Kinomics: characterizing the therapeutically validated kinase space

CDK4/6 inhibitor-induced bone marrow micronuclei might be caused by cell cycle arrest during erythropoiesis

BACKGROUND

A micronucleus test is generally used to evaluate the genotoxic potential of chemicals. Exaggerated erythropoiesis, as occurs following bleeding, may induce an unexpected increase in micronucleus frequency. This false positive result would be typical in a genotoxicity study due to the enhanced progression of the cell cycle that restores decreased blood cells. The cyclin-dependent kinase (CDK) family is known to play an essential role in preventing genomic instability. Conversely, a selective CDK4/6 inhibitor PD0332991, clinically named Palbociclib, is reported to have genotoxic potential, shown by positive results in both in vitro and in vivo micronucleus studies. To clarify the mechanism by which cell cycle arrest induced by a CDK4/6 inhibitor increases micronucleus frequency, we investigated the positive results of the bone marrow micronucleus test conducted with PD0332991.

RESULTS

Rats treated with PD0332991 exhibited increased micronucleus frequency in an in vivo bone marrow micronucleus test whereas it was not increased by treatment in human lymphoblastoid TK6 cells. In addition, all other genotoxicity tests including the Ames test and the comet assay showed negative results with PD0332991. Interestingly, PD0332991 treatment led to an increase in erythrocyte size in rats and affected the size distribution of erythrocytes, including the micronucleus. The mean corpuscular volume of reticulocytes (MCVr) in the PD0332991 treatment group was significantly increased compared to that of the vehicle control (83.8 fL in the PD0332991, and 71.6 fL in the vehicle control.). Further, the average micronucleated erythrocytes (MNE) size of the PD0332991 group and vehicle control was 8.2 and 7.3 µm, respectively. In the histogram, the vehicle control showed a monomodal distribution with a peak near 7.3 µm. In contrast, the PD0332991 group showed a bimodal distribution with peaks around 7.5 and 8.5 µm. Micronucleated erythrocytes in the PD0332991 group were significantly larger than those in the vehicle control. These results suggest that the increase in micronucleus frequency induced by the CDK4/6 inhibitor is not due to genotoxicity, but is attributable to disturbance of the cell cycle, differentiation, and enucleation of erythroblasts.

CONCLUSIONS

It was suggested that the positive outcome of the in vivo bone marrow micronucleus test resulting from treatment with PD0332991 could not be attributed to its genotoxicity. Further studies to clarify the mechanism of action can contribute to the development of drug candidate compounds lacking intrinsic genotoxic effects.

Cardiac Safety of Kinase Inhibitors - Improving Understanding and Prediction of Liabilities in Drug Discovery Using Human Stem Cell-Derived Models

Many small molecule kinase inhibitors (SMKIs) used to fight cancer have been associated with cardiotoxicity in the clinic. Therefore, preventing their failure in clinical development is a priority for preclinical discovery. Our study focused on the integration and concurrent measurement of ATP, apoptosis dynamics and functional cardiac indexes in human stem cell-derived cardiomyocytes (hSC-CMs) to provide further insights into molecular determinants of compromised cardiac function. Ten out of the fourteen tested SMKIs resulted in a biologically relevant decrease in either beating rate or base impedance (cell number index), illustrating cardiotoxicity as one of the major safety liabilities of SMKIs, in particular of those involved in the PI3K–AKT pathway. Pearson's correlation analysis indicated a good correlation between the different read-outs of functional importance. Therefore, measurement of ATP concentrations and apoptosis in vitro could provide important insight into mechanisms of cardiotoxicity. Detailed investigation of the cellular signals facilitated multi-parameter evaluation allowing integrative assessment of cardiomyocyte behavior. The resulting correlation can be used as a tool to highlight changes in cardiac function and potentially to categorize drugs based on their mechanisms of action.

Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases

Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

Application of the adverse outcome pathway framework to genotoxic modes of action

In May 2017, the Health and Environmental Sciences Institute's Genetic Toxicology Technical Committee hosted a workshop to discuss whether mode of action (MOA) investigation is enhanced through the application of the adverse outcome pathway (AOP) framework. As AOPs are a relatively new approach in genetic toxicology, this report describes how AOPs could be harnessed to advance MOA analysis of genotoxicity pathways using five example case studies. Each of these genetic toxicology AOPs proposed for further development includes the relevant molecular initiating events, key events, and adverse outcomes (AOs), identification and/or further development of the appropriate assays to link an agent to these events, and discussion regarding the biological plausibility of the proposed AOP. A key difference between these proposed genetic toxicology AOPs versus traditional AOPs is that the AO is a genetic toxicology endpoint of potential significance in risk characterization, in contrast to an adverse state of an organism or a population. The first two detailed case studies describe provisional AOPs for aurora kinase inhibition and tubulin binding, leading to the common AO of aneuploidy. The remaining three case studies highlight provisional AOPs that lead to chromosome breakage or mutation via indirect DNA interaction (inhibition of topoisomerase II, production of cellular reactive oxygen species, and inhibition of DNA synthesis). These case studies serve as starting points for genotoxicity AOPs that could ultimately be published and utilized by the broader toxicology community and illustrate the practical considerations and evidence required to formalize such AOPs so that they may be applied to genetic toxicity evaluation schemes. Environ. Mol. Mutagen. 61:114-134, 2020. © 2019 Wiley Periodicals, Inc.

Computational Strategy Revealing the Structural Determinant of Ligand Selectivity towards Highly Similar Protein Targets

BACKGROUND

Poor selectivity of drug candidates may lead to toxicity and side effects accounting for as high as 60% failure rate, thus, the selectivity is consistently significant and challenging for drug discovery.

OBJECTIVE

To find highly specific small molecules towards very similar protein targets, multiple strategies are always employed, including (1) To make use of the diverse shape of binding pocket to avoid steric bump; (2) To increase binding affinities for favorite residues; (3) To achieve selectivity through allosteric regulation of target; (4) To stabalize the inactive conformation of protein target and (5) To occupy dual binding pockets of single target.

CONCLUSION

In this review, we summarize computational strategies along with examples of their successful applications in designing selective ligands, with the aim to provide insights into everdiversifying drug development practice and inspire medicinal chemists to utilize computational strategies to avoid potential side effects due to low selectivity of ligands.

Idea2Data: Toward a New Paradigm for Drug Discovery

Increasing the success rate and throughput of drug discovery will require efficiency improvements throughout the process that is currently used in the pharmaceutical community, including the crucial step of identifying hit compounds to act as drivers for subsequent optimization. Hit identification can be carried out through large compound collection screening and often involves the generation and testing of many hypotheses based on available knowledge. In practice, hypothesis generation can involve the selection of promising chemical structures from compound collections using predictive models built from previous screening/assay results. Available physical collections, typically used during hit identification, are of the order of 10⁶ compounds but represent only a small fraction of the small molecule drug-like chemical space. In an effort to survey a larger portion of chemical space and eliminate inefficiencies during hit identification, we introduce a new process, termed Idea2Data (I2D) that tightly integrates computational and experimental components of the drug discovery process. I2D provides the ability to connect a vast virtual collection of compounds readily synthesizable on automated synthesis systems with computational predictive models for the identification of promising structures. This new paradigm enables researchers to process billions of virtual molecules and select structures that can be prepared on automated systems and made available for biological testing, allowing for timely hypothesis testing and follow-up. Since its introduction, I2D has positively impacted several portfolio efforts through identification of new chemical scaffolds and functionalization of existing scaffolds. In this Innovations paper, we describe the I2D process and present an application for the discovery of new ULK inhibitors.

Targets and mechanisms of chemically induced aneuploidy. Part 1 of the report of the 2017 IWGT workgroup on assessing the risk of aneugens for carcinogenesis and hereditary diseases

An aneuploidy workgroup was established as part of the 7th International Workshops on Genotoxicity Testing. The workgroup conducted a review of the scientific literature on the biological mechanisms of aneuploidy in mammalian cells and methods used to detect chemical aneugens. In addition, the current regulatory framework was discussed, with the objective to arrive at consensus statements on the ramifications of exposure to chemical aneugens for human health risk assessment. As part of these efforts, the workgroup explored the use of adverse outcome pathways (AOPs) to document mechanisms of chemically induced aneuploidy in mammalian somatic cells. The group worked on two molecular initiating events (MIEs), tubulin binding and binding to the catalytic domain of aurora kinase B, which result in several adverse outcomes, including aneuploidy. The workgroup agreed that the AOP framework provides a useful approach to link evidence for MIEs with aneuploidy on a cellular level. The evidence linking chemically induced aneuploidy with carcinogenicity and hereditary disease was also reviewed and is presented in two companion papers. In addition, the group came to the consensus that the current regulatory test batteries, while not ideal, are sufficient for the identification of aneugens and human risk assessment. While it is obvious that there are many different MIEs that could lead to the induction of aneuploidy, the most commonly observed mechanisms involving chemical aneugens are related to tubulin binding and, to a lesser extent, inhibition of mitotic kinases. The comprehensive review presented here should help with the identification and risk management of aneugenic agents.

Unveiling the Kinomes of Leishmania infantum and L. braziliensis Empowers the Discovery of New Kinase Targets and Antileishmanial Compounds

Leishmaniasis is a neglected tropical disease caused by parasites of the genus Leishmania (NTD) endemic in 98 countries. Although some drugs are available, current treatments deal with issues such as toxicity, low efficacy, and emergence of resistance. Therefore, there is an urgent need to identify new targets for the development of new antileishmanial drugs. Protein kinases (PKs), which play an essential role in many biological processes, have become potential drug targets for many parasitic diseases. A refined bioinformatics pipeline was applied in order to define and compare the kinomes of L. infantum and L. braziliensis, species that cause cutaneous and visceral manifestations of leishmaniasis in the Americas, the latter being potentially fatal if untreated. Respectively, 224 and 221 PKs were identified in L. infantum and L. braziliensis overall. Almost all unclassified eukaryotic PKs were assigned to six of nine major kinase groups and, consequently, most have been classified into family and subfamily. Furthermore, revealing the kinomes for both Leishmania species allowed for the prioritization of potential drug targets that could be explored for discovering new drugs against leishmaniasis. Finally, we used a drug repurposing approach and prioritized seven approved drugs and investigational compounds to be experimentally tested against Leishmania. Trametinib and NMS-1286937 inhibited the growth of L. infantum and L. braziliensis promastigotes and amastigotes and therefore might be good candidates for the drug repurposing pipeline.

Kinase-targeted cancer therapies: progress, challenges and future directions

The human genome encodes 538 protein kinases that transfer a γ-phosphate group from ATP to serine, threonine, or tyrosine residues. Many of these kinases are associated with human cancer initiation and progression. The recent development of small-molecule kinase inhibitors for the treatment of diverse types of cancer has proven successful in clinical therapy. Significantly, protein kinases are the second most targeted group of drug targets, after the G-protein-coupled receptors. Since the development of the first protein kinase inhibitor, in the early 1980s, 37 kinase inhibitors have received FDA approval for treatment of malignancies such as breast and lung cancer. Furthermore, about 150 kinase-targeted drugs are in clinical phase trials, and many kinase-specific inhibitors are in the preclinical stage of drug development. Nevertheless, many factors confound the clinical efficacy of these molecules. Specific tumor genetics, tumor microenvironment, drug resistance, and pharmacogenomics determine how useful a compound will be in the treatment of a given cancer. This review provides an overview of kinase-targeted drug discovery and development in relation to oncology and highlights the challenges and future potential for kinase-targeted cancer therapies.

Enzymatic biofuel cell-based self-powered biosensing of protein kinase activity and inhibition via thiophosphorylation-mediated interface engineering

We developed a facile and ultrasensitive EBFC-based self-powered biosensor of protein kinase A activity and inhibition via thiophosphorylation-mediated interface engineering.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

A Chemogenomic Analysis of the Human Proteome: Application to Enzyme Families

Sequence-based phylogenies (SBP) are well-established tools for describing relationships between proteins. They have been used extensively to predict the behavior and sensitivity toward inhibitors of enzymes within a family. The utility of this approach diminishes when comparing proteins with little sequence homology. Even within an enzyme family, SBPs must be complemented by an orthogonal method that is independent of sequence to better predict enzymatic behavior. A chemogenomic approach is demonstrated here that uses the inhibition profile of a 130,000 diverse molecule library to uncover relationships within a set of enzymes. The profile is used to construct a semimetric additive distance matrix. This matrix, in turn, defines a sequence-independent phylogeny (SIP). The method was applied to 97 enzymes (kinases, proteases, and phosphatases). SIP does not use structural information from the molecules used for establishing the profile, thus providing a more heuristic method than the current approaches, which require knowledge of the specific inhibitor's structure. Within enzyme families, SIP shows a good overall correlation with SBP. More interestingly, SIP uncovers distances within families that are not recognizable by sequence-based methods. In addition, SIP allows the determination of distance between enzymes with no sequence homology, thus uncovering novel relationships not predicted by SBP. This chemogenomic approach, used in conjunction with SBP, should prove to be a powerful tool for choosing target combinations for drug discovery programs as well as for guiding the selection of profiling and liability targets. ( Journal of Biomolecular Screening 2007:972-982)

Effect of Glucuronidation on the Potential of Kaempferol to Inhibit Serine/Threonine Protein Kinases

To study the effect of metabolic conjugation of flavonoids on the potential to inhibit protein kinase activity, the inhibitory effects of the dietary flavonol kaempferol and its major plasma conjugate kaempferol-3-O-glucuronide on protein kinases were studied. To this end, the inhibition of the phosphorylation activity of recombinant protein kinase A (PKA) and of cell lysate from the hepatocellular carcinoma cell line HepG2 on 141 putative serine/threonine phosphorylation sites derived from human proteins was assessed. Glucuronidation reduced the inhibitory potency of kaempferol on the phosphorylation activity of PKA and HepG2 lysate on average about 16 and 3.5 times, respectively, but did not appear to affect the target selectivity for kinases present in the lysate. The data demonstrate that, upon glucuronidation, kaempferol retains part of its intrinsic kinase inhibition potential, which implies that K3G does not necessarily need to be deconjugated to the aglycone for a potential inhibitory effect on protein kinases.

Toxicology Strategies for Drug Discovery: Present and Future

Attrition due to nonclinical safety represents a major issue for the productivity of pharmaceutical research and development (R&D) organizations, especially during the compound optimization stages of drug discovery and the early stages of clinical development. Focusing on decreasing nonclinical safety-related attrition is not a new concept, and various approaches have been experimented with over the last two decades. Front-loading testing funnels in Discovery with in vitro toxicity assays designed to rapidly identify unfavorable molecules was the approach adopted by most pharmaceutical R&D organizations a few years ago. However, this approach has also a non-negligible opportunity cost. Hence, significant refinements to the "fail early, fail often" paradigm have been proposed recently to reflect the complexity of accurately categorizing compounds with early data points without taking into account other important contextual aspects, in particular efficacious systemic and tissue exposures. This review provides an overview of toxicology approaches and models that can be used in pharmaceutical Discovery at the series/lead identification and lead optimization stages to guide and inform chemistry efforts, as well as a personal view on how to best use them to meet nonclinical safety-related attrition objectives consistent with a sustainable pharmaceutical R&D model. The scope of this review is limited to small molecules, as large molecules are associated with challenges that are quite different. Finally, a perspective on how several emerging technologies may impact toxicity evaluation is also provided.

Kinase phosphorylation monitoring with i-motif DNA cross-linked SERS probes

We propose an ultrasensitive SERS-based peptide biosensor platform to monitor phosphorylation catalyzed by kinase in a dynamic format. The developed SERS strategy has a short response time with potential to monitor phosphorylation in live cells.

Antihormone induced compensatory signalling in breast cancer: an adverse event in the development of endocrine resistance

Using MCF7 breast cancer cells, it has been shown that antihormones promote expression/activity of oestrogen-repressed tyrosine kinases, notably EGFR, HER2 and Src. These inductive events confer responsiveness to targeted inhibitors (e.g., gefitinib, trastuzumab, saracatinib). We observed that these antihormone-induced phenomena are common to ER+HER2- and ER+HER2+ breast cancer models in vitro, where targeting of EGFR, HER2 or Src alongside antihormone improves antitumour response and delays/prevents endocrine resistance. Such targeted inhibitors also subvert acquired endocrine resistant cells which retain increased EGFR, HER2 and Src (e.g., TAMR and FASR models derived after 6-12 months of tamoxifen or Faslodex treatment). Thus, antihormone-induced tyrosine kinases comprise "compensatory signalling" crucial in limiting maximal initial antihormone response and subsequently driving acquired resistance in vitro. However, despite such convincing preclinical findings from our group and others, clinical trials examining equivalent antigrowth factor strategies have proved relatively disappointing. Our new studies deciphering underlying causes reveal that further antihormone-promoted events could be pivotal in vivo. Firstly, Faslodex induces HER3 and HER4 which sensitise ER+ cells to heregulin, a paracrine growth factor that overcomes endocrine response and diminishes antitumour effect of agents targeting EGFR, HER2 or Src alongside antihormone. Secondly, extended antihormone exposure (experienced by ER+ cells prior to adjuvant clinical relapse) can "reprogramme" the compensatory kinase profile in vitro, hindering candidate antigrowth factor targeting of endocrine resistance. Faslodex resistant cells maintained with this antihormone for 3 years in vitro lose EGFR/HER2 dependency, gaining alternative mitogenic/invasion kinases. Deciphering these previously unrecognised antihormone-induced events could provide superior treatments to control endocrine relapse in the clinic.

Ten things you should know about protein kinases: IUPHAR Review 14

Many human malignancies are associated with aberrant regulation of protein or lipid kinases due to mutations, chromosomal rearrangements and/or gene amplification. Protein and lipid kinases represent an important target class for treating human disorders. This review focus on 'the 10 things you should know about protein kinases and their inhibitors', including a short introduction on the history of protein kinases and their inhibitors and ending with a perspective on kinase drug discovery. Although the '10 things' have been, to a certain extent, chosen arbitrarily, they cover in a comprehensive way the past and present efforts in kinase drug discovery and summarize the status quo of the current kinase inhibitors as well as knowledge about kinase structure and binding modes. Besides describing the potentials of protein kinase inhibitors as drugs, this review also focus on their limitations, particularly on how to circumvent emerging resistance against kinase inhibitors in oncological indications.

Fragment-based design of kinase inhibitors: a practical guide

Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

International Union of Basic and Clinical Pharmacology. XC. multisite pharmacology: recommendations for the nomenclature of receptor allosterism and allosteric ligands

Allosteric interactions play vital roles in metabolic processes and signal transduction and, more recently, have become the focus of numerous pharmacological studies because of the potential for discovering more target-selective chemical probes and therapeutic agents. In addition to classic early studies on enzymes, there are now examples of small molecule allosteric modulators for all superfamilies of receptors encoded by the genome, including ligand- and voltage-gated ion channels, G protein-coupled receptors, nuclear hormone receptors, and receptor tyrosine kinases. As a consequence, a vast array of pharmacologic behaviors has been ascribed to allosteric ligands that can vary in a target-, ligand-, and cell-/tissue-dependent manner. The current article presents an overview of allostery as applied to receptor families and approaches for detecting and validating allosteric interactions and gives recommendations for the nomenclature of allosteric ligands and their properties.

Recently targeted kinases and their inhibitors-the path to clinical trials

Protein kinases have emerged as one of the most important drug target families for the treatment of cancer. To date, 28 inhibitors with reported activity versus one or multiple kinases have been approved for clinical use. However, the majority of new clinical trials are focused on new subindications using already approved kinase inhibitors or target well validated kinase targets with novel inhibitors. In contrast, relatively few clinical trials have been initiated using specific inhibitors that inhibit novel kinase targets, despite significant validation efforts in the public domain. Analysis of the target validation history of first in class kinase inhibitors revealed a long delay between initial disease association and development of inhibitors. As part of this analysis, we have investigated which first in class inhibitor that entered phase I clinical trials over the last five years and also considered which research approaches that were used to validate them.

Profiling the kinome for drug discovery

The human kinome is made up of 518 distinctive serine/threonine and tyrosine kinases, which are key components of virtually every mammalian signal transduction pathway. Consequently, kinases provide a compelling target family for the development of small molecule inhibitors, which could be used as tools to delineate the mechanism of action for biological processes and potentially be used as therapeutics to treat human diseases such as cancer. A myriad of recent technological advances have accelerated our understanding of kinome function, its relationship to tumorigenic development, and have contributed to the progression of small molecule kinase inhibitors into the clinic. Essential to the continued growth of the field are informatics tools that can assist in interpreting disparate and voluminous data sets and correctly guide decision making processes. These advances are expected to have a dramatic impact on kinase drug development and clinical diagnoses and treatment in the near future.:

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.

The resistance tetrad: amino acid hotspots for kinome-wide exploitation of drug-resistant protein kinase alleles

Acquired resistance to targeted kinase inhibitors is a well-documented clinical problem that is potentially fatal for patients to whom a suitable back-up is not available. However, protein kinase alleles that promote resistance to inhibitors can be exploited experimentally as gold-standards for "on"- and "off"-target validation strategies and constitute a powerful resource for assessing the ability of new or combined therapies to override resistance. Clinical resistance to kinase inhibitors is an evident in all tyrosine kinase-driven malignancies, where high rates of mutation drive tumor evolution toward the insidious drug-resistant (DR) state through a variety of mechanisms. Unfortunately, this problem is likely to intensify in the future as the number of target kinases, approved inhibitors, and clinical indications increase. To empower the analysis of resistance in kinases, we have validated a bioinformatic, structural, and cellular workflow for designing and evaluating resistance at key mutational hotspots among kinome members. In this chapter, we discuss how mutation of amino acids in the gatekeeper and hinge-loop regions (collectively termed the "resistance tetrad") and the DFG motif represent an effective approach for generating panels of DR kinase alleles for chemical genetics and biological target validation.

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases

Background

‘Phylogenetic trees’ are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis. In this work, we analyzed a dataset comprising 157 compounds, which have been tested at concentrations of 1 μM and 10 μM against a panel of 225 human protein kinases in full-matrix experiments, aiming to explain kinase promiscuity and selectivity against inhibitors. Compounds were described by chemical features, which were used to represent kinases (i.e. each kinase had an active set of features and an inactive set).

Results

Using this representation, a bioactivity-based classification was made of the kinome, which partially resembles previous sequence-based classifications, where particularly kinases from the TK, CDK, CLK and AGC branches cluster together. However, we were also able to show that in approximately 57% of cases, on average 6 kinase inhibitors exhibit activity against kinases which are located at a large distance in the sequence-based classification (at a relative distance of 0.6 – 0.8 on a scale from 0 to 1), but are correctly located closer to each other in our bioactivity-based tree (distance 0 – 0.4). Despite this improvement on sequence-based classification, also the bioactivity-based classification needed further attention: for approximately 80% of all analyzed kinases, kinases classified as neighbors according to the bioactivity-based classification also show high SAR similarity (i.e. a high fraction of shared active compounds and therefore, interaction with similar inhibitors). However, in the remaining ~20% of cases a clear relationship between kinase bioactivity profile similarity and shared active compounds could not be established, which is in agreement with previously published atypical SAR (such as for LCK, FGFR1, AKT2, DAPK1, TGFR1, MK12 and AKT1).

Conclusions

In this work we were hence able to show that (1) targets (here kinases) with few shared activities are difficult to establish neighborhood relationships for, and (2) phylogenetic tree representations make implicit assumptions (i.e. that neighboring kinases exhibit similar interaction profiles with inhibitors) that are not always suitable for analyses of bioactivity space. While both points have been implicitly alluded to before, this is to the information of the authors the first study that explores both points on a comprehensive basis. Excluding kinases with few shared activities improved the situation greatly (the percentage of kinases for which no neighborhood relationship could be established dropped from 20% to only 4%). We can conclude that all of the above findings need to be taken into account when performing chemogenomics analyses, also for other target classes.

Dual leucine zipper kinase as a therapeutic target for neurodegenerative conditions

Dual leucine zipper kinase (DLK) is a serine/threonine protein kinase that is a member of the mixed lineage kinase subfamily. Mixed lineage kinases are upstream MAP3Ks that activate the JNK pathway. DLK is primarily responsible for activating JNK and mediating the apoptotic stress response in various cell types, specifically neurons. Inhibition and knockdown of DLK has been demonstrated to have neuroprotective effects in cellular and animal models of Alzheimer’s disease, glaucoma, Parkinson’s disease and other neurodegenerative conditions. Several series of ATP-binding site inhibitors have been identified through profiling efforts providing launch points for future medicinal chemistry programs.

Innovations in studying in vivo cell behavior and pharmacology in complex tissues--microvascular endothelial cells in the spotlight

Many studies on the molecular control underlying normal cell behavior and cellular responses to disease stimuli and pharmacological intervention are conducted in single-cell culture systems, while the read-out of cellular engagement in disease and responsiveness to drugs in vivo is often based on overall tissue responses. As the majority of drugs under development aim to specifically interact with molecular targets in subsets of cells in complex tissues, this approach poses a major experimental discrepancy that prevents successful development of new therapeutics. In this review, we address the shortcomings of the use of artificial (single) cell systems and of whole tissue analyses in creating a better understanding of cell engagement in disease and of the true effects of drugs. We focus on microvascular endothelial cells that actively engage in a wide range of physiological and pathological processes. We propose a new strategy in which in vivo molecular control of cells is studied directly in the diseased endothelium instead of at a (far) distance from the site where drugs have to act, thereby accounting for tissue-controlled cell responses. The strategy uses laser microdissection-based enrichment of microvascular endothelium which, when combined with transcriptome and (phospho)proteome analyses, provides a factual view on their status in their complex microenvironment. Combining this with miniaturized sample handling using microfluidic devices enables handling the minute sample input that results from this strategy. The multidisciplinary approach proposed will enable compartmentalized analysis of cell behavior and drug effects in complex tissue to become widely implemented in daily biomedical research and drug development practice.

Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not. Experimentally, these interactions can be identified by in vitro binding assay - an expensive and laborious procedure that is not applicable on a large scale. Therefore, there is an urgent need to develop statistically efficient approaches for identifying kinase-inhibitor interactions. For the first time, the quantitative binding affinities of kinase-inhibitor pairs are differentiated as a measurement to define if an inhibitor interacts with a kinase, and then a chemogenomics framework using an unbiased set of general integrated features (drug descriptors and protein descriptors) and random forest (RF) is employed to construct a predictive model which can accurately classify kinase-inhibitor pairs. Our results show that RF with integrated features gave prediction accuracy of 93.76%, sensitivity of 92.26%, and specificity of 95.27%, respectively. The results are superior to those by only considering two separated spaces (chemical space and protein space), demonstrating that these integrated features contribute cooperatively. Based on the constructed model, we provided a high confidence list of drug-target associations for subsequent experimental investigation guidance at a low false discovery rate.

Discovery of 3-phenyl-1H-5-pyrazolylamine derivatives containing a urea pharmacophore as potent and efficacious inhibitors of FMS-like tyrosine kinase-3 (FLT3)

Preclinical investigations and early clinical trials suggest that FLT3 inhibitors are a viable therapy for acute myeloid leukemia. However, early clinical data have been underwhelming due to incomplete inhibition of FLT3. We have developed 3-phenyl-1H-5-pyrazolylamine as an efficient template for kinase inhibitors. Structure-activity relationships led to the discovery of sulfonamide, carbamate and urea series of FLT3 inhibitors. Previous studies showed that the sulfonamide 4 and carbamate 5 series were potent and selective FLT3 inhibitors with good in vivo efficacy. Herein, we describe the urea series, which we found to be potent inhibitors of FLT3 and VEGFR2. Some inhibited growth of FLT3-mutated MOLM-13 cells more strongly than the FLT3 inhibitors sorafenib (2) and ABT-869 (3). In preliminary in vivo toxicity studies of the four most active compounds, 10f was found to be the least toxic. A further in vivo efficacy study demonstrated that 10f achieved complete tumor regression in a higher proportion of MOLM-13 xenograft mice than 4 and 5 (70% vs 10% and 40%). These results show that compound 10f possesses improved pharmacologic and selectivity profiles and could be more effective than previously disclosed FLT3 inhibitors in the treatment of acute myeloid leukemia.

What general conclusions can we draw from kinase profiling data sets?

Understanding general selectivity trends across the kinome has implications ranging from target selection, compound prioritization, toxicity and patient tailoring. Several recent publications have described the characterization of kinase inhibitors via large assay panels, offering a range of generalizations that influenced kinase inhibitor research trends. Since a subset of profiled inhibitors overlap across reports, we evaluated the concordance of activity results for the same compound-kinase pairs across four data sources generated from different kinase biochemical assay technologies. Overall, 77% of all results are within 3 fold or qualitatively in agreement across sources. However, the agreement for active compounds is only 37%, indicating that different profiling panels are in better agreement to determine a compound's lack of activity rather than degree of activity. Low concordance is also found when comparing the promiscuity of kinase targets evaluated from different sources, and the pharmacological similarity of kinases. In contrast, the overall promiscuity of kinase inhibitors was consistent across sources. We highlight the difficulty of drawing general conclusions from such data by showing that no significant selectivity difference distinguishes type I vs. type II inhibitors, and limited kinase space similarity that is consistent across different sources. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).

Kinome-wide activity modeling from diverse public high-quality data sets

Large corpora of kinase small molecule inhibitor data are accessible to public sector research from thousands of journal article and patent publications. These data have been generated employing a wide variety of assay methodologies and experimental procedures by numerous laboratories. Here we ask the question how applicable these heterogeneous data sets are to predict kinase activities and which characteristics of the data sets contribute to their utility. We accessed almost 500,000 molecules from the Kinase Knowledge Base (KKB) and after rigorous aggregation and standardization generated over 180 distinct data sets covering all major groups of the human kinome. To assess the value of the data sets, we generated hundreds of classification and regression models. Their rigorous cross-validation and characterization demonstrated highly predictive classification and quantitative models for the majority of kinase targets if a minimum required number of active compounds or structure-activity data points were available. We then applied the best classifiers to compounds most recently profiled in the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program and found good agreement of profiling results with predicted activities. Our results indicate that, although heterogeneous in nature, the publically accessible data sets are exceedingly valuable and well suited to develop highly accurate predictors for practical Kinome-wide virtual screening applications and to complement experimental kinase profiling.

On the origins of drug polypharmacology

The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?

Mapping small molecule binding data to structural domains

BACKGROUND

Large-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins.

RESULTS

In this implementation of our heuristic, ligand binding to protein targets from the ChEMBL database was mapped to structural domains as defined by profiles contained within the Pfam-A database. Our mapping suggests that the majority of assay targets within the current version of the ChEMBL database bind ligands through a small number of highly prevalent domains, and conversely the majority of Pfam domains sampled by our data play no currently established role in ligand binding. Validation studies, carried out firstly against Uniprot entries with expert binding-site annotation and secondly against entries in the wwPDB repository of crystallographic protein structures, demonstrate that our simple heuristic maps ligand binding to the correct domain in about 90 percent of all assessed cases. Using the mappings obtained with our heuristic, we have assembled ligand sets associated with each Pfam domain.

CONCLUSIONS

Small molecule binding has been mapped to Pfam-A domains of protein targets in the ChEMBL bioactivity database. The result of this mapping is an enriched annotation of small molecule bioactivity data and a grouping of activity classes following the Pfam-A specifications of protein domains. This is valuable for data-focused approaches in drug discovery, for example when extrapolating potential targets of a small molecule with known activity against one or few targets, or in the assessment of a potential target for drug discovery or screening studies.

Molecular characterization of novel trispecific ErbB-cMet-IGF1R antibodies and their antigen-binding properties

Therapeutic antibodies are well established drugs in diverse medical indications. Their success invigorates research on multi-specific antibodies in order to enhance drug efficacy by co-targeting of receptors and addressing key questions of emerging resistance mechanisms. Despite challenges in production, multi-specific antibodies are potentially more potent biologics for cancer therapy. However, so far only bispecific antibody formats have entered clinical phase testing. For future design of antibodies allowing even more targeting specificities, an understanding of the antigen-binding properties of such molecules is crucial. To this end, we have generated different IgG-like TriMAbs (trispecific, trivalent and tetravalent antibodies) directed against prominent cell surface antigens often deregulated in tumor biology. A combination of surface plasmon resonance and isothermal titration calorimetry techniques enables quantitative assessment of the antigen-binding properties of TriMAbs. We demonstrate that the kinetic profiles for the individual antigens are similar to the parental antibodies and all antigens can be bound simultaneously even in the presence of FcγRIIIa. Furthermore, cooperative binding of TriMAbs to their antigens was demonstrated. All antibodies are fully functional and inhibit receptor phosphorylation and cellular growth. TriMAbs are therefore ideal candidates for future applications in various therapeutic areas.

Kinome profiling

The use of arrays in genomics has led to a fast and reliable way to screen the transcriptome of an organism. It can be automated and analysis tools have become available and hence the technique has become widely used within the past few years. Signal-transduction routes rely mainly on the phosphorylation status of already available proteins; therefore kinases are central players in signal-transduction routes. The array technology can now also be used for the analysis of the kinome. To enable array analysis, consensus peptides for kinases are spot on a solid support. After incubation with cell lysates and in the presence of radioactive ATP, radioactive peptides can be visualized and the kinases that are active in the cells can be determined. The present paper reviews comprehensively the different kinome array platforms available and results obtained hitherto using such platforms. It will appear that this technology does not disappoint its high expectations and is especially powerful because of its species independence. Nevertheless, improvements are still possible and I shall also sketch future possible directions.

3-Phenyl-1H-5-pyrazolylamine-based derivatives as potent and efficacious inhibitors of FMS-like tyrosine kinase-3 (FLT3)

A new class of FLT3 inhibitors has been identified based on the 3-phenyl-1H-5-pyrazolylamine scaffold. The structure-activity relationships led to the discovery of two carbamate series, and some potent compounds within these two series exhibited better growth inhibition of FLT3-mutated MOLM-13 cells than FLT3 inhibitors sorafenib (2) and ABT-869 (3). In particular, compound 8d exhibited the ability to regress tumors in mouse xenograft model using MOLM-13 cells.

Advancing cancer drug discovery towards more agile development of targeted combination therapies

Current drug-discovery strategies are typically 'target-centric' and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized 'on-target' potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.

N⁴-(3-Bromophenyl)-7-(substituted benzyl) pyrrolo[2,3-d]pyrimidines as potent multiple receptor tyrosine kinase inhibitors: design, synthesis, and in vivo evaluation

With the goal of developing multitargeted receptor tyrosine kinase inhibitors that display potent inhibition against PDGFRβ and VEGFR-2 we designed and synthesized eleven N(4)-(3-bromophenyl)-7-(substitutedbenzyl) pyrrolo[2,3-d]pyrimidines 9a-19a. These compounds were obtained from the key intermediate N(4)-(3-bromophenyl)-7H-pyrrolo[2,3-d]pyrimidine-2,4-diamine 29. Various arylmethyl groups were regiospecifically attached at the N7 of 29 via sodium hydride induced alkylation with substituted arylmethyl halides. Compounds 11a and 19a were potent dual inhibitors of PDGFRβ and VEGFR-2. In a COLO-205, in vivo tumor mouse model 11a demonstrated inhibition of tumor growth, metastasis, and tumor angiogenesis that was better than or comparable to the standard compound TSU-68 (SU6668, 8).

Targeting cancer with peptide aptamers

A major endeavour in cancer chemotherapy is to develop agents that specifically target a biomolecule of interest. There are two main classes of targeting agents: small molecules and biologics. Among biologics (e.g.: antibodies), DNA, RNA but also peptide aptamers are relatively recent agents. Peptide aptamers are seldom described but represent attractive agents that can inhibit a growing panel of oncotargets including Heat Shock Proteins. Potential pitfalls and coming challenges towards successful clinical trials are presented such as optimizing the delivery of peptide aptamers thanks to Nanotechnology.

Global analysis of small molecule binding to related protein targets

We report on the integration of pharmacological data and homology information for a large scale analysis of small molecule binding to related targets. Differences in small molecule binding have been assessed for curated pairs of human to rat orthologs and also for recently diverged human paralogs. Our analysis shows that in general, small molecule binding is conserved for pairs of human to rat orthologs. Using statistical tests, we identified a small number of cases where small molecule binding is different between human and rat, some of which had previously been reported in the literature. Knowledge of species specific pharmacology can be advantageous for drug discovery, where rats are frequently used as a model system. For human paralogs, we demonstrate a global correlation between sequence identity and the binding of small molecules with equivalent affinity. Our findings provide an initial general model relating small molecule binding and sequence divergence, containing the foundations for a general model to anticipate and predict within-target-family selectivity.

Many drugs are small molecules that specifically bind to proteins involved in disease related processes. In this way, drugs modulate the function of a targeted protein and ultimately the process causing the disease. The development of drugs crucially relies on assays that measure the potency of the effect a small molecule exerts on its protein target. We compared the potencies of small molecules measured for human proteins and the corresponding (orthologous) protein in rat. Our results suggest that, after subtraction of statistical noise, most human proteins are equally susceptible to small molecule binding as their orthologs in rats. However, we identified a small number of exceptions to this rule, for example the histamine H3 receptor, a protein of the central nervous system. We also compared the potency of small molecules measured against a human protein and another member of the same protein family. In drug development it is often desired to target a protein selectively over other related proteins. The observed differences were generally greater than the statistical noise, indicating that most of the small molecules in our study have some degree of selectivity within protein families.