Profile of the GSK published protein kinase inhibitor set across ATP-dependent and-independent luciferases: implications for reporter-gene assays

Current advances in the use of bioluminescence assays for drug discovery: an update of the last ten years

INTRODUCTION

Bioluminescence is a well-established optical detection technique widely used in several bioanalytical applications, including high-throughput and high-content screenings. Thanks to advances in synthetic biology techniques and deep learning, a wide portfolio of luciferases is now available with tuned emission wavelengths, kinetics, and high stability. These luciferases can be implemented in the drug discovery and development pipeline, allowing high sensitivity and multiplexing capability.

AREAS COVERED

This review summarizes the latest advancements of bioluminescent systems as toolsets in drug discovery programs for in vitro applications. Particular attention is paid to the most advanced bioluminescence-based technologies for drug screening over the past 10 years (from 2013 to 2023) such as cell-free assays, cell-based assays based on genetically modified cells, bioluminescence resonance energy transfer, and protein complementation assays in 2D and 3D cell models.

EXPERT OPINION

The availability of tuned bioluminescent proteins with improved emission and stability properties is vital for the development of bioluminescence assays for drug discovery, spanning from reporter gene technology to protein-protein techniques. Further studies, combining machine learning with synthetic biology, will be necessary to obtain new tools for sustainable and highly predictive bioluminescent drug discovery platforms.

High-throughput screening identifies cell cycle-associated signaling cascades that regulate a multienzyme glucosome assembly in human cells

We have previously demonstrated that human liver-type phosphofructokinase 1 (PFK1) recruits other rate-determining enzymes in glucose metabolism to organize multienzyme metabolic assemblies, termed glucosomes, in human cells. However, it has remained largely elusive how glucosomes are reversibly assembled and disassembled to functionally regulate glucose metabolism and thus contribute to human cell biology. We developed a high-content quantitative high-throughput screening (qHTS) assay to identify regulatory mechanisms that control PFK1-mediated glucosome assemblies from stably transfected HeLa Tet-On cells. Initial qHTS with a library of pharmacologically active compounds directed following efforts to kinase-inhibitor enriched collections. Consequently, three compounds that were known to inhibit cyclin-dependent kinase 2, ribosomal protein S6 kinase and Aurora kinase A, respectively, were identified and further validated under high-resolution fluorescence single-cell microscopy. Subsequent knockdown studies using small-hairpin RNAs further confirmed an active role of Aurora kinase A on the formation of PFK1 assemblies in HeLa cells. Importantly, all the identified protein kinases here have been investigated as key signaling nodes of one specific cascade that controls cell cycle progression in human cells. Collectively, our qHTS approaches unravel a cell cycle-associated signaling network that regulates the formation of PFK1-mediated glucosome assembly in human cells.

Kinase-Modulated Bioluminescent Indicators Enable Noninvasive Imaging of Drug Activity in the Brain

Aberrant kinase activity contributes to the pathogenesis of brain cancers, neurodegeneration, and neuropsychiatric diseases, but identifying kinase inhibitors that function in the brain is challenging. Drug levels in blood do not predict efficacy in the brain because the blood-brain barrier prevents entry of most compounds. Rather, assessing kinase inhibition in the brain requires tissue dissection and biochemical analysis, a time-consuming and resource-intensive process. Here, we report kinase-modulated bioluminescent indicators (KiMBIs) for noninvasive longitudinal imaging of drug activity in the brain based on a recently optimized luciferase-luciferin system. We develop an ERK KiMBI to report inhibitors of the Ras-Raf-MEK-ERK pathway, for which no bioluminescent indicators previously existed. ERK KiMBI discriminates between brain-penetrant and nonpenetrant MEK inhibitors, reveals blood-tumor barrier leakiness in xenograft models, and reports MEK inhibitor pharmacodynamics in native brain tissues and intracranial xenografts. Finally, we use ERK KiMBI to screen ERK inhibitors for brain efficacy, identifying temuterkib as a promising brain-active ERK inhibitor, a result not predicted from chemical characteristics alone. Thus, KiMBIs enable the rapid identification and pharmacodynamic characterization of kinase inhibitors suitable for treating brain diseases.

qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data

High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standard HTS data can be simply plotted as an x-y graph usually represented as % activity of a compound tested at a single concentration vs compound ID, whereas quantitative HTS (qHTS) data incorporates a third axis represented by concentration. By virtue of the additional data points arising from the compound titration and the incorporation of logistic fit parameters that define the concentration-response curve, such as EC50 and Hill slope, qHTS data has been challenging to display on a single graph. Here we provide a flexible solution to the rapid plotting of complete qHTS data sets to produce a 3-axis plot we call qHTS Waterfall Plots. The software described here can be generally applied to any 3-axis dataset and is available as both an R package and an R shiny application.

