Proteins in harmony: Tuning selectivity in early drug discovery

This thesis describes the importance of being able to control the selectivity of potential drug candidates. It explains how computational models are employed to predict and rationalize compound-protein binding (affinity) and therewith, selectivity of compounds. Moreover, it shows that selectivity can purposely be tuned to target either a single protein or an entire panel of proteins. The challenges of selectivity modeling are addressed based on case studies in the sodium-dependent glucose co-transporters, G protein-coupled receptors, and kinases.Medicinal Chemistr

Data calibration for the MASCARA and bRing instruments

Aims: MASCARA and bRing are photometric surveys designed to detect variability caused by exoplanets in stars with $m_V < 8.4$. Such variability signals are typically small and require an accurate calibration algorithm, tailored to the survey, in order to be detected. This paper presents the methods developed to calibrate the raw photometry of the MASCARA and bRing stations and characterizes the performance of the methods and instruments. Methods: For the primary calibration a modified version of the coarse decorrelation algorithm is used, which corrects for the extinction due to the earth's atmosphere, the camera transmission, and intrapixel variations. Residual trends are removed from the light curves of individual stars using empirical secondary calibration methods. In order to optimize these methods, as well as characterize the performance of the instruments, transit signals were injected in the data. Results: After optimal calibration an RMS scatter of 10 mmag at $m_V \sim 7.5$ is achieved in the light curves. By injecting transit signals with periods between one and five days in the MASCARA data obtained by the La Palma station over the course of one year, we demonstrate that MASCARA La Palma is able to recover 84.0, 60.5 and 20.7% of signals with depths of 2, 1 and 0.5% respectively, with a strong dependency on the observed declination, recovering 65.4% of all transit signals at $\delta > 0^\circ$ versus 35.8% at $\delta < 0^\circ$. Using the full three years of data obtained by MASCARA La Palma to date, similar recovery rates are extended to periods up to ten days. We derive a preliminary occurrence rate for hot Jupiters around A-stars of ${>} 0.4 \%$, knowing that many hot Jupiters are still overlooked. In the era of TESS, MASCARA and bRing will provide an interesting synergy for finding long-period (${>} 13.5$ days) transiting gas-giant planets around the brightest stars.Comment: 18 pages, 17 figures, accepted for publication in A&

Quantitative prediction of selectivity between the A1 and A2A adenosine receptors

Medicinal Chemistr

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.Medicinal Chemistr

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.Medicinal Chemistr

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.Toxicolog

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 alpha-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 alpha-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

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 50 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.Toxicolog

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references

Background: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. Objectives: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). Methods: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. Results: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. Conclusions: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2&nbsp;years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an&nbsp;area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (&gt; 0.6) of the original MIPs were considerably decreased after removing the tumours (&lt; 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL