Psychometric evaluation of the Decision Support System (DSS) for municipal nurses encountering health deterioration among older adults.

BACKGROUND: A valid and reliable tool is crucial for municipal registered nurses (RNs) to make quick decisions in older adults who show rapid signs of health deterioration. The aim of this study was to investigate the psychometric properties of the Decision Support System (DSS) among older adults in the municipal healthcare system. METHODS: Firstly, we utilized the Rasch dichotomous model to analyze the DSS assessments (n=281) that were collected from municipal RNs working with older adults in the municipal healthcare system. We examined the properties of the DSS in terms of its unidimensionality, item fit, and separation indices. Secondly, to investigate inter-rater agreement in using the DSS, four experienced municipal RNs used the DSS to assess 60 health deterioration scenarios presented by one human patient simulators. The 60 DSS assessments were then analyzed using the ICC (2,1), percentage agreement, and Cohen κ statistics. RESULTS: The sample of older adults had a mean age of 82.8 (SD 11.7). The DSS met the criteria for unidimensionality, although two items did not meet the item fit statistics when all the DSS items were analyzed together. The person separation index was 0.47, indicating a limited level of separation among the sample. The item separation index was 11.43, suggesting that the DSS has good ability to discriminate between and separate the items. At the overall DSS level, inter-rater agreements were good according to the ICC. At the individual DSS item level, the percentage agreements were 75% or above, while the Cohen κ statistics ranged from 0.46 to 1.00. CONCLUSIONS: The Rasch analysis revealed that the psychometric properties of the instrument were acceptable, although further research with a larger sample size and more items is needed. The DSS has the potential to assist municipal RNs in making clinical decisions regarding health deterioration in older adults, thereby avoiding unnecessary emergency admistion and helping.

Designed switch from covalent to non-covalent inhibitors of carboxylesterase Notum activity.

N-Acyl indolines 4 are potent, non-covalent Notum inhibitors developed from a covalent virtual screening hit 2a. The lead compounds were simple to synthesise, achieved excellent potency in a biochemical Notum-OPTS assay and restored Wnt signalling in a cell-based TCF/LEF reporter assay. Multiple high resolution X-ray structures established a common binding mode of these inhibitors with the indoline bound centred in the palmiteolate pocket with key interactions being aromatic stacking and a water mediated hydrogen bond to the oxyanion hole. These N-acyl indolines 4 will be useful tools for use in vitro studies to investigate the role of Notum in disease models, especially when paired with a structurally related covalent inhibitor (e.g. 4w and 2a). Overall, this study highlights the designed switch from covalent to non-covalent Notum inhibitors and so illustrates a complementary approach for hit generation and target inhibition.

Swefoot - The Swedish national quality register for foot and ankle surgery.

BACKGROUND: Population-based register data could be used to improve our knowledge of patients surgically treated for foot and ankle disorders. The quality register Swefoot was recently created to collect surgical and patient-reported data of foot and ankle surgery. This manuscript aims to describe the development and current use of the register. METHODS: The development of Swefoot started in 2014 and currently, data on 16 different diagnoses are collected in 49 units performing foot and ankle surgery. Registrations are performed by the surgeon and the patient. RESULTS: Between 2014 and 2020 approximately 20,000 surgical procedures have been registered. 75.1% of the registered patients were women, 9.3% were smokers, 9.3% had a concomitant rheumatoid disease, and 18.4% a BMI larger than 30 kg/m2. CONCLUSIONS: The Swefoot is a unique national register for foot and ankle surgery. It is by now possible to present demographic, surgical, and outcome parameters based on Swefoot.

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.

Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable. In cases where the test data have drifted away from the descriptor space of the training data, or where assay setups have changed, this assumption might not be fulfilled and the models are not guaranteed to be valid. In this study, the performance of internally valid CP models when applied to either newer time-split data or to external data was evaluated. In detail, temporal data drifts were analysed based on twelve datasets from the ChEMBL database. In addition, discrepancies between models trained on publicly-available data and applied to proprietary data for the liver toxicity and MNT in vivo endpoints were investigated. In most cases, a drastic decrease in the validity of the models was observed when applied to the time-split or external (holdout) test sets. To overcome the decrease in model validity, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Updating the calibration set generally improved the validity, restoring it completely to its expected value in many cases. The restored validity is the first requisite for applying the CP models with confidence. However, the increased validity comes at the cost of a decrease in model efficiency, as more predictions are identified as inconclusive. This study presents a strategy to recalibrate CP models to mitigate the effects of data drifts. Updating the calibration sets without having to retrain the model has proven to be a useful approach to restore the validity of most models.

Design of a Potent, Selective, and Brain-Penetrant Inhibitor of Wnt-Deactivating Enzyme Notum by Optimization of a Crystallographic Fragment Hit.

Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human diseases such as colorectal cancer and Alzheimer's disease, supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we have described the discovery and profile of 8l (ARUK3001185) as a potent, selective, and brain-penetrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identified 6a and 6b as a pair of outstanding hits. Fragment development of 6 delivered 8l that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in pharmacology screens showed 8l to be selective against serine hydrolases, kinases, and drug targets.

Virtual Screening Directly Identifies New Fragment-Sized Inhibitors of Carboxylesterase Notum with Nanomolar Activity.

Notum is a negative regulator of Wnt signaling acting through the hydrolysis of a palmitoleoylate ester, which is required for Wnt activity. Inhibitors of Notum could be of use in diseases where dysfunctional Notum activity is an underlying cause. A docking-based virtual screen (VS) of a large commercial library was used to shortlist 952 compounds for experimental validation as inhibitors of Notum. The VS was successful with 31 compounds having an IC50 < 500 nM. A critical selection process was then applied with two clusters and two singletons (1-4d) selected for hit validation. Optimization of 4d guided by structural biology identified potent inhibitors of Notum activity that restored Wnt/ß-catenin signaling in cell-based models. The [1,2,4]triazolo[4,3-b]pyradizin-3(2H)-one series 4 represent a new chemical class of Notum inhibitors and the first to be discovered by a VS campaign. These results demonstrate the value of VS with well-designed docking models based on X-ray structures.

Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning.

Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.

Structural Insights into Notum Covalent Inhibition.

The carboxylesterase Notum hydrolyzes a palmitoleate moiety from Wingless/Integrated(Wnt) ligands and deactivates Wnt signaling. Notum inhibitors can restore Wnt signaling which may be of therapeutic benefit for pathologies such as osteoporosis and Alzheimer's disease. We report the identification of a novel class of covalent Notum inhibitors, 4-(indolin-1-yl)-4-oxobutanoate esters. High-resolution crystal structures of the Notum inhibitor complexes reveal a common covalent adduct formed between the nucleophile serine-232 and hydrolyzed butyric esters. The covalent interaction in solution was confirmed by mass spectrometry analysis. Inhibitory potencies vary depending on the warheads used. Mechanistically, the resulting acyl-enzyme intermediate carbonyl atom is positioned at an unfavorable angle for the approach of the active site water, which, combined with strong hydrophobic interactions with the enzyme pocket residues, hinders the intermediate from being further processed and results in covalent inhibition. These insights into Notum catalytic inhibition may guide development of more potent Notum inhibitors.

Deep Learning-Based Conformal Prediction of Toxicity.

Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.

Functional Diversity Facilitates Stability Under Environmental Changes in an Outdoor Microalgal Cultivation System.

Functionally uniform monocultures have remained the paradigm in microalgal cultivation despite the apparent challenges to avoid invasions by other microorganisms. A mixed microbial consortium approach has the potential to optimize and maintain biomass production despite of seasonal changes and to be more resilient toward contaminations. Here we present a 3-year outdoor production of mixed consortia of locally adapted microalgae and bacteria in cold temperate latitude. Microalgal consortia were cultivated in flat panel photobioreactors using brackish Baltic Sea water and CO2 from a cement factory (Degerhamn, Cementa AB, Heidelberg Cement Group) as a sustainable CO2 source. To evaluate the ability of the microbial consortia to maintain stable biomass production while exposed to seasonal changes in both light and temperature, we tracked changes in the microbial community using molecular methods (16S and 18S rDNA amplicon sequencing) and monitored the biomass production and quality (lipid, protein, and carbohydrate content) over 3 years. Despite changes in environmental conditions, the mixed consortia maintained stable biomass production by alternating between two different predominant green microalgae (Monoraphidium and Mychonastes) with complementary tolerance to temperature. The bacterial population was few taxa co-occured over time and the composition did not have any connection to the shifts in microalgal taxa. We propose that a locally adapted and mixed microalgal consortia, with complementary traits, can be useful for optimizing yield of commercial scale microalgal cultivation.

Assessing the calibration in toxicological in vitro models with conformal prediction.

Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy-exchanging the calibration data only-is convenient as it does not require retraining of the underlying model.

TREM2/PLCγ2 signalling in immune cells: function, structural insight, and potential therapeutic modulation.

The central role of the resident innate immune cells of the brain (microglia) in neurodegeneration has become clear over the past few years largely through genome-wide association studies (GWAS), and has rapidly become an active area of research. However, a mechanistic understanding (gene to function) has lagged behind. That is now beginning to change, as exemplified by a number of recent exciting and important reports that provide insight into the function of two key gene products - TREM2 (Triggering Receptor Expressed On Myeloid Cells 2) and PLCγ2 (Phospholipase C gamma2) - in microglia, and their role in neurodegenerative disorders. In this review we explore and discuss these recent advances and the opportunities that they may provide for the development of new therapies.

Carboxylesterase Notum Is a Druggable Target to Modulate Wnt Signaling.

Regulation of the Wnt signaling pathway is critically important for a number of cellular processes in both development and adult mammalian biology. This Perspective will provide a summary of current and emerging therapeutic opportunities in modulating Wnt signaling, especially through inhibition of Notum carboxylesterase activity. Notum was recently shown to act as a negative regulator of Wnt signaling through the removal of an essential palmitoleate group. Inhibition of Notum activity may represent a new approach to treat disease where aberrant Notum activity has been identified as the underlying cause. Reliable screening technologies are available to identify inhibitors of Notum, and structural studies are accelerating the discovery of new inhibitors. A selection of these hits have been optimized to give fit-for-purpose small molecule inhibitors of Notum. Three noteworthy examples are LP-922056 (26), ABC99 (27), and ARUK3001185 (28), which are complementary chemical tools for exploring the role of Notum in Wnt signaling.

Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding.

Iterative screening is a process in which screening is done in batches, with each batch filled by using machine learning to select the most promising compounds from the library based on the previous results. We believe iterative screening is poised to enhance the screening process by improving hit finding while at the same time reducing the number of compounds screened. In addition, we see this process as a key enabler of next-generation high-throughput screening (HTS), which uses more complex assays that better describe the biology but demand more resource per screened compound. To demonstrate the utility of these methods, we retrospectively analyze HTS data from PubChem with a focus on machine learning-based screening strategies that can be readily implemented in practice. Our results show that over a variety of HTS experimental paradigms, an iterative screening setup that screens a total of 35% of the screening collection over as few as three iterations has a median return rate of approximately 70% of the active compounds. Increasing the portion of the library screened to 50% yields median returns of approximately 80% of actives. Using six iterations increases these return rates to 78% and 90%, respectively. The best results were achieved with machine learning models that can be run on a standard desktop. By demonstrating that the utility of iterative screening holds true even with a small number of iterations, and without requiring significant computational resources, we provide a roadmap for the practical implementation of these techniques in hit finding.

Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports.

Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the reporting of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs and adverse events (AEs) in humans from two sources of data: the Side Effect Resource, derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System, derived from postmarketing surveillance. The ratio of a drug's therapeutic unbound plasma concentration over the drug's in vitro potency against a given protein was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the positive predictive value and the fraction of drugs with AEs that can be detected. However, considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 9 carbonic anhydrases, of which CA5B is significantly associated with cholestatic jaundice. We include the full quantitative data on associations between measured and predicted in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.

5-Phenyl-1,3,4-oxadiazol-2(3H)-ones Are Potent Inhibitors of Notum Carboxylesterase Activity Identified by the Optimization of a Crystallographic Fragment Screening Hit.

Carboxylesterase Notum is a negative regulator of the Wnt signaling pathway. There is an emerging understanding of the role Notum plays in disease, supporting the need to discover new small-molecule inhibitors. A crystallographic X-ray fragment screen was performed, which identified fragment hit 1,2,3-triazole 7 as an attractive starting point for a structure-based drug design hit-to-lead program. Optimization of 7 identified oxadiazol-2-one 23dd as a preferred example with properties consistent with drug-like chemical space. Screening 23dd in a cell-based TCF/LEF reporter gene assay restored the activation of Wnt signaling in the presence of Notum. Mouse pharmacokinetic studies with oral administration of 23dd demonstrated good plasma exposure and partial blood-brain barrier penetration. Significant progress was made in developing fragment hit 7 into lead 23dd (>600-fold increase in activity), making it suitable as a new chemical tool for exploring the role of Notum-mediated regulation of Wnt signaling.

Screening of a Custom-Designed Acid Fragment Library Identifies 1-Phenylpyrroles and 1-Phenylpyrrolidines as Inhibitors of Notum Carboxylesterase Activity.

The Wnt family of proteins are secreted signaling proteins that play key roles in regulating cellular functions. Recently, carboxylesterase Notum was shown to act as a negative regulator of Wnt signaling by mediating the removal of an essential palmitoleate. Here we disclose two new chemical scaffolds that inhibit Notum enzymatic activity. Our approach was to create a fragment library of 250 acids for screening against Notum in a biochemical assay followed by structure determination by X-ray crystallography. Twenty fragments were identified as hits for Notum inhibition, and 14 of these fragments were shown to bind in the palmitoleate pocket of Notum. Optimization of 1-phenylpyrrole 20, guided by structure-based drug design, identified 20z as the most potent compound from this series. Similarly, the optimization of 1-phenylpyrrolidine 8 gave acid 26. This work demonstrates that inhibition of Notum activity can be achieved by small, drug-like molecules possessing favorable in vitro ADME profiles.

Positively selected modifications in the pore of TbAQP2 allow pentamidine to enter Trypanosoma brucei.

Mutations in the Trypanosoma brucei aquaporin AQP2 are associated with resistance to pentamidine and melarsoprol. We show that TbAQP2 but not TbAQP3 was positively selected for increased pore size from a common ancestor aquaporin. We demonstrate that TbAQP2's unique architecture permits pentamidine permeation through its central pore and show how specific mutations in highly conserved motifs affect drug permeation. Introduction of key TbAQP2 amino acids into TbAQP3 renders the latter permeable to pentamidine. Molecular dynamics demonstrates that permeation by dicationic pentamidine is energetically favourable in TbAQP2, driven by the membrane potential, although aquaporins are normally strictly impermeable for ionic species. We also identify the structural determinants that make pentamidine a permeant although most other diamidine drugs are excluded. Our results have wide-ranging implications for optimising antitrypanosomal drugs and averting cross-resistance. Moreover, these new insights in aquaporin permeation may allow the pharmacological exploitation of other members of this ubiquitous gene family.

African sleeping sickness is a potentially deadly illness caused by the parasite Trypanosoma brucei. The disease is treatable, but many of the current treatments are old and are becoming increasingly ineffective. For instance, resistance is growing against pentamidine, a drug used in the early stages in the disease, as well as against melarsoprol, which is deployed when the infection has progressed to the brain. Usually, cases resistant to pentamidine are also resistant to melarsoprol, but it is still unclear why, as the drugs are chemically unrelated. Studies have shown that changes in a water channel called aquaglyceroporin 2 (TbAQP2) contribute to drug resistance in African sleeping sickness; this suggests that it plays a role in allowing drugs to kill the parasite. This molecular 'drain pipe' extends through the surface of T. brucei, and should allow only water and a molecule called glycerol in and out of the cell. In particular, the channel should be too narrow to allow pentamidine or melarsoprol to pass through. One possibility is that, in T. brucei, the TbAQP2 channel is abnormally wide compared to other members of its family. Alternatively, pentamidine and melarsoprol may only bind to TbAQP2, and then 'hitch a ride' when the protein is taken into the parasite as part of the natural cycle of surface protein replacement. Alghamdi et al. aimed to tease out these hypotheses. Computer models of the structure of the protein were paired with engineered changes in the key areas of the channel to show that, in T. brucei, TbAQP2 provides a much broader gateway into the cell than observed for similar proteins. In addition, genetic analysis showed that this version of TbAQP2 has been actively selected for during the evolution process of T. brucei. This suggests that the parasite somehow benefits from this wider aquaglyceroporin variant. This is a new resistance mechanism, and it is possible that aquaglyceroporins are also larger than expected in other infectious microbes. The work by Alghamdi et al. therefore provides insight into how other germs may become resistant to drugs.