Developing a Kinase Chemogenomic Set: Facilitating Investigation into Kinase Biology by Linking Phenotypes to Targets

Advances in increasingly complex phenotypic screening with lower throughput have necessitated the screening of smaller more highly annotated sets. One such collection of compounds which has been recently assembled is the kinase chemogenomic set. This is a set of curated kinase inhibitors built upon previous iterations, PKIS and PKIS2, and donations from our partners. Each compound in the set has been carefully selected based on selectivity, potency, and kinome coverage. These compounds as a set have been made available to the scientific community, enabling phenotypic screens to identify kinases that drive novel biology. Additionally, the associated data deposited in the public domain have also been used to inform new inhibitor design. Further expansion of this set to complete kinome coverage will allow for a greater understanding of kinase biology and its role in disease.

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein–ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Defactinib inhibits PYK2 phosphorylation of IRF5 and reduces intestinal inflammation

Interferon regulating factor 5 (IRF5) is a multifunctional regulator of immune responses, and has a key pathogenic function in gut inflammation, but how IRF5 is modulated is still unclear. Having performed a kinase inhibitor library screening in macrophages, here we identify protein-tyrosine kinase 2-beta (PTK2B/PYK2) as a putative IRF5 kinase. PYK2-deficient macrophages display impaired endogenous IRF5 activation, leading to reduction of inflammatory gene expression. Meanwhile, a PYK2 inhibitor, defactinib, has a similar effect on IRF5 activation in vitro, and induces a transcriptomic signature in macrophages similar to that caused by IRF5 deficiency. Finally, defactinib reduces pro-inflammatory cytokines in human colon biopsies from patients with ulcerative colitis, as well as in a mouse colitis model. Our results thus implicate a function of PYK2 in regulating the inflammatory response in the gut via the IRF5 innate sensing pathway, thereby opening opportunities for related therapeutic interventions for inflammatory bowel diseases and other inflammatory conditions.

Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.

Phosphorylated WNK kinase networks in recoded bacteria recapitulate physiological function

Advances in genetic code expansion have enabled the production of proteins containing site-specific, authentic post-translational modifications. Here, we use a recoded bacterial strain with an expanded genetic code to encode phosphoserine into a human kinase protein. We directly encode phosphoserine into WNK1 (with-no-lysine [K] 1) or WNK4 kinases at multiple, distinct sites, which produced activated, phosphorylated WNK that phosphorylated and activated SPAK/OSR kinases, thereby synthetically activating this human kinase network in recoded bacteria. We used this approach to identify biochemical properties of WNK kinases, a motif for SPAK substrates, and small-molecule kinase inhibitors for phosphorylated SPAK. We show that the kinase inhibitors modulate SPAK substrates in cells, alter cell volume, and reduce migration of glioblastoma cells. Our work establishes a protein-engineering platform technology that demonstrates that synthetically active WNK kinase networks can accurately model cellular systems and can be used more broadly to target networks of phosphorylated proteins for research and discovery.

The Ste20-like kinase - a Jack of all trades?

Over the past 20 years, the Ste20-like kinase (SLK; also known as STK2) has emerged as a central regulator of cytoskeletal dynamics. Reorganization of the cytoskeleton is necessary for a plethora of biological processes including apoptosis, proliferation, migration, tissue repair and signaling. Several studies have also uncovered a role for SLK in disease progression and cancer. Here, we review the recent findings in the SLK field and summarize the various roles of SLK in different animal models and discuss the biochemical mechanisms regulating SLK activity. Together, these studies have revealed multiple roles for SLK in coupling cytoskeletal dynamics to cell growth, in muscle repair and in negative-feedback loops critical for cancer progression. Furthermore, the ability of SLK to regulate some systems appears to be kinase activity independent, suggesting that it may be an important scaffold for signal transduction pathways. These various findings reveal highly complex functions and regulation patterns of SLK in development and disease, making it a potential therapeutic target.

Inhibition of GCK-IV kinases dissociates cell death and axon regeneration in CNS neurons

Axonal injury plays a major role in many neurodegenerative diseases. The dual leucine zipper kinase (DLK) signaling pathway is a key regulator of axonal injury-induced neuronal cell death; however, DLK also has an important role in promoting axonal outgrowth. Therefore, inhibiting DLK as a therapeutic approach for neurodegenerative diseases is limited to a neuroprotective outcome without axon regeneration, prohibiting restoration of function. In fact, there are currently no strategies that provide long-term neuroprotection and axonal regeneration after injury. Here, we identified the germinal cell kinase four (GCK-IV) family of kinases as targets to maximize neuroprotection while promoting axon regeneration, making it an attractive therapeutic approach for a subset of neurodegenerative diseases.

Axon injury is a hallmark of many neurodegenerative diseases, often resulting in neuronal cell death and functional impairment. Dual leucine zipper kinase (DLK) has emerged as a key mediator of this process. However, while DLK inhibition is robustly protective in a wide range of neurodegenerative disease models, it also inhibits axonal regeneration. Indeed, there are no genetic perturbations that are known to both improve long-term survival and promote regeneration. To identify such a neuroprotective target, we conducted a set of complementary high-throughput screens using a protein kinase inhibitor library in human stem cell-derived retinal ganglion cells (hRGCs). Overlapping compounds that promoted both neuroprotection and neurite outgrowth were bioinformatically deconvoluted to identify specific kinases that regulated neuronal death and axon regeneration. This work identified the role of germinal cell kinase four (GCK-IV) kinases in cell death and additionally revealed their unexpected activity in suppressing axon regeneration. Using an adeno-associated virus (AAV) approach, coupled with genome editing, we validated that GCK-IV kinase knockout improves neuronal survival, comparable to that of DLK knockout, while simultaneously promoting axon regeneration. Finally, we also found that GCK-IV kinase inhibition also prevented the attrition of RGCs in developing retinal organoid cultures without compromising axon outgrowth, addressing a major issue in the field of stem cell-derived retinas. Together, these results demonstrate a role for the GCK-IV kinases in dissociating the cell death and axonal outgrowth in neurons and their druggability provides for therapeutic options for neurodegenerative diseases.

Characterisation of utrophin modulator SMT C1100 as a non-competitive inhibitor of firefly luciferase

Firefly luciferase (FLuc) is a powerful tool for molecular and cellular biology, and popular in high-throughput screening and drug discovery. However, FLuc assays have been plagued with positive and negative artefacts due to stabilisation and inhibition by small molecules from a range of chemical classes. Here we disclose Phase II clinical compound SMT C1100 for the treatment of Duchenne muscular dystrophy as an FLuc inhibitor (K_D of 0.40 ± 0.15 µM). Enzyme kinetic studies using SMT C1100 and other non-competitive inhibitors including resveratrol and NFκBAI4 identified previously undescribed modes of inhibition with respect to FLuc's luciferyl adenylate intermediate. Employing a photoaffinity strategy to identify SMT C1100's binding site, a photolabelled SMT C1100 probe instead underwent FLuc-dependent photooxidation. Our findings support novel binding sites on FLuc for non-competitive inhibitors.

Validation of the protein kinase PfCLK3 as a multistage cross-species malarial drug target

The requirement for next-generation antimalarials to be both curative and transmission-blocking necessitates the identification of previously undiscovered druggable molecular pathways. We identified a selective inhibitor of the Plasmodium falciparum protein kinase PfCLK3, which we used in combination with chemogenetics to validate PfCLK3 as a drug target acting at multiple parasite life stages. Consistent with a role for PfCLK3 in RNA splicing, inhibition resulted in the down-regulation of more than 400 essential parasite genes. Inhibition of PfCLK3 mediated rapid killing of asexual liver- and blood-stage P. falciparum and blockade of gametocyte development, thereby preventing transmission, and also showed parasiticidal activity against P. berghei and P. knowlesi Hence, our data establish PfCLK3 as a target for drugs, with the potential to offer a cure-to be prophylactic and transmission blocking in malaria.

Identification of a novel class of RIP1/RIP3 dual inhibitors that impede cell death and inflammation in mouse abdominal aortic aneurysm models

Receptor interacting protein kinase-1 and -3 (RIP1 and RIP3) are essential mediators of cell death processes and participate in inflammatory responses. Our group recently demonstrated that gene deletion of Rip3 or pharmacological inhibition of RIP1 attenuated pathogenesis of abdominal aortic aneurysm (AAA), a life-threatening degenerative vascular disease characterized by depletion of smooth muscle cells (SMCs), inflammation, negative extracellular matrix remodeling, and progressive expansion of aorta. The goal of this study was to develop drug candidates for AAA and other disease conditions involving cell death and inflammation. We screened 1141 kinase inhibitors for their ability to block necroptosis using the RIP1 inhibitor Necrostatin-1s (Nec-1s) as a selection baseline. Positive compounds were further screened for cytotoxicity and virtual binding to RIP3. A cluster of top hits, represented by GSK2593074A (GSK'074), displayed structural similarity to the established RIP3 inhibitor GSK'843. In multiple cell types including mouse SMCs, fibroblasts (L929), bone marrow derived macrophages (BMDM), and human colon epithelial cells (HT29), GSK'074 inhibited necroptosis with an IC50 of ~3 nM. Furthermore, GSK'074, but not Nec-1s, blocked cytokine production by SMCs. Biochemical analyses identified both RIP1 and RIP3 as the biological targets of GSK'074. Unlike GSK'843 which causes profound apoptosis at high doses (>3 µM), GSK'074 showed no detectable cytotoxicity even at 20 µM. Daily intraperitoneal injection of GSK'074 at 0.93 mg/kg significantly attenuated aortic expansion in two mouse models of AAA (calcium phosphate: DMSO 66.06 ± 9.17% vs GSK'074 27.36 ± 8.25%, P < 0.05; Angiotensin II: DMSO 85.39 ± 15.76% vs GSK'074 36.28 ± 5.76%, P < 0.05). Histologically, GSK'074 treatment diminished cell death and macrophage infiltration in aneurysm-prone aortae. Together, our data suggest that GSK'074 represents a new class of necroptosis inhibitors with dual targeting ability to both RIP1 and RIP3. The high potency and minimum cytotoxicity make GSK'074 a desirable drug candidate of pharmacological therapies to attenuate AAA progression and other necroptosis related diseases.

