Targeting NOX2 with Bivalent Small-Molecule p47phox-p22phox Inhibitors.

Nicotinamide adenine dinucleotide phosphate oxidase isoform 2 (NOX2) is an enzymatic complex whose function is the regulated generation of reactive oxygen species (ROS). NOX2 activity is central to redox signaling events and antibacterial response, but excessive ROS production by NOX2 leads to oxidative stress and inflammation in a range of diseases. The protein-protein interaction between the NOX2 subunits p47phox and p22phox is essential for NOX2 activation, thus p47phox is a potential drug target. Previously, we identified 2-aminoquinoline as a fragment hit toward p47phoxSH3A-B and converted it to a bivalent small-molecule p47phox-p22phox inhibitor (Ki = 20 µM). Here, we systematically optimized the bivalent compounds by exploring linker types and positioning as well as substituents on the 2-aminoquinoline part and characterized the bivalent binding mode with biophysical methods. We identified several compounds with submicromolar binding affinities and cellular activity and thereby demonstrated that p47phox can be targeted by potent small molecules.

Small-molecule modulators of tumor necrosis factor signaling.

Tumor necrosis factor (TNF) is a pleiotropic cytokine with a major role in immune system homeostasis and is involved in many inflammatory and autoimmune diseases, such as rheumatoid arthritis (RA), psoriasis, Alzheimer's disease (AD), and multiple sclerosis (MS). Thus, TNF and its receptors, TNFR1 and TNFR2, are relevant pharmacological targets. Biologics have been developed to block TNF-dependent signaling cascades, but they display serious side effects, and their pharmacological effectiveness decreases over time because of their immunogenicity. In this review, we present recent discoveries in small molecules targeting TNF and its receptors and discuss alternative strategies for modulating TNF signaling.

Development of Noncovalent Small-Molecule Keap1-Nrf2 Inhibitors by Fragment-Based Drug Discovery.

Targeting the protein-protein interaction (PPI) between the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) and its repressor, Kelch-like ECH-associated protein 1 (Keap1), constitutes a promising strategy for treating diseases involving oxidative stress and inflammation. Here, a fragment-based drug discovery (FBDD) campaign resulted in novel, high-affinity (Ki = 280 nM), and cell-active noncovalent small-molecule Keap1-Nrf2 PPI inhibitors. We screened 2500 fragments using orthogonal assays─fluorescence polarization (FP), thermal shift assay (TSA), and surface plasmon resonance (SPR)─and validated the hits by saturation transfer difference (STD) NMR, leading to 28 high-priority hits. Thirteen co-structures showed fragments binding mainly in the P4 and P5 subpockets of Keap1's Kelch domain, and three fluorenone-based fragments featuring a novel binding mode were optimized by structure-based drug discovery. We thereby disclose several fragment hits, including their binding modes, and show how FBDD can be performed to find new small-molecule Keap1-Nrf2 PPI inhibitors.

Deconstructing Noncovalent Kelch-like ECH-Associated Protein 1 (Keap1) Inhibitors into Fragments to Reconstruct New Potent Compounds.

Targeting the protein-protein interaction (PPI) between nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) is a potential therapeutic strategy to control diseases involving oxidative stress. Here, six classes of known small-molecule Keap1-Nrf2 PPI inhibitors were dissected into 77 fragments in a fragment-based deconstruction reconstruction (FBDR) study and tested in four orthogonal assays. This gave 17 fragment hits of which six were shown by X-ray crystallography to bind in the Keap1 Kelch binding pocket. Two hits were merged into compound 8 with a 220-380-fold stronger affinity (Ki = 16 µM) relative to the parent fragments. Systematic optimization resulted in several novel analogues with Ki values of 0.04-0.5 µM, binding modes determined by X-ray crystallography, and enhanced microsomal stability. This demonstrates how FBDR can be used to find new fragment hits, elucidate important ligand-protein interactions, and identify new potent inhibitors of the Keap1-Nrf2 PPI.

Targeting the Redox Landscape in Cancer Therapy.

Reactive oxygen species (ROS) are produced predominantly by the mitochondrial electron transport chain and by NADPH oxidases in peroxisomes and in the endoplasmic reticulum. The antioxidative defense counters overproduction of ROS with detoxifying enzymes and molecular scavengers, for instance, superoxide dismutase and glutathione, in order to restore redox homeostasis. Mutations in the redox landscape can induce carcinogenesis, whereas increased ROS production can perpetuate cancer development. Moreover, cancer cells can increase production of antioxidants, leading to resistance against chemo- or radiotherapy. Research has been developing pharmaceuticals to target the redox landscape in cancer. For instance, inhibition of key players in the redox landscape aims to modulate ROS production in order to prevent tumor development or to sensitize cancer cells in radiotherapy. Besides the redox landscape of a single cell, alternative strategies take aim at the multi-cellular level. Extracellular vesicles, such as exosomes, are crucial for the development of the hypoxic tumor microenvironment, and hence are explored as target and as drug delivery systems in cancer therapy. This review summarizes the current pharmaceutical and experimental interventions of the cancer redox landscape.

Developing Inhibitors of the p47phox-p22phox Protein-Protein Interaction by Fragment-Based Drug Discovery.

Nicotinamide adenine dinucleotide phosphate oxidase isoform 2 is an enzyme complex, which generates reactive oxygen species and contributes to oxidative stress. The p47phox-p22phox interaction is critical for the activation of the catalytical NOX2 domain, and p47phox is a potential target for therapeutic intervention. By screening 2500 fragments using fluorescence polarization and a thermal shift assay and validation by surface plasmon resonance, we found eight hits toward the tandem SH3 domain of p47phox (p47phoxSH3A-B) with KD values of 400-600 µM. Structural studies revealed that fragments 1 and 2 bound two separate binding sites in the elongated conformation of p47phoxSH3A-B and these competed with p22phox for binding to p47phoxSH3A-B. Chemical optimization led to a dimeric compound with the ability to potently inhibit the p47phoxSH3A-B-p22phox interaction (Ki of 20 µM). Thereby, we reveal a new way of targeting p47phox and present the first report of drug-like molecules with the ability to bind p47phox and inhibit its interaction with p22phox.

A Comparative Assessment Study of Known Small-Molecule Keap1-Nrf2 Protein-Protein Interaction Inhibitors: Chemical Synthesis, Binding Properties, and Cellular Activity.

Inhibiting the protein-protein interaction (PPI) between the transcription factor Nrf2 and its repressor protein Keap1 has emerged as a promising strategy to target oxidative stress in diseases, including central nervous system (CNS) disorders. Numerous non-covalent small-molecule Keap1-Nrf2 PPI inhibitors have been reported to date, but many feature suboptimal physicochemical properties for permeating the blood-brain barrier, while others contain problematic structural moieties. Here, we present the first side-by-side assessment of all reported Keap1-Nrf2 PPI inhibitor classes using fluorescence polarization, thermal shift assay, and surface plasmon resonance-and further evaluate the compounds in an NQO1 induction cell assay and in counter tests for nonspecific activities. Surprisingly, half of the compounds were inactive or deviated substantially from reported activities, while we confirm the cross-assay activities for others. Through this study, we have identified the most promising Keap1-Nrf2 inhibitors that can serve as pharmacological probes or starting points for developing CNS-active Keap1 inhibitors.

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

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

Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches.

In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Evaluating the predictivity of virtual screening for ABL kinase inhibitors to hinder drug resistance.

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. 'Dual active' inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies.