A guide to picking the most selective kinase inhibitor tool compounds for pharmacological validation of drug targets

Solubility, pH-Solubility Profile, pH-Rate Profile, and Kinetic Stability of the Tyrosine Kinase Inhibitor, Alectinib

Alectinib HCl (ALBHCl) is a tyrosine kinase inhibitor used for non-small cell lung carcinoma (NSCLC). The aim of this study is to unlock some of the physicochemical properties of ALBHCL that serve as a database for any future studies. A solubility study of ALBHCL was performed in different solvents. Also, photostability was tested in the solution and solid states, and the order of reaction and rate constant were calculated. In addition to the pH solubility relation, the pH-rate relation at different temperatures was also studied, and the profiles were constructed. A solubility study was also performed in different media for the purpose of optimizing suitable sink conditions for the in vitro dissolution testing of solid dosage forms. Solubility tests in multiple solvents and pH conditions revealed that the highest solubility was in DMSO, methanol, and chloroform, with acidic media yielding the maximum solubility but degrading at rather low pH levels. ALBHCL proved unstable at high temperatures and under light exposure, with varying stability across different pH levels. The optimal dissolution media for in vitro oral dosage form evaluation were determined, achieving sink conditions at pH levels of 6.8 and 4.5 with specific additives. This study enhances the existing database on ALBHCL's physicochemical properties, emphasizing the importance of pH optimization in pharmaceutical processes and providing valuable insights into its pharmaceutical application.

Discovery and optimization of narrow spectrum inhibitors of Tousled like kinase 2 (TLK2) using quantitative structure activity relationships

The oxindole scaffold has been the center of several kinase drug discovery programs, some of which have led to approved medicines. A series of two oxindole matched pairs from the literature were identified where TLK2 was potently inhibited as an off-target kinase. The oxindole has long been considered a promiscuous kinase inhibitor template, but across these four specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different ranging from narrow to broad spectrum kinome coverage. We synthesized a large series of analogues, utilizing quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, kinome profiling, and small-molecule x-ray structural analysis to optimize TLK2 inhibition and kinome selectivity. This resulted in the identification of several narrow spectrum, sub-family selective, chemical tool compounds including 128 (UNC-CA2-103) that could enable elucidation of TLK2 biology.

Discovery and Optimization of Narrow Spectrum Inhibitors of Tousled Like Kinase 2 (TLK2) Using Quantitative Structure Activity Relationships

The oxindole scaffold has been the center of several kinase drug discovery programs, some of which have led to approved medicines. A series of two oxindole matched pairs from the literature were identified where TLK2 was a potent off-target kinase. The oxindole has long been considered a promiscuous inhibitor template, but across these 4 specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different from narrow to broad spectrum coverage. We synthesized a large series of analogues and through quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, small-molecule x-ray structural analysis and kinome profiling, narrow spectrum, sub-family selective, chemical tool compounds were identified to enable elucidation of TLK2 biology.

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.

Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition

While diet and nutrition are modifiable risk factors for many chronic and infectious diseases, their role in cancer prevention and control remains under investigation. The lack of clarity of some diet-cancer relationships reflects the ongoing debate about the relative contribution of genetic factors, environmental exposures, and replicative errors in stem cell division as determinate drivers of cancer risk. In addition, dietary guidance has often been based upon research assuming that the effects of diet and nutrition on carcinogenesis would be uniform across populations and for various tumor types arising in a specific organ, i.e., that one size fits all. Herein, we present a paradigm for investigating precision dietary patterns that leverages the approaches that led to successful small-molecule inhibitors in cancer treatment, namely understanding the pharmacokinetics and pharmacodynamics of small molecules for targeting carcinogenic mechanisms. We challenge the scientific community to refine the paradigm presented and to conduct proof-in-concept experiments that integrate existing knowledge (drug development, natural products, and the food metabolome) with developments in artificial intelligence to design and then test dietary patterns predicted to elicit drug-like effects on target tissues for cancer prevention and control. We refer to this precision approach as dietary oncopharmacognosy and envision it as the crosswalk between the currently defined fields of precision oncology and precision nutrition with the goal of reducing cancer deaths.

Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles

There have been more than 70 FDA-approved drugs to target the ATP binding site of kinases, mainly in the field of oncology. These compounds are usually developed to target specific kinases, but in practice, most of these drugs are multi-kinase inhibitors that leverage the conserved nature of the ATP pocket across multiple kinases to increase their clinical efficacy. To utilize kinase inhibitors in targeted therapy and outside of oncology, a narrower kinome profile and an understanding of the toxicity profile is imperative. This is essential when considering treating chronic diseases with kinase targets, including neurodegeneration and inflammation. This will require the exploration of inhibitor chemical space and an in-depth understanding of off-target interactions. We have developed an early pipeline toxicity screening platform that uses supervised machine learning (ML) to classify test compounds’ cell stress phenotypes relative to a training set of on-market and withdrawn drugs. Here, we apply it to better understand the toxophores of some literature kinase inhibitor scaffolds, looking specifically at a series of 4-anilinoquinoline and 4-anilinoquinazoline model libraries.

