Search

This poster is a summary of scientific work done in https://doi.org/10.1093/bioadv/vbad045. It was presented in a workshop titled "Infectious Diseases Beyond COVID-19", organized by Leopoldina Institute (https://www.leopoldina.org/en/events/event/event/3054/). &nbsp

E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multiclass machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries for rational design of E3 ligase binders.

BackgroundTheADAMTS7locus was genome-wide significantly associated with coronary artery disease (CAD). Lack of the extracellular matrix (ECM) protease ADAMTS-7 was shown to reduce atherosclerotic plaque formation.ObjectiveTo identify molecular mechanisms and downstream targets of ADAMTS-7 mediating risk of atherosclerosis.MethodsTargets of ADAMTS-7 were identified by high-resolution mass spectrometry of atherosclerotic plaques from Apoe-/- and Apoe-/-Adamts7-/- mice. ECM proteins were identified using solubility profiling. Putative targets were validated using immunofluorescence,in vitrodegradation assays, co-immunoprecipitation, and Förster resonance energy transfer (FRET)-based protein-protein interaction assays.ADAMTS7expression was measured in fibrous caps of human carotid artery plaques.ResultsIn humans,ADAMTS7expression was higher in caps of unstable as compared to stable carotid plaques. Compared to Apoe-/- mice, atherosclerotic aortas of Apoe-/- mice lacking Adamts-7 (Apoe-/-Adamts7-/-) contained higher protein levels of tissue inhibitor of metalloproteases 1 (Timp-1). In co-immunoprecipitation experiments, the catalytic domain of ADAMTS-7 bound to TIMP-1, which was degraded in the presence of ADAMTS-7in vitro.ADAMTS-7 reduced the inhibitory capacity of TIMP-1 at its canonical target matrix metalloprotease 9 (MMP-9) As a downstream mechanism, we investigated collagen content in plaques of Apoe-/- and Apoe-/-Adamts7-/- mice after Western diet. Picrosirius red staining of the aortic root revealed less collagen as a readout of higher MMP-9 activity in Apoe-/- as compared to Apoe-/- Adamts7-/- mice. In order to facilitate high-throughput screening for ADAMTS-7 inhibitors with the aim to decrease TIMP-1 degradation, we designed a FRET-based assay targeting the ADAMTS-7 catalytic site.ConclusionADAMTS-7,which is induced in unstable atherosclerotic plaques, decreases TIMP-1 stability reducing its inhibitory effect on MMP-9, which is known to promote collagen degradation and is likewise genome-wide significantly associated with CAD. Disrupting the interaction of ADAMTS-7 and TIMP-1 might be a strategy to increase collagen content and plaque stability for reduction of atherosclerosis-related events.

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimer’s disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231172520 for Quantification and prospective evaluation of serum NfL and GFAP as blood-derived biomarkers of outcome in acute ischemic stroke patients by Federica Ferrari, Daniela Rossi, Alessandra Ricciardi, Carlo Morasso, Liliana Brambilla, Sara Albasini, Renzo Vanna, Chiara Fassio, Tatjana Begenisic, Marianna Loi, Daniela Bossi, Alberto Zaliani, Elisa Alberici, Claudio Lisi, Andrea Morotti, Anna Cavallini, Federico Mazzacane, Antonio Nardone, Fabio Corsi and Marta Truffi in Journal of Cerebral Blood Flow & Metabolism

SummaryAdverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources and thus neglect a wealth of information that is uncovered by fusion of different data sources, including biological protein function, gene expression, chemical compound structure, cell-based imaging, etc. In this work we propose an integrative and explainable Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event.MotivationAdverse drug events are a major risk for failure of late-stage clinical trials. Attempts to prevent adverse drug events in preclinical drug development include experimental procedures for measuring liver-toxicity, cardio-toxicity, etc. Yet these procedures are costly and cannot fully guarantee success in later clinical studies, specifically in situations without a reliable animal model. Computational approaches developed for adverse event prediction have shown to be valuable, but are mostly limited to single data sources. Our approach successfully integrates various data sources on protein functions, gene expression, chemical compound structures and more, into the prediction of adverse events. A main distinguishing characteristic is the explainability of our model predictions which allow further insight into biological mechanisms.

