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

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Glycogen synthase kinase 3 inhibition controls Mycobacterium tuberculosis infection

Compounds targeting host control of infectious diseases provide an attractive alternative to antimicrobials. A phenotypic screen of a kinase library identified compounds targeting glycogen synthase kinase 3 as potent inhibitors of Mycobacterium tuberculosis (Mtb) intracellular growth in the human THP-1 cell line and primary human monocytes-derived macrophages (hMDM). CRISPR knockouts and siRNA silencing showed that GSK3 isoforms are needed for the growth of Mtb and that a selected compound, P-4423632 targets GSK3β. GSK3 inhibition was associated with macrophage apoptosis governed by the Mtb secreted protein tyrosine phosphatase A (PtpA). Phospho-proteome analysis of macrophages response to infection revealed a wide array of host signaling and apoptosis pathways controlled by GSK3 and targeted by P-4423632. P-4423632 was additionally found to be active against other intracellular pathogens. Our findings strengthen the notion that targeting host signaling to promote the infected cell's innate antimicrobial capacity is a feasible and attractive host-directed therapy approach.

Illuminating function of the understudied druggable kinome

The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.

Dual Inhibition of Mycobacterium tuberculosis and the Host TGFBR1 by an Anilinoquinazoline

Tuberculosis (TB) control is complicated by the emergence of drug resistance. Promising strategies to prevent drug resistance are the targeting of nonreplicating, drug-tolerant bacterial populations and targeting of the host, but inhibitors and targets for either are still rare. In a cell-based screen of ATP-competitive inhibitors, we identified compounds with in vitro activity against replicating Mycobacterium tuberculosis (Mtb), and an anilinoquinazoline (AQA) that also had potent activity against nonreplicating and persistent Mtb. AQA was originally developed to inhibit human transforming growth factor receptor 1 (TGFBR1), a host kinase that is predicted to have host-adverse effects during Mtb infection. The structure-activity relationship of this dually active compound identified the pyridyl-6-methyl group as being required for potent Mtb inhibition but a liability for P450 metabolism. Pyrrolopyrimidine (43) emerged as the optimal compound that balanced micromolar inhibition of nonreplicating Mtb and TGFBR1 while also demonstrating improved metabolic stability and pharmacokinetic profiles.

Selective ROCK Inhibitor Enhances Blood Flow Recovery after Hindlimb Ischemia

The impairment in microvascular network formation could delay the restoration of blood flow after acute limb ischemia. A high-content screen of a GSK-published kinase inhibitor library identified a set of ROCK inhibitor hits enhancing endothelial network formation. Subsequent kinase activity profiling against a panel of 224 protein kinases showed that two indazole-based ROCK inhibitor hits exhibited high selectivity for ROCK1 and ROCK2 isoforms compared to other ROCK inhibitors. One of the chemical entities, GSK429286, was selected for follow-up studies. We found that GSK429286 was ten times more potent in enhancing endothelial tube formation than Fasudil, a classic ROCK inhibitor. ROCK1 inhibition by RNAi phenocopied the angiogenic phenotype of the GSK429286 compound. Using an organotypic angiogenesis co-culture assay, we showed that GSK429286 formed a dense vascular network with thicker endothelial tubes. Next, mice received either vehicle or GSK429286 (10 mg/kg i.p.) for seven days after hindlimb ischemia induction. As assessed by laser speckle contrast imaging, GSK429286 potentiated blood flow recovery after ischemia induction. At the histological level, we found that GSK429286 significantly increased the size of new microvessels in the regenerating areas of ischemic muscles compared with vehicle-treated ones. Our findings reveal that selective ROCK inhibitors have in vitro pro-angiogenic properties and therapeutic potential to restore blood flow in limb ischemia.

Targeting multidrug resistant Staphylococcus infections with bacterial histidine kinase inhibitors

The emergence of drug-resistant bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), which are not susceptible to current antibiotics has necessitated the development of novel approaches and targets to tackle this growing challenge. Bacterial two-component systems (TCSs) play a central role in the adaptative response of bacteria to their ever-changing environment. They are linked to antibiotic resistance and bacterial virulence making the proteins of the TCSs, histidine kinases and response regulators, attractive for the development of novel antibacterial drugs. Here, we developed a suite of maleimide-based compounds that we evaluated against a model histidine kinase, HK853, in vitro and in silico. The most potent leads were then assessed for their ability to decrease the pathogenicity and virulence of MRSA, resulting in the identification of a molecule that decreased the lesion size caused by a methicillin-resistant S. aureus skin infection by 65% in a murine model.

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors

Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine learning models have been generated to aid in simplifying and improving the prediction of candidate compounds. Models tuned for predicting kinase inhibitors have been established. However, an effective model can be limited by the size of the chosen training dataset. In this study, we tested several machine learning models to predict potential kinase inhibitors. A dataset was curated from a number of publicly available repositories. This resulted in a comprehensive dataset covering more than half of the human kinome. More than 2,000 kinase models were established using different model approaches. The performances of the models were compared, and the Keras-MLP model was determined to be the best performing model. The model was then used to screen a chemical library for potential inhibitors targeting platelet-derived growth factor receptor-β (PDGFRB). Several PDGFRB candidates were selected, and in vitro assays confirmed four compounds with PDGFRB inhibitory activity and IC₅₀ values in the nanomolar range. These results show the effectiveness of machine learning models trained on the reported dataset. This report would aid in the establishment of machine learning models as well as in the discovery of novel kinase inhibitors.

QSAR via Multisite λ-Dynamics in the Orphaned TSSK1B Kinase

Multisite λ-dynamics (MSλD) is a novel method for the calculation of relative free energies of binding for ligands to their targeted receptors. It can be readily used to examine a large number of molecules with multiple functional groups at multiple sites around a common core. This makes MSλD a powerful tool in structure-based drug design. In the present study, MSλD is applied to calculate the relative binding free energies of 1296 inhibitors to the testis specific serine kinase 1B (TSSK1B), a validated target for male contraception. For this system, MSλD requires significantly fewer computational resources compared to traditional free energy methods like free energy perturbation or thermodynamic integration. From MSλD simulations, we examined whether modifications of a ligand at two different sites are coupled or not. Based on our calculations, we established a quantitative structure-activity relationship (QSAR) for this set of molecules and identified a site in the ligand where further modification, such as adding more polar groups, may lead to increased binding affinity. This article is protected by copyright. All rights reserved.

In vivo quantitative high-throughput screening for drug discovery and comparative toxicology

Quantitative high-throughput screening (qHTS) pharmacologically evaluates chemical libraries for therapeutic uses, toxicological risk and, increasingly, for academic probe discovery. Phenotypic high-throughput screening assays interrogate molecular pathways, often relying on cell culture systems, historically less focused on multicellular organisms. Caenorhabditis elegans has served as a eukaryotic model organism for human biology by virtue of genetic conservation and experimental tractability. Here, a paradigm enabling C. elegans qHTS using 384-well microtiter plate laser-scanning cytometry is described, in which GFP-expressing organisms revealing phenotype-modifying structure-activity relationships guide subsequent life-stage and proteomic analyses, and Escherichia coli bacterial ghosts, a non-replicating nutrient source, allow compound exposures over two life cycles, mitigating bacterial overgrowth complications. We demonstrate the method with libraries of anti-infective agents, or substances of toxicological concern. Each was tested in seven-point titration to assess the feasibility of nematode-based in vivo qHTS, and examples of follow-up strategies were provided to study organism-based chemotype selectivity and subsequent network perturbations with a physiological impact. We anticipate that this qHTS approach will enable analysis of C. elegans orthologous phenotypes of human pathologies to facilitate drug library profiling for a range of therapeutic indications.

Toward inhibition of human cytomegalovirus replication with compounds targeting cellular proteins

Antiviral therapy for human cytomegalovirus (HCMV) currently relies upon direct-acting antiviral drugs. However, it is now well known that these drugs have shortcomings, which limit their use. Here I review the identification and investigation of compounds targeting cellular proteins that have anti-HCMV activity and could supersede those anti-HCMV drugs currently in use. This includes discussion of drug repurposing, for example the use of artemisinin compounds, and discussion of new directions to identify compounds that target cellular factors in HCMV-infected cells, for example screening of kinase inhibitors. In addition, I highlight developing areas such as the use of machine learning and emphasize how interaction with fields outside virology will be critical for development of anti-HCMV compounds.

Opposing roles of CLK SR kinases in controlling HIV-1 gene expression and latency

BACKGROUND

The generation of over 69 spliced HIV-1 mRNAs from one primary transcript by alternative RNA splicing emphasizes the central role that RNA processing plays in HIV-1 replication. Control is mediated in part through the action of host SR proteins whose activity is regulated by multiple SR kinases (CLK1-4, SRPKs).

METHODS

Both shRNA depletion and small molecule inhibitors of host SR kinases were used in T cell lines and primary cells to evaluate the role of these factors in the regulation of HIV-1 gene expression. Effects on virus expression were assessed using western blotting, RT-qPCR, and immunofluorescence.

RESULTS

The studies demonstrate that SR kinases play distinct roles; depletion of CLK1 enhanced HIV-1 gene expression, reduction of CLK2 or SRPK1 suppressed it, whereas CLK3 depletion had a modest impact. The opposing effects of CLK1 vs. CLK2 depletion were due to action at distinct steps; reduction of CLK1 increased HIV-1 promoter activity while depletion of CLK2 affected steps after transcript initiation. Reduced CLK1 expression also enhanced the response to several latency reversing agents, in part, by increasing the frequency of responding cells, consistent with a role in regulating provirus latency. To determine whether small molecule modulation of SR kinase function could be used to control HIV-1 replication, we screened a GSK library of protein kinase inhibitors (PKIS) and identified several pyrazolo[1,5-b] pyridazine derivatives that suppress HIV-1 gene expression/replication with an EC₅₀ ~ 50 nM. The compounds suppressed HIV-1 protein and viral RNA accumulation with minimal impact on cell viability, inhibiting CLK1 and CLK2 but not CLK3 function, thereby selectively altering the abundance of individual CLK and SR proteins in cells.

CONCLUSIONS

These findings demonstrate the unique roles played by individual SR kinases in regulating HIV-1 gene expression, validating the targeting of these functions to either enhance latency reversal, essential for "Kick-and-Kill" strategies, or to silence HIV protein expression for "Block-and-Lock" strategies.

Temozolomide-induced guanine mutations create exploitable vulnerabilities of guanine-rich DNA and RNA regions in drug-resistant gliomas

Temozolomide (TMZ) is a chemotherapeutic agent that has been the first-line standard of care for the aggressive brain cancer glioblastoma (GBM) since 2005. Although initially beneficial, TMZ resistance is universal and second-line interventions are an unmet clinical need. Here, we took advantage of the known mechanism of action of TMZ to target guanines (G) and investigated G-rich G-quadruplex (G4) and splice site changes that occur upon TMZ resistance. We report that TMZ-resistant GBM has guanine mutations that disrupt the G-rich DNA G4s and splice sites that lead to deregulated alternative splicing. These alterations create vulnerabilities, which are selectively targeted by either the G4-stabilizing drug TMPyP4 or a novel splicing kinase inhibitor of cdc2-like kinase. Last, we show that the G4 and RNA binding protein EWSR1 aggregates in the cytoplasm in TMZ-resistant GBM cells and patient samples. Together, our findings provide insight into targetable vulnerabilities of TMZ-resistant GBM and present cytoplasmic EWSR1 as a putative biomarker.

Bayes optimal informer sets for early-stage drug discovery

An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Informer based ranking (IBR) methods address the prioritization problem when the compounds have provided bioactivity data on other potentially relevant targets. An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target. We formalize the problem as a two-stage decision problem and introduce the Bayes Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a flexible model of the initial bioactivity data, a relevant loss function, and effective computational schemes to resolve the two-step design problem. We evaluate BOISE and compare it to other IBR strategies in two retrospective studies, one on protein-kinase inhibition and the other on anti-cancer drug sensitivity. In both empirical settings BOISE exhibits better predictive performance than available methods. It also behaves well with missing data, where methods that use matrix completion show worse predictive performance. This article is protected by copyright. All rights reserved.

Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development

Predicting compound-protein interactions (CPIs) is of great importance for drug discovery and repositioning, yet still challenging mainly due to the sparse nature of CPI matrixes, resulting in poor generalization performance. Hence, unlike typical CPI prediction models focused on representation learning or model selection, we propose a deep neural network-based strategy, PCM-AAE, that re-explores and augments the pharmacological space of kinase inhibitors by introducing the adversarial auto-encoder model (AAE) to improve the generalization of the prediction model. To complete the data space, we constructed Ensemble of PCM-AAE (EPA), an ensemble model that quickly and accurately yields quantitative predictions of binding affinity between any human kinase and inhibitor. In rigorous internal validation, EPA showed excellent performance, consistently outperforming the model trained with the imbalanced set, especially for targets with relatively fewer training data points. Improved prediction accuracy of EPA for external datasets enhances its generalization ability, making it possible to gracefully handle previously unseen kinases and inhibitors. EPA showed promising potential when directly applied to virtual screening and off-target prediction, exhibiting its practicality in hit prediction. Our strategy is expected to facilitate kinase-centric drug development, as well as to solve more challenging prediction problems with insufficient data points.

A role for AKT1 in nonsense-mediated mRNA decay

Nonsense-mediated mRNA decay (NMD) is a highly regulated quality control mechanism through which mRNAs harboring a premature termination codon are degraded. It is also a regulatory pathway for some genes. This mechanism is subject to various levels of regulation, including phosphorylation. To date only one kinase, SMG1, has been described to participate in NMD, by targeting the central NMD factor UPF1. Here, screening of a kinase inhibitor library revealed as putative NMD inhibitors several molecules targeting the protein kinase AKT1. We present evidence demonstrating that AKT1, a central player in the PI3K/AKT/mTOR signaling pathway, plays an essential role in NMD, being recruited by the UPF3X protein to phosphorylate UPF1. As AKT1 is often overactivated in cancer cells and as this should result in increased NMD efficiency, the possibility that this increase might affect cancer processes and be targeted in cancer therapy is discussed.

Kinase Inhibitor Library Screening Identifies the Cancer Therapeutic Sorafenib and Structurally Similar Compounds as Strong Inhibitors of the Fungal Pathogen Histoplasma capsulatum

Histoplasma capsulatum is a dimorphic fungal pathogen endemic to the midwestern and southern United States. It causes mycoses ranging from subclinical respiratory infections to severe systemic disease, and is of particular concern for immunocompromised patients in endemic areas. Clinical management of histoplasmosis relies on protracted regimens of antifungal drugs whose effectiveness can be limited by toxicity. In this study, we hypothesize that conserved biochemical signaling pathways in the eukaryotic domain can be leveraged to repurpose kinase inhibitors as antifungal compounds. We conducted a screen of two kinase inhibitor libraries to identify compounds inhibiting the growth of Histoplasma capsulatum in the pathogenic yeast form. Our approach identified seven compounds with an elongated hydrophobic polyaromatic structure, five of which share a molecular motif including a urea unit linking a halogenated benzene ring and a para-substituted polyaromatic group. The top hits include the cancer therapeutic Sorafenib, which inhibits growth of Histoplasma in vitro and in a macrophage infection model with low host cell cytotoxicity. Our results reveal the possibility of repurposing Sorafenib or derivatives thereof as therapy for histoplasmosis, and suggest that repurposing of libraries developed for human cellular targets may be a fruitful source of antifungal discovery.

A cell-based drug discovery assay identifies inhibition of cell stress responses as a new approach to treatment of epidermolysis bullosa simplex

In the skin fragility disorder epidermolysis bullosa simplex (EBS), mutations in keratin 14 (K14, also known as KRT14) or keratin 5 (K5, also known as KRT5) lead to keratinocyte rupture and skin blistering. Severe forms of EBS are associated with cytoplasmic protein aggregates, with elevated kinase activation of ERK1 and ERK2 (ERK1/2; also known as MAPK3 and MAPK1, respectively), suggesting intrinsic stress caused by misfolded keratin protein. Human keratinocyte EBS reporter cells stably expressing GFP-tagged EBS-mimetic mutant K14 were used to optimize a semi-automated system to quantify the effects of test compounds on keratin aggregates. Screening of a protein kinase inhibitor library identified several candidates that reduced aggregates and impacted on epidermal growth factor receptor (EGFR) signalling. EGF ligand exposure induced keratin aggregates in EBS reporter keratinocytes, which was reversible by EGFR inhibition. EBS keratinocytes treated with a known EGFR inhibitor, afatinib, were driven out of activation and towards quiescence with minimal cell death. Aggregate reduction was accompanied by denser keratin filament networks with enhanced intercellular cohesion and resilience, which when extrapolated to a whole tissue context would predict reduced epidermal fragility in EBS patients. This assay system provides a powerful tool for discovery and development of new pathway intervention therapeutic avenues for EBS.

CDK12 inhibition reduces abnormalities in cells from patients with myotonic dystrophy and in a mouse model

Myotonic dystrophy type 1 (DM1) is an RNA-based disease with no current treatment. It is caused by a transcribed CTG repeat expansion within the 3' untranslated region of the dystrophia myotonica protein kinase (DMPK) gene. Mutant repeat expansion transcripts remain in the nuclei of patients' cells, forming distinct microscopically detectable foci that contribute substantially to the pathophysiology of the condition. Here, we report small-molecule inhibitors that remove nuclear foci and have beneficial effects in the HSA^LR mouse model, reducing transgene expression, leading to improvements in myotonia, splicing, and centralized nuclei. Using chemoproteomics in combination with cell-based assays, we identify cyclin-dependent kinase 12 (CDK12) as a druggable target for this condition. CDK12 is a protein elevated in DM1 cell lines and patient muscle biopsies, and our results showed that its inhibition led to reduced expression of repeat expansion RNA. Some of the inhibitors identified in this study are currently the subject of clinical trials for other indications and provide valuable starting points for a drug development program in DM1.

Reducing False Positives through the Application of Fluorescence Lifetime Technology: A Comparative Study Using TYK2 Kinase as a Model System

The predominant assay detection methodologies used for enzyme inhibitor identification during early-stage drug discovery are fluorescence-based. Each fluorophore has a characteristic fluorescence decay, known as the fluorescence lifetime, that occurs throughout a nanosecond-to-millisecond timescale. The measurement of fluorescence lifetime as a reporter for biological activity is less common than fluorescence intensity, even though the latter has numerous issues that can lead to false-positive readouts. The confirmation of hit compounds as true inhibitors requires additional assays, cost, and time to progress from hit identification to lead drug-candidate optimization. To explore whether the use of fluorescence lifetime technology (FLT) can offer comparable benefits to label-free-based approaches such as RapidFire mass spectroscopy (RF-MS) and a superior readout compared to time-resolved fluorescence resonance energy transfer (TR-FRET), three equivalent assays were developed against the clinically validated tyrosine kinase 2 (TYK2) and screened against annotated compound sets. FLT provided a marked decrease in the number of false-positive hits when compared to TR-FRET. Further cellular screening confirmed that a number of potential inhibitors directly interacted with TYK2 and inhibited the downstream phosphorylation of the signal transducer and activator of transcription 4 protein (STAT4).

A survey of the kinome pharmacopeia reveals multiple scaffolds and targets for the development of novel anthelmintics

Over one billion people are currently infected with a parasitic nematode. Symptoms can include anemia, malnutrition, developmental delay, and in severe cases, death. Resistance is emerging to the anthelmintics currently used to treat nematode infection, prompting the need to develop new anthelmintics. Towards this end, we identified a set of kinases that may be targeted in a nematode-selective manner. We first screened 2040 inhibitors of vertebrate kinases for those that impair the model nematode Caenorhabditis elegans. By determining whether the terminal phenotype induced by each kinase inhibitor matched that of the predicted target mutant in C. elegans, we identified 17 druggable nematode kinase targets. Of these, we found that nematode EGFR, MEK1, and PLK1 kinases have diverged from vertebrates within their drug-binding pocket. For each of these targets, we identified small molecule scaffolds that may be further modified to develop nematode-selective inhibitors. Nematode EGFR, MEK1, and PLK1 therefore represent key targets for the development of new anthelmintic medicines.

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.

Crystal Structure and Inhibitor Identifications Reveal Targeting Opportunity for the Atypical MAPK Kinase ERK3

Extracellular signal-regulated kinase 3 (ERK3), known also as mitogen-activated protein kinase 6 (MAPK6), is an atypical member of MAPK kinase family, which has been poorly studied. Little is known regarding its function in biological processes, yet this atypical kinase has been suggested to play important roles in the migration and invasiveness of certain cancers. The lack of tools, such as a selective inhibitor, hampers the study of ERK3 biology. Here, we report the crystal structure of the kinase domain of this atypical MAPK kinase, providing molecular insights into its distinct ATP binding pocket compared to the classical MAPK ERK2, explaining differences in their inhibitor binding properties. Medium-scale small molecule screening identified a number of inhibitors, several of which unexpectedly exhibited remarkably high inhibitory potencies. The crystal structure of CLK1 in complex with CAF052, one of the most potent inhibitors identified for ERK3, revealed typical type-I binding mode of the inhibitor, which by structural comparison could likely be maintained in ERK3. Together with the presented structural insights, these diverse chemical scaffolds displaying both reversible and irreversible modes of action, will serve as a starting point for the development of selective inhibitors for ERK3, which will be beneficial for elucidating the important functions of this understudied kinase.