Kinases control of regulated cell death revealing druggable targets for Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world. Motor impairment seen in PD is associated with dopaminergic neurotoxicity in the striatum, and dopaminergic neuronal death in the substantia nigra pars compacta. Cell death has a significant effect on the development and progression of PD. Extensive research over the last few decades has unveiled new regulated cell death (RCD) mechanisms that are not dependent on apoptosis such as necroptosis, ferroptosis, and others. In this review, we will overview the mechanistic pathways of different types of RCD. Unlike accidental cell death, RCD subroutines can be regulated and the RCD-associated kinases are potential druggable targets. Hence, we will address an overview and analysis of different kinases regulating apoptosis such as receptor-interacting protein kinase 1 (RIPK-1), RIPK3, mixed lineage kinase (MLK), Ataxia telangiectasia muted (ATM), cyclin-dependent kinase (CDK), death-associated protein kinase 1 (DAPK1), Apoptosis-signaling kinase-1 (ASK-1), and Leucine-rich repeat kinase-2 (LRRK2). In addition to the role of RIPK1, RIPK3, and Mixed Lineage Kinase Domain like Pseudokinase (MLKL) in necroptosis. We also overview functions of AMP-kinase (AMPK), protein kinase C (PKC), RIPK3, and ATM in ferroptosis. We will recap the anti-apoptotic, anti-necroptotic, and anti-ferroptotic effects of different kinase inhibitors in different models of PD. Finally, we will discuss future challenges in the repositioning of kinase inhibitors in PD. In conclusion, this review kicks-start targeting RCD from a kinases perspective, opening novel therapeutic disease-modifying therapeutic avenues for PD.

Natural Products in Precision Oncology: Plant-Based Small Molecule Inhibitors of Protein Kinases for Cancer Chemoprevention

Striking progress is being made in cancer treatment by using small molecule inhibitors of specific protein kinases that are products of genes recognized as drivers for a specific type of cancer. However, the cost of newly developed drugs is high, and these pharmaceuticals are neither affordable nor accessible in most parts of the world. Accordingly, this narrative review aims to probe how these recent successes in cancer treatment can be reverse-engineered into affordable and accessible approaches for the global community. This challenge is addressed through the lens of cancer chemoprevention, defined as using pharmacological agents of natural or synthetic origin to impede, arrest, or reverse carcinogenesis at any stage in the disease process. In this regard, prevention refers to reducing cancer-related deaths. Recognizing the clinical successes and limitations of protein kinase inhibitor treatment strategies, the disciplines of pharmacognosy and chemotaxonomy are juxtaposed with current efforts to exploit the cancer kinome to describe a conceptual framework for developing a natural product-based approach for precision oncology.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

An open source plant kinase chemogenomics set

One hundred twenty-nine protein kinases, selected to represent the diversity of the rice (Oryza sativa) kinome, were cloned and tested for expression in Escherichia coli. Forty of these rice kinases were purified and screened using differential scanning fluorimetry (DSF) against 627 diverse kinase inhibitors, with a range of structures and activities targeting diverse human kinases. Thirty-seven active compounds were then tested for their ability to modify primary root development in Arabidopsis. Of these, 14 compounds caused a significant reduction of primary root length compared with control plants. Two of these inhibitory compounds bind to the predicted orthologue of Arabidopsis PSKR1, one of two receptors for PSK, a small sulfated peptide that positively controls root development. The reduced root length phenotype could not be rescued by the exogenous addition of the PSK peptide, suggesting that chemical treatment may inhibit both PSKR1 and its closely related receptor PSKR2. Six of the compounds acting as root growth inhibitors in Arabidopsis conferred the same effect in rice. Compound RAF265 (CHIR-265), previously shown to bind the human kinase BRAF (B-Raf proto-oncogene, serine/threonine kinase), also binds to nine highly conserved rice kinases tested. The binding of human and rice kinases to the same compound suggests that human kinase inhibitor sets will be useful for dissecting the function of plant kinases.

Target-specific compound selectivity for multi-target drug discovery and repurposing

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

Improved Electrophile Design for Exquisite Covalent Molecule Selectivity

Covalent inhibitors are viable therapeutics. However, off-target reactivity challenges the field. Chemists have attempted to solve this issue by varying the reactivity attributes of electrophilic warheads. Here, we report the development of an approach to increase the selectivity of covalent molecules that is independent of warhead reactivity features and can be used in concert with existing methods. Using the scaffold of the Bruton's tyrosine kinase (BTK) inhibitor Ibrutinib for our proof-of-concept, we reasoned that increasing the steric bulk of fumarate-based electrophiles on Ibrutinib should improve selectivity via the steric exclusion of off-targets but retain rates of cysteine reactivity comparable to that of an acrylamide. Using chemical proteomic techniques, we demonstrate that elaboration of the electrophile to a tert-butyl (t-Bu) fumarate ester decreases time-dependent off-target reactivity and abolishes time-independent off-target reactivity. While an alkyne-bearing probe analogue of Ibrutinib has 247 protein targets, our t-Bu fumarate probe analogue has only 7. Of these 7 targets, BTK is the only time-independent target. The t-Bu inhibitor itself is also more selective for BTK, reducing off-targets by 70%. We investigated the consequences of treatment with Ibrutinib and our t-Bu analogue and discovered that only 8 proteins are downregulated in response to treatment with the t-Bu analogue compared to 107 with Ibrutinib. Of these 8 proteins, 7 are also downregulated by Ibrutinib and a majority of these targets are associated with BTK biology. Taken together, these findings reveal an opportunity to increase cysteine-reactive covalent inhibitor selectivity through electrophilic structure optimization.

