Search

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance. This research received funding from the Innovative Medicines Initiative 2 Joint Undertaking (IMI2 JU) under grant agreement eTRANSAFE (777365), the European Union’s Horizon 2020 ELIXIR-CONVERGE Project (871075) and research and innovation programme under grant agreement No 964537 (RISK-HUNT3R).

IntroductionHealthcare workers are vulnerable to adverse mental health impacts of COVID-19. We assessed prevalence of mental disorders and associated factors during the first wave of the pandemic among healthcare professionals in Spain.MethodsAll workers in 18 healthcare institutions (6 AACC) in Spain were invited to a series of online surveys assessing a wide range of individual characteristics, COVID-19 infection status and exposure, and mental health status. Here we report: current mental disorders (Major Depressive Disorder-MDD- [PHQ-8≥10], Generalized Anxiety Disorder-GAD- [GAD-7≥10], Panic attacks, Posttraumatic Stress Disorder –PTSD- [PCL-5≥7]; and Substance Use Disorder –SUD-[CAGE-AID≥2]. Severe disability assessed by the Sheehan Disability Scale was used to identify “disabling” current mental disorders.Results9,138 healthcare workers participated. Prevalence of screen-positive disorder: 28.1% MDD; 22.5% GAD, 24.0% Panic; 22.2% PTSD; and 6.2% SUD. Overall 45.7% presented any current and 14.5% any disabling current mental disorder. Healthcare workers with prior lifetime mental disorders had almost twice the prevalence of current disorders than those without. Adjusting for all other variables, odds of any disabling mental disorder were: prior lifetime disorders (TUS: OR=5.74; 95%CI 2.53-13.03; Mood: OR=3.23; 95%CI:2.27-4.60; Anxiety: OR=3.03; 95%CI:2.53-3.62); age category 18-29 years (OR=1.36; 95%CI:1.02-1.82), caring “all of the time” for COVID-19 patients (OR=5.19; 95%CI: 3.61-7.46), female gender (OR=1.58; 95%CI: 1.27-1.96) and having being in quarantine or isolated (OR= 1.60; 95CI:1.31-1.95).ConclusionsCurrent mental disorders were very frequent among Spanish healthcare workers during the first wave of COVID-19. As the pandemic enters its second wave, careful monitoring and support is needed for healthcare workers, especially those with previous mental disorders and those caring COVID-19 very often.

Creating selection criteria and guidelines, to aid identification of IMI datasets with the potential to generate high societal impact upon FAIRification, is an important goal of FAIRplus. This deliverable report describes the process put in place to identify, evaluate and select projects. Two thirds of projects to be addressed by FAIRplus will cover societal priorities of H2020, namely; 1) promoting healthy ageing[1]; 2) addressing chronic diseases; 3) neurodegenerative diseases and 4) emergence of antibiotic resistance. In addition, we have identified cross-cutting projects “Cross” which provide toolssuch as cell lines, biomarkers and animal models, which enable research progression in the primary priority areas. In the first period, some 25 projects have been identified based on the application of the criteria and discussions are ongoing with these consortia representativesin order to provide a steady flow of datasets into FAIRplus. [1]seehttp://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing

Background: Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed drugs for the treatment of depressive disorders. Some people share information about their experiences with antidepressants on social media platforms such as Twitter. Analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users' behavior. Objective: This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were likely to be under SSRI treatment, in comparison to the tweets posted by the same users when they were less likely to be taking this medication. Methods: In the first step, the timelines of Twitter users mentioning SSRI antidepressants in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning an SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before or more than 90 days after any tweet mentioning an SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. Results: The number of tweets generated per day by the users when they were in treatment was higher than it was when they were in the unknown-treatment period (P=.001). When the users were in treatment, the mean percentage of tweets posted during the daytime (from 8 AM to midnight) increased in comparison to the unknown-treatment period (P=.002). The number of characters and words per tweet was higher when the users were in treatment (P=.03 and P=.02, respectively). Regarding linguistic features, the percentage of pronouns that were first-person singular was higher when users were in treatment (P=.008). Conclusions: Behavioral and linguistic changes have been detected when users with depression are taking antidepressant medication. These features can provide interesting insights for monitoring the evolution of this disease, as well as offering additional information related to treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression. We received support from the Agency for Management of University and Research Grants in Catalonia (Spain) for the incorporation of new research personnel (FI2016) and from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 802750 (FAIRplus) with the support of the European Union’s Horizon 2020 research and innovation programme and European Federation of Pharmaceutical Industries and Associations Companies.

