Structure-Activity Relationship Studies of α-Ketoamides as Inhibitors of the Phospholipase A and Acyltransferase Enzyme Family.

The phospholipase A and acyltransferase (PLAAT) family of cysteine hydrolases consists of five members, which are involved in the Ca2+-independent production of N-acylphosphatidylethanolamines (NAPEs). NAPEs are lipid precursors for bioactive N-acylethanolamines (NAEs) that are involved in various physiological processes such as food intake, pain, inflammation, stress, and anxiety. Recently, we identified α-ketoamides as the first pan-active PLAAT inhibitor scaffold that reduced arachidonic acid levels in PLAAT3-overexpressing U2OS cells and in HepG2 cells. Here, we report the structure-activity relationships of the α-ketoamide series using activity-based protein profiling. This led to the identification of LEI-301, a nanomolar potent inhibitor for the PLAAT family members. LEI-301 reduced the NAE levels, including anandamide, in cells overexpressing PLAAT2 or PLAAT5. Collectively, LEI-301 may help to dissect the physiological role of the PLAATs.

Design and pharmacological profile of a novel covalent partial agonist for the adenosine A1 receptor.

Partial agonists for G protein-coupled receptors (GPCRs) provide opportunities for novel pharmacotherapies with enhanced on-target safety compared to full agonists. For the human adenosine A1 receptor (hA1AR) this has led to the discovery of capadenoson, which has been in phase IIa clinical trials for heart failure. Accordingly, the design and profiling of novel hA1AR partial agonists has become an important research focus. In this study, we report on LUF7746, a capadenoson derivative bearing an electrophilic fluorosulfonyl moiety, as an irreversibly binding hA1AR modulator. Meanwhile, a nonreactive ligand bearing a methylsulfonyl moiety, LUF7747, was designed as a control probe in our study. In a radioligand binding assay, LUF7746's apparent affinity increased to nanomolar range with longer pre-incubation time, suggesting an increasing level of covalent binding over time. Moreover, compared to the reference full agonist CPA, LUF7746 was a partial agonist in a hA1AR-mediated G protein activation assay and resistant to blockade with an antagonist/inverse agonist. An in silico structure-based docking study combined with site-directed mutagenesis of the hA1AR demonstrated that amino acid Y2717.36 was the primary anchor point for the covalent interaction. Additionally, a label-free whole-cell assay was set up to identify LUF7746's irreversible activation of an A1 receptor-mediated cell morphological response. These results led us to conclude that LUF7746 is a novel covalent hA1AR partial agonist and a valuable chemical probe for further mapping the receptor activation process. It may also serve as a prototype for a therapeutic approach in which a covalent partial agonist may cause less on-target side effects, conferring enhanced safety compared to a full agonist.

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors.

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of <60% (at a concentration of 10 µM) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC50 value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.

Quantitative prediction of selectivity between the A1 and A2A adenosine receptors.

The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A1 and A2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two drug targets by directly training on the difference in bioactivity, modeling the selectivity-window. The quality of this model was good as shown by the performances for fivefold cross-validation: ROC A1AR-selective 0.88 ± 0.04 and ROC A2AAR-selective 0.80 ± 0.07. To increase the accuracy of this selectivity model even further, inactive compounds were identified and removed prior to selectivity prediction by a combination of statistical models and structure-based docking. As a result, selectivity between the A1 and A2A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.

Advances and Challenges in Computational Target Prediction.

Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.

Development of a Retinal-Based Probe for the Profiling of Retinaldehyde Dehydrogenases in Cancer Cells.

Retinaldehyde dehydrogenases belong to a superfamily of enzymes that regulate cell differentiation and are responsible for detoxification of anticancer drugs. Chemical tools and methods are of great utility to visualize and quantify aldehyde dehydrogenase (ALDH) activity in health and disease. Here, we present the discovery of a first-in-class chemical probe based on retinal, the endogenous substrate of retinal ALDHs. We unveil the utility of this probe in quantitating ALDH isozyme activity in a panel of cancer cells via both fluorescence and chemical proteomic approaches. We demonstrate that our probe is superior to the widely used ALDEFLUOR assay to explain the ability of breast cancer (stem) cells to produce all-trans retinoic acid. Furthermore, our probe revealed the cellular selectivity profile of an advanced ALDH1A1 inhibitor, thereby prompting us to investigate the nature of its cytotoxicity. Our results showcase the application of substrate-based probes in interrogating pathologically relevant enzyme activities. They also highlight the general power of chemical proteomics in driving the discovery of new biological insights and its utility to guide drug discovery efforts.

Rapid screening and identification of ACE inhibitors in snake venoms using at-line nanofractionation LC-MS.

This study presents an analytical method for the screening of snake venoms for inhibitors of the angiotensin-converting enzyme (ACE) and a strategy for their rapid identification. The method is based on an at-line nanofractionation approach, which combines liquid chromatography (LC), mass spectrometry (MS), and pharmacology in one platform. After initial LC separation of a crude venom, a post-column flow split is introduced enabling parallel MS identification and high-resolution fractionation onto 384-well plates. The plates are subsequently freeze-dried and used in a fluorescence-based ACE activity assay to determine the ability of the nanofractions to inhibit ACE activity. Once the bioactive wells are identified, the parallel MS data reveals the masses corresponding to the activities found. Narrowing down of possible bioactive candidates is provided by comparison of bioactivity profiles after reversed-phase liquid chromatography (RPLC) and after hydrophilic interaction chromatography (HILIC) of a crude venom. Additional nanoLC-MS/MS analysis is performed on the content of the bioactive nanofractions to determine peptide sequences. The method described was optimized, evaluated, and successfully applied for screening of 30 snake venoms for the presence of ACE inhibitors. As a result, two new bioactive peptides were identified: pELWPRPHVPP in Crotalus viridis viridis venom with IC50 = 1.1 µM and pEWPPWPPRPPIPP in Cerastes cerastes cerastes venom with IC50 = 3.5 µM. The identified peptides possess a high sequence similarity to other bradykinin-potentiating peptides (BPPs), which are known ACE inhibitors found in snake venoms.