Discovery and Characterization of BAY-805, a Potent and Selective Inhibitor of Ubiquitin-Specific Protease USP21.

USP21 belongs to the ubiquitin-specific protease (USP) subfamily of deubiquitinating enzymes (DUBs). Due to its relevance in tumor development and growth, USP21 has been reported as a promising novel therapeutic target for cancer treatment. Herein, we present the discovery of the first highly potent and selective USP21 inhibitor. Following high-throughput screening and subsequent structure-based optimization, we identified BAY-805 to be a non-covalent inhibitor with low nanomolar affinity for USP21 and high selectivity over other DUB targets as well as kinases, proteases, and other common off-targets. Furthermore, surface plasmon resonance (SPR) and cellular thermal shift assays (CETSA) demonstrated high-affinity target engagement of BAY-805, resulting in strong NF-κB activation in a cell-based reporter assay. To the best of our knowledge, BAY-805 is the first potent and selective USP21 inhibitor and represents a valuable high-quality in vitro chemical probe to further explore the complex biology of USP21.

De novo pyrimidine synthesis is a targetable vulnerability in IDH mutant glioma.

Mutations affecting isocitrate dehydrogenase (IDH) enzymes are prevalent in glioma, leukemia, and other cancers. Although mutant IDH inhibitors are effective against leukemia, they seem to be less active in aggressive glioma, underscoring the need for alternative treatment strategies. Through a chemical synthetic lethality screen, we discovered that IDH1-mutant glioma cells are hypersensitive to drugs targeting enzymes in the de novo pyrimidine nucleotide synthesis pathway, including dihydroorotate dehydrogenase (DHODH). We developed a genetically engineered mouse model of mutant IDH1-driven astrocytoma and used it and multiple patient-derived models to show that the brain-penetrant DHODH inhibitor BAY 2402234 displays monotherapy efficacy against IDH-mutant gliomas. Mechanistically, this reflects an obligate dependence of glioma cells on the de novo pyrimidine synthesis pathway and mutant IDH's ability to sensitize to DNA damage upon nucleotide pool imbalance. Our work outlines a tumor-selective, biomarker-guided therapeutic strategy that is poised for clinical translation.

Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care.

OBJECTIVE: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. METHODS AND PROCEDURES: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). RESULTS: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. CONCLUSION: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. CLINICAL IMPACT: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieved.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study.

BACKGROUND: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. OBJECTIVE: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. METHODS: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. RESULTS: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (-1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). CONCLUSIONS: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

Assessing Visual Exploratory Activity of Athletes in Virtual Reality Using Head Motion Characteristics.

Maximizing performance success in sports is about continuous learning and adaptation processes. Aside from physiological, technical and emotional performance factors, previous research focused on perceptual skills, revealing their importance for decision-making. This includes deriving relevant environmental information as a result of eye, head and body movement interaction. However, to evaluate visual exploratory activity (VEA), generally utilized laboratory settings have restrictions that disregard the representativeness of assessment environments and/or decouple coherent cognitive and motor tasks. In vivo studies, however, are costly and hard to reproduce. Furthermore, the application of elaborate methods like eye tracking are cumbersome to implement and necessitate expert knowledge to interpret results correctly. In this paper, we introduce a virtual reality-based reproducible assessment method allowing the evaluation of VEA. To give insights into perceptual-cognitive processes, an easily interpretable head movement-based metric, quantifying VEA of athletes, is investigated. Our results align with comparable in vivo experiments and consequently extend them by showing the validity of the implemented approach as well as the use of virtual reality to determine characteristics among different skill levels. The findings imply that the developed method could provide accurate assessments while improving the control, validity and interpretability, which in turn informs future research and developments.

Discovery of the SMYD3 Inhibitor BAY-6035 Using Thermal Shift Assay (TSA)-Based High-Throughput Screening.