Comorbid diseases are an important concern in oncology since they can affect the choice and effectiveness of treatment. What is particularly relevant is the fact that the diagnosis of depression in cancer patients has an important impact on the quality of life of these patients. Although there is no consensus about a specific relationship of depression with certain cancer types, some authors have proposed that depression constitutes a risk factor for cancer. The objective of this study is to identify the presence of comorbidities in a massive EHR system, between depression and the 10 most common cancers in women and men and to determine if there is a preferred temporal ordering in the co-occurrence of these diseases. All the cancers studied showed a significant co-occurrence with depression, more specifically, twice more frequent than what could be expected by chance. A preferred directionality was identified between some of the comorbid diseases, such as breast cancer followed by depression, and depression followed by either stomach cancer, colorectal cancer or lung cancer. Future work will address other potential factors that have an influence on the likelihood of suffering from depression in patients with cancer, such as drug therapies received, exposure to substance of abuse or other comorbidities. We received support from the IMI-JU under grant agreement no.115372 (EMIF), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and from the EU H2020 Research & Innovation Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics: Creating medically-driven integrative bioinformatics applications focused on oncology, CNS disorders and their comorbidities).

Additional file 9.

Creating selection criteria and guidelines, to aid identification of IMI datasets with the potential to generate high societal impact upon FAIRification, is an important goal of FAIRplus. This deliverable report describes the process put in place to identify, evaluate and select projects. Two thirds of projects to be addressed by FAIRplus will cover societal priorities of H2020, namely; 1) promoting healthy ageing[1]; 2) addressing chronic diseases; 3) neurodegenerative diseases and 4) emergence of antibiotic resistance. In addition, we have identified cross-cutting projects “Cross” which provide tools such as cell lines, biomarkers and animal models, which enable research progression in the primary priority areas. In the first period, some 25 projects have been identified based on the application of the criteria and discussions are ongoing with these consortia representatives in order to provide a steady flow of datasets into FAIRplus. [1] see http://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing, This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA companies.

Additional file 9.

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided This work was supported by a grant from the European Comission (CancerGrid, FP-6 LCHC-CT-2006-037559), http://ec.europa.eu/research/fp6/index_en.cfm

Progress in healthcare and biomedical research involves taking advantage of the huge amount of clinical data and biological knowledge that already exists and is currently being generated. Bioinformatic methods and tools have become essential for data extraction, management, and integration. However, it is necessary to overcome two barriers: the deficit of integrative computational approaches that combine different types of data and sources, and the lack of active involvement of the potential users in the process of creating applications for the management of biomedical information.

Cholesterol is a key component of cell membranes with a proven modulatory role on the function and ligand-binding properties of G-protein-coupled receptors (GPCRs). Crystal structures of prototypical GPCRs such as the adenosine A2A receptor (A2AR) have confirmed that cholesterol finds stable binding sites at the receptor surface suggesting an allosteric role of this lipid. Here we combine experimental and computational approaches to show that cholesterol can spontaneously enter the A2AR-binding pocket from the membrane milieu using the same portal gate previously suggested for opsin ligands. We confirm the presence of cholesterol inside the receptor by chemical modification of the A2AR interior in a biotinylation assay. Overall, we show that cholesterol’s impact on A2AR-binding affinity goes beyond pure allosteric modulation and unveils a new interaction mode between cholesterol and the A2AR that could potentially apply to other GPCRs.

Molecular dynamics (MD) simulations are a well-established technique to characterize the structural motions of biological systems at atomic resolution. However, accessing, viewing, and sharing MD trajectories is typically restricted by large file sizes and the need for specialized software, which limits the audience to which this data is available. The aim of this thesis is to extend the outreach of MD simulations by providing online resources that facilitate the dissemination, visual inspection, and analysis of this data. For that, we present GPCRmd and SCoV2-MD, two online resources focused on proteins with high biomedical interest: G protein-coupled receptors (GPCRs) and the proteome of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), respectively. We also showcase the capabilities of GPCRmd and SCoV2-MD for exploring key aspects of protein dynamics. Overall, these platforms have the potential to promote data “Findability, Accessibility, Interoperability, and Reusability” in the MD field, supporting the FAIR principles for scientific data management. Les simulacions de dinàmica molecular (MD, per les seves sigles en anglès) són una tècnica ben establerta per caracteritzar els moviments estructurals de sistemes biològics amb una resolució atòmica. No obstant això, l'accés, la visualització i la compartició de trajectòries de MD solen estar restringits per la gran mida dels seus fitxers i la necessitat de programari especialitzat, que limita el públic al qual estan disponibles aquestes dades. L'objectiu d'aquesta tesi és ampliar la difusió de les simulacions de MD proporcionant recursos en línia que facilitin la compartició, inspecció visual i anàlisi d'aquestes dades. Per això, presentem GPCRmd i SCoV2-MD, dos recursos en línia centrats en proteïnes d'alt interès biomèdic: els receptors acoblats a proteïnes G (coneguts com a GPCRs, per les seves sigles en anglès) i el proteoma del coronavirus 2 de la síndrome respiratòria aguda greu (SARS-CoV-2), respectivament. També mostrem les capacitats de GPCRmd i SCoV2-MD per explorar aspectes clau de la dinàmica de proteïnes. En definitiva, aquestes plataformes tenen el potencial de promoure la cercabilitat, l'accessibilitat, la interoperabilitat i la reutilització de dades en l'àmbit de la MD, donant suport als principis FAIR (acrònim de l’anglès Findable, Accessible, Interoperable and Reusable) per a la gestió de dades científiques.

Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, thoughts and feelings of people’s daily lives, including those of patients suffering from mental disorders such as depression. Based on the characteristics and linguistic features of the tweets, it is possible to identify signs of depression among Twitter users. Moreover, the effect of antidepressant treatments can be linked to changes in the features of the tweets posted by depressive users. The analysis of this huge volume and diversity of data, the so-called “Big Data”, can provide relevant information about the course of mental disorders and the treatments these patients are receiving, which allows us to detect, monitor and predict depressive disorders. This thesis presents different studies carried out on Twitter data in the Spanish language, with the aim of detecting behavioral and linguistic patterns associated to depression, which can constitute the basis of new and complementary tools for the diagnose and follow-up of patients suffering from this disease

In this thesis, we present GPCRmd, an online repository with a submission system and visualization platform specifically designed for storing and providing easy access to molecular dynamics (MD) data of G protein-coupled receptor (GPCR). This database stores MD trajectories and necessary metadata (e.g. force-field, simulation software, integration time-step) for posterior analysis, ensuring data reproducibility and integrity. Importantly, we demonstrate the usefulness of implemented analysis tools in two case studies related to GPCR signaling bias and membrane-induced GPCR modulation. These tools enabled us to detect important structural rearrangement in the initial phase of β-arrestin signaling in the δ-opioid receptor. In addition, we captured relevant molecular mechanisms which are responsible for cholesterol-induced modulation of the 5-HT2A receptor. Aquí presentem el GPCRmd, un repositori en línia amb un sistema de dipòsit i una plataforma de visualització dissenyats específicament per oferir l’emmagatzematge i un fàcil accés a dades dinàmica molecular (MD) de receptors acoblats a proteïnes G (GPCRs). Aquesta base de dades emmagatzema trajectòries de MD i les metadades (per exemple, el programari de simulació, el camp de força o el temps d'integració) necessàries per a una anàlisi posterior, garantint la reproductibilitat i la integritat de les dades. És important destacar la utilitat de les eines d'anàlisi implementades en dos estudis de cas relacionats amb el biaix de senyalització de GPCRs i la modulació de GPCRs induïda per membrana. Aquestes eines ens han permès detectar una important reordenació estructural en la fase inicial de senyalització per β-arrestina en el receptor δ-opioide. A més, hem capturat el mecanismes moleculars rellevants responsables de la modulació induïda per colesterol del receptor 5-HT2A.

International audience; How do people appropriate their virtual hand representation when interacting in virtual environments? In order to answer this question , we conducted an experiment studying the sense of embodiment when interacting with three different virtual hand representations , each one providing a different degree of visual realism but keeping the same control mechanism. The main experimental task was a Pick-and-Place task in which participants had to grasp a virtual cube and place it to an indicated position while avoiding an obstacle (brick, barbed wire or fire). An additional task was considered in which participants had to perform a potentially dangerous operation towards their virtual hand: place their virtual hand close to a virtual spinning saw. Both qualitative measures and questionnaire data were gathered in order to assess the sense of agency and ownership towards each virtual hand. Results show that the sense of agency is stronger for less realistic virtual hands which also provide less mismatch between the participant's actions and the animation of the virtual hand. In contrast, the sense of ownership is increased for the human virtual hand which provides a direct mapping between the degrees of freedom of the real and virtual hand.

Understanding how receptor-ligand interactions occur is a first step towards designing new drugs. The complete reconstruction of the binding process in a drug-receptor system provides all the physical-chemistry variables for rational design of inhibitors of a chosen target, an important step in drug discovery. Although very powerful, direct experimental observation of full binding processes is very hard to perform. In this thesis, by using high-throughput molecular dynamics in the distributed computing project GPUGRID.net and analysing the resulting data by Markov state models (MSM), we successfully estimated kinetics, thermodynamics and binding modes for different molecular systems. In the initial works, we focused on estimating the potency of inhibitor-protein complexes. In subsequent studies, we described more complex pictures of binding, taking into account the receptor dynamics or other binding molecules. The results are promising and establish the methodology as a very powerful tool in the first stages of the drug discovery pipeline., Comprender las interacciones entre proteína y ligando es el primer paso para diseñar nuevos medicamentos. Llegar a reconstruir completamente este proceso de unión proporciona todas las variables físico-químicas para una optimización racional, un paso muy importante en el descubrimiento de fármacos. Pese a que esto ofrece muchas ventajas, todavía es complicado observar estos procesos experimentalmente. En esta tesis, utilizando simulaciones moleculares de alto rendimiento (HTMD) mediante el proyecto distribuido GPUGRID.net y análisis por Markov state models (MSM), hemos obtenido datos cinéticos, termodinámicos y modos de unión para varios sistemas. En los primeros trabajos nos centramos en estimar la afinidad entre complejos inhibidor-proteína. En trabajos posteriores, logramos caracterizar completamente rutas de unión del ligando teniendo en cuenta los confórmeros de la proteína u otros ligandos presentes. Los resultados son prometedores y establecen la utilidad de HTMD en las primeras fases de descubrimiento de fármacos

Una de las principales cuestiones en farmacología molecular de los GPCR es entender los mecanismos estructurales de las siete hélices transmembrana (TM) que se producen para estabilizar ya sea Rg o los diferentes estados R*. Para entender el mecanismo que cambia el equilibrio del conjunto a un estado activo R* se construyeron tres de los receptores de la serotonina (5-HT4, 5-HT6, y 5 HT7) sobre la base de su información más reciente de cristalografía de rayos X. Dando lugar a dos modelos de cada receptor: una inactiva y otra activa. Los modelos, mejorados y evaluados con la ayuda de datos farmacológicos y químicos se utilizaron principalmente para comprender la interacción entre un ligando y su receptor y su mecanismo de acción. Estos hallazgos estructurales pueden a su vez resultar útiles para el diseño de nuevos fármacos más eficaces y selectivos., One of the main questions in G protein coupled receptors (GPCRs) molecular pharmacology is to understand the structural arrangements of the seven transmembrane (TM) helices that occur to stabilize either the ground state (Rg) or different active states (R*) of the receptors. In order to understand the mechanism that shift the equilibrium of the ensemble to an active R* state models of the inactive and the active state of three serotonin receptors (5-HT4, 5-HT6, and 5-HT7) were built based on the latest information from X-ray crystallography. The resulting models were mainly used to understand the interaction between a ligand and its receptor and the mechanism of action. With the help of pharmacological and chemical data these models and complexes were improved and evaluated. These findings may prove valuable for structural based drug discovery efforts and facilitate the design of more effective and selective pharmaceuticals.

El objetivo de la presente tesis es el de aportar conocimiento sobre la bioquímica y la farmacología de los receptores de adenosina, así como entender las relaciones entre estructura química y actividad farmacológica de los ligandos existentes para estos receptores. Con este objetivo se han empleado distintas técnicas y metodologías del diseño de fármacos asistido por ordenador. Los resultados presentados en este trabajo incluyen:· El desarrollo de una estrategia original para la selección de una muestra que cubra adecuadamente la diversidad molecular existente en una base de datos de compuestos químicos· La construcción de un modelo de la región transmembrana del receptor A1 humano de adenosina, en el que se ha localizado y caracterizado un sitio de unión de agonistas compatible con los datos experimentales.· Predicciones teóricas de las energías de unión de ligandos, realizadas a partir de los complejos agonista-receptor predichos sobre el modelo mencionado, obteniendo un grado de acuerdo con los datos experimentales que resulta esperanzador, The goal of the present thesis is to gain knowledge about the biochemistry and pharmacology of adenosine receptors, as well as to understand structure-activity relationships for the existing ligands for this receptors. In order to achieve this goal, we have used several techniques and methodologies from the computer-aided drug design field. Results presented in this work include:· The development of an original strategy of selection of a maximum diversity sample that adequately covers the original molecular diversity contained in a compound database· The building of the transmembrane region of a human A1 adenosine receptor model. In such a model, an agonists binding site has been located and characterized, showing agreement with experimental data.· The resulting ligand-receptor complexes have been studied with computational approaches for the prediction of ligand-binding free energies. A nice correlation with experimental results was observed

Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET (http://www.psygenet.org/) has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype-phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators' tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene-disease associ- ations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL: http://www.psygenet.org. PsyGeNET corpus: http://www.psygenet.org/ds/PsyGeNET/results/psygenetCorpus.tar We received support from ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026), IMI-JU under grants agreements no. 115191 (Open PHACTS)] and no. 115372 (EMIF), resources of which are composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D+i 2013-2016, funded by ISCIII and FEDER. MRA, SMR and MCBG are supported RD16/0017/0007; OV, FF and MT are supported by RD16/0017/0010; and AS and FJP are supported by RD16/0017/0001, by Instituto de Salud Carlos III, Red de Trastornos Adictivos (RTA-Retics-ISCIII). AGS acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the 'María de Maeztu' Programme for Units of Excellence in R&D (MDM-2014-0370). Funding for open access: EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics).