Ferrán Sanz

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field.

Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

Healthcare workers are vulnerable to adverse mental health impacts of the COVID-19 pandemic. We assessed prevalence of mental disorders and associated factors during the first wave of the pandemic among healthcare professionals in Spain.All workers in 18 healthcare institutions (6 AACC) in Spain were invited to web-based surveys assessing individual characteristics, COVID-19 infection status and exposure, and mental health status (May 5 - September 7, 2020). We report: probable 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 probable "disabling" current mental disorders.9,138 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. Workers with pre-pandemic lifetime mental disorders had almost twice the prevalence 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).One in seven Spanish healthcare workers screened positive for a disabling mental disorder during the first wave of the COVID-19 pandemic. Workers reporting pre-pandemic lifetime mental disorders, those frequently exposed to COVID-19 patients, infected or quarantined/isolated, female workers, and auxiliary nurses should be considered groups in need of mental health monitoring and support.

Depression and AnxietyVolume 38, Issue 5 p. 528-544 RESEARCH ARTICLEOpen Access Thirty-day suicidal thoughts and behaviors among hospital workers during the first wave of the Spain COVID-19 outbreak Philippe Mortier, Corresponding Author Philippe Mortier [email protected] orcid.org/0000-0003-2113-6241 Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Correspondence Philippe Mortier, IMIM, CIBERESP, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected] Jordi Alonso, IMIM, CIBERESP, UPF, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected]Search for more papers by this authorGemma Vilagut, Gemma Vilagut Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, SpainSearch for more papers by this authorMontse Ferrer, Montse Ferrer Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Universitat Autònoma de Barcelona (UAB), Barcelona, SpainSearch for more papers by this authorConsol Serra, Consol Serra CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Parc de Salut Mar PSMAR, Barcelona, Spain CiSAL-Centro de Investigación en Salud Laboral, IMIM/UPF, Barcelona, SpainSearch for more papers by this authorJuan D. Molina, Juan D. Molina orcid.org/0000-0001-8561-8130 Villaverde Mental Health Center, Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, Hospital Universitario 12 de Octubre, Madrid, Spain Research Institute Hospital 12 de Octubre (i+12), Madrid, Spain Faculty of Health Sciences, Francisco de Vitoria University, Madrid, Spain CIBER Salud Mental (CIBERSAM), Madrid, SpainSearch for more papers by this authorNieves López-Fresneña, Nieves López-Fresneña Hospital General Universitario Gregorio Marañón, Madrid, SpainSearch for more papers by this authorTeresa Puig, Teresa Puig Universitat Autònoma de Barcelona (UAB), Barcelona, Spain Department of Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain CIBER Enfermedades Cardiovasculares (CIBERCV), Madrid, SpainSearch for more papers by this authorJosé M. Pelayo-Terán, José M. Pelayo-Terán Hospital El Bierzo, León, SpainSearch for more papers by this authorJosé I. Pijoan, José I. Pijoan CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Hospital Universitario Cruces/OSI EEC, Bilbao, SpainSearch for more papers by this authorJosé I. Emparanza, José I. Emparanza CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Hospital Universitario Donostia, San Sebastián, SpainSearch for more papers by this authorMeritxell Espuga, Meritxell Espuga Occupational Health Service, Hospital Universitari Vall d'Hebron, Barcelona, SpainSearch for more papers by this authorNieves Plana, Nieves Plana CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Príncipe de Asturias University Hospital, Alcalá de Henares, Madrid, SpainSearch for more papers by this authorAna González-Pinto, Ana González-Pinto CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Universitario Araba-Santiago, Vitoria-Gasteiz, SpainSearch for more papers by this authorRafael M. Ortí-Lucas, Rafael M. Ortí-Lucas CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Clínic Universitari, Valencia, SpainSearch for more papers by this authorAlma M. de Salázar, Alma M. de Salázar UGC Salud Mental, Hospital Universitario Torrecárdenas, Almería, SpainSearch for more papers by this authorCristina Rius, Cristina Rius CIBER Salud Mental (CIBERSAM), Madrid, Spain Agència de Salut Pública de Barcelona, Barcelona, SpainSearch for more papers by this authorEnric Aragonès, Enric Aragonès Institut d'Investigació en Atenció Primària IDIAP Jordi Gol, Barcelona, Spain Atenció Primària Camp de Tarragona, Institut Català de la Salut, Tarragona, SpainSearch for more papers by this authorIsabel del Cura-González, Isabel del Cura-González Research Unit, Primary Care Management, Madrid Health Service, Madrid, Spain Department of Medical Specialities and Public Health, King Juan Carlos University, Madrid, Spain Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, SpainSearch for more papers by this authorAndrés Aragón-Peña, Andrés Aragón-Peña Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, Spain Epidemiology Unit, Regional Ministry of Health, Community of Madrid, Madrid, SpainSearch for more papers by this authorMireia Campos, Mireia Campos Service of Prevention of Labor Risks, Medical Emergencies System, Generalitat de Catalunya, Barcelona, SpainSearch for more papers by this authorMara Parellada, Mara Parellada CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital General Universitario Gregorio Marañón, Madrid, SpainSearch for more papers by this authorAurora Pérez-Zapata, Aurora Pérez-Zapata Príncipe de Asturias University Hospital, Alcalá de Henares, Madrid, SpainSearch for more papers by this authorMaria João Forjaz, Maria João Forjaz National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII), Madrid, Spain Health Services Research Network on Chronic Diseases (REDISSEC), Madrid, SpainSearch for more papers by this authorFerran Sanz, Ferran Sanz Research Progamme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain Instituto Nacional de Bioinformatica—ELIXIR-ES, Madrid, SpainSearch for more papers by this authorJosep M. Haro, Josep M. Haro Universitat Autònoma de Barcelona (UAB), Barcelona, Spain CIBER Salud Mental (CIBERSAM), Madrid, Spain Parc Sanitari Sant Joan de Déu, Barcelona, SpainSearch for more papers by this authorEduard Vieta, Eduard Vieta CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorVíctor Pérez-Solà, Víctor Pérez-Solà Universitat Autònoma de Barcelona (UAB), Barcelona, Spain Parc de Salut Mar PSMAR, Barcelona, Spain CIBER Salud Mental (CIBERSAM), Madrid, SpainSearch for more papers by this authorRonald C. Kessler, Ronald C. Kessler orcid.org/0000-0003-4831-2305 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorRonny Bruffaerts, Ronny Bruffaerts Center for Public Health Psychiatry, Universitair Psychiatrisch Centrum, KU Leuven, Leuven, BelgiumSearch for more papers by this authorJordi Alonso, Corresponding Author Jordi Alonso [email protected] orcid.org/0000-0001-8627-9636 Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain Correspondence Philippe Mortier, IMIM, CIBERESP, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected] Jordi Alonso, IMIM, CIBERESP, UPF, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected]Search for more papers by this authorthe MINDCOVID Working Group, the MINDCOVID Working GroupSearch for more papers by this author Philippe Mortier, Corresponding Author Philippe Mortier [email protected] orcid.org/0000-0003-2113-6241 Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Correspondence Philippe Mortier, IMIM, CIBERESP, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected] Jordi Alonso, IMIM, CIBERESP, UPF, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected]Search for more papers by this authorGemma Vilagut, Gemma Vilagut Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, SpainSearch for more papers by this authorMontse Ferrer, Montse Ferrer Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Universitat Autònoma de Barcelona (UAB), Barcelona, SpainSearch for more papers by this authorConsol Serra, Consol Serra CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Parc de Salut Mar PSMAR, Barcelona, Spain CiSAL-Centro de Investigación en Salud Laboral, IMIM/UPF, Barcelona, SpainSearch for more papers by this authorJuan D. Molina, Juan D. Molina orcid.org/0000-0001-8561-8130 Villaverde Mental Health Center, Clinical Management Area of Psychiatry and Mental Health, Psychiatric Service, Hospital Universitario 12 de Octubre, Madrid, Spain Research Institute Hospital 12 de Octubre (i+12), Madrid, Spain Faculty of Health Sciences, Francisco de Vitoria University, Madrid, Spain CIBER Salud Mental (CIBERSAM), Madrid, SpainSearch for more papers by this authorNieves López-Fresneña, Nieves López-Fresneña Hospital General Universitario Gregorio Marañón, Madrid, SpainSearch for more papers by this authorTeresa Puig, Teresa Puig Universitat Autònoma de Barcelona (UAB), Barcelona, Spain Department of Epidemiology and Public Health, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain CIBER Enfermedades Cardiovasculares (CIBERCV), Madrid, SpainSearch for more papers by this authorJosé M. Pelayo-Terán, José M. Pelayo-Terán Hospital El Bierzo, León, SpainSearch for more papers by this authorJosé I. Pijoan, José I. Pijoan CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Hospital Universitario Cruces/OSI EEC, Bilbao, SpainSearch for more papers by this authorJosé I. Emparanza, José I. Emparanza CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Hospital Universitario Donostia, San Sebastián, SpainSearch for more papers by this authorMeritxell Espuga, Meritxell Espuga Occupational Health Service, Hospital Universitari Vall d'Hebron, Barcelona, SpainSearch for more papers by this authorNieves Plana, Nieves Plana CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Príncipe de Asturias University Hospital, Alcalá de Henares, Madrid, SpainSearch for more papers by this authorAna González-Pinto, Ana González-Pinto CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Universitario Araba-Santiago, Vitoria-Gasteiz, SpainSearch for more papers by this authorRafael M. Ortí-Lucas, Rafael M. Ortí-Lucas CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Clínic Universitari, Valencia, SpainSearch for more papers by this authorAlma M. de Salázar, Alma M. de Salázar UGC Salud Mental, Hospital Universitario Torrecárdenas, Almería, SpainSearch for more papers by this authorCristina Rius, Cristina Rius CIBER Salud Mental (CIBERSAM), Madrid, Spain Agència de Salut Pública de Barcelona, Barcelona, SpainSearch for more papers by this authorEnric Aragonès, Enric Aragonès Institut d'Investigació en Atenció Primària IDIAP Jordi Gol, Barcelona, Spain Atenció Primària Camp de Tarragona, Institut Català de la Salut, Tarragona, SpainSearch for more papers by this authorIsabel del Cura-González, Isabel del Cura-González Research Unit, Primary Care Management, Madrid Health Service, Madrid, Spain Department of Medical Specialities and Public Health, King Juan Carlos University, Madrid, Spain Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, SpainSearch for more papers by this authorAndrés Aragón-Peña, Andrés Aragón-Peña Fundación Investigación e Innovación Biosanitaria de AP, Comunidad de Madrid, Madrid, Spain Epidemiology Unit, Regional Ministry of Health, Community of Madrid, Madrid, SpainSearch for more papers by this authorMireia Campos, Mireia Campos Service of Prevention of Labor Risks, Medical Emergencies System, Generalitat de Catalunya, Barcelona, SpainSearch for more papers by this authorMara Parellada, Mara Parellada CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital General Universitario Gregorio Marañón, Madrid, SpainSearch for more papers by this authorAurora Pérez-Zapata, Aurora Pérez-Zapata Príncipe de Asturias University Hospital, Alcalá de Henares, Madrid, SpainSearch for more papers by this authorMaria João Forjaz, Maria João Forjaz National Center of Epidemiology, Instituto de Salud Carlos III (ISCIII), Madrid, Spain Health Services Research Network on Chronic Diseases (REDISSEC), Madrid, SpainSearch for more papers by this authorFerran Sanz, Ferran Sanz Research Progamme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain Instituto Nacional de Bioinformatica—ELIXIR-ES, Madrid, SpainSearch for more papers by this authorJosep M. Haro, Josep M. Haro Universitat Autònoma de Barcelona (UAB), Barcelona, Spain CIBER Salud Mental (CIBERSAM), Madrid, Spain Parc Sanitari Sant Joan de Déu, Barcelona, SpainSearch for more papers by this authorEduard Vieta, Eduard Vieta CIBER Salud Mental (CIBERSAM), Madrid, Spain Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorVíctor Pérez-Solà, Víctor Pérez-Solà Universitat Autònoma de Barcelona (UAB), Barcelona, Spain Parc de Salut Mar PSMAR, Barcelona, Spain CIBER Salud Mental (CIBERSAM), Madrid, SpainSearch for more papers by this authorRonald C. Kessler, Ronald C. Kessler orcid.org/0000-0003-4831-2305 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USASearch for more papers by this authorRonny Bruffaerts, Ronny Bruffaerts Center for Public Health Psychiatry, Universitair Psychiatrisch Centrum, KU Leuven, Leuven, BelgiumSearch for more papers by this authorJordi Alonso, Corresponding Author Jordi Alonso [email protected] orcid.org/0000-0001-8627-9636 Health Services Research Unit, IMIM-Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain Correspondence Philippe Mortier, IMIM, CIBERESP, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected] Jordi Alonso, IMIM, CIBERESP, UPF, IMIM, PRBB Bldg, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain. Email: [email protected]Search for more papers by this authorthe MINDCOVID Working Group, the MINDCOVID Working GroupSearch for more papers by this author First published: 04 January 2021 https://doi.org/10.1002/da.23129Citations: 52AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Background Healthcare workers are a key occupational group at risk for suicidal thoughts and behaviors (STB). We investigated the prevalence and correlates of STB among hospital workers during the first wave of the Spain COVID-19 outbreak (March–July 2020). Methods Data come from the baseline assessment of a cohort of Spanish hospital workers (n = 5450), recruited from 10 hospitals just after the height of the coronavirus disease 2019 (COVID-19) outbreak (May 5–July 23, 2020). Web-based self-report surveys assessed 30-day STB, individual characteristics, and potentially modifiable contextual factors related to hospital workers' work and financial situation. Results Thirty-day STB prevalence was estimated at 8.4% (4.9% passive ideation only, 3.5% active ideation with or without a plan or attempt). A total of n = 6 professionals attempted suicide in the past 30 days. In adjusted models, 30-day STB remained significantly associated with pre-pandemic lifetime mood (odds ratio [OR] = 2.92) and anxiety disorder (OR = 1.90). Significant modifiable factors included a perceived lack of coordination, communication, personnel, or supervision at work (population-attributable risk proportion [PARP] = 50.5%), and financial stress (PARP = 44.1%). Conclusions and Relevance Thirty-day STB among hospital workers during the first wave of the Spain COVID-19 outbreak was high. Hospital preparedness for virus outbreaks should be increased, and strong governmental policy response is needed to increase financial security among hospital workers. 