Extending kinome coverage by analysis of kinase inhibitor broad profiling data

Structure-Activity Relationship Study and Design Strategies of Hydantoin, Thiazolidinedione, and Rhodanine-Based Kinase Inhibitors: A Two-Decade Review

Cancer is one of the most prominent causes of the rapidly growing mortality numbers worldwide. Cancer originates from normal cells that have acquired the capability to alter their molecular, biochemical, and cellular traits. The alteration of cell signaling enzymes, such as kinases, can initiate and amplify cancer progression. As a curative method, the targeted therapy utilized small molecules' capability to inhibit kinase's cellular function. This review provides a brief history (1999-2023) of Small Molecule Kinase Inhibitors (SMKIs) discovery with their molecular perspective. Furthermore, this current review also addresses the application and the development of hydantoin, thiazolidinedione, and rhodanine-based derivatives as kinase inhibitors toward several subclasses (EGFR, PI3K, VEGFR, Pim, c-Met, CDK, IGFR, and ERK) accompanied by their structure-activity relationship study and their molecular interactions. The present work summarizes and compiles all the important structural information essential for developing hydantoin, thiazolidinedione, and rhodanine-based kinase inhibitors to improve their potency in the future.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Palbociclib (PD 0332991) Interaction with Kinases. Theoretical and Comparative Molecular Docking Study

The optimized geometry of palbociclib, (PD 0332991) (8-cyclopentyl-6-ethanoyl-5-methyl-2-(5-(piperazin-1-yl)pyridin-2-ylamino)pyrido[2,3-d]pyrimidin-7(8H)-one), electrostatic potential map, molecular orbitals were calculated using the density functional theory. The geometry was used in a molecular docking study of palbociclib-kinase complexes, results could be explained by the charge of the nitrogen and oxygen atoms within the palbociclib. Energy gap of HOMO-LUMO surfaces, could help to explain the reactivity of the ligand and the hydrogen bonding with three different kinases, two of CDK6 and one of CDK4 type. Docking results are similar and complementary with literature reports using molecular dynamics, were hydrogen bonding was obtained and analyzed. The promiscuity of three kinases with palbociclib was detected by the docking results, thus, palbociclib could be used in other types of cancer besides myeloid leukemia. Some similarities are found with CDK4/CDK6 kinases which allow us to determine that palbociclib could be used to control other resistant inhibitor types of cancer.

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

BACKGROUND

Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

METHODS

CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

RESULTS

Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.

CONCLUSIONS

CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

BRD9 Inhibition by Natural Polyphenols Targets DNA Damage/Repair and Apoptosis in Human Colon Cancer Cells

Epigenetic mechanisms play an important role in the etiology of colorectal cancer (CRC) and other malignancies due, in part, to deregulated bromodomain (BRD) functions. Inhibitors of the bromodomain and extraterminal (BET) family have entered into clinical trials as anticancer agents, and interest has grown in other acetyl 'reader' proteins as therapeutic targets, including non-BET member bromodomain-containing protein 9 (BRD9). We report here that overexpression of BRD9 is associated with poor prognosis in CRC patients, and that siRNA-mediated knockdown of BRD9 decreased cell viability and activated apoptosis in human colon cancer cells, coincident with increased DNA damage. Seeking natural compounds as BRD9 antagonists, molecular docking in silico identified several polyphenols such as Epigallocatechin-3-gallate (EGCG), Equol, Quercetin, and Aspalathin, with favorable binding energies, supported by BROMOscan^® (DiscoverX) and isothermal titration calorimetry experiments. Polyphenols mimicked BRD9 knockdown and iBRD9 treatment in reducing colon cancer cell viability, inhibiting colony formation, and enhancing DNA damage and apoptosis. Normal colonic epithelial cells were unaffected, signifying cancer-specific effects. These findings suggest that natural polyphenols recognize and target BRD9 for inhibition, and might serve as useful lead compounds for bromodomain therapeutics in the clinical setting.

KOPI: Kinase inhibitOr Proteome Impact analysis

Kinase inhibitors often exert on/off-target effects, and efficient data analysis is essential for assessing these effects on the proteome. We developed a workflow for rapidly performing such a proteomic assessment, termed as kinase inhibitor proteome impact analysis (KOPI). We demonstrate KOPI’s utility with staurosporine (STS) on the leukemic K562 cell proteome. We identified systematically staurosporine’s non-kinome interactors, and showed for the first time that it caused paradoxical hyper- and biphasic phosphorylation.

Tracking mutation and drug-driven alterations of oncokinase conformations

Numerous kinases act as central nodes of cellular signaling networks. As such, many of these enzymes function as molecular switches for coordinating spatiotemporal signal transmission. Typically, it is the compartmentalized phosphorylation of protein substrates which relays the transient input signal to determine decisive physiological cell responses. Genomic alterations affect kinase abundance and/or their activities which contribute to the malignant transformation, progression, and metastasis of human cancers. Thus, major drug discovery efforts have been made to identify lead molecules targeting clinically relevant oncokinases. The concept of personalized medicine aims to apply the therapeutic agent with the highest efficacy towards a patient-specific mutation. Here, we discuss the implementation of a cell-based reporter system which may foster the decision-making process to identify the most promising lead-molecules. We present a modular kinase conformation (KinCon) biosensor platform for live-cell analyses of kinase activity states. This biosensor facilitates the recording of kinase activity conformations of the wild-type and the respective mutated kinase upon lead molecule exposure. We reflect proof-of-principle studies demonstrating how this technology has been extended to profile drug properties of the full-length kinases BRAF and MEK1 in intact cells. Further, we pinpoint how this technology may open new avenues for systematic and patient-tailored drug discovery efforts. Overall, this precision-medicineoriented biosensor concept aims to determine kinase inhibitor specificity and anticipate their drug efficacies.

Targeted therapy for LIMD1-deficient non-small cell lung cancer subtypes

An early event in lung oncogenesis is loss of the tumour suppressor gene LIMD1 (LIM domains containing 1); this encodes a scaffold protein, which suppresses tumorigenesis via a number of different mechanisms. Approximately 45% of non-small cell lung cancers (NSCLC) are deficient in LIMD1, yet this subtype of NSCLC has been overlooked in preclinical and clinical investigations. Defining therapeutic targets in these LIMD1 loss-of-function patients is difficult due to a lack of 'druggable' targets, thus alternative approaches are required. To this end, we performed the first drug repurposing screen to identify compounds that confer synthetic lethality with LIMD1 loss in NSCLC cells. PF-477736 was shown to selectively target LIMD1-deficient cells in vitro through inhibition of multiple kinases, inducing cell death via apoptosis. Furthermore, PF-477736 was effective in treating LIMD1-/- tumours in subcutaneous xenograft models, with no significant effect in LIMD1+/+ cells. We have identified a novel drug tool with significant preclinical characterisation that serves as an excellent candidate to explore and define LIMD1-deficient cancers as a new therapeutic subgroup of critical unmet need.

VRK1 Depletion Facilitates the Synthetic Lethality of Temozolomide and Olaparib in Glioblastoma Cells

Background

Glioblastomas treated with temozolomide frequently develop resistance to pharmacological treatments. Therefore, there is a need to find alternative drug targets to reduce treatment resistance based on tumor dependencies. A possibility is to target simultaneously two proteins from different DNA-damage repair pathways to facilitate tumor cell death. Therefore, we tested whether targeting the human chromatin kinase VRK1 by RNA interference can identify this protein as a novel molecular target to reduce the dependence on temozolomide in combination with olaparib, based on synthetic lethality.

Materials and Methods

Depletion of VRK1, an enzyme that regulates chromatin dynamic reorganization and facilitates resistance to DNA damage, was performed in glioblastoma cells treated with temozolomide, an alkylating agent used for GBM treatment; and olaparib, an inhibitor of PARP-1, used as sensitizer. Two genetically different human glioblastoma cell lines, LN-18 and LN-229, were used for these experiments. The effect on the DNA-damage response was followed by determination of sequential steps in this process: H4K16ac, γH2AX, H4K20me2, and 53BP1.

Results

The combination of temozolomide and olaparib increased DNA damage detected by labeling free DNA ends, and chromatin relaxation detected by H4K16ac. The combination of both drugs, at lower doses, resulted in an increase in the DNA damage response detected by the formation of γH2AX and 53BP1 foci. VRK1 depletion did not prevent the generation of DNA damage in TUNEL assays, but significantly impaired the DNA damage response induced by temozolomide and olaparib, and mediated by γH2AX, H4K20me2, and 53BP1. The combination of these drugs in VRK1 depleted cells resulted in an increase of glioblastoma cell death detected by annexin V and the processing of PARP-1 and caspase-3.

Conclusion

Depletion of the chromatin kinase VRK1 promotes tumor cell death at lower doses of a combination of temozolomide and olaparib treatments, and can be a novel alternative target for therapies based on synthetic lethality.

Engineering Selectivity for Reduced Toxicity of Bacterial Kinase Inhibitors Using Structure-Guided Medicinal Chemistry

Tuberculosis is a major global public health concern, and new drugs are needed to combat both the typical form and the increasingly common drug-resistant form of this disease. The essential tuberculosis kinase PknB is an attractive drug development target because of its central importance in several critical signaling cascades. A major hurdle in kinase inhibitor development is the reduction of toxicity due to nonspecific kinase activity in host cells. Here a novel class of PknB inhibitors was developed from hit aminopyrimidine 1 (GW779439X), which was originally designed for human CDK4 but failed to progress clinically because of high toxicity and low specificity. Replacing the pyrazolopyridazine headgroup of the original hit with substituted pyridine or phenyl headgroups resulted in a reduction of Cdk activity and a 3-fold improvement in specificity over the human kinome while maintaining PknB activity. This also resulted in improved microbiological activity and reduced toxicity in THP-1 cells and zebrafish.

KinCon: Cell-based recording of full-length kinase conformations

The spectrum of kinase alterations displays distinct functional characteristics and requires kinase mutation-oriented strategies for therapeutic interference. Besides phosphotransferase activity, protein abundance, and intermolecular interactions, particular patient-mutations promote pathological kinase conformations. Despite major advances in identifying lead molecules targeting clinically relevant oncokinase functions, still many kinases are neglected and not part of drug discovery efforts. One explanation is attributed to challenges in tracking kinase activities. Chemical probes are needed to functionally annotate kinase functions, whose activities may not always depend on catalyzing phospho-transfer. Such non-catalytic kinase functions are related to transitions of full-length kinase conformations. Recent findings underline that cell-based reporter systems can be adapted to record conformation changes of kinases. Here, we discuss the possible applications of an extendable kinase conformation (KinCon) reporter toolbox for live-cell recording of kinase states. KinCon is a genetically encoded bioluminescence-based biosensor platform, which can be subjected for measurements of conformation dynamics of mutated kinases upon small molecule inhibitor exposure. We hypothesize that such biosensors can be utilized to delineate the molecular modus operandi for kinase and pseudokinase regulation. This should pave the path for full-length kinase-targeted drug discovery efforts aiming to identify single and combinatory kinase inhibitor therapies with increased specificity and efficacy.

GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth.. [DOI: 10.1101/2020.05.20.107458]

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.

Discovery of a novel and highly selective CDK9 kinase inhibitor (JSH-009) with potent antitumor efficacy in preclinical acute myeloid leukemia models

Acute myeloid leukemia (AML) is reported to be vulnerable to transcription disruption due to transcriptional addiction. Cyclin-dependent kinase 9 (CDK9), which regulates transcriptional elongation, has attracted extensive attention as a drug target. Although several inhibitors, such as alvocidib and dinaciclib, have shown potent therapeutic effects in clinical trials on AML, the lack of high selectivity for CDK9 and other CDKs has limited their optimal clinical efficacy. Therefore, developing highly selective CDK9 inhibitors is still imperative for the efficacy and safety profile in treating AML. Here, we report a novel highly selective CDK9 inhibitor, JSH-009, which exhibited high potency against CDK9 and displayed great selectivity over 468 kinases/mutants. It also demonstrates impressive in vitro and in vivo antileukemic efficacy in preclinical models of AML, which makes JSH-009 a useful pharmacological tool for elucidating CDK9-mediated transcription and a novel therapeutic candidate for AML.

A chemical toolbox for the study of bromodomains and epigenetic signaling

Bromodomains (BRDs) are conserved protein interaction modules which recognize (read) acetyl-lysine modifications, however their role(s) in regulating cellular states and their potential as targets for the development of targeted treatment strategies is poorly understood. Here we present a set of 25 chemical probes, selective small molecule inhibitors, covering 29 human bromodomain targets. We comprehensively evaluate the selectivity of this probe-set using BROMOscan and demonstrate the utility of the set identifying roles of BRDs in cellular processes and potential translational applications. For instance, we discovered crosstalk between histone acetylation and the glycolytic pathway resulting in a vulnerability of breast cancer cell lines under conditions of glucose deprivation or GLUT1 inhibition to inhibition of BRPF2/3 BRDs. This chemical probe-set will serve as a resource for future applications in the discovery of new physiological roles of bromodomain proteins in normal and disease states, and as a toolset for bromodomain target validation.

Bromodomains are conserved protein interaction modules that recognize acetyl-lysine modifications. Here the authors present a set of 25 selective small molecule inhibitors covering 29 human bromodomain targets and comprehensively evaluate the selectivity of this probe-set.

Drug Sensitivity Screening and Targeted Pathway Analysis Reveal a Multi-Driver Proliferative Mechanism and Suggest a Strategy of Combination Targeted Therapy for Colorectal Cancer Cells

Treatment of colorectal cancer mostly relies on traditional therapeutic approaches, such as surgery and chemotherapy. Limited options of targeted therapy for colorectal cancer narrowly focus on blocking cancer-generic targets VEGFR and EGFR. Identifying the oncogenic drivers, understanding their contribution to proliferation, and finding inhibitors to block such drivers are the keys to developing targeted therapy for colorectal cancer. In this study, ten colorectal cancer cell lines were screened against a panel of protein kinase inhibitors blocking key oncogenic signaling pathways. The results show that four of the 10 cell lines did not respond to any kinase inhibitors significantly, the other six were mildly inhibited by AZD-6244, BMS-754807, and/or dasatinib. Mechanistic analyses demonstrate that these inhibitors independently block the MAP kinase pathway, IR/IGF-1R/AKT pathway, and Src kinases, suggesting a multi-driver nature of proliferative signaling in these cells. Most of these cell lines were potently and synergistically inhibited by pair-wise combinations of these drugs. Furthermore, seven of the 10 cell lines were inhibited by the triple combination of AZD-6244/BMS-754807/dasatinib with IC₅₀’s between 10 and 84 nM. These results suggest that combination targeted therapy may be an effective strategy against colorectal cancer.

Kinase inhibitor library screening identifies synergistic drug combinations effective in sensitive and resistant melanoma cells

Background

Melanoma is the most aggressive and deadly form of skin cancer with increasing case numbers worldwide. The development of inhibitors targeting mutated BRAF (found in around 60% of melanoma patients) has markedly improved overall survival of patients with late-stage tumors, even more so when combined with MEK inhibitors targeting the same signaling pathway. However, invariably patients become resistant to this targeted therapy resulting in rapid progression with treatment-refractory disease. The purpose of this study was the identification of new kinase inhibitors that do not lead to the development of resistance in combination with BRAF inhibitors (BRAFi), or that could be of clinical benefit as a 2nd line treatment for late-stage melanoma patients that have already developed resistance.

Methods

We have screened a 274-compound kinase inhibitor library in 3 BRAF mutant melanoma cell lines (each one sensitive or made resistant to 2 distinct BRAFi). The screening results were validated by dose-response studies and confirmed the killing efficacies of many kinase inhibitors. Two different tools were applied to investigate and quantify potential synergistic effects of drug combinations: the Chou-Talalay method and the Synergyfinder application. In order to exclude that resistance to the new treatments might occur at later time points, synergistic combinations were administered to fluorescently labelled parental and resistant cells over a period of > 10 weeks.

Results

Eight inhibitors targeting Wee1, Checkpoint kinase 1/2, Aurora kinase, MEK, Polo-like kinase, PI3K and Focal adhesion kinase killed melanoma cells synergistically when combined with a BRAFi. Additionally, combination of a Wee1 and Chk inhibitor showed synergistic killing effects not only on sensitive cell lines, but also on intrinsically BRAFi- and treatment induced-resistant melanoma cells. First in vivo studies confirmed these observations. Interestingly, continuous treatment with several of these drugs, alone or in combination, did not lead to emergence of resistance.

Conclusions

Here, we have identified new, previously unexplored (in the framework of BRAFi resistance) inhibitors that have an effect not only on sensitive but also on BRAFi-resistant cells. These promising combinations together with the new immunotherapies could be an important step towards improved 1st and 2nd line treatments for late-stage melanoma patients.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1038-x) contains supplementary material, which is available to authorized users.

Drug target ontology to classify and integrate drug discovery data

Background

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.

Results

As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.

Conclusions

DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (http://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (http://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Electronic supplementary material

The online version of this article (10.1186/s13326-017-0161-x) contains supplementary material, which is available to authorized users.

Protocols for the Design of Kinase-focused Compound Libraries

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery

Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (F_ic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined F_ic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. F_ic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery.

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Industrial medicinal chemistry insights: neuroscience hit generation at Janssen

The role of medicinal chemistry has changed over the past 10 years. Chemistry had become one step in a process; funneling the output of high-throughput screening (HTS) on to the next stage. The goal to identify the ideal clinical compound remains, but the means to achieve this have changed. Modern medicinal chemistry is responsible for integrating innovation throughout early drug discovery, including new screening paradigms, computational approaches, novel synthetic chemistry, gene-family screening, investigating routes of delivery, and so on. In this Foundation Review, we show how a successful medicinal chemistry team has a broad impact and requires multidisciplinary expertise in these areas.

Measuring Kinase Activity-A Global Challenge

The kinase enzymes within a cell, known collectively as the kinome, play crucial roles in many signaling pathways, including survival, motility, differentiation, stress response, and many more. Aberrant signaling through kinase pathways is often linked to cancer, among other diseases. A major area of scientific research involves understanding the relationships between kinases, their targets, and how the kinome adapts to perturbations of the cellular system. This review will discuss many of the current and developing methods for studying kinase activity, and evaluate their applications, advantages, and disadvantages. J. Cell. Biochem. 118: 3595-3606, 2017. © 2017 Wiley Periodicals, Inc.

Novel lead structures with both Plasmodium falciparum gametocytocidal and asexual blood stage activity identified from high throughput compound screening

BACKGROUND

Blocking malaria transmission is an important step in eradicating malaria. In the field, transmission requires the production of sexual stage Plasmodium parasites, called gametocytes, which are not effectively killed by the commonly used anti-malarials allowing individuals to remain infectious after clearance of asexual parasites.

METHODS

To identify new gametocytocidal compounds, a library of 45,056 compounds with diverse structures was screened using a high throughput gametocyte viability assay. The characteristics of active hits were further evaluated against asexual stage parasites in a growth inhibition assay. Their cytotoxicity were tested against mammalian cells in a cytotoxicity assay. The chemical scaffold similarity of active hits were studied using scaffold cluster analysis.

RESULTS

A set of 23 compounds were identified and further confirmed for their activity against gametocytes. All the 23 confirmed compounds possess dual-activities against both gametocytes responsible for human to mosquito transmission and asexual parasites that cause the clinical symptoms. Three of these compounds were fourfold more active against gametocytes than asexual parasites. Further cheminformatic analysis revealed three sets of novel scaffolds, including highly selective 4-1H-pyrazol-5-yl piperidine analogs.

CONCLUSIONS

This study revealed important new structural scaffolds that can be used as starting points for dual activity anti-malarial drug development.

The use of novel selectivity metrics in kinase research

Background

Compound selectivity is an important issue when developing a new drug. In many instances, a lack of selectivity can translate to increased toxicity. Protein kinases are particularly concerned with this issue because they share high sequence and structural similarity. However, selectivity may be assessed early on using data generated from protein kinase profiling panels.

Results

To guide lead optimization in drug discovery projects, we propose herein two new selectivity metrics, namely window score (WS) and ranking score (RS). These metrics can be applied to standard in vitro data–including intrinsic enzyme activity/affinity (Ki, IC₅₀ or percentage of inhibition), cell-based potency (percentage of effect, EC₅₀) or even kinetics data (Kd, Kon and Koff). They are both easy to compute and offer different viewpoints from which to consider compound selectivity.

Conclusions

We performed a comparative analysis of their respective performance on several data sets against already published selectivity metrics and analyzed how they might influence compound selection. Our results showed that the two new metrics bring additional information to prioritize compound selection.

Graphical Abstract

Two novel metrics were developed to better estimate selectivity of compounds screened on multiple proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1413-y) contains supplementary material, which is available to authorized users.

Translational Prospects and Challenges in Human Induced Pluripotent Stem Cell Research in Drug Discovery

Despite continuous efforts to improve the process of drug discovery and development, achieving success at the clinical stage remains challenging because of a persistent translational gap between the preclinical and clinical settings. Under these circumstances, the discovery of human induced pluripotent stem (iPS) cells has brought new hope to the drug discovery field because they enable scientists to humanize a variety of pharmacological and toxicological models in vitro. The availability of human iPS cell-derived cells, particularly as an alternative for difficult-to-access tissues and organs, is increasing steadily; however, their use in the field of translational medicine remains challenging. Biomarkers are an essential part of the translational effort to shift new discoveries from bench to bedside as they provide a measurable indicator with which to evaluate pharmacological and toxicological effects in both the preclinical and clinical settings. In general, during the preclinical stage of the drug development process, in vitro models that are established to recapitulate human diseases are validated by using a set of biomarkers; however, their translatability to a clinical setting remains problematic. This review provides an overview of current strategies for human iPS cell-based drug discovery from the perspective of translational research, and discusses the importance of early consideration of clinically relevant biomarkers.

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools.

Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach

Targeted polypharmacology of kinases has emerged as a promising strategy to design efficient and safe therapeutics. Here, we perform a systematic study of kinase-ligand binding modes for the human structural kinome at scale (208 kinases, 1777 unique ligands, and their complexes) by integrating chemical genomics and structural genomics data and by introducing a functional site interaction fingerprint (Fs-IFP) method. New insights into kinase-ligand binding modes were obtained. We establish relationships between the features of binding modes, the ligands, and the binding pockets, respectively. We also drive the intrinsic binding specificity and which correlation with amino acid conservation. Third, we explore the landscape of the binding modes and highlight the regions of "selectivity pocket" and "selectivity entrance". Finally, we demonstrate that Fs-IFP similarity is directly correlated to the experimentally determined profile. These improve our understanding of kinase-ligand interactions and contribute to the design of novel polypharmacological therapies targeting kinases.

Protein Kinase Selectivity Profiling Using Microfluid Mobility Shift Assays

Biochemical selectivity profiling is an integral part of early drug development. Typically compounds from optimization phase are regularly tested for off-target activities within or across target families. This article presents workflow and critical aspects of biochemical protein kinase profiling based on microfluidic mobility shift assays.

Polypharmacology in Precision Oncology: Current Applications and Future Prospects

Over the past decade, a more comprehensive, large-scale approach to studying cancer genetics and biology has revealed the challenges of tumor heterogeneity, adaption, evolution and drug resistance, while systems-based pharmacology and chemical biology strategies have uncovered a much more complex interaction between drugs and the human proteome than was previously anticipated. In this mini-review we assess the progress and potential of drug polypharmacology in biomarker-driven precision oncology. Polypharmacology not only provides great opportunities for drug repurposing to exploit off-target effects in a new single-target indication but through simultaneous blockade of multiple targets or pathways offers exciting opportunities to slow, overcome or even prevent inherent or adaptive drug resistance. We highlight the many challenges associated with exploiting known or desired polypharmacology in drug design and development, and assess computational and experimental methods to uncover unknown polypharmacology. A comprehensive understanding of the intricate links between polypharmacology, efficacy and safety is urgently needed if we are to tackle the enduring challenge of cancer drug resistance and to fully exploit polypharmacology for the ultimate benefit of cancer patients.

Discovery of wrightiadione as a novel template for the TrkA kinase inhibitors

Enzymatic kinase assays and docking simulation studies have shown that the natural product wrightiadione displays inhibitory activity toward TrkA and PLK3. In this study, the template of wrightiadione served as a starting point for Trk inhibitor development campaigns. Molecular simulation provided structural insights for the design of derivatives that were efficiently generated by our recently developed 3-step tandem synthetic approach, resulting in the discovery of compound 2h with biochemical potency at the single-digit micromolar level.

Integration of Ligand and Structure Based Approaches for CSAR-2014

The prediction of binding poses and affinities is an area of active interest in computer-aided drug design (CADD). Given the documented limitations with either ligand or structure based approaches, we employed an integrated approach and developed a rapid protocol for binding mode and affinity predictions. This workflow was applied to the three protein targets of Community Structure-Activity Resource-2014 (CSAR-2014) exercise: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA (guanine-N(1))-methyltransferase (TrmD). Our docking and scoring workflow incorporates compound clustering and ligand and protein structure based pharmacophore modeling, followed by local docking, minimization, and scoring. While the former part of the protocol ensures high-quality ligand alignments and mapping, the subsequent minimization and scoring provides the predicted binding modes and affinities. We made blind predictions of docking pose for 1, 5, and 14 ligands docked into 1, 2, and 12 crystal structures of FXa, SYK, and TrmD, respectively. The resulting 174 poses were compared with cocrystallized structures (1, 5, and 14 complexes) made available at the end of CSAR. Our predicted poses were related to the experimentally determined structures with a mean root-mean-square deviation value of 3.4 Å. Further, we were able to classify high and low affinity ligands with the area under the curve values of 0.47, 0.60, and 0.69 for FXa, SYK, and TrmD, respectively, indicating the validity of our approach in at least two of the three systems. Detailed critical analysis of the results and CSAR methodology ranking procedures suggested that a straightforward application of our workflow has limitations, as some of the performance measures do not reflect the actual utility of pose and affinity predictions in the biological context of individual systems.

Identification of Bexarotene as a PPARγ Antagonist with HDX

The retinoid x receptors (RXRs) are the pharmacological target of Bexarotene, an antineoplastic agent indicated for the treatment of cutaneous T cell lymphoma (CTCL). The RXRs form heterodimers with several nuclear receptors (NRs), including peroxisome proliferator-activated receptor gamma (PPARγ), to regulate target gene expression through cooperative recruitment of transcriptional machinery. Here we have applied hydrogen/deuterium exchange (HDX) mass spectrometry to characterize the effects of Bexarotene on the conformational plasticity of the intact RXRα:PPARγ heterodimer. Interestingly, addition of Bexarotene to PPARγ in the absence of RXRα induced protection from solvent exchange, suggesting direct receptor binding. This observation was confirmed using a competitive binding assay. Furthermore, Bexarotene functioned as a PPARγ antagonist able to alter rosiglitazone induced transactivation in a cell based promoter:reporter transactivation assay. Together these results highlight the complex polypharmacology of lipophilic NR targeted small molecules and the utility of HDX for identifying and characterizing these interactions.