Publications by authors named "Gerard J P van Westen"

56 Publications

Structural insights into the mechanisms of action of functionally distinct classes of Chikungunya virus nonstructural protein 1 inhibitors.

Antimicrob Agents Chemother 2021 Apr 19. Epub 2021 Apr 19.

Department of Medical Microbiology, Leiden University Medical Center, Leiden, the Netherlands

Chikungunya virus (CHIKV) nonstructural protein 1 (nsP1) harbours the methyltransferase (MTase) and guanylyltransferase (GTase) activities needed for viral RNA capping and represents a promising antiviral drug target. We compared the antiviral efficacy of nsP1 inhibitors belonging to the MADTP, CHVB and FHNA series [6'-fluoro-homoneplanocin A (FHNA), its 3'-keto form and 6'-β-Fluoro-homoaristeromycin]. Cell-based phenotypic cross-resistance assays revealed that the CHVB and MADTP series shared a similar mode of action that differed from that of the FHNA series. In biochemical assays with purified Semliki Forest virus and CHIKV nsP1, CHVB compounds strongly inhibited MTase and GTase activities, while MADTP-372 had a moderate inhibitory effect. FHNA did not directly inhibit enzymatic activity of CHIKV nsP1. The first of its kind molecular docking studies with the cryo-EM structure of CHIKV nsP1, which is assembled into a dodecameric ring, revealed that the MADTP and CHVB series bind at the SAM-binding site in the capping domain, where they would function as (non)competitive inhibitors. The FHNA series was predicted to bind at the secondary binding pocket in the Ring-Aperture Membrane-Binding and Oligomerization domain, potentially interfering with membrane binding and oligomerization of nsP1. Our cell-based and enzymatic assays, in combination with molecular docking and mapping of compound-resistance mutations to the nsP1 structure allowed us to group nsP1 inhibitors into functionally distinct classes. This study identified druggable pockets in the nsP1 dodecameric structure and provides a basis for rational design, optimization and combination of inhibitors of this unique and promising antiviral drug target.
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http://dx.doi.org/10.1128/AAC.02566-20DOI Listing
April 2021

Crystal Structure and Subsequent Ligand Design of a Nonriboside Partial Agonist Bound to the Adenosine A Receptor.

J Med Chem 2021 Apr 25;64(7):3827-3842. Epub 2021 Mar 25.

Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.

In this study, we determined the crystal structure of an engineered human adenosine A receptor bound to a partial agonist and compared it to structures cocrystallized with either a full agonist or an antagonist/inverse agonist. The interaction between the partial agonist, belonging to a class of dicyanopyridines, and amino acids in the ligand binding pocket inspired us to develop a small library of derivatives and assess their affinity in radioligand binding studies and potency and intrinsic activity in a functional, label-free, intact cell assay. It appeared that some of the derivatives retained the partial agonist profile, whereas other ligands turned into inverse agonists. We rationalized this remarkable behavior with additional computational docking studies.
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http://dx.doi.org/10.1021/acs.jmedchem.0c01856DOI Listing
April 2021

Quantitative prediction of selectivity between the A and A adenosine receptors.

J Cheminform 2020 May 13;12(1):33. Epub 2020 May 13.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

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 A and A 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 AAR-selective 0.88 ± 0.04 and ROC AAR-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 A and A adenosine receptors was predicted effectively using the selectivity-window model. The approach presented here can be readily applied to other selectivity cases.
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http://dx.doi.org/10.1186/s13321-020-00438-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222572PMC
May 2020

G protein-coupled receptors expressed and studied in yeast. The adenosine receptor as a prime example.

Biochem Pharmacol 2020 Dec 16:114370. Epub 2020 Dec 16.

Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333 CC Leiden, The Netherlands.

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins with around 800 members in the human genome/proteome. Extracellular signals such as hormones and neurotransmitters regulate various biological processes via GPCRs, with GPCRs being the bodily target of 30-40% of current drugs on the market. Complete identification and understanding of GPCR functionality will provide opportunities for novel drug discovery. Yeast expresses three different endogenous GPCRs regulating pheromone and sugar sensing, with the pheromone pathway offering perspectives for the characterization of heterologous GPCR signaling. Moreover, yeast offers a ''null" background for studies on mammalian GPCRs, including GPCR activation and signaling, ligand identification, and characterization of disease-related mutations. This review focuses on modifications of the yeast pheromone signaling pathway for functional GPCR studies, and on opportunities and usage of the yeast system as a platform for human GPCR studies. Finally, this review discusses in some further detail studies of adenosine receptors heterologously expressed in yeast, and what Geoff Burnstock thought of this approach.
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http://dx.doi.org/10.1016/j.bcp.2020.114370DOI Listing
December 2020

The association between body temperature and electrocardiographic parameters in normothermic healthy volunteers.

