analogues.

Winning the relay: The first total synthesis of stemaphylline N-oxide has been completed utilizing a bistandem relay ring-closing-metathesis (RRCM) strategy, necessitated by the conformation of the requisite tetraene. This effort also gave unnatural 9a-epi-stemaphylline and 9a-epi-stemaphylline N-oxide.

A novel multi-modal drug repurposing approach for identification of potent ACK1 inhibitors.

Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ∼1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.

From laptop to benchtop to bedside: structure-based drug design on protein targets.

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.

Non inflammatory boronate based glucose-responsive insulin delivery systems.

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L-40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger.

3-Formylchromone interacts with cysteine 38 in p65 protein and with cysteine 179 in IκBα kinase, leading to down-regulation of nuclear factor-κB (NF-κB)-regulated gene products and sensitization of tumor cells.

3-Formylchromone (3-FC) has been associated with anticancer potential through a mechanism yet to be elucidated. Because of the critical role of NF-κB in tumorigenesis, we investigated the effect of this agent on the NF-κB activation pathway. Whether activated by inflammatory agents (such as TNF-α and endotoxin) or tumor promoters (such as phorbol ester and okadaic acid), 3-FC suppressed NF-κB activation. It also inhibited constitutive NF-κB expressed by most tumor cells. This activity correlated with sequential inhibition of IκBα kinase (IKK) activation, IκBα phosphorylation, IκBα degradation, p65 phosphorylation, p65 nuclear translocation, and reporter gene expression. We found that 3-FC inhibited the direct binding of p65 to DNA, and this binding was reversed by a reducing agent, thus suggesting a role for the cysteine residue. Furthermore, mutation of Cys38 to Ser in p65 abolished this effect of the chromone. This result was confirmed by a docking study. 3-FC also inhibited IKK activation directly, and the reducing agent reversed this inhibition. Furthermore, mutation of Cys179 to Ala in IKK abolished the effect of the chromone. Suppression of NF-κB activation led to inhibition of anti-apoptotic (Bcl-2, Bcl-xL, survivin, and cIAP-1), proliferative (cyclin D1 and COX-2), invasive (MMP-9 and ICAM-1), and angiogenic (VEGF) gene products and sensitization of tumor cells to cytokines. Thus, this study shows that modification of cysteine residues in IKK and p65 by 3-FC leads to inhibition of the NF-κB activation pathway, suppression of anti-apoptotic gene products, and potentiation of apoptosis in tumor cells.

Design, synthesis, and diversification of 3,5-substituted enone library.

This paper describes the synthesis of a 300 member library of 3,5-substituted enones. The synthesis starts with 6 different bromoenones that are accessed from the corresponding 1,3 diones. These bromides are then diversified by Suzuki coupling with a variety of aromatic and vinyl boronic acids. Additionally a small series of triazoles was synthesized by a Sonogashira coupling reaction dipolar cycloaddition sequence. The library was analyzed by principal component analysis to examine its diversity.

Docking methods for structure-based library design.

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models.

G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the ß(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.

Ligand-steered modeling and docking: A benchmarking study in class A G-protein-coupled receptors.

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.

2,3-Dihydro-1-benzofuran derivatives as a series of potent selective cannabinoid receptor 2 agonists: design, synthesis, and binding mode prediction through ligand-steered modeling.

We recently discovered and reported a series of N-alkyl-isatin acylhydrazone derivatives that are potent cannabinoid receptor 2 (CB(2)) agonists. In an effort to improve the druglike properties of these compounds and to better understand and improve the treatment of neuropathic pain, we designed and synthesized a new series of 2,3-dihydro-1-benzofuran derivatives bearing an asymmetric carbon atom that behave as potent selective CB(2) agonists. We used a multidisciplinary medicinal chemistry approach with binding mode prediction through ligand-steered modeling. Enantiomer separation and configuration assignment were carried out for the racemic mixture for the most selective compound, MDA7 (compound 18). It appeared that the S enantiomer, compound MDA104 (compound 33), was the active enantiomer. Compounds MDA42 (compound 19) and MDA39 (compound 30) were the most potent at CB(2). MDA42 was tested in a model of neuropathic pain and exhibited activity in the same range as that of MDA7. Preliminary ADMET studies for MDA7 were performed and did not reveal any problems.

Homology modeling in drug discovery: current trends and applications.

As structural genomics (SG) projects continue to deposit representative 3D structures of proteins, homology modeling methods will play an increasing role in structure-based drug discovery. Although computational structure prediction methods provide a cost-effective alternative in the absence of experimental structures, developing accurate enough models still remains a big challenge. In this contribution, we report the current developments in this field, discuss in silico modeling limitations, and review the successful application of this technique to different stages of the drug discovery process.

6-Methoxy-N-alkyl isatin acylhydrazone derivatives as a novel series of potent selective cannabinoid receptor 2 inverse agonists: design, synthesis, and binding mode prediction.

Recently, we discovered and reported a series of N-alkyl isatin acylhydrazone derivatives that are potent CB2 agonists. Here, we describe a novel series of selective CB2 inverse agonists resulting from introduction of a methoxy moiety in position 6 of the isatin scaffold. These novel 6-methoxy-N-alkyl isatin acylhydrazone derivatives exhibited high CB2 functional activity and selectivity at human CB2. Compound 16 (MDA77) had high activity (EC(50) = 5.82 nM) at CB2 and no activity at CB1. Compound 15 (MDA55) (K(i) = 89.9 nM, EC(50) = 88.2 nM at CB2) inhibited the effect of compound 1 (MDA7), a selective CB2 agonist, in an animal model of neuropathic pain. The molecular modeling study presented here represents a first study of CB2 based on the structure of beta(2)-adrenergic receptor. A ligand-based homology model of the CB2 binding site was developed, and on the basis of our results, we propose a general binding mode for this class of inverse agonists with CB2.

High-throughput and in silico screenings in drug discovery.

BACKGROUND: In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. OBJECTIVE: Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. CONCLUSION: Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.