Synthesis and SAR evaluation of coumarin derivatives as potent cannabinoid receptor agonists.

We report the development and extensive structure-activity relationship evaluation of a series of modified coumarins as cannabinoid receptor ligands. In radioligand, and [35S]GTPγS binding assays the CB receptor binding affinities and efficacies of the new ligands were determined. Furthermore, we used a ligand-based docking approach to validate the empirical observed results. In conclusion, several crucial structural requirements were identified. The most potent coumarins like 3-butyl-7-(1-butylcyclopentyl)-5-hydroxy-2H-chromen-2-one (36b, Ki CB2 13.7 nM, EC50 18 nM), 7-(1-butylcyclohexyl)-5-hydroxy-3-propyl-2H-chromen-2-one (39b, Ki CB2 6.5 nM, EC50 4.51 nM) showed a CB2 selective agonistic profile with low nanomolar affinities.

Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs.

Proteins often have both orthosteric and allosteric binding sites. Endogenous ligands, such as hormones and neurotransmitters, bind to the orthosteric site, while synthetic ligands may bind to orthosteric or allosteric sites, which has become a focal point in drug discovery. Usually, such allosteric modulators bind to a protein noncompetitively with its endogenous ligand or substrate. The growing interest in allosteric modulators has resulted in a substantial increase of these entities and their features such as binding data in chemical libraries and databases. Although this data surge fuels research focused on allosteric modulators, binding data is unfortunately not always clearly indicated as being allosteric or orthosteric. Therefore, allosteric binding data is difficult to retrieve from databases that contain a mixture of allosteric and orthosteric compounds. This decreases model performance when statistical methods, such as machine learning models, are applied. In previous work we generated an allosteric data subset of ChEMBL release 14. In the current study an improved text mining approach is used to retrieve the allosteric and orthosteric binding types from the literature in ChEMBL release 22. Moreover, convolutional deep neural networks were constructed to predict the binding types of compounds for class A G protein-coupled receptors (GPCRs). Temporal split validation showed the model predictiveness with Matthews correlation coefficient (MCC) = 0.54, sensitivity allosteric = 0.54, and sensitivity orthosteric = 0.94. Finally, this study shows that the inclusion of accurate binding types increases binding predictions by including them as descriptor (MCC = 0.27 improved to MCC = 0.34; validated for class A GPCRs, trained on all GPCRs). Although the focus of this study is mainly on class A GPCRs, binding types for all protein classes in ChEMBL were obtained and explored. The data set is included as a supplement to this study, allowing the reader to select the compounds and binding types of interest.

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.

Novel natural and synthetic inhibitors of solute carriers SGLT1 and SGLT2.

Selective analogs of the natural glycoside phloridzin are marketed drugs that reduce hyperglycemia in diabetes by inhibiting the active sodium glucose cotransporter SGLT2 in the kidneys. In addition, intestinal SGLT1 is now recognized as a target for glycemic control. To expand available type 2 diabetes remedies, we aimed to find novel SGLT1 inhibitors beyond the chemical space of glycosides. We screened a bioactive compound library for SGLT1 inhibitors and tested primary hits and additional structurally similar molecules on SGLT1 and SGLT2 (SGLT1/2). Novel SGLT1/2 inhibitors were discovered in separate chemical clusters of natural and synthetic compounds. These have IC50-values in the 10-100 µmol/L range. The most potent identified novel inhibitors from different chemical clusters are (SGLT1-IC50 Mean ± SD, SGLT2-IC50 Mean ± SD): (+)-pteryxin (12 ± 2 µmol/L, 9 ± 4 µmol/L), (+)-ε-viniferin (58 ± 18 µmol/L, 110 µmol/L), quinidine (62 µmol/L, 56 µmol/L), cloperastine (9 ± 3 µmol/L, 9 ± 7 µmol/L), bepridil (10 ± 5 µmol/L, 14 ± 12 µmol/L), trihexyphenidyl (12 ± 1 µmol/L, 20 ± 13 µmol/L) and bupivacaine (23 ± 14 µmol/L, 43 ± 29 µmol/L). The discovered natural inhibitors may be further investigated as new potential (prophylactic) agents for controlling dietary glucose uptake. The new diverse structure activity data can provide a starting point for the optimization of novel SGLT1/2 inhibitors and support the development of virtual SGLT1/2 inhibitor screening models.

Development of Ubiquitin-Based Probe for Metalloprotease Deubiquitinases.

Deubiquitinases (DUBs) are a family of enzymes that regulate the ubiquitin signaling cascade by removing ubiquitin from specific proteins in response to distinct signals. DUBs that belong to the metalloprotease family (metalloDUBs) contain Zn2+ in their active sites and are an integral part of distinct cellular protein complexes. Little is known about these enzymes because of the lack of specific probes. Described here is a Ub-based probe that contains a ubiquitin moiety modified at its C-terminus with a Zn2+ chelating group based on 8-mercaptoquinoline, and a modification at the N-terminus with either a fluorescent tag or a pull-down tag. The probe is validated using Rpn11, a metalloDUB found in the 26S proteasome complex. This probe binds to metalloDUBs and efficiently pulled down overexpressed metalloDUBs from a HeLa cell lysate. Such probes may be used to study the mechanism of metalloDUBs in detail and allow better understanding of their biochemical processes.

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.

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling.

Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins.

Activity-Based Protein Profiling Identifies α-Ketoamides as Inhibitors for Phospholipase A2 Group XVI.

Phospholipase A2, group XVI (PLA2G16) is a thiol hydrolase from the HRASLS family that regulates lipolysis in adipose tissue and has been identified as a host factor enabling the cellular entry of picornaviruses. Chemical tools are essential to visualize and control PLA2G16 activity, but they have not been reported to date. Here, we show that MB064, which is a fluorescent lipase probe, also labels recombinant and endogenously expressed PLA2G16. Competitive activity-based protein profiling (ABPP) using MB064 enabled the discovery of α-ketoamides as the first selective PLA2G16 inhibitors. LEI110 was identified as a potent PLA2G16 inhibitor ( Ki = 20 nM) that reduces cellular arachidonic acid levels and oleic acid-induced lipolysis in human HepG2 cells. Gel-based ABPP and chemical proteomics showed that LEI110 is a selective pan-inhibitor of the HRASLS family of thiol hydrolases (i.e., PLA2G16, HRASLS2, RARRES3 and iNAT). Molecular dynamic simulations of LEI110 in the reported crystal structure of PLA2G16 provided insight in the potential ligand-protein interactions to explain its binding mode. In conclusion, we have developed the first selective inhibitor that can be used to study the cellular role of PLA2G16.

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands.

Drugs have become an essential part of our lives due to their ability to improve people's health and quality of life. However, for many diseases, approved drugs are not yet available or existing drugs have undesirable side effects, making the pharmaceutical industry strive to discover new drugs and active compounds. The development of drugs is an expensive process, which typically starts with the detection of candidate molecules (screening) after a protein target has been identified. To this end, the use of high-performance screening techniques has become a critical issue in order to palliate the high costs. Therefore, the popularity of computer-based screening (often called virtual screening or in silico screening) has rapidly increased during the last decade. A wide variety of Machine Learning (ML) techniques has been used in conjunction with chemical structure and physicochemical properties for screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently (iii) Multiple Classifier Systems (MCS). Here, we apply an MCS for virtual screening (D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine (1,834,362 compounds), was virtually screened to identify 48,232 potential active molecules using D2-MCS. Identified molecules were ranked to select 21 promising novel compounds for in vitro evaluation. Experimental validation confirmed six highly active hits (> 50% displacement at 10 µM and subsequent Ki determination) and an additional five medium active hits (> 25% displacement at 10 µM). Hence, D2-MCS provided a hit rate of 29% for highly active compounds and an overall hit rate of 52%.

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.