Solid Tumors and Kinase Inhibition: Management and Therapy Efficacy Evolution

The increasing numbers of cancer cases worldwide and the exceedingly high mortality rates of some tumor subtypes raise the question about if the current protocols for cancer management are effective and what has been done to improve upon oncologic patients’ prognoses. The traditional chemo-immunotherapy options for cancer treatment focus on the use of cytotoxic agents that are able to overcome neoplastic clones’ survival mechanisms and induce apoptosis, as well as on the ability to capacitate the host’s immune system to hinder the continuous growth of malignant cells. The need to avert the highly toxic profiles of conventional chemo-immunotherapy and to overcome the emerging cases of tumor multidrug resistance has fueled a growing interest in the field of precision medicine and targeted molecular therapies in the last couple of decades, although relatively new alternatives in oncologic practices, the increased specificity, and the positive clinical outcomes achieved through targeted molecular therapies have already consolidated them as promising prospects for the future of cancer management. In recent years, the development and application of targeted drugs as tyrosine kinase inhibitors have enabled cancer treatment to enter the era of specificity. In addition, the combined use of targeted therapy, immunotherapy, and traditional chemotherapy has innovated the standard treatment for many malignancies, bringing new light to patients with recurrent tumors. This article comprises a series of clinical trials that, in the past 5 years, utilized kinase inhibitors (KIs) as a monotherapy or in combination with other cytotoxic agents to treat patients afflicted with solid tumors. The results, with varying degrees of efficacy, are reported.

A novel JAK/ROCK inhibitor, CPL409116, demonstrates potent efficacy in the mouse model of systemic lupus erythematosus

Systemic lupus erythematosus is a chronic inflammatory disease, in which treatment is still limited due to suboptimal efficacy and toxicities associated with the available therapies. JAK kinases are well known to play an important role in systemic lupus erythematous. There is growing evidence that ROCK kinases are also important in disease development. In this paper, we present the results of the development of CPL409116, a dual JAK and ROCK inhibitor. The studies we performed demonstrate that this molecule is an effective JAK and ROCK inhibitor which efficiently blocks disease progression in NZBWF1/J mouse models of systemic lupus erythematous.

Decreased Proteasomal Function Induces Neuronal Loss and Memory Impairment

Alzheimer disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia worldwide. There is considerable evidence of age-related disruption of proteostasis being responsible for the development of AD. The proteasome is a multicatalytic enzyme complex that degrades both normal and damaged proteins, and an age-related decline in its activity has been implicated in age-related pathologies. Although proteasomal dysfunction is assumed to be a key AD hallmark, it remains unclear whether its role in disease onset is causative or secondary. In this study, we demonstrate that mice with proteasomal dysfunction exhibited memory impairment with associated neuronal loss, accumulation of phosphorylated tau, and activation of endoplasmic reticulum (ER) stress-related apoptosis pathways. Impaired proteasomal activity also activated ER stress-related apoptosis pathways in HT-22, a murine hippocampal neuronal cell line. HT-22 cell death, caused by proteasomal inhibition, was prevented by an inhibitor of c-Jun N-terminal kinase, an ER stress-related molecule. Collective evidence suggests that impaired proteasomal activity alters proteostasis, and subsequent ER stress-mediated pathways play pivotal roles in neuronal loss. Because aging decreases proteasomal function, age-related impairment of proteasomes may be involved in the development and progression of AD in elderly patients.

Clinically Precedented Protein Kinases: Rationale for Their Use in Neurodegenerative Disease

Kinases are an intensively studied drug target class in current pharmacological research as evidenced by the large number of kinase inhibitors being assessed in clinical trials. Kinase-targeted therapies have potential for treatment of a broad array of indications including central nervous system (CNS) disorders. In addition to the many variables which contribute to identification of a successful therapeutic molecule, drug discovery for CNS-related disorders also requires significant consideration of access to the target organ and specifically crossing the blood-brain barrier (BBB). To date, only a small number of kinase inhibitors have been reported that are specifically designed to be BBB permeable, which nonetheless demonstrates the potential for success. This review considers the potential for kinase inhibitors in the context of unmet medical need for neurodegenerative disease. A subset of kinases that have been the focus of clinical investigations over a 10-year period have been identified and discussed individually. For each kinase target, the data underpinning the validity of each in the context of neurodegenerative disease is critically evaluated. Selected molecules for each kinase are identified with information on modality, binding site and CNS penetrance, if known. Current clinical development in neurodegenerative disease are summarized. Collectively, the review indicates that kinase targets with sufficient rationale warrant careful design approaches with an emphasis on improving brain penetrance and selectivity.

JAK2 regulates Nav1.6 channel function via FGF14Y158 phosphorylation

BACKGROUND

Protein interactions between voltage-gated sodium (Nav) channels and accessory proteins play an essential role in neuronal firing and plasticity. However, a surprisingly limited number of kinases have been identified as regulators of these molecular complexes. We hypothesized that numerous as-of-yet unidentified kinases indirectly regulate the Nav channel via modulation of the intracellular fibroblast growth factor 14 (FGF14), an accessory protein with numerous unexplored phosphomotifs and required for channel function in neurons.

METHODS

Here we present results from an in-cell high-throughput screening (HTS) against the FGF14: Nav1.6 complex using >3000 diverse compounds targeting an extensive range of signaling pathways. Regulation by top kinase targets was then explored using in vitro phosphorylation, biophysics, mass-spectrometry and patch-clamp electrophysiology.

RESULTS

Compounds targeting Janus kinase 2 (JAK2) were over-represented among HTS hits. Phosphomotif scans supported by mass spectrometry revealed FGF14Y158, a site previously shown to mediate both FGF14 homodimerization and interactions with Nav1.6, as a JAK2 phosphorylation site. Following inhibition of JAK2, FGF14 homodimerization increased in a manner directly inverse to FGF14:Nav1.6 complex formation, but not in the presence of the FGF14Y158A mutant. Patch-clamp electrophysiology revealed that through Y158, JAK2 controls FGF14-dependent modulation of Nav1.6 channels. In hippocampal CA1 pyramidal neurons, the JAK2 inhibitor Fedratinib reduced firing by a mechanism that is dependent upon expression of FGF14.

CONCLUSIONS

These studies point toward a novel mechanism by which levels of JAK2 in neurons could directly influence firing and plasticity by controlling the FGF14 dimerization equilibrium, and thereby the availability of monomeric species for interaction with Nav1.6.

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.

Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.

P38α MAPK Signaling-A Robust Therapeutic Target for Rab5-Mediated Neurodegenerative Disease

Multifactorial pathologies, involving one or more aggregated protein(s) and neuroinflammation are common in major neurodegenerative diseases, such as Alzheimer's disease and dementia with Lewy bodies. This complexity of multiple pathogenic drivers is one potential explanation for the lack of success or, at best, the partial therapeutic effects, respectively, with approaches that have targeted one specific driver, e.g., amyloid-beta, in Alzheimer's disease. Since the endosome-associated protein Rab5 appears to be a convergence point for many, if not all the most prominent pathogenic drivers, it has emerged as a major therapeutic target for neurodegenerative disease. Further, since the alpha isoform of p38 mitogen-activated protein kinase (p38α) is a major regulator of Rab5 activity and its effectors, a biology that is distinct from the classical nuclear targets of p38 signaling, brain-penetrant selective p38α kinase inhibitors provide the opportunity for significant therapeutic advances in neurogenerative disease through normalizing dysregulated Rab5 activity. In this review, we provide a brief summary of the role of Rab5 in the cell and its association with neurodegenerative disease pathogenesis. We then discuss the connection between Rab5 and p38α and summarize the evidence that through modulating Rab5 activity there are therapeutic opportunities in neurodegenerative diseases for p38α kinase inhibitors.

Landscape of drug-resistance mutations in kinase regulatory hotspots

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase's secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.

RET isoform-specific interaction with scaffold protein Ezrin promotes cell migration and chemotaxis in lung adenocarcinoma

OBJECTIVES

Increased expression of REarranged during Transfection (RET) kinase is reported in 10-20 % of lung adenocarcinomas (LUAD) and is associated with metastasis and reduced survival. Ezrin is a scaffold protein that promotes protein interactions with the actin cytoskeleton to regulate cell migration and is also associated with invasion and metastasis in cancers. RET isoforms interact with unique combinations of scaffold proteins to promote distinct signaling pathways. We hypothesized that RET isoforms associate distinctly with Ezrin for cytoskeletal reorganization and LUAD cell migration processes.

METHODS

HCC1833 and A549 LUAD, SH-SY5Y neuroblastoma or HEK-293 cells expressing RET and Ezrin were stimulated with the RET ligand glial cell line-derived neurotrophic factor (GDNF) and treated with RET, Ezrin or Src inhibitors. Co-immunoprecipitation or pull-down assays coupled to immunoblotting were used to investigate protein activation and interactions. Immunofluorescence confocal microscopy assessed LUAD cytoskeletal reorganization and colocalization of RET and Ezrin. Live-cell fluorescence imaging was used to measure cell migration and chemotaxis.

RESULTS

GDNF promoted activation, interaction and colocalization of RET51 isoform and Ezrin. Inhibition of RET or Src impaired Ezrin interactions with RET and Src. GDNF stimulation enhanced the formation of actin-rich filopodia, in which both RET and Ezrin were enriched, and promoted chemotaxis in LUAD cells. However, inhibition of RET, Src or Ezrin suppressed filopodia formation, reduced colocalization of Ezrin with RET, and impaired cell migration and/ or chemotaxis. We further showed that GDNF-mediated activation of RET and Ezrin promoted RhoA-GTPase activity and signaling of ROCK1 and ROCK2 in LUAD cells.

CONCLUSIONS

Expression and activation of RET51 mediates unique protein interactions with Ezrin to promote LUAD cell chemotaxis for cancer cell dissemination, which may have implications in LUAD metastatic progression.

An activator of G protein-coupled receptor and MEK1/2-ERK1/2 signaling inhibits HIV-1 replication by altering viral RNA processing

The ability of HIV-1 to evolve resistance to combined antiretroviral therapies (cARTs) has stimulated research into alternative means of controlling this infection. We assayed >60 modulators of RNA alternative splicing (AS) to identify new inhibitors of HIV-1 RNA processing-a segment of the viral lifecycle not targeted by current drugs-and discovered compound N-[4-chloro-3-(trifluoromethyl)phenyl]-7-nitro-2,1,3-benzoxadiazol-4-amine (5342191) as a potent inhibitor of both wild-type (Ba-L, NL4-3, LAI, IIIB, and N54) and drug-resistant strains of HIV-1 (IC50: ~700 nM) with no significant effect on cell viability at doses tested. 5342191 blocks expression of four essential HIV-1 structural and regulatory proteins (Gag, Env, Tat, and Rev) without affecting total protein synthesis of the cell. This response is associated with altered unspliced (US) and singly-spliced (SS) HIV-1 RNA accumulation (~60% reduction) and transport to the cytoplasm (loss of Rev) whereas parallel analysis of cellular RNAs revealed less than a 0.7% of host alternative splicing (AS) events (0.25-0.67% by ≥ 10-20%), gene expression (0.01-0.46% by ≥ 2-5 fold), and protein abundance (0.02-0.34% by ≥ 1.5-2 fold) being affected. Decreased expression of Tat, but not Gag/Env, upon 5342191 treatment was reversed by a proteasome inhibitor, suggesting that this compound alters the synthesis/degradation of this key viral factor. Consistent with an affect on HIV-1 RNA processing, 5342191 treatment of cells altered the abundance and phosphorylation of serine/arginine-rich splicing factor (SRSF) 1, 3, and 4. Despite the activation of several intracellular signaling pathways by 5342191 (Ras, MEK1/2-ERK1/2, and JNK1/2/3), inhibition of HIV-1 gene expression by this compound could be reversed by pre-treatment with either a G-protein α-subunit inhibitor or two different MEK1/2 inhibitors. These observations demonstrate enhanced sensitivity of HIV-1 gene expression to small changes in host RNA processing and highlights the potential of modulating host intracellular signaling as an alternative approach for controlling HIV-1 infection.

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.

Design and Analysis of the 4-Anilinoquin(az)oline Kinase Inhibition Profiles of GAK/SLK/STK10 Using Quantitative Structure-Activity Relationships

The 4-anilinoquinoline and 4-anilinoquinazoline ring systems have been the focus of significant efforts in prior kinase drug discovery programs, which have led to approved medicines. Broad kinome profiles of these compounds have now been assessed with the advent of advanced screening technologies. These ring systems, while originally designed for specific targets including epidermal growth factor receptor (EGFR), but actually display a number of potent collateral kinase targets, some of which have been associated with negative clinical outcomes. We have designed and synthesized a series of 4-anilinoquin(az)olines in order to better understand the structure-activity relationships of three main collateral kinase targets of quin(az)oline-based kinase inhibitors: cyclin G associated kinase (GAK), STE20-like serine/threonine-protein kinase (SLK) and serine/threonine-protein kinase 10 (STK10). This was achieved through a series of quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites and extensive small-molecule X-ray structural analysis.

Poly(ADP-ribose)-dependent ubiquitination and its clinical implications

ADP-ribosylation-the addition of one or multiple ADP-ribose units onto proteins-is a therapeutically important post-translational modification implicated in cancer, neurodegeneration, and infectious diseases. The protein modification regulates a broad range of biological processes, including DNA repair, transcription, RNA metabolism, and the structural integrity of nonmembranous structures. The polymeric form of ADP-ribose, poly(ADP-ribose), was recently identified as a signal for triggering protein degradation through the ubiquitin-proteasome system. Using informatics analyses, we found that these ubiquitinated substrates tend to be low abundance proteins, which may serve as rate-limiting factors within signaling networks or metabolic processes. In this review, we summarize the current literature on poly(ADP-ribose)-dependent ubiquitination (PARdU) regarding its biological mechanisms, substrates, and relevance to diseases.