1 INTRODUCTION The coronavirus disesase 2019 (COVID-19) pandemic has presented hospital workers with unprecedented challenges in terms of workload as well as health- and work-related risk and stress exposures. The latter includes exposure to COVID-19 patients and stress about getting infected or infecting loved ones, but also moral injury, that is, psychological distress resulting from actions, or the lack of them, that violate one's moral or ethical code (Litz et al., 2009). In the current context, moral injury may result from the lack of hospital preparedness for the pandemic, and may lead to traumatic experiences such as having to prioritize care, or seeing patients suffer or die from COVID-19 (Greenberg et al., 2020). In line with these concerns, rates of depression, anxiety, and sleep problems among healthcare workers during COVID-19 outbreaks are high (Muller et al., 2020; Pappa et al., 2020; Vindegaard & Benros, 2020), and those in direct contact with affected patients report posttraumatic stress and psychological distress (Kisely et al., 2020). These adverse mental health outcomes are well-known risk factors for suicidal thoughts and behaviors (STB; Franklin et al., 2017). High rates of STB among healthcare professionals during virus outbreaks can therefore be expected (Gunnell et al., 2020) especially since this population segment already has increased risk for suicidal ideation (Tyssen et al., 2001) and suicide (Dutheil et al., 2019; Hawton et al., 2011) under normal working conditions. No research to date focused on STB during a virus outbreak in this key occupational group at risk (Salazar de Pablo et al., 2020). In addition, previous studies among hospital workers active during the COVID-19 pandemic predominantly focused on healthcare workers (mostly doctors or nurses; Muller et al., 2020), while many hospital workers not involved in patient care may also be at risk for adverse mental health. Spain was among those countries whose healthcare systems came under extreme pressure during the first wave of the COVID-19 pandemic (March–July 2020; Arango, 2020). The Spanish government declared a state of alarm on March 14, 2020, and between the beginning of March and mid-April, more than 2000 new cases were reported daily. The healthcare system nearly collapsed during April–May due to lack of intensive care unit beds, ventilators, and healthcare personnel (Red Nacional de Vigilancia Epidemiólogica [RENAVE], 2020). By the time the situation stabilized in early July, Spain had the eighth highest number of confirmed cases (i.e., 249,659 on 01/07/2020), and the fifth highest COVID death rate (i.e., 60.7/100,000 on 01/07/2020) in the world (Roser et al., 2020). We present data from the MIND/COVID project (MIND/COVID-19, 2020), a national multiple-cohort study of the mental health impact of the COVID-19 pandemic in Spain. We report here on the baseline assessment of the hospital workers cohort, conducted just after the height of the virus outbreak (May 5–July 23, 2020), when demands on the Spanish public healthcare system were substantially increased. The objectives of the current report are to examine baseline prevalence of 30-day STB and to investigate the relationship of potentially modifiable contextual factors related to hospital workers' perceived work and financial situation, with 30-day STB. 2 METHODS Study design, population, and sampling The study design consists in a multicenter, prospective, observational cohort study of Spanish hospital workers. A convenience sample of 10 hospitals from four autonomous communities in Spain (i.e., the Basque Country, Castile and Leon, Catalonia, and the Community of Madrid) agreed to participate. Hospitals were selected to reflect the geographical and sociodemographic variability in Spain. All participating hospitals came from regions with high COVID-19 caseloads. Here we report on the baseline assessment of the cohort, which consists of de-identified web-based self-report surveys (May 5–July 23, 2020), conducted soon after the first wave of the COVID-19 outbreak in Spain. In each participating hospital, hospital representatives contacted all employed hospital workers to participate using the hospitals' administrative email distribution lists (i.e., census sampling). The invitation email included an anonymous link to access the web-based survey platform (qualtrics.com). Informed consent was obtained from all participants at the first survey page. Two reminder emails were sent within a 2–4 weeks period after the initial invitation. The study complies with the principles established by national and international regulations, including the Declaration of Helsinki and the Code of Ethics. The study was approved by the Research Integrity and Good Scientific Practices Committee of IMIM-Parc de Salut Mar, Barcelona, Spain (2020/9203/I), and by all participating centers' institutional review boards (IRBs). Measures 2.2.1 STB A modified self-report version of selected items from the Columbia Suicide Severity Rating Scale (C-SSRS; Posner et al., 2011), also used in other large-scale epidemiological studies (e.g., Nock et al., 2014), assessed suicidal thoughts and behaviors in the past 30 days, including passive suicidal ideation ("wish you were dead or would go to sleep and never wake up"), active suicidal ideation ("have thoughts of killing yourself"), suicide plans ("think about how you might kill yourself [e.g., taking pills, shooting yourself] or work out a plan of how to kill yourself"), and suicide attempt ("make a suicide attempt [i.e., purposefully hurt yourself with at least some intent to die]"). 2.2.2 Potentially modifiable contextual factors Potentially modifiable contextual factors refer to factors that are related to hospital workers' work and financial situation, that are relevant with regard to the COVID-19 outbreak, and that are potentially modifiable in the future. Six work-related factors were assessed: (1) the average weekly hours worked, categorized into 40 h/week or less, 41–50 h/week, and 51 h or more per week; (2) the perceived lack of coordination, communication, personnel, or supervision at work, using four 5-level Likert-type items ranging from "none of the time" to "all of the time." Items were summed and rescaled to a 0.0–4.0 Likert scale (Cronbach α = .858); (3) the perceived frequency of lack of protective equipment, using a 5-level Likert-type item ranging from "none of the time" to "all of the time;" (4) the perceived efficiency of the available protective equipment, using a 4-level Likert-type item ranging from "sufficient" to "completely insufficient;" (5) having had to make decisions regarding prioritizing care among COVID-19 patients (assessed among medical doctors and nurses only); and (6) having had patient(s) in care that died from COVID-19. All items included a specific time frame, that is, "since the onset of the virus outbreak in Spain." Two factors related to hospital workers' financial situation were assessed: (1) having suffered a significant loss in personal or familial income due to the COVID-19 pandemic; and (2) financial stress, using two 5-level Likert-type items that assessed stress regarding one's financial situation (Dohrenwend et al., 1978) and stress regarding job loss or loss of income because of COVID-19, with response options ranging from "none" to "very severe." Items were summed and rescaled to 0.0–4.0 Likert scale (Cronbach α = .821). 2.2.3 Individual characteristics Twelve individual characteristics were assessed: (1) age; (2) gender; (3) marital status; (4) having children in care; (5) self-reported lifetime mental disorders before the onset of the COVID-19 outbreak, using Composite International Diagnostic Interview (CIDI) 3.0 adapted screener items (Kessler & Üstün, 2004), including mood (i.e., depressive and bipolar disorders), anxiety (i.e., panic, generalized anxiety, and obsessive–compulsive disorders), substance use (i.e., alcohol, illicit drugs, and prescription drugs with or without prescription), and other disorders; (6) profession, categorized into five categories: medical doctors, nurses, auxiliary nurses, other professions involved in patient care (i.e., midwives; dentists or odontologists; pharmaceutical, laboratory, or radiology technicians; psychologists, physiotherapists, social workers, patient transport) and other professions not involved in patient care (i.e., administrative and management personnel, logistic support [e.g. food, maintenance, supplies], research-only personnel); (7) the frequency of direct exposure to COVID-19 patients during professional activity, using one 5-level Likert type item, ranging from "none of the time" to "all of the time;" (8) changes in assigned functions, team, or working location, categorized into having changed to a specific COVID-19-related work location (e.g., emergency room, COVID ward, fever clinic, intensive care unit, quarantine center, field hospital), having changed of team or assigned functions, and all others; (9) the frequency of working at home during the COVID-19 outbreak, using a 6-level Likert item, ranging from "never" to "always;" (10) COVID-19 infection history, categorized into having been hospitalized for COVID-19, having had a positive COVID-19 test or medical diagnosis not requiring hospitalization, and all others; (11) having been in isolation of quarantine because of exposure to COVID-19-infected person(s); and (12) having close ones infected with COVID-19. Data representativeness and quality A total of 5450 hospital workers participated (response rate = 11.8%). It is important to note that the survey view rate (i.e., the proportion of hospital workers that opened the invitation email; Eysenbach, 2004) is unknown, except for one hospital (26.4%), suggesting low survey view rates, and questioning the validity of the response rate as an indicator of data representativeness. Post-stratification weights were used in all analyses to adjust for potential nonresponse bias, taking into account sample versus target population differences in age, gender, and profession. Differences in post-stratifying variables between our sample and the target population were small, suggesting good data representativeness. See Table S1 and S2 for more details on response rates and poststratification. To further assess data representativeness, we compared our observed COVID-19 infection rates stratified by Autonomous Community in Spain (range 8.4%–21.8%) with official seroprevalence results, and found that they are in very close agreement (see Table S3). For this study, analyses were restricted to n = 5169 (94.8%) that completed all STB items. An additional n = 5 were excluded because they did identify with neither male or female gender (of those, n = 1 reported 30-day passive ideation only; n = 2 reported a 30-day suicide attempt). No statistical differences in gender or age were found between those that completed the STB items and those that did not (females 80.8% vs. 82.1%, χ2(1) = 0.315, p = .574; mean age 42.9 vs. 42.1, t(5448] = −1.18, p = .240). Median % missingness per variable in the analysis sample was 1.4% (see Table S4). Missing data were handled using multivariate imputation by chained equations (van Buuren, 2012). Statistical analysis All analyses were conducted with SAS version 9.4, and R version 3.6.2. First, prevalence estimates of 30-day STB were estimated, both total and stratified by individual characteristics, with associated modified Rao–Scott χ2 tests and Fisher-Exact tests. Second, we estimated multivariable associations between 30-day STB and individual characteristics. Logistic regression was used for all multivariable analysis. Third, we estimated the multivariable association between 30-day STB and each potentially modifiable contextual factor separately, adjusting for all individual characteristics. Fourth, we identified the subset of individual characteristics and potentially modifiable contextual factors that best explain STB in multivariable models, using the lasso shrinkage method (Hastie et al., 2009), optimizing the Bayesian Information Criterion. Variance was estimated using the Taylor series linearization method taking into account post-stratification and within-hospital clustering of data. Potential deviations from a continuous linear effect in the logit were assessed using likelihood ratio tests comparing full categorical versus continuous variable specifications. All analyses were adjusted for hospital membership and time of survey. Fifth, population-attributable risk proportions (PARP; Krysinska & Martin, 2009), and associated bootstrap percentile confidence intervals (500 replicatio