PKIS deep dive yields a chemical starting point for dark kinases and a cell active BRSK2 inhibitor

The Published Kinase Inhibitor Set (PKIS) is a publicly-available chemogenomic library distributed to more than 300 laboratories by GlaxoSmithKline (GSK) between 2011 and 2015 and by SGC-UNC from 2015 to 2017. Screening this library of well-annotated, published kinase inhibitors has yielded a plethora of data in diverse therapeutic and scientific areas, funded applications, publications, and provided impactful pre-clinical results. GW296115 is a compound that was included in PKIS based on its promising selectivity following profiling against 260 human kinases. Herein we present more comprehensive profiling data for 403 wild type human kinases and follow-up enzymatic screening results for GW296115. This more thorough investigation of GW296115 has confirmed it as a potent inhibitor of kinases including BRSK1 and BRSK2 that were identified in the original panel of 260 kinases as well as surfaced other kinases that it potently inhibits. Based on these new kinome-wide screening results, we report that GW296115 is an inhibitor of several members of the Illuminating the Druggable Genome (IDG) list of understudied dark kinases. Specifically, our results establish GW296115 as a potent lead chemical tool that inhibits six IDG kinases with IC50 values less than 100 nM. Focused studies establish that GW296115 is cell active, and directly engages BRSK2. Further evaluation showed that GW296115 downregulates BRSK2-driven phosphorylation and downstream signaling. Therefore, we present GW296115 as a cell-active chemical tool that can be used to interrogate the poorly characterized function(s) of BRSK2.

DFG-1 Residue Controls Inhibitor Binding Mode and Affinity, Providing a Basis for Rational Design of Kinase Inhibitor Selectivity

Selectivity remains a challenge for ATP-mimetic kinase inhibitors, an issue that may be overcome by targeting unique residues or binding pockets. However, to date only few strategies have been developed. Here we identify that bulky residues located N-terminal to the DFG motif (DFG-1) represent an opportunity for designing highly selective inhibitors with unexpected binding modes. We demonstrate that several diverse inhibitors exerted selective, noncanonical binding modes that exclusively target large hydrophobic DFG-1 residues present in many kinases including PIM, CK1, DAPK, and CLK. By use of the CLK family as a model, structural and biochemical data revealed that the DFG-1 valine controlled a noncanonical binding mode in CLK1, providing a rationale for selectivity over the closely related CLK3 which harbors a smaller DFG-1 alanine. Our data suggest that targeting the restricted back pocket in the small fraction of kinases that harbor bulky DFG-1 residues offers a versatile selectivity filter for inhibitor design.

Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Selectivity is a key requirement of high-quality chemical probes and lead medicines; however, methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade the target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.

Mining Public Domain Data to Develop Selective DYRK1A Inhibitors

Kinases represent one of the most intensively pursued groups of targets in modern-day drug discovery. Often it is desirable to achieve selective inhibition of the kinase of interest over the remaining ∼500 kinases in the human kinome. This is especially true when inhibitors are intended to be used to study the biology of the target of interest. We present a pipeline of open-source software that analyzes public domain data to repurpose compounds that have been used in previous kinase inhibitor development projects. We define the dual-specificity tyrosine-regulated kinase 1A (DYRK1A) as the kinase of interest, and by addition of a single methyl group to the chosen starting point we remove glycogen synthase kinase β (GSK3β) and cyclin-dependent kinase (CDK) inhibition. Thus, in an efficient manner we repurpose a GSK3β/CDK chemotype to deliver 8b, a highly selective DYRK1A inhibitor.

A Semi-automated and Scalable 3D Spheroid Assay to Study Neuroblast Migration

The subventricular zone of the mammalian brain is the major source of adult born neurons. These neuroblasts normally migrate long distances to the olfactory bulbs but can be re-routed to locations of injury and promote neuroregeneration. Mechanistic understanding and pharmacological targets regulating neuroblast migration is sparse. Furthermore, lack of migration assays limits development of pharmaceutical interventions targeting neuroblast recruitment. We therefore developed a physiologically relevant 3D neuroblast spheroid migration assay that permits the investigation of large numbers of interventions. To verify the assay, 1,012 kinase inhibitors were screened for their effects on migration. Several induced significant increases or decreases in migration. MuSK and PIK3CB were selected as putative targets and their knockdown validated increased neuroblast migration. Thus, compounds identified through this assay system could be explored for their potential in augmenting neuroblast recruitment to sites of injury for neuroregeneration, or for decreasing malignant invasion.

CagA-ASPP2 complex mediates loss of cell polarity and favors H. pylori colonization of human gastric organoids

The main risk factor for stomach cancer, the third most common cause of cancer death worldwide, is infection with Helicobacter pylori bacterial strains that inject cytotoxin-associated gene A (CagA). As the first described bacterial oncoprotein, CagA causes gastric epithelial cell transformation by promoting an epithelial-to-mesenchymal transition (EMT)-like phenotype that disrupts junctions and enhances motility and invasiveness of the infected cells. However, the mechanism by which CagA disrupts gastric epithelial cell polarity to achieve its oncogenicity is not fully understood. Here we found that the apoptosis-stimulating protein of p53 2 (ASPP2), a host tumor suppressor and an important CagA target, contributes to the survival of cagA-positive H. pylori in the lumen of infected gastric organoids. Mechanistically, the CagA-ASPP2 interaction is a key event that promotes remodeling of the partitioning-defective (PAR) polarity complex and leads to loss of cell polarity of infected cells. Blockade of cagA-positive H. pylori ASPP2 signaling by inhibitors of the EGFR (epidermal growth factor receptor) signaling pathway-identified by a high-content imaging screen-or by a CagA-binding ASPP2 peptide, prevents the loss of cell polarity and decreases the survival of H. pylori in infected organoids. These findings suggest that maintaining the host cell-polarity barrier would reduce the detrimental consequences of infection by pathogenic bacteria, such as H. pylori, that exploit the epithelial mucosal surface to colonize the host environment.

Cytoskeletal Proteins in Cancer and Intracellular Stress: A Therapeutic Perspective

Cytoskeletal proteins, which consist of different sub-families of proteins including microtubules, actin and intermediate filaments, are essential for survival and cellular processes in both normal as well as cancer cells. However, in cancer cells, these mechanisms can be altered to promote tumour development and progression, whereby the functions of cytoskeletal proteins are co-opted to facilitate increased migrative and invasive capabilities, proliferation, as well as resistance to cellular and environmental stresses. Herein, we discuss the cytoskeletal responses to important intracellular stresses (such as mitochondrial, endoplasmic reticulum and oxidative stresses), and delineate the consequences of these responses, including effects on oncogenic signalling. In addition, we elaborate how the cytoskeleton and its associated molecules present themselves as therapeutic targets. The potential and limitations of targeting new classes of cytoskeletal proteins are also explored, in the context of developing novel strategies that impact cancer progression.

Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.

In the early stages of drug discovery efforts, computational models are used to predict activity and prioritize compounds for experimental testing. New targets commonly lack the data necessary to build effective models, and the screening needed to generate that experimental data can be costly. We seek to improve the efficiency of the initial screening phase, and of the process of prioritizing compounds for subsequent screening.

We choose a small informer set of compounds based on publicly available prior screening data on distinct targets. We then collect experimental data on these informer compounds and use that data to predict the activity of other compounds in the set for the target of interest. Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening. We find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds. We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets (PKIS) chemogenomic data and in prospective experimental screens against three additional non-human kinase targets.

Application of a MYC degradation screen identifies sensitivity to CDK9 inhibitors in KRAS-mutant pancreatic cancer

Stabilization of the MYC oncoprotein by KRAS signaling critically promotes the growth of pancreatic ductal adenocarcinoma (PDAC). Thus, understanding how MYC protein stability is regulated may lead to effective therapies. Here, we used a previously developed, flow cytometry-based assay that screened a library of >800 protein kinase inhibitors and identified compounds that promoted either the stability or degradation of MYC in a KRAS-mutant PDAC cell line. We validated compounds that stabilized or destabilized MYC and then focused on one compound, UNC10112785, that induced the substantial loss of MYC protein in both two-dimensional (2D) and 3D cell cultures. We determined that this compound is a potent CDK9 inhibitor with a previously uncharacterized scaffold, caused MYC loss through both transcriptional and posttranslational mechanisms, and suppresses PDAC anchorage-dependent and anchorage-independent growth. We discovered that CDK9 enhanced MYC protein stability through a previously unknown, KRAS-independent mechanism involving direct phosphorylation of MYC at Ser⁶² Our study thus not only identifies a potential therapeutic target for patients with KRAS-mutant PDAC but also presents the application of a screening strategy that can be more broadly adapted to identify regulators of protein stability.

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

BACKGROUND

Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

METHODS

Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay.

RESULTS

Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model).

CONCLUSIONS

These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets

The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.

A Perspective on Extreme Open Science: Companies Sharing Compounds without Restriction

Although the human genome provides the blueprint for life, most of the proteins it encodes remain poorly studied. This perspective describes how one group of scientists, in seeking new targets for drug discovery, used open science through unrestricted sharing of small molecules to shed light on dark matter of the genome. Starting initially with a single pharmaceutical company before expanding to multiple companies, a precedent was established for sharing published kinase inhibitors as chemical tools. The integration of open science and kinase chemogenomics has supported the study of many new potential drug targets by the scientific community.

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.

In Silico Screen and Structural Analysis Identifies Bacterial Kinase Inhibitors which Act with β-Lactams To Inhibit Mycobacterial Growth

New tools and concepts are needed to combat antimicrobial resistance. Actinomycetes and firmicutes share several eukaryotic-like Ser/Thr kinases (eSTK) that offer antibiotic development opportunities, including PknB, an essential mycobacterial eSTK. Despite successful development of potent biochemical PknB inhibitors by many groups, clinically useful microbiologic activity has been elusive. Additionally, PknB kinetics are not fully described, nor are structures with specific inhibitors available to inform inhibitor design. We used computational modeling with available structural information to identify human kinase inhibitors predicted to bind PknB, and we selected hits based on drug-like characteristics intended to increase the likelihood of cell entry. The computational model suggested a family of inhibitors, the imidazopyridine aminofurazans (IPAs), bind PknB with high affinity. We performed an in-depth characterization of PknB and found that these inhibitors biochemically inhibit PknB, with potency roughly following the predicted models. A novel X-ray structure confirmed that the inhibitors bound as predicted and made favorable protein contacts with the target. These inhibitors also have antimicrobial activity toward mycobacteria and nocardia. We demonstrated that the inhibitors are uniquely potentiated by β-lactams but not antibiotics traditionally used to treat mycobacteria, consistent with PknB's role in sensing cell wall stress. This is the first demonstration in the phylum actinobacteria that some β-lactam antibiotics could be more effective if paired with a PknB inhibitor. Collectively, our data show that in silico modeling can be used as a tool to discover promising drug leads, and the inhibitors we discovered can act with clinically relevant antibiotics to restore their efficacy against bacteria with limited treatment options.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

GW779439X and Its Pyrazolopyridazine Derivatives Inhibit the Serine/Threonine Kinase Stk1 and Act As Antibiotic Adjuvants against β-Lactam-Resistant Staphylococcus aureus

As antibiotic resistance rises, there is a need for strategies such as antibiotic adjuvants to conserve already-established antibiotics. A family of bacterial kinases known as the penicillin-binding-protein and serine/threonine kinase-associated (PASTA) kinases has attracted attention as targets for antibiotic adjuvants for β-lactams. Here, we report that the pyrazolopyridazine GW779439X sensitizes methicillin-resistant Staphylococcus aureus (MRSA) to various β-lactams through inhibition of the PASTA kinase Stk1. GW779439X potentiates β-lactam activity against multiple MRSA and MSSA isolates, including the sensitization of a ceftaroline-resistant isolate to ceftaroline. In silico modeling was used to guide the synthesis of GW779439X derivatives. The presence and orientation of GW779439X's methylpiperazine moiety was crucial for robust biochemical and microbiologic activity. Taken together, our data provide a proof of concept for developing the pyrazolopyridazines as selective Stk1 inhibitors which act across S. aureus isolates.

Hydrophobic Derivatives of Glycopeptide Antibiotics as Inhibitors of Protein Kinases

As key regulators of cell signaling, protein kinases (PKs) are attractive targets for therapeutic intervention in a variety of diseases. Herein, we report for the first time the inhibitory activity of polycyclic peptides, particularly, derivatives of glycopeptide antibiotics teicoplanin and eremomycin, against a panel of 12 recombinant human protein kinases and two protein kinases (CK1 and CK2) isolated from rat liver. Several of the investigated compounds inhibited various PKs with IC50 values below 10 μM and caused >90% suppression of the enzyme activity at 10 µM concentration. Kinetic analysis of the protein kinase CK2α inhibition by the teicoplanin aglycon analogue (7) demonstrated the non-competitive mechanism of inhibition (with regard to ATP). Interestingly, the inhibitory activity of some investigated compounds correlated with the earlier described antiviral activity against HIV, HCV, and other corona- and flaviviruses.

Application of Integrated Drug Screening/Kinome Analysis to Identify Inhibitors of Gemcitabine-Resistant Pancreatic Cancer Cell Growth

Continuous exposure of a pancreatic cancer cell line MIA PaCa-2 (MiaS) to gemcitabine resulted in the formation of a gemcitabine-resistant subline (MiaR). In an effort to discover kinase inhibitors that inhibited MiaR growth, MiaR cells were exposed to kinase inhibitors (PKIS-1 library) in a 384-well screening format. Three compounds (UNC10112721A, UNC10112652A, and UNC10112793A) were identified that inhibited the growth of MiaR cells by more than 50% (at 50 nM). Two compounds (UNC10112721A and UNC10112652A) were classified as cyclin-dependent kinase (CDK) inhibitors, whereas UNC10112793A was reported to be a PLK inhibitor. Dose-response experiments supported the efficacy of these compounds to inhibit growth and increase apoptosis in 2D cultures of these cells. However, only UNC10112721A significantly inhibited the growth of 3D spheroids composed of MiaR cells and GFP-tagged cancer-associated fibroblasts. Multiplexed inhibitor bead (MIB)-mass spectrometry (MS) kinome competition experiments identified CDK9, CLK1-4, DYRK1A, and CSNK1 as major kinase targets for UNC10112721A in MiaR cells. Another CDK9 inhibitor (CDK-IN-2) replicated the growth inhibitory effects of UNC10112721A, whereas inhibitors against the CLK, DYRK, or CSNK1 kinases had no effect. In summary, these studies describe a coordinated approach to discover novel kinase inhibitors, evaluate their efficacy in 3D models, and define their specificity against the kinome.

BRAF/MAPK and GSK3 signaling converges to control MITF nuclear export

The close integration of the MAPK, PI3K, and WNT signaling pathways underpins much of development and is deregulated in cancer. In principle, combinatorial posttranslational modification of key lineage-specific transcription factors would be an effective means to integrate critical signaling events. Understanding how this might be achieved is central to deciphering the impact of microenvironmental cues in development and disease. The microphthalmia-associated transcription factor MITF plays a crucial role in the development of melanocytes, the retinal pigment epithelium, osteoclasts, and mast cells and acts as a lineage survival oncogene in melanoma. MITF coordinates survival, differentiation, cell-cycle progression, cell migration, metabolism, and lysosome biogenesis. However, how the activity of this key transcription factor is controlled remains poorly understood. Here, we show that GSK3, downstream from both the PI3K and Wnt pathways, and BRAF/MAPK signaling converges to control MITF nuclear export. Phosphorylation of the melanocyte MITF-M isoform in response to BRAF/MAPK signaling primes for phosphorylation by GSK3, a kinase inhibited by both PI3K and Wnt signaling. Dual phosphorylation, but not monophosphorylation, then promotes MITF nuclear export by activating a previously unrecognized hydrophobic export signal. Nonmelanocyte MITF isoforms exhibit poor regulation by MAPK signaling, but instead their export is controlled by mTOR. We uncover here an unanticipated mode of MITF regulation that integrates the output of key developmental and cancer-associated signaling pathways to gate MITF flux through the import-export cycle. The results have significant implications for our understanding of melanoma progression and stem cell renewal.

Identification of lead anti-human cytomegalovirus compounds targeting MAP4K4 via machine learning analysis of kinase inhibitor screening data

Chemogenomic approaches involving highly annotated compound sets and cell based high throughput screening are emerging as a means to identify novel drug targets. We have previously screened a collection of highly characterized kinase inhibitors (Khan et al., Journal of General Virology, 2016) to identify compounds that increase or decrease expression of a human cytomegalovirus (HCMV) protein in infected cells. To identify potential novel anti-HCMV drug targets we used a machine learning approach to relate our phenotypic data from the aforementioned screen to kinase inhibition profiling of compounds used in this screen. Several of the potential targets had no previously reported role in HCMV replication. We focused on one potential anti-HCMV target, MAPK4K, and identified lead compounds inhibiting MAP4K4 that have anti-HCMV activity with little cellular cytotoxicity. We found that treatment of HCMV infected cells with inhibitors of MAP4K4, or an siRNA that inhibited MAP4K4 production, reduced HCMV replication and impaired detection of IE2-60, a viral protein necessary for efficient HCMV replication. Our findings demonstrate the potential of this machine learning approach to identify novel anti-viral drug targets, which can inform the discovery of novel anti-viral lead compounds.

High-throughput screen for compounds that modulate neurite growth of human induced pluripotent stem cell-derived neurons

Development of technology platforms to perform compound screens of human induced pluripotent stem cell (hiPSC)-derived neurons with relatively high throughput is essential to realize their potential for drug discovery. Here, we demonstrate the feasibility of high-throughput screening of hiPSC-derived neurons using a high-content, image-based approach focused on neurite growth, a process that is fundamental to formation of neural networks and nerve regeneration. From a collection of 4421 bioactive small molecules, we identified 108 hit compounds, including 37 approved drugs, that target molecules or pathways known to regulate neurite growth, as well as those not previously associated with this process. These data provide evidence that many pathways and targets known to play roles in neurite growth have similar activities in hiPSC-derived neurons that can be identified in an unbiased phenotypic screen. The data also suggest that hiPSC-derived neurons provide a useful system to study the mechanisms of action and off-target activities of the approved drugs identified as hits, leading to a better understanding of their clinical efficacy and toxicity, especially in the context of specific human genetic backgrounds. Finally, the hit set we report constitutes a sublibrary of approved drugs and tool compounds that modulate neurites. This sublibrary will be invaluable for phenotypic analyses and interrogation of hiPSC-based disease models as probes for defining phenotypic differences and cellular vulnerabilities in patient versus control cells, as well as for investigations of the molecular mechanisms underlying human neurite growth in development and maintenance of neuronal networks, and nerve regeneration.

Summary: High-throughput, small molecule screening of hiPSC-derived neurons using a high-content, image-based approach focused on neurite growth identified hit compounds, including approved drugs, which target molecules or pathways known to regulate neurite growth.

The polo-like kinase 1 inhibitor volasertib synergistically increases radiation efficacy in glioma stem cells

Background

Despite the availability of hundreds of cancer drugs, there is insufficient data on the efficacy of these drugs on the extremely heterogeneous tumor cell populations of glioblastoma (GBM).

Results

The PKIS of 357 compounds was initially evaluated in 15 different GSC lines which then led to a more focused screening of the 21 most highly active compounds in 11 unique GSC lines using HTS screening for cell viability. We further validated the HTS result with the second-generation PLK1 inhibitor volasertib as a single agent and in combination with ionizing radiation (IR). In vitro studies showed that volasertib inhibited cell viability, and high levels of the anti-apoptotic protein Bcl-xL expression were highly correlated with volasertib resistance. Volasertib sensitized GSCs to radiation therapy by enhancing G2/M arrest and by inducing apoptosis. Colony-formation assay demonstrated that volasertib plus IR synergistically inhibited colony formation. In intracranial xenograft mouse models, the combination of volasertib and radiation significantly inhibited GSC tumor growth and prolonged median survival compared with radiation treatment alone due to inhibition of cell proliferation, enhancement of DNA damage, and induction of apoptosis.

Conclusions

Our results reinforce the potential therapeutic efficacy of volasertib in combination with radiation for the treatment of GBM.

Methods

We used high-throughput screening (HTS) to identify drugs, out of 357 compounds in the published Protein Kinase Inhibitor Set, with the greatest efficacy against a panel of glioma stem cells (GSCs), which are representative of the classic cancer genome atlas (TCGA) molecular subtypes.

A screen for kinase inhibitors identifies antimicrobial imidazopyridine aminofurazans as specific inhibitors of the Listeria monocytogenes PASTA kinase PrkA

Bacterial signaling systems such as protein kinases and quorum sensing have become increasingly attractive targets for the development of novel antimicrobial agents in a time of rising antibiotic resistance. The family of bacterial Penicillin-binding-protein And Serine/Threonine kinase-Associated (PASTA) kinases is of particular interest due to the role of these kinases in regulating resistance to β-lactam antibiotics. As such, small-molecule kinase inhibitors that target PASTA kinases may prove beneficial as treatments adjunctive to β-lactam therapy. Despite this interest, only limited progress has been made in identifying functional inhibitors of the PASTA kinases that have both activity against the intact microbe and high kinase specificity. Here, we report the results of a small-molecule screen that identified GSK690693, an imidazopyridine aminofurazan-type kinase inhibitor that increases the sensitivity of the intracellular pathogen Listeria monocytogenes to various β-lactams by inhibiting the PASTA kinase PrkA. GSK690693 potently inhibited PrkA kinase activity biochemically and exhibited significant selectivity for PrkA relative to the Staphylococcus aureus PASTA kinase Stk1. Furthermore, other imidazopyridine aminofurazans could effectively inhibit PrkA and potentiate β-lactam antibiotic activity to varying degrees. The presence of the 2-methyl-3-butyn-2-ol (alkynol) moiety was important for both biochemical and antimicrobial activity. Finally, mutagenesis studies demonstrated residues in the back pocket of the active site are important for GSK690693 selectivity. These data suggest that targeted screens can successfully identify PASTA kinase inhibitors with both biochemical and antimicrobial specificity. Moreover, the imidazopyridine aminofurazans represent a family of PASTA kinase inhibitors that have the potential to be optimized for selective PASTA kinase inhibition.

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.

Progress towards a public chemogenomic set for protein kinases and a call for contributions

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.