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... Show moreRetinaldehyde 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. Show less
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most common causes of end-stage renal failure, caused by mutations in PKD1 or PKD2 genes. Tolvaptan, the only drug approved for... Show moreAutosomal Dominant Polycystic Kidney Disease (ADPKD) is one of the most common causes of end-stage renal failure, caused by mutations in PKD1 or PKD2 genes. Tolvaptan, the only drug approved for ADPKD treatment, results in serious side-effects, warranting the need for novel drugs.\nIn this study, we applied RNA-sequencing of Pkd1cko mice at different disease stages, and with/without drug treatment to identify genes involved in ADPKD progression that were further used to identify novel drug candidates for ADPKD. We followed an integrative computational approach using a combination of gene expression profiling, bioinformatics and cheminformatics data.\nWe identified 1162 genes that had a normalized expression after treating the mice with drugs proven effective in preclinical models. Intersecting these genes with target affinity profiles for clinically-approved drugs in ChEMBL, resulted in the identification of 116 drugs targeting 29 proteins, of which several are previously linked to Polycystic Kidney Disease such as Rosiglitazone. Further testing the efficacy of six candidate drugs for inhibition of cyst swelling using a human 3D-cyst assay, revealed that three of the six had cyst-growth reducing effects with limited toxicity.\nOur data further establishes drug repurposing as a robust drug discovery method, with three promising drug candidates identified for ADPKD treatment (Meclofenamic Acid, Gamolenic Acid and Birinapant). Our strategy that combines multiple-omics data, can be extended for ADPKD and other diseases in the future.\nEuropean Union's Seventh Framework Program, Dutch Technology Foundation Stichting Technische Wetenschappen and the Dutch Kidney Foundation. Show less
Hassing, G.J.; Wall, H.E.C. van der; Westen, G.J.P. van; Kemme, M.J.B.; Adiyaman, A.; Elvan, A.; ... ; Gal, P. 2019
Elevated blood pressure induces electrocardiographic changes and is associated with an increase in cardiovascular disease later in life compared to normal blood pressure levels. The purpose of this... Show moreElevated blood pressure induces electrocardiographic changes and is associated with an increase in cardiovascular disease later in life compared to normal blood pressure levels. The purpose of this study was to evaluate the association between normal to high normal blood pressure values (90–139/50–89 mmHg) and electrocardiographic parameters related to cardiac changes in hypertension in healthy young adults.Data from 1449 volunteers aged 18–30 years collected at our centre were analyzed. Only subjects considered healthy by a physician after review of collected data with systolic blood pressure values between 90 and 139 mmHg and diastolic blood pressure values between 50 and 89 mmHg were included. Subjects were divided into groups with 10 mmHg systolic blood pressure increment between groups for analysis of electrocardiographic differences. Backward multivariate regression analysis with systolic and diastolic blood pressure as a continuous variable was performed.The mean age was 22.7 ± 3.0 years, 73.7% were male. P-wave area, ventricular activation time, QRS-duration, Sokolow–Lyon voltages, Cornell Product, J-point–T-peak duration corrected for heart rate and maximum T-wave duration were significantly different between systolic blood pressure groups. In the multivariate model with gender, body mass index and cholesterol, ventricular rate (standardized coefficient (SC): +0.182, p < .001), ventricular activation time in lead V6 (SC= +0.065, p = .048), Sokolow–Lyon voltage (SC= +0.135, p < .001), and Cornell product (SC= +0.137, p < .001) were independently associated with systolic blood pressure, while ventricular rate (SC= +0.179, p < .001), P-wave area in lead V1 (SC= +0.079, p = .020), and Cornell product (SC= +0.091, p = .006) were independently associated with diastolic blood pressure.Blood pressure-related electrocardiographic changes were observed incrementally in a healthy young population with blood pressure in the normal range. These changes were an increased ventricular rate, increased atrial surface area, ventricular activation time and increased ventricular hypertrophy indices on a standard 12 lead electrocardiogram. Show less
Ruano‑Ordás, D.; Burggraaff, L.; Liu, R.; Horst, C. van der; Heitman, L.H.; Emmerich, M.T.M.; ... ; Westen, G.J.P. van 2019
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... Show moreDrugs 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%. Show less
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... Show moreDeubiquitinases (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. Show less
Oranje, P.; Gouka, R.; Burggraaff, L.; Vermeer, M.; Chalet, C.; Duchateau, G.; ... ; Westen, G.J.P. van 2019
