Measuring and interpreting the selectivity of protein kinase inhibitors

Protein kinase inhibitors are a well-established class of clinically useful drugs, particularly for the treatment of cancer. Achieving inhibitor selectivity for particular protein kinases often remains a significant challenge in the development of new small molecules as drugs or as tools for chemical biology research. This review summarises the methodologies available for measuring kinase inhibitor selectivity, both in vitro and in cells. The interpretation of kinase inhibitor selectivity data is discussed, particularly with reference to the structural biology of the protein targets. Measurement and prediction of kinase inhibitor selectivity will be important for the development of new multi-targeted kinase inhibitors.     

Identification of intracellular targets of small molecular weight chemical compounds using affinity chromatography

Efforts to characterize small molecular weight chemical inhibitors of pharmacological interest tend to identify molecules with high efficiency and selectivity, to meet the two criteria required for the clinical development of a drug: efficacy and harmlessness. Drug candidates are expected to inhibit efficiently the target they have been optimized against (for example, a particular type of protein kinase). These hits are also designed to not interfere (or as little as possible) with the activity of other cellular enzymes/proteins to reduce undesired side effects. Here we discuss the use of immobilized drugs as affinity chromatography matrices to purify and identify their bona fide intracellular targets. This method not only allows the systematic investigation of the selectivity of pharmacological compounds but also the anticipation of their putative adverse effects.     

Kinomics-structural biology and chemogenomics of kinase inhibitors and targets

Classifying kinases based entirely on small molecule selectivity data is a new approach to drug discovery that allows scientists to understand relationships between targets. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and polypharmacology of molecules in related targets. Currently, structural information is available for relatively few of the protein kinases encoded in the human genome (7% of the estimated 518); however, even the current knowledge base, when paired with structure-based design techniques, can assist in the identification and optimization of novel kinase inhibitors across the entire protein class. Chemogenomics attempts to combine genomic data, structural biological data, classical dendrograms, and selectivity data to explore, define, and classify the medicinally relevant kinase space. Exploitation of this information in the discovery of kinase inhibitors defines practical kinase chemogenomics (kinomics). In this paper, we review the available information on kinase targets and their inhibitors, and present the relationships between the various classification schema for kinase space. In particular, we present the first dendrogram of kinases based entirely on small molecule selectivity data. We find that the selectivity dendrogram differs from sequence-based clustering mostly in the higher-level groupings of the smaller clusters, and remains very comparable for closely homologous targets. Highly homologous kinases are, on average, inhibited comparably by small molecules. This observation, although intuitive, is very important to the process of target selection, as one would expect difficulty in achieving inhibitor selectivity for kinases that share high sequence identity.     

High affinity targets of protein kinase inhibitors have similar residues at the positions energetically important for binding

Inhibition of protein kinase activity is a focus of intense drug discovery efforts in several therapeutic areas. Major challenges facing the field include understanding of the factors determining the selectivity of kinase inhibitors and the development of compounds with the desired selectivity profile. Here, we report the analysis of sequence variability among high and low affinity targets of eight different small molecule kinase inhibitors (BIRB796, Tarceva, NU6102, Gleevec, SB203580, balanol, H89, PP1). It is observed that all high affinity targets of each inhibitor are found among a relatively small number of kinases, which have similar residues at the specific positions important for binding. The findings are highly statistically significant, and allow one to exclude the majority of kinases in a genome from a list of likely targets for an inhibitor. The findings have implications for the design of novel inhibitors with a desired selectivity profile (e.g. targeted at multiple kinases), the discovery of new targets for kinase inhibitor drugs, comparative analysis of different in vivo models, and the design of "a-la-carte" chemical libraries tailored for individual kinases.     

Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors

We describe a chemical proteomics approach to profile the interaction of small molecules with hundreds of endogenously expressed protein kinases and purine-binding proteins. This subproteome is captured by immobilized nonselective kinase inhibitors (kinobeads), and the bound proteins are quantified in parallel by mass spectrometry using isobaric tags for relative and absolute quantification (iTRAQ). By measuring the competition with the affinity matrix, we assess the binding of drugs to their targets in cell lysates and in cells. By mapping drug-induced changes in the phosphorylation state of the captured proteome, we also analyze signaling pathways downstream of target kinases. Quantitative profiling of the drugs imatinib (Gleevec), dasatinib (Sprycel) and bosutinib in K562 cells confirms known targets including ABL and SRC family kinases and identifies the receptor tyrosine kinase DDR1 and the oxidoreductase NQO2 as novel targets of imatinib. The data suggest that our approach is a valuable tool for drug discovery.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

High-throughput assays for promiscuous inhibitors

High-throughput screening (HTS) searches large libraries of chemical compounds for those that can modulate the activity of a particular biological target; it is the dominant technique used in early-stage drug discovery. A key problem in HTS is the prevalence of nonspecific or 'promiscuous' inhibitors. These molecules have peculiar properties, act on unrelated targets and can dominate the results from screening campaigns. Several explanations have been proposed to account for promiscuous inhibitors, including chemical reactivity, interference in assay read-out, high molecular flexibility and hydrophobicity. The diversity of these models reflects the apparently unrelated molecules whose behaviors they seek to explain. However, a single mechanism may explain the effects of many promiscuous inhibitors: some organic molecules form large colloid-like aggregates that sequester and thereby inhibit enzymes. Hits from HTS, leads for drug discovery and even several drugs appear to act through this mechanism at micromolar concentrations. Here, we report two rapid assays for detecting promiscuous aggregates that we tested against 1,030 'drug-like' molecules. The results from these assays were used to test two preliminary computational models of this phenomenon and as benchmarks to develop new models.     

Developing hybrid molecule therapeutics for diverse enzyme inhibitory action: Active role of coumarin-based structural leads in drug discovery

Hybrid drugs featuring two or more potentially bioactive pharmacophores have been recognized as advanced and superior chemical entities to simultaneously modulate multiple drug targets of multifactorial diseases, thus overcoming the severe side effects associated with a single drug molecule. The selection of these chemical moieties to produce hybrid structures with druggable properties is generally facilitated by the observed and/or anticipated synergistic pharmacological activities of the individual molecules. In this perspective, coumarin template has extensively been studied in pursuit of structurally diverse leads for drug development due to high affinity and specificity to different molecular targets. This review highlights the most commonly exploited approaches conceptualizing the design and construction of hybrid molecules by coupling two or more individual fragments with or without an appropriate linker. In addition to the design strategies, this review also summarizes and reflects on the therapeutic potential of these hybrid molecules for diverse enzyme inhibitory action as well as their observed structure-activity relationship (SAR). Several key features of the synthesized hybrid structures that assert a profound impact on the inhibitory function have also been discussed alongside computational investigations, inhibitor molecular diversity and selectivity toward multiple drug targets. Finally, these drug discovery and development efforts should serve as a handy reference aiming to provide a useful platform for the exploration of new coumarin-based compounds with enhanced enzyme inhibitory profile.     

Identification of Novel Inhibitors of M. tuberculosis Growth Using Whole Cell Based High-Throughput Screening

Despite the urgent need for new antitubercular drugs, few are on the horizon. To combat the problem of emerging drug resistance, structurally unique chemical entities that inhibit new targets will be required. Here we describe our investigations using whole cell screening of a diverse collection of small molecules as a methodology for identifying novel inhibitors that target new pathways for Mycobacterium tuberculosis drug discovery. We find that conducting primary screens using model mycobacterial species may limit the potential for identifying new inhibitors with efficacy against M. tuberculosis. In addition, we confirm the importance of developing in vitro assay conditions that are reflective of in vivo biology for maximizing the proportion of hits from whole cell screening that are likely to have activity in vivo. Finally, we describe the identification and characterization of two novel inhibitors that target steps in M. tuberculosis cell wall biosynthesis. The first is a novel benzimidazole that targets mycobacterial membrane protein large 3 (MmpL3), a proposed transporter for cell wall mycolic acids. The second is a nitro-triazole that inhibits decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1), an epimerase required for cell wall biosynthesis. These proteins are both among the small number of new targets that have been identified by forward chemical genetics using resistance generation coupled with genome sequencing. This suggests that methodologies currently employed for screening and target identification may lead to a bias in target discovery and that alternative methods should be explored.     

PI3K-independent AKT activation in cancers: a treasure trove for novel therapeutics

AKT/PKB serine threonine kinase, a critical signaling molecule promoting cell growth and survival pathways, is frequently dysregulated in many cancers. Although phosphatidylinositol-3-OH kinase (PI3K), a lipid kinase, is well characterized as a major regulator of AKT activation in response to a variety of ligands, recent studies highlight a diverse group of tyrosine (Ack1/TNK2, Src, PTK6) and serine/threonine (TBK1, IKBKE, DNAPKcs) kinases that activate AKT directly to promote its pro-proliferative signaling functions. While some of these alternate AKT activating kinases respond to growth factors, others respond to inflammatory and genotoxic stimuli. A common theme emerging from these studies is that aberrant or hyperactivation of these alternate kinases is often associated with malignancy. Consequently, evaluating the use of small molecular inhibitors against these alternate AKT activating kinases at earlier stages of cancer therapy may overcome the pressing problem of drug resistance surfacing especially in patients treated with PI3K inhibitors.     

The Small Molecules AZD0530 and Dasatinib Inhibit Dengue Virus RNA Replication via Fyn Kinase

In this study, we characterized the antiviral mechanism of action of AZD0530 and dasatinib, two pharmacological inhibitors of host kinases, that also inhibit dengue virus (DV) infection. Using Northern blot and reporter replicon assays, we demonstrated that both small molecules inhibit the DV2 infectious cycle at the step of steady-state RNA replication. In order to identify the cellular target of AZD0530 and dasatinib mediating this anti-DV2 activity, we examined the effects of RNA interference (RNAi)-mediated depletion of the major kinases known to be inhibited by these small molecules. We determined that Fyn kinase, a target of both AZD0530 and dasatinib, is involved in DV2 RNA replication and is probably a major mediator of the anti-DV activity of these compounds. Furthermore, serial passaging of DV2 in the presence of dasatinib led to the identification of a mutation in the transmembrane domain 3 of the NS4B protein that overcomes the inhibition of RNA replication by AZD0530, dasatinib, and Fyn RNAi. Although we observed that dasatinib also inhibits DV2 particle assembly and/or secretion, this activity does not appear to be mediated by Src-family kinases. Together, our results suggest that AZD0530 and dasatinib inhibit DV at the step of viral RNA replication and demonstrate a critical role for Fyn kinase in this viral process. The antiviral activity of these compounds in vitro makes them useful pharmacological tools to validate Fyn or other host kinases as anti-DV targets in vivo.     

ANTI-HBV SPECIFIC β-L-2′-DEOXYNUCLEOSIDES

A unique series of simple unnatural L-nucleosides that specifically inhibit hepatitis B virus (HBV) replication has been discovered. These molecules have in common a hydroxyl group in the 3′-position (3′-OH) of the β-L-2′-deoxyribose sugar that confers antiviral activity specifically against hepadnaviruses. Replacement of the 3′-OH broadens activity to other viruses. Substitution in the base decreases antiviral potency and selectivity. Human DNA polymerases and mitochondrial function are not effected. Plasma viremia is reduced up to 8 logs in a woodchuck model of chronic HBV infection. These investigational drugs, used alone or in combination, are expected to offer new therapeutic options for patients with chronic HBV infection.     

Molecular models of protein kinase 6 from Plasmodium falciparum

Cyclin-dependent kinases (CDKs) have been identified as potential targets for development of drugs, mainly against cancer. These studies generated a vast library of chemical inhibitors of CDKs, and some of these molecules can also inhibit kinases identified in the Plasmodium falciparum genome. Here we describe structural models for Protein Kinase 6 from P. falciparum (PfPK6) complexed with Roscovitine and Olomoucine. These models show clear structural evidence for differences observed in the inhibition, and may help designing inhibitors for PfPK6 generating new potential drugs against malaria. Figure Ribbon diagram of PfPK6 complexed with a roscovitine and b olomoucine     

Analysis of water patterns in protein kinase binding sites

Deregulation of protein kinases is associated with numerous diseases, making them important targets for drug discovery. The majority of drugs target the catalytic site of these proteins, but due to the high level of similarity within the ATP binding sites of protein kinases, it is often difficult to achieve the required pharmacological selectivity. In this study, we describe the identification and subsequent analysis of water patterns in the ATP binding sites of 171 protein kinase structures, comprising 19 different kinases from various branches of the kinome, and demonstrate that structurally similar binding sites often have significantly different water patterns. We show that the observed variations in water patterns of different, but structurally similar kinases can be exploited in the structure-based design of potent and selective kinase inhibitors.     

Characterizing the role of the dark kinome in neurodegenerative disease – A mini review

BackgroundDrugs that modulate previously unexplored targets could potentially slow or halt the progression of neurodegenerative diseases. Several candidate proteins lie within the dark kinome, those human kinases that have not been well characterized. Much of the kinome (~80%) remains poorly studied, and these targets likely harbor untapped biological potential.Scope of reviewThis review highlights the significance of kinases as mediators of aberrant pathways in neurodegeneration and provides examples of published high-quality small molecules that modulate some of these kinases.Major conclusionsThere is a need for continued efforts to develop high-quality chemical tools to illuminate the function of understudied kinases in the brain. Potent and selective small molecules enable accurate pairing of an observed phenotype with a protein target.General significanceThe examples discussed herein support the premise that validation of therapeutic hypotheses surrounding kinase targets can be accomplished via small molecules and they can serve as the basis for disease-focused drug development campaigns.     

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.     

Signaling protein networks as targets of new antineoplastic drugs

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from "-omic" approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, "intelligent" drug candidates. An additional medicinal chemistry strategy (via 40-50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.     

Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.     

Proteome-wide identification of cellular targets affected by bisindolylmaleimide-type protein kinase C inhibitors

Bisindolylmaleimide compounds such as GF109203X are potent inhibitors of protein kinase C (PKC) activity. Although bisindolylmaleimides are not entirely selective for PKC and are known to inhibit a few other protein kinases, these reagents have been extensively used to study the functional roles of PKC family enzymes in cellular signal transduction for more than a decade. Here, we establish a proteomics approach to gain further insights into the cellular effects of this compound class. Functional immobilization of suitable bisindolylmaleimide analogues in combination with the specific purification of cellular binding proteins by affinity chromatography led to the identification of several known and previously unknown enzyme targets. Subsequent in vitro binding and activity assays confirmed the protein kinases Ste20-related kinase and cyclin-dependent kinase 2 (CDK2) and the non-protein kinases adenosine kinase and quinone reductase type 2 as novel targets of bisindolylmaleimide inhibitors. As observed specifically for CDK2, minor chemical variation of the ligand by immobilizing the closely related bisindolylmaleimides III, VIII, and X dramatically affected target binding. These observed changes in affinity correlated with both the measured IC(50) values for in vitro CDK2 inhibition and results from molecular docking into the CDK2 crystal structure. Moreover, the conditions for affinity purification could be adapted in a way that immobilized bisindolylmaleimide III selectively interacted with either PKC alpha or ribosomal S6 protein kinase 1 only after activation of these kinases. Thus, we have established an efficient technique for the rapid identification of cellular bisindolylmaleimide targets and further demonstrate the comparative selectivity profiling of closely related kinase inhibitors within a cellular proteome.     

Developing kinase inhibitors for malaria: an opportunity or liability?

Highly druggable and essential to almost all aspects of cellular life, the protein and phosphoinositide kinase gene families offer a wealth of potential targets for pharmacological modulation for both noncommunicable and infectious diseases. Despite the success of kinase inhibitors in oncology and other disease indications, targeting kinases comes with significant challenges. Key hurdles for kinase drug discovery include selectivity and acquired resistance. The phosphatidylinositol 4-kinase beta inhibitor MMV390048 showed good efficacy in Phase 2a clinical trials, demonstrating the potential of kinase inhibitors for malaria treatment. Here we argue that the potential benefits of Plasmodium kinase inhibitors outweigh the risks, and we highlight the opportunity for designed polypharmacology to reduce the risk of resistance.