SARS-CoV-2 infection is still spreading worldwide, and new antiviral therapies are an urgent need to complement the approved vaccine preparations. SARS-CoV-2 nps13 helicase is a validated drug target participating in the viral replication complex and possessing two associated activities: RNA unwinding and 5′-triphosphatase. In the search of SARS-CoV-2 direct antiviral agents, we established biochemical assays for both SARS-CoV-2 nps13-associated enzyme activities and screened both in silico and in vitro a small in-house library of natural compounds. Myricetin, quercetin, kaempferol, and flavanone were found to inhibit the SARS-CoV-2 nps13 unwinding activity at nanomolar concentrations, while licoflavone C was shown to block both SARS-CoV-2 nps13 activities at micromolar concentrations. Mode of action studies showed that all compounds are nsp13 noncompetitive inhibitors versus ATP, while computational studies suggested that they can bind both nucleotide and 5′-RNA nsp13 binding sites, with licoflavone C showing a unique pattern of interaction with nsp13 amino acid residues. Overall, we report for the first time natural flavonoids as selective inhibitors of SARS-CoV-2 nps13 helicase with low micromolar activity.

SARS-CoV-2 caused worldwide the current outbreak called COVID-19. Despite multiple countermeasures implemented, there is an urgent global need for new potent and efficient antiviral drugs against this pathogen. In this context, the main protease (Mpro) of SARS-CoV-2 is an essential viral enzyme and plays a pivotal role in viral replication and transcription. Its specific cleavage of polypeptides after a glutamine residue has been considered as a key element to design novel antiviral drugs. Herein, we reported the design, synthesis and structure-activity relationships of novel α-ketoamides as covalent reversible inhibitors of Mpro, exploiting the PADAM oxidation route. The reported compounds showed μM to nM activities in enzymatic and in the antiviral cell-based assays against SARS-CoV-2 Mpro. In order to assess inhibitors’ binding mode, two co-crystal structures of SARS-CoV-2 Mpro in complex with our inhibitors were solved, which confirmed the covalent binding of the keto amide moiety to the catalytic Cys145 residue of Mpro. Finally, in order to interrogate potential broad-spectrum properties, we assessed a selection of compounds against MERS Mpro where they showed nM inhibitory potency, thus highlighting their potential as broad-spectrum coronavirus inhibitors.

SummaryThe outbreak of Monkeypox virus (MPXV) infection in May 2022 is declared a global health emergency by WHO. As of 28th July, 21067 cases have been confirmed and the numbers are on the rise. Unfortunately, MPXV pathophysiology and its underlying mechanisms are not yet understood. Likewise, the knowledge of biochemicals and drugs used against MPXV and their downstream effects is sparse. In this work, using Knowledge Graph (KG) representations we have depicted chemical and biological aspects of MPXV. To achieve this, we have collected and rationally assembled several biological study results, assays, drug candidates, and preclinical evidence to form a dynamic and comprehensive network. The KG is compliant with FAIR annotations allowing seamless transformation and integration to/with other formats and infrastructures.Availability and implementationThe Monkeypox knowledge graph is publicly available at https://github.com/Fraunhofer-ITMP/mpox-kgSupplementary informationSupplementary data are available at Bioinformatics online.ContactReagon.Karki@itmp.fraunhofer.de

SARS-CoV-2 caused worldwide the current outbreak called COVID-19. Despite multiple countermeasures implemented, there is an urgent global need for new potent and efficient antiviral drugs against this pathogen. In this context, the main protease (M

Identification of reliable and accessible biomarkers to characterize ischemic stroke patients’ prognosis remains a clinical challenge. Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are markers of brain injury, detectable in blood by high-sensitive technologies. Our aim was to measure serum NfL and GFAP after stroke, and to evaluate their correlation with functional outcome and the scores in rehabilitation scales at 3-month follow-up. Stroke patients were prospectively enrolled in a longitudinal observational study within 24 hours from symptom onset (D1) and monitored after 7 (D7), 30 ± 3 (M1) and 90 ± 5 (M3) days. At each time-point serum NfL and GFAP levels were measured by Single Molecule Array and correlated with National Institute of Health Stroke Scale (NIHSS), modified Rankin scale (mRS), Trunk Control Test (TCT), Functional Ambulation Classification (FAC) and Functional Independence Measure (FIM) scores. Serum NfL and GFAP showed different temporal profiles: NfL increased after stroke with a peak value at D7; GFAP showed an earlier peak at D1. NfL and GFAP concentrations correlated with clinical/rehabilitation outcomes both longitudinally and prospectively. Multivariate analysis revealed that NfL-D7 and GFAP-D1 were independent predictors of 3-month NIHSS, TCT, FAC and FIM scores, with NfL being the biomarker with the best predictive performance.

This deliverable summarises the work of WP1 to deliver Findable, Accessible, Interoperable and Re-usable (FAIR) services in the context of EOSC-Life’s funding calls, using these to improve FAIR services, service uptake and to inform sustainable development and future use. We describe service delivery and development around the FAIR principles and present the funded projects which have driven our implementation of FAIR Services. We address sustainability and describe the processes used to engage the EOSC-Life funded projects, as well as future work.

This deliverable summarises the work of WP1 to deliver Findable, Accessible, Interoperable and Re-usable (FAIR) services in the context of EOSC-Life’s funding calls, using these to improve FAIR services, service uptake and to inform sustainable development and future use. We describe service delivery and development around the FAIR principles and present the funded projects which have driven our implementation of FAIR Services. We address sustainability and describe the processes used to engage the EOSC-Life funded projects, as well as future work.

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

The present Excel workbook template collects in several sub-table the relevant dimensions to be scored in order to deliver a scorecard for any scientific project in Pharmaceutical environment. The template has been used during IMI-FAIRplus project to collect information and score-prioritize project datasetsto allow a more rational rank for further FAIRification activity downstream.

The present Excel workbook template collects in several sub-table the relevant dimensions to be scored in order to deliver a scorecard for any scientific project in Pharmaceutical environment. The template has been used during IMI-FAIRplus project to collect information and score-prioritize project datasetsto allow a more rational rank for further FAIRification activity downstream.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer’s Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

International journal of molecular sciences 22(21), 11779 (2021). doi:10.3390/ijms222111779 special issue: "Topical Collection "Feature Papers in Molecular Pharmacology" / Editor: Prof. Dr. Peter Illes, Collection Editor", Published by Molecular Diversity Preservation International, Basel

Background. Progressive increase of an aging population in Western countries will result in a growth of stroke prevalence. As many stroke survivors chronically show severe disability, increase in economic, social, and medical burden could be expected in the future. Objective and subjective measures of poststroke recovery are necessary to obtain predictive information, to improve the treatments, and to better allocate resources. Aim. To explore a measure of the temporal dimension of poststroke recovery, to search for predictive association with multiple clinical variables, and to improve tailoring of poststroke treatments. Method. In this observational monocentric cohort study, 176 poststroke inpatients at their first cerebrovascular event were consecutively enrolled. A novel measure based on the time needed to reach the main milestones of motor recovery was proposed. Moreover, two commonly used outcome measures, a measure of global functioning (Functional Independence Measure (FIM™)) and a measure of neurological poststroke deficit (Fugl-Meyer scale), were collected for the investigations of possible predictors. Results. The patients showed a substantial increase in Fugl-Meyer and FIM scores during the rehabilitative treatment. The acquisition of three milestones was significantly associated with female sex (autonomous standing), length of stay and Fugl-Meyer initial score (autonomous walking), and Fugl-Meyer initial score (functional arm). These findings provided quantitative data on motor milestone reacquisition in a sample of poststroke patients. It also demonstrated the value of the Fugl-Meyer score in predicting the acquisition of two motor milestones, relevant for daily life activities. Conclusion. Systematic recording of the timescale of poststroke recovery showed that motor milestone reacquisition happens, on average and when attainable, in less than 30 days in our sample of patients. The present study underscores the importance of the Fugl-Meyer score as a possible predictor for better improvement in reacquisition times of milestone functional recovery.

One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the “reliability” of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data.

This deliverable 3.2 is a report on the demonstrator projects, the eight scientific and technical pilot projects that were selected to provide concrete scientific use-cases and guide and structure the work done in EOSC-Life to build an open digital and collaborative space for biological and medical research. We report in this deliverable the process of integration of the demonstrators within EOSC-Life, the achievement of the demonstrators who developed and made available to the scientific community several valuable resources (databases, workflows, web platform...), the actions undertaken within EOSC-Life to disseminate the demonstrator achievement and finally the results of the demonstrator survey to learn from the demonstrator experience and improve the integration of the new pilot project within EOSC-Life.