Pacing Clin Electrophysiol 2021 Jan 23;44(1):44-53. Epub 2020 Nov 23.

Centre for Human Drug Research, Leiden, The Netherlands.

Background: Previous studies reported that hypo- and hyperthermia are associated with several atrial and ventricular electrocardiographical parameters, including corrected QT (QTc) interval. Enhanced characterization of variations in QTc interval and normothermic body temperature aids in better understanding the underlying mechanism behind drug induced QTc interval effects. The analysis' objective was to investigate associations between body temperature and electrocardiographical parameters in normothermic healthy volunteers.

Methods: Data from 3023 volunteers collected at our center were retrospectively analyzed. Subjects were considered healthy after review of collected data by a physician, including a normal tympanic body temperature (35.5-37.5°C) and in sinus rhythm. A linear multivariate model with body temperature as a continuous was performed. Another multivariate analysis was performed with only the QT subintervals as independent variables and body temperature as dependent variable.

Results: Mean age was 33.8 ± 17.5 years and mean body temperature was 36.6 ± 0.4°C. Body temperature was independently associated with age (standardized coefficient [SC] = -0.255, P < .001), female gender (SC = +0.209, P < .001), heart rate (SC = +0.231, P < .001), P-wave axis (SC = -0.051, P < .001), J-point elevation in lead V4 (SC = -0.121, P < .001), and QTcF duration (SC = -0.061, P = .002). In contrast, other atrial and atrioventricular (AV) nodal parameters were not independently associated with body temperature. QT subinterval analysis revealed that only QRS duration (SC = -0.121, P < .001) was independently associated with body temperature.

Conclusion: Body temperature in normothermic healthy volunteers was associated with heart rate, P-wave axis, J-point amplitude in lead V4, and ventricular conductivity, the latter primarily through prolongation of the QRS duration.
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http://dx.doi.org/10.1111/pace.14120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894493PMC
January 2021

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

J Chem Inf Model 2020 10 5;60(10):4664-4672. Epub 2020 Oct 5.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

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.
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http://dx.doi.org/10.1021/acs.jcim.0c00695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592116PMC
October 2020

Computational Approaches for De Novo Drug Design: Past, Present, and Future.

Methods Mol Biol 2021 ;2190:139-165

Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.

Drug discovery is time- and resource-consuming. To this end, computational approaches that are applied in de novo drug design play an important role to improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were exploited to search for new drug molecules to meet multiple objectives. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm. There has been particular interest in the potential application of deep learning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.
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http://dx.doi.org/10.1007/978-1-0716-0826-5_6DOI Listing
March 2021

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

J Med Chem 2020 09 13;63(17):9340-9359. Epub 2020 Aug 13.

Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University & Oncode Institute, 2300 RA Leiden, The Netherlands.

The phospholipase A and acyltransferase (PLAAT) family of cysteine hydrolases consists of five members, which are involved in the Ca-independent production of -acylphosphatidylethanolamines (NAPEs). NAPEs are lipid precursors for bioactive -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 , a nanomolar potent inhibitor for the PLAAT family members. reduced the NAE levels, including anandamide, in cells overexpressing PLAAT2 or PLAAT5. Collectively, may help to dissect the physiological role of the PLAATs.
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http://dx.doi.org/10.1021/acs.jmedchem.0c00522DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498158PMC
September 2020

Chemical genetics strategy to profile kinase target engagement reveals role of FES in neutrophil phagocytosis.

Nat Commun 2020 06 25;11(1):3216. Epub 2020 Jun 25.

Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University & Oncode Institute, Leiden, The Netherlands.

Chemical tools to monitor drug-target engagement of endogenously expressed protein kinases are highly desirable for preclinical target validation in drug discovery. Here, we describe a chemical genetics strategy to selectively study target engagement of endogenous kinases. By substituting a serine residue into cysteine at the DFG-1 position in the ATP-binding pocket, we sensitize the non-receptor tyrosine kinase FES towards covalent labeling by a complementary fluorescent chemical probe. This mutation is introduced in the endogenous FES gene of HL-60 cells using CRISPR/Cas9 gene editing. Leveraging the temporal and acute control offered by our strategy, we show that FES activity is dispensable for differentiation of HL-60 cells towards macrophages. Instead, FES plays a key role in neutrophil phagocytosis via SYK kinase activation. This chemical genetics strategy holds promise as a target validation method for kinases.
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http://dx.doi.org/10.1038/s41467-020-17027-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316778PMC
June 2020

Characterization of cancer-related somatic mutations in the adenosine A receptor.

Eur J Pharmacol 2020 Aug 26;880:173126. Epub 2020 Apr 26.

Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333 CC, Leiden, the Netherlands. Electronic address:

In cancer, G protein-coupled receptors (GPCRs) are involved in tumor progression and metastasis. In this study we particularly examined one GPCR, the adenosine A receptor. This receptor is activated by high concentrations of its endogenous ligand adenosine, which suppresses the immune response to fight tumor progression. A series of adenosine A receptor mutations were retrieved from the Cancer Genome Atlas harboring data from patient samples with different cancer types. The main goal of this work was to investigate the pharmacology of these mutant receptors using a 'single-GPCR-one-G protein' yeast assay technology. Concentration-growth curves were obtained with the full agonist NECA for the wild-type receptor and 15 mutants. Compared to wild-type receptor, the constitutive activity levels in mutant receptors F141L, Y202C and L310P were high, while the potency and efficacy of NECA and BAY 60-6583 on Y202C was lower. A 33- and 26-fold higher constitutive activity on F141L and L310P was reduced to wild-type levels in response to the inverse agonist ZM241385. These constitutively active mutants may thus be tumor promoting. Mutant receptors F259S and Y113F showed a more than one log-unit decrease in potency. A complete loss of activation was observed in mutant receptors C29R, W130C and P249L. All mutations were characterized at the structural level, generating hypotheses of their roles on modulating the receptor conformational equilibrium. Taken together, this study is the first to investigate the nature of adenosine A receptor cancer mutations and may thus provide insights in mutant receptor function in cancer.
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http://dx.doi.org/10.1016/j.ejphar.2020.173126DOI Listing
August 2020

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

J Chem Inf Model 2020 09 12;60(9):4283-4295. Epub 2020 May 12.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

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 pIC 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.
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http://dx.doi.org/10.1021/acs.jcim.9b01204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525794PMC
September 2020

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Authors:
Giulio Superti-Furga Daniel Lackner Tabea Wiedmer Alvaro Ingles-Prieto Barbara Barbosa Enrico Girardi Ulrich Goldmann Bettina Gürtl Kristaps Klavins Christoph Klimek Sabrina Lindinger Eva Liñeiro-Retes André C Müller Svenja Onstein Gregor Redinger Daniela Reil Vitaly Sedlyarov Gernot Wolf Matthew Crawford Robert Everley David Hepworth Shenping Liu Stephen Noell Mary Piotrowski Robert Stanton Hui Zhang Salvatore Corallino Andrea Faedo Maria Insidioso Giovanna Maresca Loredana Redaelli Francesca Sassone Lia Scarabottolo Michela Stucchi Paola Tarroni Sara Tremolada Helena Batoulis Andreas Becker Eckhard Bender Yung-Ning Chang Alexander Ehrmann Anke Müller-Fahrnow Vera Pütter Diana Zindel Bradford Hamilton Martin Lenter Diana Santacruz Coralie Viollet Charles Whitehurst Kai Johnsson Philipp Leippe Birgit Baumgarten Lena Chang Yvonne Ibig Martin Pfeifer Jürgen Reinhardt Julian Schönbett Paul Selzer Klaus Seuwen Charles Bettembourg Bruno Biton Jörg Czech Hélène de Foucauld Michel Didier Thomas Licher Vincent Mikol Antje Pommereau Frédéric Puech Veeranagouda Yaligara Aled Edwards Brandon J Bongers Laura H Heitman Ad P IJzerman Huub J Sijben Gerard J P van Westen Justine Grixti Douglas B Kell Farah Mughal Neil Swainston Marina Wright-Muelas Tina Bohstedt Nicola Burgess-Brown Liz Carpenter Katharina Dürr Jesper Hansen Andreea Scacioc Giulia Banci Claire Colas Daniela Digles Gerhard Ecker Barbara Füzi Viktoria Gamsjäger Melanie Grandits Riccardo Martini Florentina Troger Patrick Altermatt Cédric Doucerain Franz Dürrenberger Vania Manolova Anna-Lena Steck Hanna Sundström Maria Wilhelm Claire M Steppan

Nat Rev Drug Discov 2020 07;19(7):429-430

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http://dx.doi.org/10.1038/d41573-020-00056-6DOI Listing
July 2020

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

ACS Cent Sci 2019 Dec 12;5(12):1965-1974. Epub 2019 Dec 12.

Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University, Leiden 2300 RA, The Netherlands.

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 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.
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http://dx.doi.org/10.1021/acscentsci.9b01022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936097PMC
December 2019

Prioritization of novel ADPKD drug candidates from disease-stage specific gene expression profiles.

EBioMedicine 2020 Jan 24;51:102585. Epub 2019 Dec 24.

Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands. Electronic address:

Background: 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 ADPKD treatment, results in serious side-effects, warranting the need for novel drugs.

Methods: In 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.

Findings: We 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.

Interpretation: Our 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.

Funding: European Union's Seventh Framework Program, Dutch Technology Foundation Stichting Technische Wetenschappen and the Dutch Kidney Foundation.
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http://dx.doi.org/10.1016/j.ebiom.2019.11.046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000333PMC
January 2020

Proteochemometrics - recent developments in bioactivity and selectivity modeling.

Drug Discov Today Technol 2019 Dec 20;32-33:89-98. Epub 2020 Sep 20.

Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands. Electronic address:

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.
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http://dx.doi.org/10.1016/j.ddtec.2020.08.003DOI Listing
December 2019

Blood pressure-related electrocardiographic findings in healthy young individuals.

Blood Press 2020 04 12;29(2):113-122. Epub 2019 Nov 12.

Centre for Human Drug Research, Leiden, The Netherlands.

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 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,  < .001), ventricular activation time in lead V6 (SC= +0.065,  = .048), Sokolow-Lyon voltage (SC= +0.135,  < .001), and Cornell product (SC= +0.137,  < .001) were independently associated with systolic blood pressure, while ventricular rate (SC= +0.179,  < .001), P-wave area in lead V1 (SC= +0.079,  = .020), and Cornell product (SC= +0.091,  = .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.
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http://dx.doi.org/10.1080/08037051.2019.1673149DOI Listing
April 2020

A multiple classifier system identifies novel cannabinoid CB2 receptor ligands.

J Cheminform 2019 Nov 7;11(1):66. Epub 2019 Nov 7.

Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

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%.
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http://dx.doi.org/10.1186/s13321-019-0389-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6836644PMC
November 2019

Structure Kinetics Relationships and Molecular Dynamics Show Crucial Role for Heterocycle Leaving Group in Irreversible Diacylglycerol Lipase Inhibitors.

J Med Chem 2019 09 30;62(17):7910-7922. Epub 2019 Aug 30.

Molecular Physiology, Leiden Institute of Chemistry , Leiden University , 2300RA Leiden , The Netherlands.

Drug discovery programs of covalent irreversible, mechanism-based enzyme inhibitors often focus on optimization of potency as determined by IC-values in biochemical assays. These assays do not allow the characterization of the binding activity () and reactivity () 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 (p) 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 p 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.
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http://dx.doi.org/10.1021/acs.jmedchem.9b00686DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745892PMC
September 2019

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

Pharmacol Res Perspect 2019 08 30;7(4):e00504. Epub 2019 Jul 30.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research Leiden University Leiden The Netherlands.

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 IC-values in the 10-100 μmol/L range. The most potent identified novel inhibitors from different chemical clusters are (SGLT1-IC Mean ± SD, SGLT2-IC 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.
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http://dx.doi.org/10.1002/prp2.504DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6664820PMC
August 2019

Development of Ubiquitin-Based Probe for Metalloprotease Deubiquitinases.

Angew Chem Int Ed Engl 2019 10 28;58(41):14477-14482. Epub 2019 Aug 28.

Department of Cell Biology II, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.

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 Zn 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 Zn 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.
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http://dx.doi.org/10.1002/anie.201906790DOI Listing
October 2019

An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A receptor.

J Cheminform 2019 May 24;11(1):35. Epub 2019 May 24.

Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands.

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 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 A 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.
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http://dx.doi.org/10.1186/s13321-019-0355-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534880PMC
May 2019

In vitro 3D phenotypic drug screen identifies celastrol as an effective in vivo inhibitor of polycystic kidney disease.

J Mol Cell Biol 2020 08;12(8):644-653

Department of Human Genetics, Leiden University Medical Center (LUMC), Leiden, The Netherlands.

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 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.
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http://dx.doi.org/10.1093/jmcb/mjz029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683017PMC
August 2020

Author Correction: Linking drug target and pathway activation for effective therapy using multi-task learning.

Sci Rep 2019 May 3;9(1):7106. Epub 2019 May 3.

RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, Germany.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
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http://dx.doi.org/10.1038/s41598-019-43503-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499772PMC
May 2019

Advances and Challenges in Computational Target Prediction.

J Chem Inf Model 2019 05 28;59(5):1728-1742. Epub 2019 Feb 28.

In silico Toxicology, Institute of Physiology , Charité - Universitätsmedizin Berlin , Charitéplatz 1 , 10117 Berlin , Germany.

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.
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http://dx.doi.org/10.1021/acs.jcim.8b00832DOI Listing
May 2019

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

J Cheminform 2019 Feb 14;11(1):15. Epub 2019 Feb 14.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

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.
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http://dx.doi.org/10.1186/s13321-019-0337-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689890PMC
February 2019

Comprehensive structure-activity-relationship of azaindoles as highly potent FLT3 inhibitors.

Bioorg Med Chem 2019 03 14;27(5):692-699. Epub 2019 Jan 14.

Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University, Leiden, the Netherlands. Electronic address:

Acute myeloid leukemia (AML) is characterized by fast progression and low survival rates, in which Fms-like tyrosine kinase 3 (FLT3) receptor mutations have been identified as a driver mutation in cancer progression in a subgroup of AML patients. Clinical trials have shown emergence of drug resistant mutants, emphasizing the ongoing need for new chemical matter to enable the treatment of this disease. Here, we present the discovery and topological structure-activity relationship (SAR) study of analogs of isoquinolinesulfonamide H-89, a well-known PKA inhibitor, as FLT3 inhibitors. Surprisingly, we found that the SAR was not consistent with the observed binding mode of H-89 in PKA. Matched molecular pair analysis resulted in the identification of highly active sub-nanomolar azaindoles as novel FLT3-inhibitors. Structure based modelling using the FLT3 crystal structure suggested an alternative, flipped binding orientation of the new inhibitors.
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http://dx.doi.org/10.1016/j.bmc.2019.01.006DOI Listing
March 2019

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

ACS Chem Biol 2019 02 18;14(2):164-169. Epub 2019 Jan 18.

Department of Molecular Physiology, Leiden Institute of Chemistry , Leiden University , Leiden , The Netherlands.

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 ( K = 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.
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http://dx.doi.org/10.1021/acschembio.8b00969DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379856PMC
February 2019

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes.

J Chem Inf Model 2019 03 8;59(3):1221-1229. Epub 2018 Nov 8.

Molecular Physiology, Leiden Institute of Chemistry , Leiden University , 2333 CC Leiden , The Netherlands.

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.
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http://dx.doi.org/10.1021/acs.jcim.8b00640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437696PMC
March 2019

Linking drug target and pathway activation for effective therapy using multi-task learning.

Sci Rep 2018 05 29;8(1):8322. Epub 2018 May 29.

RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, Germany.

Despite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways' activation. A typical insight would be: "Activation of pathway Y will confer sensitivity to any drug targeting protein X". We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.
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http://dx.doi.org/10.1038/s41598-018-25947-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974390PMC
May 2018

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

AAPS J 2017 12 4;20(1):11. Epub 2017 Dec 4.

Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333, CC, Leiden, The Netherlands.

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K ) and the target dissociation rate constant on target and tissue selectivity. The K values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ-tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K and k for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K value is often not the lowest K value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.
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http://dx.doi.org/10.1208/s12248-017-0172-7DOI Listing
December 2017