SMYD3 (SET and MYND domain-containing protein 3) is a protein lysine methyltransferase that was initially described as an H3K4 methyltransferase involved in transcriptional regulation. SMYD3 has been reported to methylate and regulate several nonhistone proteins relevant to cancer, including mitogen-activated protein kinase kinase kinase 2 (MAP3K2), vascular endothelial growth factor receptor 1 (VEGFR1), and the human epidermal growth factor receptor 2 (HER2). In addition, overexpression of SMYD3 has been linked to poor prognosis in certain cancers, suggesting SMYD3 as a potential oncogene and attractive cancer drug target. Here we report the discovery of a novel SMYD3 inhibitor. We performed a thermal shift assay (TSA)-based high-throughput screening (HTS) with 410,000 compounds and identified a novel benzodiazepine-based SMYD3 inhibitor series. Crystal structures revealed that this series binds to the substrate binding site and occupies the hydrophobic lysine binding pocket via an unprecedented hydrogen bonding pattern. Biochemical assays showed substrate competitive behavior. Following optimization and extensive biophysical validation with surface plasmon resonance (SPR) analysis and isothermal titration calorimetry (ITC), we identified BAY-6035, which shows nanomolar potency and selectivity against kinases and other PKMTs. Furthermore, BAY-6035 specifically inhibits methylation of MAP3K2 by SMYD3 in a cellular mechanistic assay with an IC50 <100 nM. Moreover, we describe a congeneric negative control to BAY-6035. In summary, BAY-6035 is a novel selective and potent SMYD3 inhibitor probe that will foster the exploration of the biological role of SMYD3 in diseased and nondiseased tissues.

Crystal structures of the selenoprotein glutathione peroxidase 4 in its apo form and in complex with the covalently bound inhibitor ML162.

Wild-type human glutathione peroxidase 4 (GPX4) was co-expressed with SBP2 (selenocysteine insertion sequence-binding protein 2) in human HEK cells to achieve efficient production of this selenocysteine-containing enzyme on a preparative scale for structural biology. The protein was purified and crystallized, and the crystal structure of the wild-type form of GPX4 was determined at 1.0 Šresolution. The overall fold and the active site are conserved compared with previously determined crystal structures of mutated forms of GPX4. A mass-spectrometry-based approach was developed to monitor the reaction of the active-site selenocysteine Sec46 with covalent inhibitors. This, together with the introduction of a surface mutant (Cys66Ser), enabled the crystal structure determination of GPX4 in complex with the covalent inhibitor ML162 [(S)-enantiomer]. The mass-spectrometry-based approach described here opens the path to further co-complex crystal structures of this potential cancer drug target in complex with covalent inhibitors.

Does co-presence affect the way we perceive and respond to emotional interactions?

This study compared how two virtual display conditions of human body expressions influenced explicit and implicit dimensions of emotion perception and response behavior in women and men. Two avatars displayed emotional interactions (angry, sad, affectionate, happy) in a "pictorial" condition depicting the emotional interactive partners on a screen within a virtual environment and a "visual" condition allowing participants to share space with the avatars, thereby enhancing co-presence and agency. Subsequently to stimulus presentation, explicit valence perception and response tendency (i.e. the explicit tendency to avoid or approach the situation) were assessed on rating scales. Implicit responses, i.e. postural and autonomic responses towards the observed interactions were measured by means of postural displacement and changes in skin conductance. Results showed that self-reported presence differed between pictorial and visual conditions, however, it was not correlated with skin conductance responses. Valence perception was only marginally influenced by the virtual condition and not at all by explicit response behavior. There were gender-mediated effects on postural response tendencies as well as gender differences in explicit response behavior but not in valence perception. Exploratory analyses revealed a link between valence perception and preferred behavioral response in women but not in men. We conclude that the display condition seems to influence automatic motivational tendencies but not higher level cognitive evaluations. Moreover, intragroup differences in explicit and implicit response behavior highlight the importance of individual factors beyond gender.

Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles.

We recently described glutathione peroxidase 4 (GPX4) as a promising target for killing therapy-resistant cancer cells via ferroptosis. The onset of therapy resistance by multiple types of treatment results in a stable cell state marked by high levels of polyunsaturated lipids and an acquired dependency on GPX4. Unfortunately, all existing inhibitors of GPX4 act covalently via a reactive alkyl chloride moiety that confers poor selectivity and pharmacokinetic properties. Here, we report our discovery that masked nitrile-oxide electrophiles, which have not been explored previously as covalent cellular probes, undergo remarkable chemical transformations in cells and provide an effective strategy for selective targeting of GPX4. The new GPX4-inhibiting compounds we describe exhibit unexpected proteome-wide selectivity and, in some instances, vastly improved physiochemical and pharmacokinetic properties compared to existing chloroacetamide-based GPX4 inhibitors. These features make them superior tool compounds for biological interrogation of ferroptosis and constitute starting points for development of improved inhibitors of GPX4.

Computationally Empowered Workflow Identifies Novel Covalent Allosteric Binders for KRASG12C.

Due to its frequent mutations in multiple lethal cancers, KRAS is one of the most-studied anticancer targets nowadays. Since the discovery of the druggable allosteric binding site containing a G12C mutation, KRASG12C has been the focus of attention in oncology research. We report here a computationally driven approach aimed at identifying novel and selective KRASG12C covalent inhibitors. The workflow involved initial enumeration of virtual molecules tailored for the KRAS allosteric binding site. Tools such as pharmacophore modeling, docking, and free-energy perturbations were deployed to prioritize the compounds with the best profiles. The synthesized naphthyridinone scaffold showed the ability to react with G12C and inhibit KRASG12C . Analogues were prepared to establish structure-activity relationships, while molecular dynamics simulations and crystallization of the inhibitor-KRASG12C complex highlighted an unprecedented binding mode.

Discovery of BAY-985, a Highly Selective TBK1/IKKε Inhibitor.

The serine/threonine kinase TBK1 (TANK-binding kinase 1) and its homologue IKKε are noncanonical members of the inhibitor of the nuclear factor κB (IκB) kinase family. These kinases play important roles in multiple cellular pathways and, in particular, in inflammation. Herein, we describe our investigations on a family of benzimidazoles and the identification of the potent and highly selective TBK1/IKKε inhibitor BAY-985. BAY-985 inhibits the cellular phosphorylation of interferon regulatory factor 3 and displays antiproliferative efficacy in the melanoma cell line SK-MEL-2 but showed only weak antitumor activity in the SK-MEL-2 human melanoma xenograft model.

Optimization of PDE3A Modulators for SLFN12-Dependent Cancer Cell Killing.

6-(4-(Diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one, or DNMDP, potently and selectively inhibits phosphodiesterases 3A and 3B (PDE3A and PDE3B) and kills cancer cells by inducing PDE3A/B interactions with SFLN12. The structure-activity relationship (SAR) of DNMDP analogs was evaluated using a phenotypic viability assay, resulting in several compounds with suitable pharmacokinetic properties for in vivo analysis. One of these compounds, BRD9500, was active in an SK-MEL-3 xenograft model of cancer.

Sick Moves! Motion Parameters as Indicators of Simulator Sickness.

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.

The novel dihydroorotate dehydrogenase (DHODH) inhibitor BAY 2402234 triggers differentiation and is effective in the treatment of myeloid malignancies.

Acute myeloid leukemia (AML) is a devastating disease, with the majority of patients dying within a year of diagnosis. For patients with relapsed/refractory AML, the prognosis is particularly poor with currently available treatments. Although genetically heterogeneous, AML subtypes share a common differentiation arrest at hematopoietic progenitor stages. Overcoming this differentiation arrest has the potential to improve the long-term survival of patients, as is the case in acute promyelocytic leukemia (APL), which is characterized by a chromosomal translocation involving the retinoic acid receptor alpha gene. Treatment of APL with all-trans retinoic acid (ATRA) induces terminal differentiation and apoptosis of leukemic promyelocytes, resulting in cure rates of over 80%. Unfortunately, similarly efficacious differentiation therapies have, to date, been lacking outside of APL. Inhibition of dihydroorotate dehydrogenase (DHODH), a key enzyme in the de novo pyrimidine synthesis pathway, was recently reported to induce differentiation of diverse AML subtypes. In this report we describe the discovery and characterization of BAY 2402234 - a novel, potent, selective and orally bioavailable DHODH inhibitor that shows monotherapy efficacy and differentiation induction across multiple AML subtypes. Herein, we present the preclinical data that led to initiation of a phase I evaluation of this inhibitor in myeloid malignancies.

Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units.

Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.

Detection of fetal kicks using body-worn accelerometers during pregnancy: Trade-offs between sensors number and positioning.

Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement during pregnancy. However, many questions remain unanswered to date on the optimal setup in terms of body-worn accelerometers as well as signal processing and machine learning techniques used to detect fetal movement. In this paper, we systematically analyze the trade-offs between sensor number and positioning, the presence of reference accelerometers outside of the abdominal area and provide guidelines on dealing with class imbalance. Using a dataset of 15 measurements collected employing 6 three-axial accelerometers we show that including a reference accelerometer on the back of the participant consistently improves fetal movement detection performance regardless of the number of sensors utilized. We also show that two accelerometers plus a reference accelerometer are sufficient for optimal results.

Temporal correction of detected R-peaks in ECG signals: A crucial step to improve QRS detection algorithms.

In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g. the R-peak in electrocardiogram signals, especially in the presence of arrhythmia. This localization can be delivered by numerous solutions found in the literature which all lack an exact specification of their temporal precision. We implemented three different state-of-the-art algorithms and evaluated the precision of their R-peak localization. We suggest a method to estimate the overall R-peak temporal inaccuracy-dubbed beat slackness-of QRS detectors with respect to normal and abnormal beats. We also propose a simple algorithm that can complement existing detectors to reduce this slackness. Furthermore we define improvements to one of the three detectors allowing it to be used in real-time on mobile devices or embedded hardware. Across the entire MIT-BIH Arrhythmia Database, the average slackness of all the tested algorithms was 9ms for normal beats and 13ms for abnormal beats. Using our complementing algorithm this could be reduced to 4ms for normal beats and to 7ms for abnormal beats. The presented methods can be used to significantly improve the precision of R-peak detection and provide an additional measurement for QRS detector performance.

Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.

Somnography using unobtrusive motion sensors and Android-based mobile phones.

Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages. The gold standard method polysomnography (PSG) requires subjects to stay overnight in a sleep laboratory and to wear a series of obtrusive devices. This work presents an easy to use method to perform somnography at home using unobtrusive motion sensors. Ten healthy male subjects were recorded during two consecutive nights. Sensors from the Shimmer platform were placed in the bed to record accelerometer data, while reference hypnograms were collected using a SOMNOwatch system. A series of filters were used to extract a motion feature in 30 second epochs from the accelerometer signals. The feature was used together with the ground truth information to train a Naive Bayes classifiers that distinguished wakefulness, REM and non-REM sleep. Additionally the algorithm was implemented on an Android mobile phone. Averaged over all subjects, the classifier had a mean accuracy of 79.0 % (SD 9.2%) for the three classes. The mobile phone implementation was able to run in realtime during all experiments. In future this will lead to a method for simple and unobtrusive somnography using mobile phones.

Identification of C-2 hydroxyethyl imidazopyrrolopyridines as potent JAK1 inhibitors with favorable physicochemical properties and high selectivity over JAK2.

Herein we report on the structure-based discovery of a C-2 hydroxyethyl moiety which provided consistently high levels of selectivity for JAK1 over JAK2 to the imidazopyrrolopyridine series of JAK1 inhibitors. X-ray structures of a C-2 hydroxyethyl analogue in complex with both JAK1 and JAK2 revealed differential ligand/protein interactions between the two isoforms and offered an explanation for the observed selectivity. Analysis of historical data from related molecules was used to develop a set of physicochemical compound design parameters to impart desirable properties such as acceptable membrane permeability, potent whole blood activity, and a high degree of metabolic stability. This work culminated in the identification of a highly JAK1 selective compound (31) exhibiting favorable oral bioavailability across a range of preclinical species and robust efficacy in a rat CIA model.