Selective analogs of the natural glycoside phloridzin are marketed drugs that reducehyperglycemia in diabetes by inhibiting the active sodium glucose cotransporterSGLT2 in the kidneys. In addition,... Show moreSelective analogs of the natural glycoside phloridzin are marketed drugs that reducehyperglycemia in diabetes by inhibiting the active sodium glucose cotransporterSGLT2 in the kidneys. In addition, intestinal SGLT1 is now recognized as atarget for glycemic control. To expand available type 2 diabetes remedies, weaimed to find novel SGLT1 inhibitors beyond the chemical space of glycosides. Wescreened a bioactive compound library for SGLT1 inhibitors and tested primary hitsand additional structurally similar molecules on SGLT1 and SGLT2 (SGLT1/2). NovelSGLT1/2 inhibitors were discovered in separate chemical clusters of natural and syntheticcompounds. These have IC50‐values in the 10‐100 μmol/L range. The mostpotent identified novel inhibitors from different chemical clusters are (SGLT1‐IC50Mean ± 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 structureactivity data can provide a starting point for the optimization of novel SGLT1/2 inhibitorsand support the development of virtual SGLT1/2 inhibitor screening models. Show less
Janssen, A.P.A.; Hengst, J.M.A. van; Béquignon, O.J.M.; Deng, H.; Westen, G.J.P. van; Stelt, M. van der 2019
Drug discovery programs of covalent irreversible, mechanism-based enzyme inhibitors often focus on optimization of potency as determined by IC50-values in biochemical assays. These assays do not... Show moreDrug discovery programs of covalent irreversible, mechanism-based enzyme inhibitors often focus on optimization of potency as determined by IC50-values in biochemical assays. These assays do not allow the characterization of the binding activity (Ki) and reactivity (kinact) as individual kinetic parameters of the covalent inhibitors. Here, we report the development of a kinetic substrate assay to study the influence of the acidity (pKa) of heterocyclic leaving group of triazole urea derivatives as diacylglycerol lipase (DAGL)-α inhibitors. Surprisingly, we found that the reactivity of the inhibitors did not correlate with the pKa of the leaving group, whereas the position of the nitrogen atoms in the heterocyclic core determined to a large extent the binding activity of the inhibitor. This finding was confirmed and clarified by molecular dynamics simulations on the covalently bound Michaelis−Menten complex. A deeper understanding of the binding properties of covalent serine hydrolase inhibitors is expected to aid in the discovery and development of more selective covalent inhibitors. Show less
He, J.; McLaughlin, R.P.; Noord, V.E. van der; Foekens, J.A.; Martens, J.W.M.; Westen, G.J.P. van; ... ; Water, B. van de 2019
Owing to its genetic heterogeneity and acquired resistance, triple-negative breast cancer (TNBC) is not responsive to single-targeted therapy, causing disproportional cancer-related death worldwide... Show moreOwing to its genetic heterogeneity and acquired resistance, triple-negative breast cancer (TNBC) is not responsive to single-targeted therapy, causing disproportional cancer-related death worldwide. Combined targeted therapy strategies to block interactive oncogenic signaling networks are being explored for effective treatment of the refractory TNBC subtype.A broad kinase inhibitor screen was applied to profile the proliferative responses of TNBC cells, revealing resistance of TNBC cells to inhibition of the mammalian target of rapamycin (mTOR). A systematic drug combination screen was subsequently performed to identify that AEE788, an inhibitor targeting multiple receptor tyrosine kinases (RTKs) EGFR/HER2 and VEGFR, synergizes with selective mTOR inhibitor rapamycin as well as its analogs (rapalogs) temsirolimus and everolimus to inhibit TNBC cell proliferation.The combination treatment with AEE788 and rapalog effectively inhibits phosphorylation of mTOR and 4EBP1, relieves mTOR inhibition-mediated upregulation of cyclin D1, and maintains suppression of AKT and ERK signaling, thereby sensitizing TNBC cells to the rapalogs. siRNA validation of cheminformatics-based predicted AEE788 targets has further revealed the mTOR interactive RPS6K members (RPS6KA3, RPS6KA6, RPS6KB1, and RPS6KL1) as synthetic lethal targets for rapalog combination treatment.TOR signaling is highly activated in TNBC tumors. As single rapalog treatment is insufficient to block mTOR signaling in rapalog-resistant TNBC cells, our results thus provide a potential multi-kinase inhibitor combinatorial strategy to overcome mTOR-targeted therapy resistance in TNBC cells. Show less
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... Show moreDeubiquitinases (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. Show less
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is... Show moreThe effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. Show less
Polycystic kidney disease (PKD) is a prevalent genetic disorder, characterized by the formation of kidney cysts that progressively lead to kidney failure. The currently available drug tolvaptan is... Show morePolycystic kidney disease (PKD) is a prevalent genetic disorder, characterized by the formation of kidney cysts that progressively lead to kidney failure. The currently available drug tolvaptan is not well tolerated by all patients and there remains a strong need for alternative treatments. The signaling rewiring in PKD that drives cyst formation is highly complex and not fully understood. As a consequence, the effects of drugs are sometimes difficult to predict. We previously established a high throughput microscopy phenotypic screening method for quantitative assessment of renal cyst growth. Here, we applied this 3D cyst growth phenotypic assay and screened 2320 small drug-like molecules, including approved drugs. We identified 81 active molecules that inhibit cyst growth. Multi-parametric phenotypic profiling of the effects on 3D cultured cysts discriminated molecules that showed preferred pharmacological effects above genuine toxicological properties. Celastrol, a triterpenoid from Tripterygium Wilfordii, was identified as a potent inhibitor of cyst growth in vitro. In an in vivo iKspCre-Pkd1lox,lox mouse model for PKD, celastrol inhibited the growth of renal cysts and maintained kidney function. Show less
Liu, X.; Ye, K.; Vlijmen, H.W.T. van; IJzerman, A.P.; Westen, G.J.P. van 2019
Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug... Show moreOver the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent neural networks (RNNs) have been shown to be an effective method to generate novel chemical structures in the form of SMILES. However, ligands generated by current methods have so far provided relatively low diversity and do not fully cover the whole chemical space occupied by known ligands. Here, we propose a new method (DrugEx) to discover de novo drug-like molecules. DrugEx is an RNN model (generator) trained through reinforcement learning which was integrated with a special exploration strategy. As a case study we applied our method to design ligands against the adenosine A2A receptor. From ChEMBL data, a machine learning model (predictor) was created to predict whether generated molecules are active or not. Based on this predictor as the reward function, the generator was trained by reinforcement learning without any further data. We then compared the performance of our method with two previously published methods, REINVENT and ORGANIC. We found that candidate molecules our model designed, and predicted to be active, had a larger chemical diversity and better covered the chemical space of known ligands compared to the state-of-the-art. Show less
Hassing, G.J.; Wall, H.E.C. van der; Westen, G.J.P. van; Kemme, M.J.B.; Adiyaman, A.; Elvan, A.; ... ; Gal, P. 2019
IntroductionAn increased body mass index (BMI) (>25 kg/m2) is associated with a wide range of electrocardiographic changes. However, the association between electrocardiographic changes and BMI in... Show moreIntroductionAn increased body mass index (BMI) (>25 kg/m2) is associated with a wide range of electrocardiographic changes. However, the association between electrocardiographic changes and BMI in healthy young individuals with a normal BMI (18.5–25 kg/m2) is unknown. The aim of this study was to evaluate the association between BMI and electrocardiographic parameters.MethodsData from 1,290 volunteers aged 18 to 30 years collected at our centre were analysed. Only subjects considered healthy by a physician after review of collected data with a normal BMI and in sinus rhythm were included in the analysis. Subjects with a normal BMI (18.5–25 kg/m2) were divided into BMI quartiles analysis and a backward multivariate regression analysis with a normal BMI as a continuous variable was performed.ResultsMean age was 22.7 ± 3.0 years, mean BMI was 22.0, and 73.4% were male. There were significant differences between the BMI quartiles in terms of maximum P-wave duration, P-wave balance, total P-wave area in lead V1, PR-interval duration, and heart axis. In the multivariate model maximum P-wave duration (standardised coefficient (SC) = +0.112, P < 0.001), P-wave balance in lead V1 (SC = +0.072, P < 0.001), heart axis (SC = −0.164, P < 0.001), and Sokolow-Lyon voltage (SC = −0.097, P < 0.001) were independently associated with BMI.ConclusionIncreased BMI was related with discrete electrocardiographic alterations including an increased P-wave duration, increased P-wave balance, a leftward shift of the heart axis, and decreased Sokolow-Lyon voltage on a standard twelve lead electrocardiogram in healthy young individuals with a normal BMI. Show less
Janssen, A.P.A.; Grimm, S.H.; Wijdeven, R.H.M.; Lenselink, E.B.; Neefjes, J.; Boeckel, C.A.A. van; ... ; Stelt, M. van der 2019
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... Show moreTarget 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. Show less
Burggraaff, L.; Oranje, P.; Gouka, R.; Pijl, P. van der; Geldof, M.; Vlijmen, H.W.T. van; ... ; Westen, G.J.P. van 2019
The present analysis addressed the effect of the number of ECG replicates extracted from a continuous ECG on estimated QT interval prolongation for different QT correction formulas.\nFor one... Show moreThe present analysis addressed the effect of the number of ECG replicates extracted from a continuous ECG on estimated QT interval prolongation for different QT correction formulas.\nFor one hundred healthy volunteers, who received a compound prolonging the QT interval, 18 ECG replicates within a 3 minute window were extracted from 12-lead Holter ECGs. Ten QT correction formulas were deployed and the QTc interval was controlled for baseline and placebo and averaged per dose level.\nThe mean prolongation difference was >4 ms for single and > 2 ms for triplicate ECG measurements compared to the 18 ECG replicate mean value. The difference was <0.5ms after 14 replicates. In contrast, concentration-effect analysis was independent of replicate count and also of QT correction formula.\nThe number of ECG replicates impacted the estimated QT interval prolongation for all deployed QT correction formulas. However, concentration-effect analysis was independent of both the replicate number and correction formula.\nINTRODUCTION\nMETHODS\nRESULTS\nCONCLUSION Show less