The 9H-Fluoren Vinyl Ether Derivative SAM461 Inhibits Bacterial Luciferase Activity and Protects Artemia franciscana From Luminescent Vibriosis

Vibrio campbellii is a major pathogen in aquaculture. It is a causative agent of the so-called “luminescent vibriosis,” a life-threatening condition caused by bioluminescent Vibrio spp. that often involves mass mortality of farmed shrimps. The emergence of multidrug resistant Vibrio strains raises a concern and poses a challenge for the treatment of this infection in the coming years. Inhibition of bacterial cell-to-cell communication or quorum sensing (QS) has been proposed as an alternative to antibiotic therapies. Aiming to identify novel QS disruptors, the 9H-fluroen-9yl vinyl ether derivative SAM461 was found to thwart V. campbellii bioluminescence, a QS-regulated phenotype. Phenotypic and gene expression analyses revealed, however, that the mode of action of SAM461 was unrelated to QS inhibition. Further evaluation with purified Vibrio fischeri and NanoLuc luciferases revealed enzymatic inhibition at micromolar concentrations. In silico analysis by molecular docking suggested binding of SAM461 in the active site cavities of both luciferase enzymes. Subsequent in vivo testing of SAM461 with gnotobiotic Artemia franciscana nauplii demonstrated naupliar protection against V. campbellii infection at low micromolar concentrations. Taken together, these findings suggest that suppression of luciferase activity could constitute a novel paradigm in the development of alternative anti-infective chemotherapies against luminescent vibriosis, and pave the ground for the chemical synthesis and biological characterization of derivatives with promising antimicrobial prospects.

Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1

In the current work, we applied computational methods to analyze the membrane-associated inhibitory kinase PKMYT1 and small molecule inhibitors. PKMYT1 regulates the cell cycle at G2/M transition and phosphorylates Thr14 and Tyr15 in the Cdk1-cyclin B complex. A combination of in silico and in vitro screening was applied to identify novel PKMYT1 inhibitors. The computational approach combined structural analysis, molecular docking, binding free energy calculations, and quantitative structure-activity relationship (QSAR) models. In addition, a computational fragment growing approach was applied to a set of previously identified diaminopyrimidines. Based on the derived computational models, several derivatives were synthesized and tested in vitro on PKMYT1. Novel inhibitors active in the sub-micromolar range were identified which provide the basis for further characterization of PKMYT1 as putative target for cancer therapy.

KInhibition: A Kinase Inhibitor Selection Portal

Protein kinases constitute a large class of signaling molecules frequently targeted in research and clinical uses. However, kinase inhibitors are notoriously non-specific, making it difficult to select an appropriate inhibitor for a given kinase. Available data from large-scale kinase inhibitor screens are often difficult to query. Here, we present KInhibition (https://kinhibition.fredhutch.org), an online portal that allows users to search publicly available datasets to find selective inhibitors for a chosen kinase or group of kinases. Compounds are sorted by a KInhibition Selectivity Score, calculated based on compounds' activity against the selected kinase(s) versus activity against all other kinases for which that compound has been profiled. The current version allows users to query four datasets, with a framework that can easily accommodate additional datasets. KInhibition represents a powerful platform through which researchers from broad areas of biology, chemistry, and pharmacology can easily interrogate large datasets to help guide their selection of kinase inhibitors.

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An easy-to-use tool that compiles datasets from kinase inhibitor screens

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A KInhibition Selectivity Score quantifies each compound's selectivity

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Users can select kinases, view compounds and selectivity, and download the results

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KInhibition is broadly applicable across life science disciplines

Luciferase Genes as Reporter Reactions: How to Use Them in Molecular Biology?

: The latest advances in molecular biology have made available several biotechnological tools that take advantage of the high detectability and quantum efficiency of bioluminescence (BL), with an ever-increasing number of novel applications in environmental, pharmaceutical, food, and forensic fields. Indeed, BL proteins are being used to develop ultrasensitive binding assays and cell-based assays, thanks to their high detectability and to the availability of highly sensitive BL instruments. The appealing aspect of molecular biology tools relying on BL reactions is their general applicability in both in vitro assays, such as cell cultures or purified proteins, and in vivo settings, such as in whole-animal BL imaging. The aim of this chapter is to provide the reader with an overview of state-of-the-art bioluminescent tools based on luciferase genes, highlighting molecular biology strategies that have been applied so far, together with some selected examples.

Immuno-detection by sequencing enables large-scale high-dimensional phenotyping in cells

Cell-based small molecule screening is an effective strategy leading to new medicines. Scientists in the pharmaceutical industry as well as in academia have made tremendous progress in developing both large-scale and smaller-scale screening assays. However, an accessible and universal technology for measuring large numbers of molecular and cellular phenotypes in many samples in parallel is not available. Here we present the immuno-detection by sequencing (ID-seq) technology that combines antibody-based protein detection and DNA-sequencing via DNA-tagged antibodies. We use ID-seq to simultaneously measure 70 (phospho-)proteins in primary human epidermal stem cells to screen the effects of ~300 kinase inhibitor probes to characterise the role of 225 kinases. The results show an association between decreased mTOR signalling and increased differentiation and uncover 13 kinases potentially regulating epidermal renewal through distinct mechanisms. Taken together, our work establishes ID-seq as a flexible solution for large-scale high-dimensional phenotyping in fixed cell populations.

Detecting proteins and post-translational modifications is important for drug screens, but the number of proteins measurable simultaneously is limited. Here the authors use antibodies tagged with DNA barcodes and high-throughput sequencing to detect up to 70 (phospho-)proteins in stem cells.

Differential prioritization of therapies to subtypes of triple negative breast cancer using a systems medicine method

Triple negative breast cancer (TNBC) is a group of cancers whose heterogeneity and shortage of effective drug therapies has prompted efforts to divide these cancers into molecular subtypes. Our computational platform, entitled GenEx-TNBC, applies concepts in systems biology and polypharmacology to prioritize thousands of approved and experimental drugs for therapeutic potential against each molecular subtype of TNBC. Using patient-based and cell line-based gene expression data, we constructed networks to describe the biological perturbation associated with each TNBC subtype at multiple levels of biological action. These networks were analyzed for statistical coincidence with drug action networks stemming from known drug-protein targets, while accounting for the direction of disease modulation for coinciding entities. GenEx-TNBC successfully designated drugs, and drug classes, that were previously shown to be broadly effective or subtype-specific against TNBC, as well as novel agents. We further performed biological validation of the platform by testing the relative sensitivities of three cell lines, representing three distinct TNBC subtypes, to several small molecules according to the degree of predicted biological coincidence with each subtype. GenEx-TNBC is the first computational platform to associate drugs to diseases based on inverse relationships with multi-scale disease mechanisms mapped from global gene expression of a disease. This method may be useful for directing current efforts in preclinical drug development surrounding TNBC, and may offer insights into the targetable mechanisms of each TNBC subtype.

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

Kinase inhibitor screening using artificial neural networks and engineered cardiac biowires

Kinase inhibitors are often used as cancer targeting agents for their ability to prevent the activation of cell growth and proliferation signals. Cardiotoxic effects have been identified for some marketed kinase inhibitors that were not detected during clinical trials. We hypothesize that more predictive cardiac functional assessments of kinase inhibitors on human myocardium can be established by combining a high-throughput two-dimensional (2D) screening assay and a high-content three-dimensional (3D) engineered cardiac tissue (Biowire^TM) based assay, and using human induced pluripotent stem cell-derived CMs (hiPSC-CMs). A subset (80) of compounds from the GlaxoSmithKline published kinase inhibitor set were tested on hiPSC-CM monolayers and significant effects on cell viability, calcium transients, and contraction frequency were observed. Artificial neural network modelling was then used to analyze the experimental results in an efficient and unbiased manner to select for kinase inhibitors with minimal effects on cell viability and function. Inhibitors of specific interest based on the modeling were evaluated in the 3D Biowire tissues. The three-dimensional Biowire platform eliminated oversensitivity in detecting both Ca²⁺ transient amplitude enhancements as well as the acute detrimental effects on cell viability due to the kinase inhibitor application as compared to the monolayer testing.

Functional annotation of chemical libraries across diverse biological processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Signalome-wide assessment of host cell response to hepatitis C virus

Host cell signalling during infection with intracellular pathogens remains poorly understood. Here we report on the use of antibody microarray technology to detect variations in the expression levels and phosphorylation status of host cell signalling proteins during hepatitis C virus (HCV) replication. Following transfection with HCV RNA, the JNK and NF-κB pathways are suppressed, while the JAK/STAT5 pathway is activated; furthermore, components of the apoptosis and cell cycle control machineries are affected in the expression and/or phosphorylation status. RNAi-based hit validation identifies components of the JAK/STAT, NF-κB, MAPK and calcium-induced pathways as modulators of HCV replication. Selective chemical inhibition of one of the identified targets, the JNK activator kinase MAP4K2, does impair HCV replication. Thus this study provides a comprehensive picture of host cell pathway mobilization by HCV and uncovers potential therapeutic targets. The strategy of identifying targets for anti-infective intervention within the host cell signalome can be applied to any intracellular pathogen.

Development of antiviral strategies depends on an understanding of virus–host interactions. Here, using HCV, Haqshenas et al. show that antibody microarray combined with a targeted siRNA screen can be a powerful tool to identify cellular signalling pathways that are important for virus replication.

Applications of chemogenomic library screening in drug discovery

The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay

Kinome-wide screening would have the advantage of providing structure-activity relationships against hundreds of targets simultaneously. Here, we report the generation of ligand-based activity prediction models for over 280 kinases by employing Machine Learning methods on an extensive data set of proprietary bioactivity data combined with open data. High quality (AUC > 0.7) was achieved for ∼200 kinases by (1) combining open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training data sets. Tests on left-out and external data indicate a high value for virtual screening projects. Importantly, the derived models are evenly distributed across the kinome tree, allowing reliable profiling prediction for all kinase branches. The prediction quality was further improved by employing experimental bioactivity fingerprints of a small kinase subset. Overall, the generated models can support various hit identification tasks, including virtual screening, compound repurposing, and the detection of potential off-targets.

Bioluminescence Methods for Assaying Kinases in Quantitative High-Throughput Screening (qHTS) Format Applied to Yes1 Tyrosine Kinase, Glucokinase, and PI5P4Kα Lipid Kinase

Assays in which the detection of a biological phenomenon is coupled to the production of bioluminescence by luciferase have gained widespread use. As firefly luciferases (FLuc) and kinases share a common substrate (ATP), coupling of a kinase to FLuc allows for the amount of ATP remaining following a kinase reaction to be assessed by quantitating the amount of luminescence produced. Alternatively, the amount of ADP produced by the kinase reaction can be coupled to FLuc through a two-step process. This chapter describes the bioluminescent assays that were developed for three classes of kinases (lipid, protein, and metabolic kinases) and miniaturized to 1536-well format, enabling their use for quantitative high-throughput (qHTS) of small-molecule libraries.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.

Identification of "Preferred" Human Kinase Inhibitors for Sleeping Sickness Lead Discovery. Are Some Kinases Better than Others for Inhibitor Repurposing?

A kinase-targeting cell-based high-throughput screen (HTS) against Trypanosoma brucei was recently reported, and this screening set included the Published Kinase Inhibitor Set (PKIS). From the PKIS was identified 53 compounds with pEC₅₀ ≥ 6. Utilizing the published data available for the PKIS, a statistical analysis of these active antiparasitic compounds was performed, allowing identification of a set of human kinases having inhibitors that show a high likelihood for blocking T. brucei cellular proliferation in vitro. This observation was confirmed by testing other established inhibitors of these human kinases and by mining past screening campaigns at GlaxoSmithKline. Overall, although the parasite targets of action are not known, inhibitors of this set of human kinases displayed an enhanced hit rate relative to a random kinase-targeting HTS campaign, suggesting that repurposing efforts should focus primarily on inhibitors of these specific human kinases. We therefore term this statistical analysis-driven approach “preferred lead repurposing”.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.

Identification and Visualization of Kinase-Specific Subpockets

The identification and design of selective compounds is important for the reduction of unwanted side effects as well as for the development of tool compounds for target validation studies. This is, in particular, true for therapeutically important protein families that possess conserved folds and have numerous members such as kinases. To support the design of selective kinase inhibitors, we developed a novel approach that allows identification of specificity determining subpockets between closely related kinases solely based on their three-dimensional structures. To account for the intrinsic flexibility of the proteins, multiple X-ray structures of the target protein of interest as well as of unwanted off-target(s) are taken into account. The binding pockets of these protein structures are calculated and fused to a combined target and off-target pocket, respectively. Subsequently, shape differences between these two combined pockets are identified via fusion rules. The approach provides a user-friendly visualization of target-specific areas in a binding pocket which should be explored when designing selective compounds. Furthermore, the approach can be easily combined with in silico alanine mutation studies to identify selectivity determining residues. The potential impact of the approach is demonstrated in four retrospective experiments on closely related kinases, i.e., p38α vs Erk2, PAK1 vs PAK4, ITK vs AurA, and BRAF vs VEGFR2. Overall, the presented approach does not require any profiling data for training purposes, provides an intuitive visualization of a large number of protein structures at once, and could also be applied to other target classes.

Non-stoichiometric inhibition in integrated lead finding - a literature review

INTRODUCTION

Non-stoichiometric inhibition summarizes different mechanisms by which low-molecular weight compounds can reproducibly inhibit high-throughput screening (HTS) and other lead finding assays without binding to a structurally defined site on their molecular target. This disqualifies such molecules from optimization by medicinal chemistry, and therefore their rapid elimination from screening hit lists is essential for productive and effective drug discovery.

AREAS COVERED

This review covers recent literature that either investigates the various mechanisms behind non-stoichiometric inhibition or suggests assays and readouts to identify them. In addition, combination of the various methods to distill promising molecules out of raw primary hit lists step-by-step is considered. Emerging technologies to demonstrate target engagement in cells are also discussed.

EXPERT OPINION

Over the last few years, awareness of non-stoichiometric inhibitors within screening libraries and HTS hit lists has considerably increased, not only in the pharmaceutical industry but also in the academic drug discovery community. This has resulted in a variety of methods to detect and handle such compounds. These range from in silico approaches to flag suspicious compounds, and counterassays to measure non-stoichiometric inhibition, to biophysical methods that positively demonstrate stoichiometric binding. In addition, novel technologies to verify target engagement within cells are becoming available. While still a time- and resource-consuming nuisance, non-stoichiometric inhibitors therefore do not fundamentally jeopardize the discovery of low molecular weight lead and drug candidates. Rather, they should be viewed as a manageable issue that with appropriate expertise can be overcome through integration of the above-mentioned approaches.

Kinase profiling in early stage drug discovery: sorting things out

Protein kinases represent one of the largest superfamilies of drugable targets and a major research area for both the pharmaceutical industry and academic groups. This has resulted in the emergence of numerous screening technologies and services dedicated to kinase profiling. In spite of this plentiful offering, the field is not without its own pitfalls, as the profusion of reported conditions and data can ultimately complicate interpretation of project results. Here, we discuss how kinase profiling was used in our early stage drug discovery efforts, from the perspective of a smaller biotech relying largely on assay outsourcing.

Comprehensive characterization of the Published Kinase Inhibitor Set

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome.

Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter

Parkin, an E3 ubiquitin ligase, is a central mediator of mitochondrial quality control and is linked to familial forms of Parkinson's disease (PD). Removal of dysfunctional mitochondria from the cell by Parkin is thought to be neuroprotective, and pharmacologically increasing Parkin levels may be a novel therapeutic approach. We used genome-editing to integrate a coincidence reporter into the PARK2 gene locus of a neuroblastoma-derived cell line and developed a quantitative high-throughput screening (qHTS) assay capable of accurately detecting subtle compound-mediated increases in endogenous PARK2 expression. Interrogation of a chemogenomic library revealed diverse chemical classes that up-regulate the PARK2 transcript, including epigenetic agents, drugs controlling cholesterol biosynthesis, and JNK inhibitors. Use of the coincidence reporter eliminated wasted time pursuing reporter-biased false positives accounting for ∼2/3 of the actives and, coupled with titration-based screening, greatly improves the efficiency of compound selection. This approach represents a strategy to revitalize reporter-gene assays for drug discovery.

PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS

Significant resources in early drug discovery are spent unknowingly pursuing artifacts and promiscuous bioactive compounds, while understanding the chemical basis for these adverse behaviors often goes unexplored in pursuit of lead compounds. Nearly all the hits from our recent sulfhydryl-scavenging high-throughput screen (HTS) targeting the histone acetyltransferase Rtt109 were such compounds. Herein, we characterize the chemical basis for assay interference and promiscuous enzymatic inhibition for several prominent chemotypes identified by this HTS, including some pan-assay interference compounds (PAINS). Protein mass spectrometry and ALARM NMR confirmed these compounds react covalently with cysteines on multiple proteins. Unfortunately, compounds containing these chemotypes have been published as screening actives in reputable journals and even touted as chemical probes or preclinical candidates. Our detailed characterization and identification of such thiol-reactive chemotypes should accelerate triage of nuisance compounds, guide screening library design, and prevent follow-up on undesirable chemical matter.

Identification and characterization of hundreds of potent and selective inhibitors of Trypanosoma brucei growth from a kinase-targeted library screening campaign

In the interest of identification of new kinase-targeting chemotypes for target and pathway analysis and drug discovery in Trypanosomal brucei, a high-throughput screen of 42,444 focused inhibitors from the GlaxoSmithKline screening collection was performed against parasite cell cultures and counter-screened against human hepatocarcinoma (HepG2) cells. In this way, we have identified 797 sub-micromolar inhibitors of T. brucei growth that are at least 100-fold selective over HepG2 cells. Importantly, 242 of these hit compounds acted rapidly in inhibiting cellular growth, 137 showed rapid cidality. A variety of in silico and in vitro physicochemical and drug metabolism properties were assessed, and human kinase selectivity data were obtained, and, based on these data, we prioritized three compounds for pharmacokinetic assessment and demonstrated parasitological cure of a murine bloodstream infection of T. brucei rhodesiense with one of these compounds (NEU-1053). This work represents a successful implementation of a unique industrial-academic collaboration model aimed at identification of high quality inhibitors that will provide the parasitology community with chemical matter that can be utilized to develop kinase-targeting tool compounds. Furthermore these results are expected to provide rich starting points for discovery of kinase-targeting tool compounds for T. brucei, and new HAT therapeutics discovery programs.

Human African trypanosomiasis, or sleeping sickness, affects 10,000 patients annually, yet current drugs for this disease are poor, with high toxicity and inconvenient dosing requirements. Trypanosoma brucei, the parasite that causes sleeping sickness, is sensitive to a class of compounds called kinase inhibitors, and our project was aimed at identifying kinase-targeting compounds that rapidly and irreversibly inhibit parasite growth. This was accomplished by high-throughput screening of over 42,000 compounds, which resulted in identification of 797 potent inhibitors of parasite growth that are non-toxic to human cells. These inhibitors were studied for the speed of their effects and reversibility of growth inhibition, and were grouped on the basis of chemical structure similarity. One compound was shown to cure mice from a bloodstream of infection of T. brucei. These compounds can now be utilized by the research community as starting points for new drug discovery, and also as tool compounds for understanding the function of kinases in T. brucei.

Seeding collaborations to advance kinase science with the GSK Published Kinase Inhibitor Set (PKIS)

To catalyze research on historically untargeted protein kinases, we created the PKIS, an annotated set of 367 small molecule kinase inhibitors. The set has been widely distributed to academic collaborators as an open access tool. It has been used to identify chemical starting points for development of chemical probes for orphan kinases and to investigate kinase signaling in high content phenotypic assays. Access to the set comes with few restrictions other than the requirement that assay results be released into the public domain for the benefit of the entire research community. Examples from the efforts of several collaborators are summarized.

Composition and applications of focus libraries to phenotypic assays

The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening

Virtual screening with docking is an integral component of drug design, particularly during hit finding phases. While successful prospective studies of virtual screening exist, it remains a significant challenge to identify best practices a priori due to the many factors that influence the final outcome, including targets, data sets, software, metrics, and expert knowledge of the users. This study investigates the extent to which ligand-based methods can be applied to improve structure-based methods. The use of ligand-based methods to modulate the number of hits identified using the protein–ligand complex and also the diversity of these hits from the crystallographic ligand is discussed. In this study, 40 CDK2 ligand complexes were used together with two external data sets containing both actives and inactives from GlaxoSmithKline (GSK) and actives and decoys from the Directory of Useful Decoys (DUD). Results show how ligand-based modeling can be used to select a more appropriate protein conformation for docking, as well as to assess the reliability of the docking experiment. The time gained by reducing the pool of virtual screening candidates via ligand-based similarity can be invested in more accurate docking procedures, as well as in downstream labor-intensive approaches (e.g., visual inspection) maximizing the use of the chemical and biological information available. This provides a framework for molecular modeling scientists that are involved in initiating virtual screening campaigns with practical advice to make best use of the information available to them.

Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci

Blood pressure (BP) is a heritable risk factor for cardiovascular disease. To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), we genotyped ~50,000 SNPs in up to 87,736 individuals of European ancestry and combined these in a meta-analysis. We replicated findings in an independent set of 68,368 individuals of European ancestry. Our analyses identified 11 previously undescribed associations in independent loci containing 31 genes including PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 at the Bonferroni-corrected array-wide significance threshold (p < 6 × 10(-7)) and confirmed 27 previously reported associations. Bioinformatic analysis of the 11 loci provided support for a putative role in hypertension of several genes, such as CDK6 and NUCB2. Analysis of potential pharmacological targets in databases of small molecules showed that ten of the genes are predicted to be a target for small molecules. In summary, we identified previously unknown loci associated with BP. Our findings extend our understanding of genes involved in BP regulation, which may provide new targets for therapeutic intervention or drug response stratification.

The ChEMBL bioactivity database: an update

ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.

Identification of potent Yes1 kinase inhibitors using a library screening approach

Yes1 kinase has been implicated as a potential therapeutic target in a number of cancers including melanomas, breast cancers, and rhabdomyosarcomas. Described here is the development of a robust and miniaturized biochemical assay for Yes1 kinase that was applied in a high throughput screen (HTS) of kinase-focused small molecule libraries. The HTS provided 144 (17% hit rate) small molecule compounds with IC₅₀ values in the sub-micromolar range. Three of the most potent Yes1 inhibitors were then examined in a cell-based assay for inhibition of cell survival in rhabdomyosarcoma cell lines. Homology models of Yes1 were generated in active and inactive conformations, and docking of inhibitors supports binding to the active conformation (DFG-in) of Yes1. This is the first report of a large high throughput enzymatic activity screen for identification of Yes1 kinase inhibitors, thereby elucidating the polypharmacology of a variety of small molecules and clinical candidates.