Synergistic effects of a cremophor EL drug delivery system and its U0126 cargo in an ex vivo model

Neuroprotection has proven clinically unsuccessful in subarachnoid hemorrhage. We believe that this is because the major component in the early damage pathway, the vascular wall, has not been given the necessary focus. U0126 is a potent inhibitor of vascular phenotypical changes, exemplified by functional endothelin B (ET_B) receptor upregulation. The current study aimed to determine the optimal dose of U0126 ex vivo and test the toxicology of this dose in vivo. To find the optimal dose and test a suitable in vivo delivery system, we applied an ex vivo model of blood flow cessation and investigated functional ET_B receptor upregulation (using a specific agonist) as the primary endpoint. The secondary endpoint was depolarization-induced contractility assessed by 60 mM K⁺ stimuli. Furthermore, an in vivo toxicology study was performed on the optimal selected doses. U0126 (10 µM) had a strong effect on the prevention of functional ET_B receptor contractility, combined with minimal effect on the depolarization-induced contractility. When cremophor EL was chosen for drug delivery, it had an inhibitory and additive effect (combined with U0126) on the ET_B receptor contractility. Hence, 10 µM U0126 in 0.5% cremophor EL seems to be a dose that will be close to the maximal inhibition observed ex vivo on basilar arteries, without exhibiting side effects in the toxicology studies. U0126 and cremophor EL are well tolerated at doses that have effect on ET_B receptor upregulation. Cremophor EL has an additional positive effect, preventing functional ET_B receptor upregulation, making it suitable as a drug delivery system.

Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets.

Identification of the Direct Substrates of the ABL Kinase via Kinase Assay Linked Phosphoproteomics with Multiple Drug Treatments

Ableson tyrosine kinase (ABL) plays essential roles in cell differentiation, division, adhesion, and stress response. However, fusion of the breakpoint cluster region (BCR) to ABL produces constitutive kinase activity that causes chronic myelogenous leukemia (CML). Small molecule tyrosine kinase inhibitors (TKIs) such as imatinib revolutionized the treatment of CML and other cancers, but acquired resistance to these inhibitors is rising. Thus, careful dissection of ABL signaling pathways is needed to find novel drug targets. Here we present a refined proteomic approach for elucidation of direct kinase substrates called kinase assay linked phosphoproteomics (KALIP). Our strategy integrates in vitro kinase assays at both the peptide and protein levels with quantitative tyrosine phosphoproteomics in response to treatment by multiple TKIs. Utilizing multiple TKIs permits elimination of off-target effects of these drugs, and overlapping the in vivo and in vitro data sets allows us to define a list of the most probable kinase substrates. Applying our approach produced a list of 60 ABL substrates, including novel and known proteins. We demonstrate that spleen tyrosine kinase (SYK) is a novel direct substrate of ABL, and we predict our proteomic strategy may facilitate identification of substrates in other cancers that have disrupted kinase signaling.

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.

Click chemistry for improvement in selectivity of quinazoline-based kinase inhibitors for mutant epidermal growth factor receptors

Discovery of mutant-selective kinase inhibitors is one of the challenges in medicinal chemistry and is a main issue for epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors. We tried to improve the selectivity of pan-HER inhibitors for mutant EGFRs. Utilizing click chemistry, triazole-tethered quinazoline derivatives were synthesized, based on a quinazoline scaffold showing pan-HER inhibition. The representative compound 5j exhibited 17- and 52-fold improved selectivity for EGFR L858R/T790M over wild-type EGFR and HER2, respectively, and demonstrated 6.7-fold more potent antiproliferative activity against PC9 cells harboring EGFR-activating mutation than gefitinib. Although the described quinazolines did not surpass pyrimidines as 3rd generation EGFR inhibitors in terms of selectivity for mutant EGFRs, our approach might provide information that would help in the identification of mutant-selective compounds among pan-HER inhibitors using the quinazoline scaffold.

Identification and antitumor activity of a novel inhibitor of the NIMA-related kinase NEK6

The NIMA (never in mitosis, gene A)-related kinase-6 (NEK6), which is implicated in cell cycle control and plays significant roles in tumorigenesis, is an attractive target for the development of novel anti-cancer drugs. Here we describe the discovery of a potent ATP site-directed inhibitor of NEK6 identified by virtual screening, adopting both structure- and ligand-based techniques. Using a homology-built model of NEK6 as well as the pharmacophoric features of known NEK6 inhibitors we identified novel binding scaffolds. Twenty-five compounds from the top ranking hits were subjected to in vitro kinase assays. The best compound, i.e. compound 8 ((5Z)-2-hydroxy-4-methyl-6-oxo-5-[(5-phenylfuran-2-yl)methylidene]-5,6-dihydropyridine-3-carbonitrile), was able to inhibit NEK6 with low micromolar IC₅₀ value, also displaying antiproliferative activity against a panel of human cancer cell lines. Our results suggest that the identified inhibitor can be used as lead candidate for the development of novel anti-cancer agents, thus opening the possibility of new therapeutic strategies.

How Selective Are Pharmacological Inhibitors of Cell-Cycle-Regulating Cyclin-Dependent Kinases?

Cyclin-dependent kinases (CDKs) are an important and emerging class of drug targets for which many small-molecule inhibitors have been developed. However, there is often insufficient data available on the selectivity of CDK inhibitors (CDKi) to attribute the effects on the presumed target CDK to these inhibitors. Here, we highlight discrepancies between the kinase selectivity of CDKi and the phenotype exhibited; we evaluated 31 CDKi (claimed to target CDK1-4) for activity toward CDKs 1, 2, 4, 5, 7, 9 and for effects on the cell cycle. Our results suggest that most CDKi should be reclassified as pan-selective and should not be used as a tool. In addition, some compounds did not even inhibit CDKs as their primary cellular targets; for example, NU6140 showed potent inhibition of Aurora kinases. We also established an online database of commercially available CDKi for critical evaluation of their utility as molecular probes. Our results should help researchers select the most relevant chemical tools for their specific applications.

Synthesis of kinase inhibitors containing a pentafluorosulfanyl moiety

A series of 3-methylidene-1H-indol-2(3H)-ones substituted with a 5- or 6-pentafluorosulfanyl group has been synthesized by a Knoevenagel condensation reaction of SF₅-substituted oxindoles with a range of aldehydes. The resulting products were characterized by X-ray crystallography studies and were tested for biological activity versus a panel of cell lines and protein kinases. Some exhibited single digit nM activity.

The target landscape of clinical kinase drugs

Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making.

An exploratory clinical study of p38α kinase inhibition in Alzheimer's disease

Objective

The aim of this study was to preliminarily evaluate an oral small molecule p38α kinase inhibitor in patients with early Alzheimer's disease (AD) for the effects on brain amyloid plaque load and episodic memory function, and to establish pharmacokinetic-pharmacodynamics correlations if any effects identified on these parameters.

Methods

Sixteen patients with early AD received a highly selective p38α inhibitor (neflamapimod) for 84 days (12 weeks). To obtain a broad range of plasma drug exposures, subjects randomized to receive either 40 mg (n = 9) or 125 mg (n = 7) twice daily. Dynamic, ¹¹C-PiB positron emission scans were performed at baseline and at Day 84 and quantitatively analyzed by reference parametric mapping. Episodic memory assessed as Wechsler Memory Scale (WMS) immediate and delayed recall composites.

Result

In the ¹¹C-PiB analyses there were no main group level effects, though in the prespecified responder analysis (>7% reduction in ¹¹C-PiB signal) there were three responders in the 40 mg, and one in the 125 mg group. There were statistically significant increases from baseline in mean WMS immediate recall score and WMS delayed recall at both day 28 (P = 0.03 and P = 0.001) and day 84 (P = 0.001 and P < 0.001). Individual subject plasma drug concentration profiles were significantly positively correlated with the change in combined WMS immediate and delayed recall (P < 0.0001, r² = 0.70). Within-subject effect size was 0.59 for immediate recall and 0.67 for delayed recall.

Interpretation

Selective p38α inhibition in patients with early AD may improve episodic memory and potentially impact β-amyloid production. These preliminary clinical findings support conduct of a longer duration placebo-controlled study, particularly to confirm the effects on episodic memory function.

Cardiac glycoside/aglycones inhibit HIV-1 gene expression by a mechanism requiring MEK1/2-ERK1/2 signaling

The capacity of HIV-1 to develop resistance to current drugs calls for innovative strategies to control this infection. We aimed at developing novel inhibitors of HIV-1 replication by targeting viral RNA processing—a stage dependent on conserved host processes. We previously reported that digoxin is a potent inhibitor of this stage. Herein, we identify 12 other cardiac glycoside/aglycones or cardiotonic steroids (CSs) that impede HIV growth in HIV-infected T cells from clinical patients at IC₅₀s (1.1–1.3 nM) that are 2–26 times below concentrations used in patients with heart conditions. We subsequently demonstrate that CSs inhibit HIV-1 gene expression in part through modulation of MEK1/2-ERK1/2 signaling via interaction with the Na⁺/K⁺-ATPase, independent of alterations in intracellular Ca²⁺. Supporting this hypothesis, depletion of the Na⁺/K⁺-ATPase or addition of a MEK1/2-ERK1/2 activator also impairs HIV-1 gene expression. Similar to digoxin, all CSs tested induce oversplicing of HIV-1 RNAs, reducing unspliced (Gag) and singly spliced RNAs (Env/p14-Tat) encoding essential HIV-1 structural/regulatory proteins. Furthermore, all CSs cause nuclear retention of genomic/unspliced RNAs, supporting viral RNA processing as the underlying mechanism for their disruption of HIV-1 replication. These findings call for further in vivo validation and supports the targeting of cellular processes to control HIV-1 infection.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

G2/M inhibitors as pharmacotherapeutic opportunities for glioblastoma: the old, the new, and the future

Glioblastoma (GBM) is one of the deadliest tumors and has a median survival of 3 months if left untreated. Despite advances in rationally targeted pharmacological approaches, the clinical care of GBM remains palliative in intent. Since the majority of altered signaling cascades involved in cancer establishment and progression eventually affect cell cycle progression, an alternative approach for cancer therapy is to develop innovative compounds that block the activity of crucial molecules needed by tumor cells to complete cell division. In this context, we review promising ongoing and future strategies for GBM therapeutics aimed towards G2/M inhibition such as anti-microtubule agents and targeted therapy against G2/M regulators like cyclin-dependent kinases, Aurora inhibitors, PLK1, BUB, 1, and BUBR1, and survivin. Moreover, we also include investigational agents in the preclinical and early clinical settings. Although several drugs were shown to be gliotoxic, most of them have not yet entered therapeutic trials. The use of either single exposure or a combination with novel compounds may lead to treatment alternatives for GBM patients in the near future.

Quantitative Prioritization of Tool Compounds for Phenotypic Screening

Phenotypic screens are increasingly utilized in drug discovery for multiple purposes such as lead and/or tool compound finding, and target discovery. Using potent and selective chemical tool compounds against well-defined targets in phenotypic screens can help elucidate biological processes modulating assay phenotypes. Unfortunately the identification of such tools from large heterogeneous bioactivity databases is nontrivial and there is repeated use of published unselective compounds as phenotypic tools. Here we describe a computational model, the compound-target tool score (TS), which is an evidence-based quantitative confidence metric that can be used to systematically rank tool compounds for targets. The identified selective and nonselective tool compounds have applications in phenotypic assays for target hypothesis validation as well as assay development.

Clinical pharmacology of anti-angiogenic drugs in oncology

Abnormal vasculature proliferation is one of the so-called hallmarks of cancer. Angiogenesis inhibitor therapies are one of the major breakthroughs in cancer treatment in the last two decades. Two types of anti-angiogenics have been approved: monoclonal antibodies and derivatives, which are injected and target the extracellular part of a receptor, and protein kinase inhibitors, which are orally taken small molecules targeting the intra-cellular Adenosine Triphosphate -pocket of different kinases. They have become an important part of some tumors' treatment, both in monotherapy or in combination. In this review, we discuss the key pharmacological concepts and the major pitfalls of anti-angiogenic prescriptions. We also review the pharmacokinetic and pharmacodynamics profile of all approved anti-angiogenic protein kinase inhibitors and the potential role of surrogate markers and of therapeutic drug monitoring.

Structural characterization of human Vaccinia-Related Kinases (VRK) bound to small-molecule inhibitors identifies different P-loop conformations

The human genome encodes two active Vaccinia-related protein kinases (VRK), VRK1 and VRK2. These proteins have been implicated in a number of cellular processes and linked to a variety of tumors. However, understanding the cellular role of VRKs and establishing their potential use as targets for therapeutic intervention has been limited by the lack of tool compounds that can specifically modulate the activity of these kinases in cells. Here we identified BI-D1870, a dihydropteridine inhibitor of RSK kinases, as a promising starting point for the development of chemical probes targeting the active VRKs. We solved co-crystal structures of both VRK1 and VRK2 bound to BI-D1870 and of VRK1 bound to two broad-spectrum inhibitors. These structures revealed that both VRKs can adopt a P-loop folded conformation, which is stabilized by different mechanisms on each protein. Based on these structures, we suggest modifications to the dihydropteridine scaffold that can be explored to produce potent and specific inhibitors towards VRK1 and VRK2.

Target Residence Time-Guided Optimization on TTK Kinase Results in Inhibitors with Potent Anti-Proliferative Activity

The protein kinase threonine tyrosine kinase (TTK; also known as Mps1) is a critical component of the spindle assembly checkpoint and a promising drug target for the treatment of aggressive cancers, such as triple negative breast cancer. While the first TTK inhibitors have entered clinical trials, little is known about how the inhibition of TTK with small-molecule compounds affects cellular activity. We studied the selective TTK inhibitor NTRC 0066-0, which was developed in our own laboratory, together with 11 TTK inhibitors developed by other companies, including Mps-BAY2b, BAY 1161909, BAY 1217389 (Bayer), TC-Mps1-12 (Shionogi), and MPI-0479605 (Myrexis). Parallel testing shows that the cellular activity of these TTK inhibitors correlates with their binding affinity to TTK and, more strongly, with target residence time. TTK inhibitors are therefore an example where target residence time determines activity in in vitro cellular assays. X-ray structures and thermal stability experiments reveal that the most potent compounds induce a shift of the glycine-rich loop as a result of binding to the catalytic lysine at position 553. This "lysine trap" disrupts the catalytic machinery. Based on these insights, we developed TTK inhibitors, based on a (5,6-dihydro)pyrimido[4,5-e]indolizine scaffold, with longer target residence times, which further exploit an allosteric pocket surrounding Lys553. Their binding mode is new for kinase inhibitors and can be classified as hybrid Type I/Type III. These inhibitors have very potent anti-proliferative activity that rivals classic cytotoxic therapy. Our findings will open up new avenues for more applications for TTK inhibitors in cancer treatment.

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.