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Perspective28 July 2009Open Access Pathway databases and tools for their exploitation: benefits, current limitations and challenges Anna Bauer-Mehren Anna Bauer-Mehren Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Laura I Furlong Corresponding Author Laura I Furlong Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Ferran Sanz Ferran Sanz Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Anna Bauer-Mehren Anna Bauer-Mehren Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Laura I Furlong Corresponding Author Laura I Furlong Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Ferran Sanz Ferran Sanz Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain Search for more papers by this author Author Information Anna Bauer-Mehren1, Laura I Furlong 1 and Ferran Sanz1 1Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain *Corresponding author. Research Unit on Biomedical Informatics, Universitat Pompeu Fabra, IMIM-Hospital del Mar, PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain. Tel.: +34 9331 60521; Fax: +34 9331 60550; E-mail: [email protected] Molecular Systems Biology (2009)5:290https://doi.org/10.1038/msb.2009.47 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue. Introduction The past decades of research have led to a better understanding of the processes involved in cell signalling. Cell signalling refers to the biochemical processes using which cells respond to cues in their internal or external environment (Alberts et al, 2007). With the advent of high throughput experimentation, the identification and characterization of the molecular components involved in cell signalling became possible in a systematic way. In addition, the discovery of the connections between each of these components promoted the reconstruction of the chain of reactions, which subsequently gives rise to a signalling pathway. Ultimately, our ability to interpret the function and regulation of cell signalling pathways is crucial for understanding the ways in which cells respond to external cues and how they communicate with each other. In this regard, the systematic collection of pathway information in the form of pathway databases and the application of mathematical analysis for pathway modelling are crucial. Several databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data (Suderman and Hallett, 2007). Furthermore, mathematical modelling emerged as a solution to study the complex behaviour of networks (Alves et al, 2006; Fisher and Henzinger, 2007; Karlebach and Shamir, 2008). The models, so far obtained, allow formulating hypothesis that can be tested in the laboratory. Iterative cycles of prediction and experimental verification have resulted in the refinement of our knowledge of cell signalling, and have shed light on different aspects of cell signalling at a systems level (regulatory aspects, such as feedback control circuits or architectural features, such as modularity). Furthermore, signalling cascades are not isolated units within the cell, but form part of a mesh of interconnected networks through which the signal elicited by an environmental cue can traverse (Yaffe, 2008). Ultimately, each cell is exposed to a variety of signalling cues, and the specificity of the response will be determined by the signalling mechanisms that are activated by the cue (Alberts et al, 2007). Recent research highlights the importance of the, so called, crosstalks between pathways, such as the recently published connections between signalling through the purinergic receptors and the Ca2+ sensing (Chaumont et al, 2008); the link between extracellular glycocalyx structure and nitric oxide signalling pathway (Tarbell and Ebong, 2008); the interactions between insulin and epidermal growth factor signalling (Borisov et al, 2009) and the crosstalk between phosphoinositide 3 kinase and Ras/extracellular signal-regulated kinase signalling pathways (Wang et al, 2009). An important goal of this research is to achieve a reconstruction of the network of interactions that gives rise to a signalling pathway in a biologically consistent and meaningful manner that in turn allows the mathematical analysis of the emerging properties of the network. In this regard, comprehensive maps of signalling pathways have been developed by manual curation from literature (Oda et al, 2005; Oda and Kitano, 2006; Calzone et al, 2008). Building such reference maps requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of the structural features of the network and its dynamic behaviour can take place. A commonly seen architecture of signalling pathways is called ‘bow-tie’, in which many input and output signals are handled by a common layer constituted by a small number of conserved components. This network architecture provides robustness and flexibility to a variety of external cues due to the redundancy of reactions that are part of the input and output layers (Kitano, 2007a). Robustness refers to the ability of an organism to compensate the effects of perturbations to maintain the organism's functions (Kitano, 2007b). Such perturbations can be changes in the availability of nutrients as well as the presence of mutagens or toxins. Moreover, systems can be subjected to functional disruptions when facing perturbations for which they are not optimized, thus showing points of fragility of the biological system (Kitano, 2007b). For instance, an undesired effect of a drug can be caused by the unwanted interaction of the drug with molecules that represent points of fragility of the physiological system (Kitano, 2007a). In contrast, drugs can be completely ineffective when the robustness of the system compensates their action. It has been suggested that crosstalks between signalling pathways contribute to the robustness of cells against perturbations (Kitano, 2007a). In addition, the points of fragility of the system are sometimes exploited by pathogens causing diseases, or represent processes that are usually found to malfunction in particular diseases, such as cancer. Diseases that arise from dysfunction in cell signalling are usually not attributed to a single gene but to the failure of emerging control mechanisms in the network. It has been reported that the loss of negative feedback loops characterizes solid tumours (Amit et al, 2007). These diseases are difficult to diagnose and treat unless accurate understanding of the underlying principles regulating the system is in place. Thus, the interpretation of the global properties of signalling pathways has important implications for the elucidation of the mechanisms that lead to complex diseases, and also for the identification of drug targets. At present, there are several repositories of information on cell signalling pathways that cover a wide range of signal transduction mechanisms and include high quality data in terms of annotation and cross references to biological databases. In principle, the scenario is prepared to make use of the information for the analysis of the behaviour of the signalling pathways. Thus, are the knowledge repositories on signalling pathways ready to realize the systems biology promise? In this article, we aim to initiate this discussion and to provide some insights on this issue. First, we present an analytical overview of current pathway databases (see Pathway databases). In section ‘Case study: EGFR signalling’, we present the results of an evaluation exercise conducted to determine the accuracy and completeness of current pathway databases in front of an expert-curated pathway used as ‘gold standard’. Moreover, we propose a strategy for the use of pathway data from public databases for network modelling (Box 1; Table I). Finally, in the section ‘Conclusions and perspectives’ we discuss the strengths and limitations of the current pathway databases and their usefulness in practical biological problems and applications. Box 1 Use of data from public pathway databases for modelling purposes Box 1 Most public, available pathway databases offer their data in BioPAX format, which was developed for detailed pathway representation and as data exchange format. For storing and sharing of computational models of biological networks, SBML has emerged as standard and is supported by most modelling software. BioPAX and SBML, the two main standards for the representation of biological networks, have been discussed in detail by others (Stromback and Lambrix, 2005; Stromback et al, 2006). In Table I, we briefly list the most important features of the SBML and BioPAX standards. A scenario in which pathway data were directly used for network modelling is proposed here. One or more pathways represented in BioPAX format are automatically retrieved from different databases and imported into a pathway visualization and analysis tool. Then, integration of the different pathways can take place to obtain a comprehensive and biologically meaningful representation of the network. In addition, annotations can be added if required or structural analysis of the network can be carried out. The resulting network, which integrates the original pathways retrieved from the databases, is exported to SBML format and subjected to modelling. If a quantitative approach is chosen, additional information, such as rate constants are required to start the modelling process. In this process, conversion between the two formats is required to achieve inter-operability between pathway and model representations. Some solutions are already available. The BioModels (http://www.ebi.ac.uk/biomodels-main/) database, which contains a variety of curated models in SBML format, offers conversion to BioPAX format. The opposite conversion, from BioPAX to SBML, would open the possibility of modelling the pathways stored in public databases. However, the inter-conversion between BioPAX and SBML is not trivial as both formats where developed for different purposes. BioPAX, for instance, does not offer the possibility to store quantitative information needed for kinetic modelling, whereas SBML does not represent relationships between nodes that are not needed for modelling and that are present in BioPAX. Examples of approaches for the conversion from BioPAX to SBML are BiNoM (Zinovyev et al, 2008), which is available as Cytoscape plugin, and SyBil, which is part of the model environment for quantitative modelling VCell (Evelo, 2009). Although compatibility of different pathway and network model exchange formats is still not completely achieved, the efforts made towards this goal represent significant contributions to pathway retrieval, integration and subsequent modelling. Table 1. Comparison between SBML and BioPAX SBML BioPAX Representation format XML (Extensible Markup Language) OWL (Web Ontology Language), XML Main purpose Representation of computational models of biological networks Pathway description with all details on reactions, components, information on cellular location etc. Entities and reactions Based on species and reactions (Hucka et al., 2003): Basic ontology based on three classes (http://www.biopax.org/): Species (proteins, small molecules etc.) Pathway (set of interactions) Reactions (how species interact) Physical entity with subclasses, such as RNA, DNA, protein, complex and small molecules Compartment (in which interactions take place) Interaction with subclasses, such as conversion having biochemicalReaction as subclass, etc. Number of pathways represented One model per SBML file Several pathways per BioPAX file possible (each object has its own RDF id and is hence uniquely identifiable) Reaction kinetics Allows representation of kinetics, including parameters for reaction rates, initial concentrations etc. No kinetics as BioPAX is not meant for modelling but pathway representation Levels Built in levels with different versions. Each level adds new features, such as the incorporation of controlled vocabularies. At the time of writing, the most stable version is SBML Level 2 BioPAX Level 1: representation of chemical reactions involved in metabolism BioPAX Level 2: adds molecular interactions and protein post-translational modifications BioPAX Level 3: any kind of biological reaction, including regulation of gene expression (BioPAX L3 is at the time of writing still in release process) The BioPAX project roadmap envisages two additional levels capturing interactions at the cellular level. (http://www.biopax.org/Docs/BioPAX_Roadmap.html) Pathway database support Reactome Reactome KEGG KEGG (only BioPAX Level 1) PID PathwayCommons Model database support BioModels BioModels (conversion from SBML to BioPAX possible) Library for reading/writing libSBML(Bornstein et al, 2008) Paxtools (http://www.biopax.org/paxtools/) Software support Standard modelling software, such as CellDesigner or Copasi (Hoops et al, 2006) Network visualization software, such as Cytoscape or VisANT Network visualization software, such as Cytoscape Pathway databases Pathway databases serve as repositories of current knowledge on cell signalling. They present pathways in a graphical format comparable to the representation in text books, as well as in standard formats allowing exchange between different software platforms and further processing by network analysis, visualization and modelling tools. At present, there exist a vast variety of databases containing biochemical reactions, such as signalling pathways or protein–protein interactions. The Pathguide resource serves as a good overview of current pathway databases (Bader et al, 2006). More than 200 pathway repositories are listed, from which over 60 are specialized on reactions in human. However, only half of them provide pathways and reactions in computer-readable formats needed for automatic retrieval and processing, and even less support standard formats, such as Biological Pathway Exchange (BioPAX) (http://www.biopax.org) and Systems Biology Markup Language (SBML) (Hucka et al, 2003). To obtain a complete view of the biological process of interest, combination of information from diverse reactions and pathways is often needed. A recent publication (Adriaens et al, 2008), describes a workflow developed for gathering and curating all information on a pathway to obtain a broad and correct representation. However, the described process heavily relies on manual intervention. Consequently, there is a need for the automation of both the pathway retrieval process and the integration of different data sources. This section is devoted to the description of main pathway databases: Reactome, Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathways, Nature Pathway Interaction Database (PID) and Pathway Commons. Table II lists all pathway databases and protein–protein interaction resources that are mentioned in this section. Table 2. Online pathway and protein–protein interaction (PPI) databases Pathway/PPI database Web link Standard exchange formats for download Web service API Reactome http://www.reactome.org BioPAX Level 2 SOAP web service API BioPAX Level 3 (only some reactions) Detailed user manual available, example client in Java SBML Level 2 KEGG http://www.genome.jp/kegg/pathway.html KGML (default format) SOAP web service API BioPAX Level 1 (only metabolic reactions) Example client in Java, Ruby, Perl SBML (using converter) Direct import into Cytoscape GPML (using converter) WikiPathways http://www.wikipathways.org GPML (default format) SOAP web service API Converters to standards, such as SBML and BioPAX are in progress Example clients in Java, Perl, Python, R NCI/Nature Pathway Interaction Database (PID) http://pid.nci.nih.gov PID XML (default format) Access through Pathway Commons BioPAX Level 2 BioCarta http://www.biocarta.com BioPAX Level 2 through NCI/ Nature Pathway Interaction Database (PID) Pathway commons http://www.pathwaycommons.org BioPAX Level 2 (default format for pathways) HTTP URL-based XML web service through cPath PSI-MI (default format for protein–protein interactions) Direct import into Cytoscape Cancer cell map http://cancer.cellmap.org BioPAX Level 2 HTTP URL-based XML web service via cPath HumanCyc http://humancyc.org BioPAX Level 2 Access through Pathway Commons and Pathway Tools (Karp et al, 2002) BioPAX Level 3 IntAct www.ebi.ac.uk/intact/ PSI-MI Access through Pathway Commons HPRD http://www.hprd.org PSI-MI Access through Pathway Commons MINT http://mint.bio.uniroma2.it/mint/ PSI-MI Access through Pathway Commons Reactome Reactome is currently one of the most complete and best-curated pathway databases. It covers reactions for any type of biological process and organizes them in a hierarchal manner. In this hierarchy, the lower level corresponds to single reactions, whereas the upper level represents the pathway as a whole. Reactome was first developed as an open source database for pathways and interactions in human. Equivalent reactions for other species are inferred from the human data (Vastrik et al, 2007), providing coverage to 22 non-human species, including mouse, rat, chicken, puffer fish, worm, fly, yeast, and Escherichia coli. Furthermore, other Reactome projects exist focusing on single species, such as the Arabidopsis Reactome (http://www.arabidopsisreactome.org). All pathway and reaction data in Reactome are extracted from biomedical experiments and literature. For this purpose, PhD-level biologists are invited to work together with the Reactome curators and editors on the curation of data on selected biological processes. Once the first outline of the biological process is created and annotated, it is inspected by peer reviewers and potential inconsistencies and errors are fixed. Every two years the data are reviewed to keep it updated (Joshi-Tope et al, 2005; Matthews et al, 2009). Moreover, cross references to different databases, such as UniProt (The UniProt Consortium, 2008), Ensembl (http://www.ensembl.org/index.html), NCBI (http://www.ncbi.nlm.nih.gov), Gene Ontology (GO) (Ashburner et al, 2000), Entrez Gene (Maglott et al, 2007), UCSC Genome Browser (http://genome.ucsc.edu), HapMap (http://www.hapmap.org), PubMed, as well as to other pathway databases, such as KEGG (Kanehisa and Goto, 2000) are provided. Pathways are presented as chains of chemical reactions and the same data model is used to describe reactions for any biological process, such as transcription, catalysis or binding (Matthews et al, 2007). Altogether, this represents a coherent view of pathway knowledge. The data model is based on classes, such as physical entity or event. Physical entities comprise proteins, DNA, RNA, small molecules but also complexes of single entities. Proteins, RNA and DNA, for which the sequence is known, are linked to the appropriate databases. Chemical entities such as small molecules are linked to ChEBI (http://www.ebi.ac.uk/chebi/init.do). An event can be either a ReactionLikeEvent, which represents reactions that convert an input into an output, or a PathwayLikeEvent, grouping together several related events. Each class possesses properties, such as information on the type of interaction (e.g. inhibition or activation). Reactome explicitly considers the different states an entity can show in a reaction. The phosphorylated and the unphosphorylated version of a protein are, for example, represented as separate entities. In addition, generalization is allowed. This means that if two different entities have exactly the same function in a reaction, such as isoenzymes, the reaction is only described once and the functional equivalent entities belong to the same defined set. Another interesting element of the Reactome data model is the use of candidate sets, which act as placeholders for all possible entities in a reaction, in case the exact entity involved in the reaction is not yet known. Reactome can either be directly browsed or queried by text search using, for instance, UniProt accession numbers. In addition, some tools for advanced queries are provided. The PathFinder tool allows connecting an input to an output molecule or event by constructing the shortest path between both. The SkyPainter tool can be used to identify events or pathways that are statistically over-represented for a list of genes or proteins. Moreover, Reactome data can be combined with other databases such as UniProt, by using the Reactome BioMart (http://www.biomart.org) tool. In addition to browsing pathways through the Reactome web interface, it is possible to download the data for local visualization and analysis using other tools. Different formats are provided for pathway download, including SBML Level 2, and BioPAX Level 2 and Level 3 (for some reactions only), as well as graphical formats. Pathway files, for instance, in BioPAX format can be directly opened in Cytoscape (Shannon et al, 2003), a software for the visualization and analysis of networks. Moreover, data can be programmatically accessed through a SOAP web service. KEGG KEGG is not only a database for pathways but consists of 19 highly interconnected databases, containing genomic, chemical and phenotypic information (Kanehisa and Goto, 2000; Kanehisa et al, 2008). Here we concentrate on the database storing biological pathways. KEGG categorizes its pathways into metabolic processes, genetic information processing, environmental information processing, including signalling pathways, cellular processes, information on human diseases and drug development. However, the best-organized and most complete information can be found for metabolic pathways. KEGG is not organism specific but covers a wide range of organisms, including human. The pathways are manually curated by experts using literature. In addition to the interconnection of all databases underlying KEGG, links to external databases, such as NCBI Entrez Gene, OMIM, UniProt and GO are provided. Pathways can either be browsed or queried by free text search. The user can search for gene names, chemical compounds or whole pathways. A tutorial on how to browse pathways in KEGG and an overview of the multiple representation formats is available (Aoki-Kinoshita & Minoru Kanehisa, 2007). Each pathway stored in KEGG can be downloaded in its own XML format named KGML, which is supported by VisANT, a software tool for pathway visualization (Hu et al, 2008b) and indirectly by Cytoscape using scripting plugins. In addition, metabolic pathways are available in BioPAX Level 1, which was especially designed for metabolic reactions, as well as in SBML. For converting KEGG metabolic pathways to SBML, a tool called KEGG2SBML (http://sbml.org/Software/KEGG2SBML) was developed. KEGG data can also be accessed using the KEGG API or KEGG FTP. Moreover, for making use of the KEGG resources, several applications exist. KegArray, for example, allows the analysis of microarray data in the context of KEGG pathways. WikiPathways A recently developed resource for pathway information that strongly differs from other pathway repositories is WikiPathways. WikiPathways is an open source project based, like Wikipedia, on the MediaWiki software (Pico et al, 2008). It serves as an open and collaborative platform for creation, edition and curation of biological pathways in different species. WikiPathways aims to achieve a public commitment to pathway storage and curation by keeping pathway creation and curation processes simple. Although the curation process of the previously described databases is subjected to experts, any user with an account on WikiPathways can create new pathways, and edit already existing ones. The pathway entities are linked to reference databases, based on the criteria provided by the editor. Hence, the identifiers depend on the chosen reference database and can therefore differ between pathways and even within a single pathway. Pathways in WikiPathways can be browsed by species and categories, for example, Metabolic Process. They can also be searched using gene, protein or pathway name or any free text query. In addition, pathways can be programmatically accessed through a web service (http://www.wikipathways.org/index.php/Help:WikiPathways_Webservice). For pathway data exchange, WikiPathways does not use standard formats like BioPAX or SBML, but offers a much simpler representation called GenMAPP Pathway Markup Language (GPML) that is compatible with visualization and analysis tools, such as Cytoscape, GenMAPP (Salomonis et al, 2007) and PathVisio (van Iersel et al, 2008). The use of GPML is in agreement with the community annotation nature of the project, as it offers a simple pathway representation and several functionalities for building network diagrams. However, inter-operability with other pathway databases is impeded, and substantial efforts towards combining WikiPathways with the other pathway repositories will be required. In this regard, some approaches with the objective of conversion between GPML and standard pathway exchange formats, such as SBML and BioPAX, are under development (Evelo, 2009). In addition, KEGG pathways in KGML format are also available in GPML format ready for download (http://www.pathvisio.org/Download#Step_3) or can be converted into GPML (http://www.bigcat.unimaas.nl/tracprojects/pathvisio/wiki/KeggConverter). The exponential growth of biological data poses a challenge to the high-quality annotation and curation of databases. In this scenario, the use of wikis for community curation of biological data have emerged in the past years with the goal of increasing quality of data annotation by combining knowledge from multiple experts (Giles, 2007; Waldrop, 2008; Hu et al, 2008a). However, their success will strongly depend on the commitment of the community and WikiPathways authors claim that the initiative represents an experiment, in which the ‘community curation’ approach is being tested (Pico et al, 2008). Thus, WikiPathways can be seen as a complementary and enhancing source of information for the major pathway databases, like Reactome or KEGG. In contrast to the aforementioned databases, the systems described below combine diverse pathway repositories, and can be seen as first attempts towards the integration of pathway information from various sources. Nature pathway interaction database PID contains data on cell signalling in humans (Schaefer et al, 2009). PID combines three different sources: the NCI-curated pathways that are obtained from peer reviewed literature, as well as pathways imported from Reactome and BioCarta. Similar to Reactome, PID structures pathways hierarchically into pathways and their sub-pathways that are called sub-networks in PID. The PID data model is based on molecular interactions in which input biomolecules are transformed into output biomolecules. Each process can be promoted or inhibited by regulators. Biomolecules are proteins, RNA, complexes or small molecules. DNA is not a part of the PID data model and only output RNA and regulator are represented in transcriptional processes. Each protein is cross-referenced to UniProt, RNA to Entrez Gene, small molecules to the Chemical Abstracts Service (CAS) registry number and complexes are annotated using GO terms. Different states of biomolecules, such as ‘active/inactive’ or ‘phosphorylated’ are part of the annotations of the biomolecule. Cellular location

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

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.

<ns3:p>Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.</ns3:p>

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd ( http://gpcrmd.org/ ), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations. GPCRmd is a community-driven online platform to visualize, analyze and share G-protein-coupled receptor (GPCR) molecular dynamics data. It currently contains simulation data representing 100% of GPCR classes, 71% of receptor subtypes and 80% of GPCR families.

Membrane levels of docosahexaenoic acid (DHA), an essential omega-3 polyunsaturated fatty acid (ω-3 PUFA), are decreased in common neuropsychiatric disorders. DHA modulates key cell membrane properties like fluidity, thereby affecting the behaviour of transmembrane proteins like G protein-coupled receptors (GPCRs). These receptors, which have special relevance for major neuropsychiatric disorders have recently been shown to form dimers or higher order oligomers, and evidence suggests that DHA levels affect GPCR function by modulating oligomerisation. In this study, we assessed the effect of membrane DHA content on the formation of a class of protein complexes with particular relevance for brain disease: adenosine A2A and dopamine D2 receptor oligomers. Using extensive multiscale computer modelling, we find a marked propensity of DHA for interaction with both A2A and D2 receptors, which leads to an increased rate of receptor oligomerisation. Bioluminescence resonance energy transfer (BRET) experiments performed on living cells suggest that this DHA effect on the oligomerisation of A2A and D2 receptors is purely kinetic. This work reveals for the first time that membrane ω-3 PUFAs play a key role in GPCR oligomerisation kinetics, which may have important implications for neuropsychiatric conditions like schizophrenia or Parkinson's disease.

PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities.The PysGeNET platform is freely available at http://www.psygenet.org/. The PsyGeNET database is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/).lfurlong@imim.esSupplementary data are available at Bioinformatics online.

Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English.The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression.This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out.In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001).Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

Importance Out-of-hospital cardiac arrest (OHCA) health care provision may be a good indicator of the recovery of the health care system involved in OHCA care following the COVID-19 pandemic. There is a lack of data regarding outcomes capable of verifying this recovery. Objective To determine whether return to spontaneous circulation, overall survival, and survival with good neurological outcome increased in patients with OHCA since the COVID-19 pandemic was brought under control in 2022 compared with prepandemic and pandemic levels. Design, Setting, and Participants This observational cohort study was conducted to examine health care response and survival with good neurological outcome at hospital discharge in patients treated following OHCA. A 3-month period, including the first wave of the pandemic (February 1 to April 30, 2020), was compared with 2 periods before (April 1, 2017, to March 31, 2018) and after (January 1 to December 31, 2022) the pandemic. Data analysis was performed in July 2023. Emergency medical services (EMS) serving a population of more than 28 million inhabitants across 10 Spanish regions participated. Patients with OHCA were included if participating EMS initiated resuscitation or continued resuscitation initiated by a first responder. Exposure The pandemic was considered to be under control following the official declaration that infection with SARS-CoV-2 was to be considered another acute respiratory infection. Main Outcome and Measures The main outcomes were return of spontaneous circulation, overall survival, and survival at hospital discharge with good neurological outcome, expressed as unimpaired or minimally impaired cerebral performance. Results A total of 14 732 patients (mean [SD] age, 64.2 [17.2] years; 10 451 [71.2%] male) were included, with 6372 OHCAs occurring during the prepandemic period, 1409 OHCAs during the pandemic period, and 6951 OHCAs during the postpandemic period. There was a higher incidence of OHCAs with a resuscitation attempt in the postpandemic period compared with the pandemic period (rate ratio, 4.93; 95% CI, 4.66-5.22; P &amp;amp;lt; .001), with lower incidence of futile resuscitation for OHCAs (2.1 per 100 000 person-years vs 1.3 per 100 000 person-years; rate ratio, 0.81; 95% CI, 0.71-0.92; P &amp;amp;lt; .001). Recovery of spontaneous circulation at hospital admission increased from 20.5% in the pandemic period to 30.5% in the postpandemic period (relative risk [RR], 1.08; 95% CI, 1.06-1.10; P &amp;amp;lt; .001). In the same way, overall survival at discharge increased from 7.6% to 11.2% (RR, 1.45; 95% CI, 1.21-1.75; P &amp;amp;lt; .001), with 6.6% of patients being discharged with good neurological status (Cerebral Performance Category Scale categories 1-2) in the pandemic period compared with 9.6% of patients in the postpandemic period (RR, 1.07; 95% CI, 1.04-1.10; P &amp;amp;lt; .001). Conclusions and Relevance In this cohort study, survival with good neurological outcome at hospital discharge following OHCA increased significantly after the COVID-19 pandemic.

By means of differential RNA display, we have isolated a cDNA corresponding to transcripts that are down-regulated upon differentiation of the goblet cell-like HT-29-M6 human colon carcinoma cell line. These transcripts encode proteins originally identified as CROC-1 on the basis of their capacity to activate transcription of c-fos. We show that these proteins are similar in sequence, and in predicted secondary and tertiary structure, to the ubiquitin-conjugating enzymes, also known as E2. Despite the similarities, these proteins lack a critical cysteine residue essential for the catalytic activity of E2 enzymes and, in vitro, they do not conjugate or transfer ubiquitin to protein substrates. These proteins constitute a distinct subfamily within the E2 protein family and are highly conserved in phylogeny from yeasts to mammals. Therefore, we have designated them UEV (ubiquitin-conjugating E2 enzyme variant) proteins, defined as proteins similar in sequence and structure to the E2 ubiquitin-conjugating enzymes but lacking their enzymatic activity (HW/GDB-approved gene symbol, UBE2V). At least two human genes code for UEV proteins, and one of them, located on chromosome 20q13.2, is expressed as at least four isoforms, generated by alternative splicing. All human cell types analyzed expressed at least one of these isoforms. Constitutive expression of exogenous human UEV in HT-29-M6 cells inhibited their capacity to differentiate upon confluence and caused both the entry of a larger proportion of cells in the division cycle and an accumulation in G2-M. This was accompanied with a profound inhibition of the mitotic kinase, cdk1. These results suggest that UEV proteins are involved in the control of differentiation and could exert their effects by altering cell cycle distribution.

G-protein coupled receptors, the largest family of proteins in the human genome, are involved in many complex signal transduction pathways, typically activated by orthosteric ligand binding and subject to allosteric modulation. Dopaminergic receptors, belonging to the class A family of G-protein coupled receptors, are known to be modulated by sodium ions from an allosteric binding site, although the details of sodium effects on the receptor have not yet been described. In an effort to understand these effects, we performed microsecond scale all-atom molecular dynamics simulations on the dopaminergic D(2) receptor, finding that sodium ions enter the receptor from the extracellular side and bind at a deep allosteric site (Asp2.50). Remarkably, the presence of a sodium ion at this allosteric site induces a conformational change of the rotamer toggle switch Trp6.48 which locks in a conformation identical to the one found in the partially inactive state of the crystallized human beta(2) adrenergic receptor. This study provides detailed quantitative information about binding of sodium ions in the D(2) receptor and reports a possibly important sodium-induced conformational change for modulation of D(2) receptor function.

The preclinical assessment of drug-induced ventricular arrhythmia, a major concern for regulators, is typically based on experimental or computational models focused on the potassium channel hERG (human ether-a-go-go-related gene, Kv11.1). Even if the role of this ion channel in the ventricular repolarization is of critical importance, the complexity of the events involved make the cardiac safety assessment based only on hERG has a high risk of producing either false positive or negative results. We introduce a multiscale simulation system aiming to produce a better cardiotoxicity assessment. At the molecular scale, the proposed system uses a combination of docking simulations on two potassium channels, hERG and KCNQ1, plus three-dimensional quantitative structure−activity relationship modeling for predicting how the tested compound will block the potassium currents IKr and IKs. The obtained results have been introduced in electrophysiological models of the cardiomyocytes and the ventricular tissue, allowing the direct prediction of the drug effects on electrocardiogram simulations. The usefulness of the whole method is illustrated by predicting the cardiotoxic effect of several compounds, including some examples in which classic hERG-based models produce false positive or negative results, yielding correct predictions for all of them. These results can be considered a proof of concept, suggesting that multiscale prediction systems can be suitable for being used for preliminary screening in lead discovery, before the compound is physically available, or in early preclinical development when they can be fed with experimentally obtained data.

Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.

Section:ChooseTop of pageAbstract <<Definition and Historical...A Perspective of PM from ...Bottlenecks and Opportuni...Implications for the Indi...Summary and Proposals for...ReferencesCITING ARTICLES

The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility. The sharing of legacy preclinical safety data among pharmaceutical companies and its integration with other information sources offers unprecedented opportunities to improve the early assessment of drug safety. Here, we discuss the experience of the eTOX project, which was established through the Innovative Medicines Initiative to explore this possibility.

Abstract The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

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.

Abstract A series is presented of 83 patients surgically explored for massive bowel infarction. Old men with previous heart disease and symptoms of peripheral atherosclerosis were primarily affected. Clinical presenting features were abdominal pain (100 per cent), peritonitis (57 per cent), shock (34 per cent) and hypothermia (26 per cent). A third-space syndrome with metabolic acidosis and uraemia was the most common physiological derangement. Age was the only factor that appeared to have influenced the surgeon's decision to perform massive bowel resection (71 years in non-resected versus 64 years in resected patients, P &amp;lt; 0.006). The overall mortality rate was 71 per cent. Forty-four patients underwent massive bowel resection (mean length of remaining small bowel 60 ± 40 cm) and twenty-four (54 per cent) survived the procedure. Axillary temperature was higher in survivors (36.7°C versus 36.1°C, P &amp;lt; 0.03). Early postoperative total plasma protein and albumin concentrations were also higher in survivors (57 versus 46 g/l, P &amp;lt; 0.005; 27 versus 22 g/l, P &amp;lt; 0.02). Patients with previous symptoms of atherosclerotic disease and high pre-operative blood urea levels also had a bad prognosis. Survivors had a mean hospital stay of 57 days and parenteral nutrition had to be maintained for a mean of 34 days. The survival rate achieved with massive resection justifies this surgical approach in selected patients with massive bowel infarction.

Abstract A prospective four-year study on the infection rate of clean operative wounds is presented. From January 1982 to June 1985, a nurse epidemiologist and a medical team assessed 4,468 operative procedures, from the day of surgery to the patients' discharge from the hospital. The infection rate was 3.2%. A higher incidence of wound infection was detected in patients requiring emergency operations (5.1%), in drained wounds (5.4%), and in patients with conditions thought to predispose to infection, such as advanced cancer, hepatic cirrhosis, diabetes, nephrotic syndrome, previous splenectomy, and treatment with immunosuppressive drugs (7.8%). Age over 65 did not influence infection rates. There were up to tenfold differences in infection indices between surgeons performing the same clean procedures. The continued monitoring of clean wound infection rates allowed the early detection and control of infection outbreaks. Providing periodic information on infection rates to the different surgical services was associated with decreasing infection rates over time.

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.

Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest. We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts. Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities. The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.

We report a novel method called ADAN (Applicability Domain ANalysis) for assessing the reliability of drug property predictions obtained by in silico methods. The assessment provided by ADAN is based on the comparison of the query compound with the training set, using six diverse similarity criteria. For every criterion, the query compound is considered out of range when the similarity value obtained is larger than the 95th percentile of the values obtained for the training set. The final outcome is a number in the range of 0-6 that expresses the number of unmet similarity criteria and allows classifying the query compound within seven reliability categories. Such categories can be further exploited to assign simpler reliability classes using a traffic light schema, to assign approximate confidence intervals or to mark the predictions as unreliable. The entire methodology has been validated simulating realistic conditions, where query compounds are structurally diverse from those in the training set. The validation exercise involved the construction of more than 1000 models. These models were built using a combination of training set, molecular descriptors, and modeling methods representative of the real predictive tasks performed in the eTOX project (a project whose objective is to predict in vivo toxicological end points in drug development). Validation results confirm the robustness of the proposed assessment methodology, which compares favorably with other classical methods based solely on the structural similarity of the compounds. ADAN characteristics make the method well-suited for estimate the quality of drug predictions obtained in extremely unfavorable conditions, like the prediction of drug toxicity end points.

Abstract Motivation: DisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web. Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected linked open data. Availability and implementation: http://rdf.disgenet.org/ Contact: support@disgenet.org

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

Longitudinal data on the mental health impact of the coronavirus disease 2019 (Covid-19) pandemic in healthcare workers is limited. We estimated prevalence, incidence and persistence of probable mental disorders in a cohort of Spanish healthcare workers (Covid-19 waves 1 and 2) -and identified associated risk factors.8996 healthcare workers evaluated on 5 May-7 September 2020 (baseline) were invited to a second web-based survey (October-December 2020). Major depressive disorder (PHQ-8 ≥ 10), generalised anxiety disorder (GAD-7 ≥ 10), panic attacks, post-traumatic stress disorder (PCL-5 ≥ 7), and alcohol use disorder (CAGE-AID ≥ 2) were assessed. Distal (pre-pandemic) and proximal (pandemic) risk factors were included. We estimated the incidence of probable mental disorders (among those without disorders at baseline) and persistence (among those with disorders at baseline). Logistic regression of individual-level [odds ratios (OR)] and population-level (population attributable risk proportions) associations were estimated, adjusting by all distal risk factors, health care centre and time of baseline interview.4809 healthcare workers participated at four months follow-up (cooperation rate = 65.7%; mean = 120 days s.d. = 22 days from baseline assessment). Follow-up prevalence of any disorder was 41.5%, (v. 45.4% at baseline, p < 0.001); incidence, 19.7% (s.e. = 1.6) and persistence, 67.7% (s.e. = 2.3). Proximal factors showing significant bivariate-adjusted associations with incidence included: work-related factors [prioritising Covid-19 patients (OR = 1.62)], stress factors [personal health-related stress (OR = 1.61)], interpersonal stress (OR = 1.53) and financial factors [significant income loss (OR = 1.37)]. Risk factors associated with persistence were largely similar.Our study indicates that the prevalence of probable mental disorders among Spanish healthcare workers during the second wave of the Covid-19 pandemic was similarly high to that after the first wave. This was in good part due to the persistence of mental disorders detected at the baseline, but with a relevant incidence of about 1 in 5 of HCWs without mental disorders during the first wave of the Covid-19 pandemic. Health-related factors, work-related factors and interpersonal stress are important risks of persistence of mental disorders and of incidence of mental disorders. Adequately addressing these factors might have prevented a considerable amount of mental health impact of the pandemic among this vulnerable population. Addressing health-related stress, work-related factors and interpersonal stress might reduce the prevalence of these disorders substantially. Study registration number: NCT04556565.

Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary.A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison.The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions.The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories.The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans.

The recently introduced GRid-INdependent Descriptors (GRIND) were designed to provide a suitable description of a series of ligands for 3D-QSAR studies not requiring the spatial superimposition of their structures. Despite the proven usefulness of the method, it was recognized that the original GRIND failed to describe appropriately the shape of the ligand molecules, which in some cases plays a major role in ligand-receptor binding. For this reason, the original descriptors have been enhanced with the addition of a molecular shape description based on the local curvature of the molecular surface. The integration of this description into the GRIND allows the generation of 3D-QSAR models able to identify both favorable and unfavorable shape complementarity in a simple and alignment-independent way. The usefulness of the new GRIND-shape description in 3D-QSAR is illustrated using two structure-activity studies: one performed on a set of xanthine-like antagonists of the A(1) adenosine receptor; another performed on a series of Plasmodium falciparum plasmepsin II inhibitors.

The aim of this work is to present the anchor-GRIND methodology. Anchor-GRIND efficiently combines a priori chemical and biological knowledge about the studied compounds with alignment-independent molecular descriptors derived from molecular interaction fields. Such descriptors are particularly useful for series of ligands sharing a common scaffold but with very diverse substituents. The method uses a specific position of the molecular structure (the "anchor point") to compare the spatial distribution of the molecular interaction fields of the substituents. The descriptors produced are more detailed and specific than the original GRIND while still avoiding the bias introduced by the alignment. Three data sets have been studied to demonstrate the usefulness of the anchor-GRIND methodology for 3D-QSAR modeling. The two first data sets respectively include congeneric series of the hepatitis C virus NS3 protease and of the acetylcholinesterase inhibitors. The third data set discriminates between factor Xa inhibitors of high and low affinity. In all the series presented, the models obtained with the anchor-GRIND are statistically sound and easy to interpret.

Once upon a time, several engineers, biologists and clinicians realized that a lot of information in biomedicine was partitioned into ‘silos’ that do not intercommunicate. These silos were a side effect of the existence of different disciplines required to, for example, develop new drugs. The engineers decided to dispose of the silos, and to put the information in axiomatic form to facilitate automatic reasoning over multiple data sources. They also decided to do this in a very open way so that effort was not duplicated. This seemed to be a very reasonable step and was welcomed by all. After much axiomatization, the engineers found that there were still issues. There was a lack of agreement on many seemingly uncomplicated ‘facts’. They had to employ curators to resolve the issues, and then it was said that the curators were ‘losing the plot’. They also found that there were not only ‘discipline silos’, but also ‘intra-discipline silos’. The ‘intra-discipline silos’ were the partitions between the different evidences and between the assertions developed from the evidences and from earlier assertions, which were based on even earlier assertions, and so on. There were not only webs of disagreement, but also chains of error. And they found that connecting facts from various silos was not so uncomplicated after all, even after axiomatization. Why was this? Because the results of scientific experiments are not axioms, even if they may be treated in this way to perform isolated “bits of tasks” (T. W. Clark) (Fig. 1). This illustrates the challenge that scientists and clinical practitioners face: the world contains a vast array of complex and diverse data, but locating and connecting the information are difficult 1-3, and deriving definitive knowledge from the data to guide research and/or for clinical practice is even harder. Many barriers that make it difficult to progress in this field were recently discussed at a scientific meeting held in Barcelona from 3 July 2012 to 4 July 2012 4 under the general title ‘Beyond Omics Revolutions: Integrative Knowledge Management for Empowered Healthcare and Research’. The meeting focused on six topics: ‘dealing with biomedical knowledge explosion for better healthcare: identifying actionable knowledge items at the point of care’; ‘exploiting patient information to enrich basic biomedical research’; ‘standards for clinical-omics integration: the semantic challenge’; ‘new information technology (IT) is supporting massive biomedical data management’; ‘systems medicine: making systems biology translational’; and ‘integrative knowledge management for improving drug R&D’. The main ideas and conclusions arising from this event are presented below. New biomedical discoveries emerge at an ever-increasing rate, but their translation into health care typically occurs slowly or not at all. There is a lack of sufficient systems that can astutely identify, clarify and hand on these advances to the relevant practitioners, in usable formats. For example, thousands of biomarkers exist, comprising a few truly useful ones amongst many others that are less useful or nonactionable. Valuable new biomarkers (diagnostic, prognostic or therapeutic) are therefore not being advanced effectively into health care. The decision of which ones to progress with is simply too onerous, given the cost of modern clinical trials and a deficiency of incentives and expertise amongst researchers who would be best placed to advance markers into development. Hence, when this translation does occur, it is usually because of a major ‘pull’ from the clinical world, rather than a ‘push’ from researchers. Clearly then, there is a need for methods and systems that can reliably and routinely identify and connect the most informative, reliable and useful information (not least biomarkers) generated by the research community. Efforts to better structure scientific knowledge, for instance, by means of nanopublications 5 or the Investigation Study Assay (ISA) commons 6, could provide key components of this solution. But the challenge is magnified by the fact that the relevant information is spread not only across research resources (e.g. literature, patents, laboratory reports, market data, medical reports and biobanks), but also in realms with less professional rigour such as social networks and patient communities (e.g. wikis, blogs and other social media platforms). Progress will therefore necessitate addressing cross-language and cross-jargon barriers, as well as all the traditional targets of interoperability such as standards for data syntax and semantics. Beyond connecting and integrating research findings, there lies the challenge of understanding this information. Education is important here, and indeed, it has been proposed that a lack of appropriate training explains the slow uptake of companion diagnostics into clinical practice 7. Tackling this will require robust guidelines on how to use pharmacogenomic information and also the provision accompanying pharmacokinetic, metabolic and drug interaction knowledge derived from the latest biomedical research. Thus, it is arguable that researchers have a responsibility to make their clinically relevant findings more understandable to the healthcare sector, perhaps in the form of user-friendly web portals or other software 8, 9. Electronic health record (EHR) developers, computerized physician order entry designers and clinical decision support system (CDSS) creators and vendors likewise need to be involved in delivering additional content for such portals and in connecting such platforms to the intended end users. Considering all the above issues, as well as the key challenges of data interpretation, some experts concluded that the overall challenge is one of ‘knowledge engineering’ (KE), rather than simply a need for better informatics, research or medical practice. Hence, it may be difficult to make real progress with biomedical researchers and clinical practitioners alone; there is a need for a new group of multidisciplinary engineers 10. This goes back to the tradition of KE for health, a field that stemmed from artificial intelligence research in the 1990s 11. However, in contrast to previous KE approaches that aimed to organize all the data to reveal absolute knowledge (which is a flawed concept, as illustrated above), there is a need for a far more pragmatic approach – ‘KE 2.0’ – to identify and make directly useful the very limited set of data and knowledge items that are both reliably proven and clinically actionable. The aim would be to explicitly address the two core information problems faced by clinicians: (i) having too much existing and new data and (ii) not having time or resources to discern reliably from uncertain and erroneous information. As shown in Supplementary Table S1, there are now many international projects that aim to integrate various types of data related to specific diseases or their pharmacological treatments. In general, however, these are not using the KE 2.0 approach, but developing new methodologies and tools for data integration and exploitation or novel strategies for massive data storage and handling. But as these types of projects make progress in consolidating and unifying the relevant data, KE 2.0 approaches can begin to be explored. However, for this to succeed, the data must be of suitable quality and breadth. Petabytes of potentially useful biomedical data are not captured in a structured format and/or made available for use by others in many situations. These include molecular ‘omics’ profiles (genomes, transcriptomes, proteomes, epigenomes, etc.), exposure to environmental chemicals exposomes, phenotype data (e.g. as recorded in clinical settings) and dynamic data (e.g. measurements at different points in time or space), all of which could contribute to improved research and health care. For instance, in the research world, primary data from high-throughput studies on a large number of subjects (e.g. genome-wide association studies) 12 typically never leave the laboratory in which they were generated; in the healthcare world, molecular profiles of individual patient, sometimes recorded per time period, are starting to be recorded but are then poorly exploited 13. It is clear that simply handling this diversity and scale of data is a challenge in itself, but that should motivate focusing more effort on the problem, rather than providing a reason for allowing the data to be lost. Many considerations relate to the quality, completeness, reliability and reproducibility of primary data and the knowledge derived from them. Relevant judgements may well be context dependent, for example, whether a biopsy from a heterogeneous tumour might be considered usefully representative of the whole tumour. Contextual metadata (data about the data) are therefore important, but such information is often not properly collected or recorded. This is directly related to current discussions about the reproducibility of research findings and the comparability of different analytical procedures. Approaches that allow consistent and repeated analysis of data sets are becoming necessary (e.g. Galaxy and GenePatterns). Questions about reproducibility concern both the data (how they were produced) and the knowledge gleaned from the data (how it was derived). Important studies of statistical and experimental design problems in contemporary scientific publications were recently reported 14, 15. One notable problem in applying KE to biomedical research data is the nature of the knowledge being engineered. Specifically, active as opposed to consolidated scientific knowledge consists of assertions supported by evidence. What we consider knowledge is a snapshot of the consensus of the scientific community on a particular subject at a given time, but this active knowledge is subjected to continuous re-evaluation where new findings change our perspective, and ‘facts’ may be refuted after some years. Essentially, no knowledge is truly absolute. A particular complication here is that of human bias or error underlying citation distortion, not least in review articles. An example is provided by a recent review in which a role for inclusion body myositis in the aetiopathology of Alzheimer's disease was suggested. Following the chain of assertions to the underlying evidence, it was found that in some cases, there was no such grounding evidence, and in other cases, its meaning had been distorted or the results misapplied or misconstrued 16. These issues contribute to the existence of intradiscipline ‘silos’, which disconnect facts and assertions from the underlying evidence. In other cases, there are discrepancies between data collected from different sources. This clearly argues the need for more information accessibility and structure and less reliance on subjective human opinion. But this itself must be balanced against the risk of proposing too many hypotheses from extensive and high-throughput data, which could easily lead to spurious associations. In this context, ongoing multiparty curation efforts from different initiatives are useful as a way to identify and organize relevant information, but they represent very costly and time-consuming tasks. Efforts on harmonization and standardization, as well as the development of software for supporting curation tasks, are therefore needed to improve and assist curators in their work. An important point to emphasize is that very different levels of evidence are needed for CDSSs compared with what is required for research grade knowledge discovery. Medical reasoning may be represented by epistemological models, which are amenable to partial automation 17, 18, and in all cases, the data should be generated or chosen to fit a purpose. Researchers, for example, must design their experiments and simulations to record as much detailed information as possible to facilitate a comprehensive exploration of the biomedical question. By contrast, clinicians must carefully define healthcare questionnaires and register only the salient medical variables pertaining to their patients to aid in clinical decision-making. Ideally, however, to avoid silos of data, both groups should always also consider the possible or likely reuse of their data. As part of this, data provenance should be carefully recorded to make possible the retrieval of the original sources and to ensure its reliability and reproducibility, which will undoubtedly have an effect on the generation of useful predictions 19. Time constraints at the Barcelona meeting precluded extending this discussion into areas of ethical and legal frameworks, but further information can be found elsewhere 20, 21. It should also be noted that the European Parliament is currently discussing a data protection directive that will underpin a new legal framework 22. Increasingly, genomic information is likely to be relevant to health care, and as such, it should ideally be stored within medical records. An example of current use would be that of personalized drug dosing. Some pharmacogenomic tests are now being used in routine clinical practice; however, they are vastly underused. Key biological data on individuals should be encapsulated in its native format in clinical data structures, with ‘bubbled-up’ items being associated with phenotypic data using clinical data standards. This then generates the question as to what standards are required to allow the efficient translation of key research findings into clinical practice and what IT paradigms will be needed to support biomedical data management. Controlled vocabularies and ontologies for the integration of diverse and heterogeneous biomedical information can provide part of the answer. Fortunately, several current initiatives support the development of ontologies to describe different aspects of biology and biomedicine (e.g. the National Center for Biomedical Ontology 23, the Open Biological and Biomedical Ontologies 24 and the Ricordo project 25). But yet more needs to be done. For instance, it is difficult to reconcile medical records with disease descriptions associated with public molecular data. This is due to the inherent complexity of diseases and the way they have been traditionally classified and described. Also, disease descriptions are heterogeneous and often dynamic, as in the case of mental illness 26. Beyond ‘standards’ perhaps, there is actually an equal need for ‘understandards’. In other words, efforts that aim to deliver the standardization capabilities required for KE 2.0, not just standards for semantic integration irrespective of common understanding. We need to make sure that in the next generation of in cerebro and in silico reasoning strategies, it is understood what is ‘meant’ by any node and edge in a network of associations. To resolve the issue that the more expressive a standard is the less interoperable it is, constraining the standards is crucial, and also enables capturing similarities whilst preserving disparities. More specifically, health data semantics and context cannot be faithfully represented using flat structures (e.g. a list of entries), rather a compositional language is required that meaningfully connects various data entries. Furthermore, health data standards need to accommodate unstructured data and text (e.g. clinicians' narrative), whilst having links to structured data entries. A lifetime comprehensive recording of personal health information including omics data is certainly desirable. This arguably calls for a new model of data stewardship: the Independent Health Record Banks (IHRB) vision 27, which would support the implementation of lifelong, cross-institutional and interoperable EHRs. This would constitute an escape from fixation with ‘legacy systems’. As long as healthcare providers are also record keepers, we will continue to have poor archives, proprietary based and isolated in silos, with most of the data semantics not represented explicitly, making it hard or impossible for CDSSs to be really effective. Instead, it is proposed that there should be a limited number of independent and regulated third parties specialized in sustaining the individual lifetime EHR, continuously curating the record and running various analyses to prepare the right infostructure for CDSSs. These tasks require unique specialization and a new kind of archive, which should provide the most complete and coherent information framework to support the health of the individual. The challenge of improving biomedical knowledge management goes hand in hand with the need for suitable education and training for all the relevant stakeholders: patients, clinicians, researchers, regulators and policymakers. In particular, clinicians need more support to improve their ability to interpret and use research findings, and researchers must learn how to take actionable findings closer to clinicians. Concomitantly, researchers need to better comprehend the problems raised in clinical practice that can be solved in the laboratory or by intensive use of IT. This reinforces the need for forums of interaction with the active participation of biomedical researchers, bioinformaticians and physicians with experience in clinical research. Hence, we should move from a one-size-fits-all education to that of stratified medicine and from this towards a truly individualized clinical exercise, following the paradigm shift towards the concept of predictive, preventive, personalized and participatory medicine (P4) 28. Finally, the active participation of citizens, via blogs and other social networks, provides a way to improve the general level of health literacy and thereby to empower all individuals regarding their role in the healthcare system. The experts who took part in the aforementioned debates in Barcelona also offer the following consensus statements: In summary, medicine is an increasingly data-intensive discipline, with a growing need to link individual patient health records to rapidly changing research knowledge for better differential diagnosis, prognosis and prediction of treatment response. Equally, biomedical research will gain enormously from the integrative analysis of clinical and multi-omics information (Fig. 2). Capitalization on these opportunities must be guided by a precise understanding of the many complex issues related to the integration of large amounts of diverse information. Partly this involves overcoming barriers between different disciplines, such as biology, medicine and computer sciences, implying a key role for ‘knowledge engineers’. This review is based on the debates held in Barcelona from 3 July 2012 to 4 July 2012 with the active participation of all authors. The debates were organized by B-Debate (an initiative of Biocat and Obra Social ‘La Caixa’) and Universitat Pompeu Fabra (Barcelona). The event was held within the framework of the European INBIOMEDvision project (funded by the EU Seventh Framework Programme for Research and Technological Development (FP7) under grant agreement no. 270107). In addition, we received support from EU FP7 project no. 200754 (GEN2PHEN) and the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115002 (eTOX) and no. 115191 (Open PHACTS), resources of which are composed of financial contribution from the EU FP7 and in kind contributions from companies of the European Federation of Pharmaceutical Industries and Associations. L.I.F received support from Instituto de Salud Carlos III Fondo Europeo de Desarollo Regional (CP10/00524). Anthony Rowe works for Janssen Research and Development, who originally developed the TranSMART platform prior to making it open source. Russ B. Altman is the founder of Personalis.com. No other conflict of interest was declared. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings.To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network.Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible.Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate.Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out.A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation.

According to current diagnostic criteria, mantle cell lymphoma (MCL) encompasses the usual, aggressive variants and rare, nonnodal cases with monoclonal asymptomatic lymphocytosis, cyclin D1-positive (MALD1). We aimed to understand the biology behind this clinical heterogeneity and to identify markers for adequate identification of MALD1 cases.We compared 17 typical MCL cases with a homogeneous group of 13 untreated MALD1 cases (median follow-up, 71 months). We conducted gene expression profiling with functional analysis in five MCL and five MALD1. Results were validated in 12 MCL and 8 MALD1 additional cases by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and in 24 MCL and 13 MALD1 cases by flow cytometry. Classification and regression trees strategy was used to generate an algorithm based on CD38 and CD200 expression by flow cytometry.We found 171 differentially expressed genes with enrichment of neoplastic behavior and cell proliferation signatures in MCL. Conversely, MALD1 was enriched in gene sets related to immune activation and inflammatory responses. CD38 and CD200 were differentially expressed between MCL and MALD1 and confirmed by flow cytometry (median CD38, 89% vs. 14%; median CD200, 0% vs. 24%, respectively). Assessment of both proteins allowed classifying 85% (11 of 13) of MALD1 cases whereas 15% remained unclassified. SOX11 expression by qRT-PCR was significantly different between MCL and MALD1 groups but did not improve the classification.We show for the first time that MALD1, in contrast to MCL, is characterized by immune activation and driven by inflammatory cues. Assessment of CD38/CD200 by flow cytometry is useful to distinguish most cases of MALD1 from MCL in the clinical setting. MALD1 should be identified and segregated from the current MCL category to avoid overdiagnosis and unnecessary treatment.

ABSTRACT Purpose Pharmacovigilance methods have advanced greatly during the last decades, making post‐market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real‐world reports. The EU‐ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in‐depth analysis of adverse drug reactions risks. Methods The EU‐ADR Web Platform exploits the wealth of data collected within a large‐scale European initiative, the EU‐ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug–event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data‐mining and statistical analysis tasks. This permits obtaining a ranked drug–event list, removing spurious entries and highlighting relationships with high risk potential. Results The EU‐ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug–event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug–event pairs can be substantiated and statistically analysed within the platform's innovative working environment. Conclusions A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU‐ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/ . Copyright © 2012 John Wiley &amp; Sons, Ltd.

The frequent occurrence of comorbidities in patients with chronic obstructive pulmonary disease (COPD) suggests that they may share pathobiological processes and/or risk factors.To explore these possibilities we compared the clinical diseasome and the molecular diseasome of 5447 COPD patients hospitalised because of an exacerbation of the disease. The clinical diseasome is a network representation of the relationships between diseases, in which diseases are connected if they co-occur more than expected at random; in the molecular diseasome, diseases are linked if they share associated genes or interaction between proteins.The results showed that about half of the disease pairs identified in the clinical diseasome had a biological counterpart in the molecular diseasome, particularly those related to inflammation and vascular tone regulation. Interestingly, the clinical diseasome of these patients appears independent of age, cumulative smoking exposure or severity of airflow limitation.These results support the existence of shared molecular mechanisms among comorbidities in COPD.

The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis.We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions.https://bitbucket.org/ibi_group/comorbidity.Supplementary data are available at Bioinformatics online.

The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of &ldquo;one target, one disease&rdquo; to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer&rsquo;s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

The genetic basis of complex diseases involves alterations on multiple genes. Unraveling the interplay between these genetic factors is key to the discovery of new biomarkers and treatments. In 2014, we introduced GUILDify, a web server that searches for genes associated to diseases, finds novel disease genes applying various network-based prioritization algorithms and proposes candidate drugs. Here, we present GUILDify v2.0, a major update and improvement of the original method, where we have included protein interaction data for seven species and 22 human tissues and incorporated the disease–gene associations from DisGeNET. To infer potential disease relationships associated with multi-morbidities, we introduced a novel feature for estimating the genetic and functional overlap of two diseases using the top-ranking genes and the associated enrichment of biological functions and pathways (as defined by GO and Reactome). The analysis of this overlap helps to identify the mechanistic role of genes and protein–protein interactions in comorbidities. Finally, we provided an R package, guildifyR, to facilitate programmatic access to GUILDify v2.0 (http://sbi.upf.edu/guildify2)

Healthcare workers are vulnerable to adverse mental health impacts of the COVID-19 pandemic. We assessed prevalence of mental disorders and associated factors during the first wave of the pandemic among healthcare professionals in Spain. All workers in 18 healthcare institutions (6 AACC) in Spain were invited to web-based surveys assessing individual characteristics, COVID-19 infection status and exposure, and mental health status (May 5 – September 7, 2020). We report: probable 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 probable “disabling” current mental disorders. 9,138 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. Workers with pre-pandemic lifetime mental disorders had almost twice the prevalence 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). One in seven Spanish healthcare workers screened positive for a disabling mental disorder during the first wave of the COVID-19 pandemic. Workers reporting pre-pandemic lifetime mental disorders, those frequently exposed to COVID-19 patients, infected or quarantined/isolated, female workers, and auxiliary nurses should be considered groups in need of mental health monitoring and support. Los profesionales sanitarios son vulnerables al impacto negativo en salud mental de la pandemia COVID-19. Evaluamos la prevalencia de trastornos mentales y factores asociados durante la primera oleada de la pandemia en sanitarios españoles. Se invitó a todos los trabajadores de 18 instituciones sanitarias españolas (6 CCAA) a encuestas en línea evaluando características individuales, estado de infección y exposición a COVID-19 y salud mental (5 Mayo – 7 Septiembre, 2020). Reportamos: probables trastornos mentales actuales (Trastorno depresivo mayor TDD [PHQ-8≥10], Trastorno de ansiedad generalizada TAG [GAD-7≥10], Ataques de pánico, Trastorno de estrés postraumático TEP [PCL-5≥7]; y Trastorno por uso de sustancias TUS [CAGE-AID≥2]. La interferencia funcional grave (Escala de Discapacidad de Sheehan) identificó los probables trastornos “discapacitantes”. Participaron 9.138 sanitarios. Prevalencia de cribado positivo: 28,1% TDD; 22,5% TAG, 24,0% Pánico; 22,2% PTE; y 6,2% TUS. En general, el 45,7% presentó algún trastorno mental actual y el 14,5% algún trastorno discapacitante. Los sanitarios con trastornos mentales previos tuvieron el doble de prevalencia que aquellos sin patología mental previa. Ajustando por todas las variables, el trastorno mental incapacitante se asoció positivamente con: trastornos previos (TUS: OR=5.74; 95%CI 2.53-13.03; Ánimo: OR=3.23; 95%CI:2.27-4.60; Ansiedad: OR=3,03; IC 95%: 2,53-3,62); edad 18-29 años (OR=1,36; IC 95%: 1,02-1,82); atender “siempre” a pacientes COVID-19 (OR=5,19; IC 95%: 3,61-7,46), género femenino (OR=1,58; IC 95%: 1,27-1,96) y haber estado en cuarentena o aislado (OR=1,60; IC 95%: 1,31-1,95). Uno de cada 7 sanitarios españoles presentaron un probable trastorno mental discapacitante durante la primera oleada de COVID-19. Aquéllos con trastornos mentales alguna vez antes de la pandemia, los que están expuestos con frecuencia a pacientes con COVID-19, los infectados o en cuarentena / aislados, las mujeres y las enfermeras auxiliares deben considerarse grupos que necesitan seguimiento y apoyo de su salud mental.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Abstract Background Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer’s disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. Methods We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before β-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis. Results We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart , Grin3b , Nr1d1 and Mc4r , and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD. Conclusions The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.

The relationships between the multi-receptor binding profile of clozapine and olanzapine and their therapeutic properties were analyzed by using novel complexes built from the recently solved β2 adrenergic receptor structure. While interactions with position 3.36 determine the binding profile of clozapine-like ligands, diversity in TM5 and TM6 is responsible for subtle differences between clozapine and olanzapine. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2452/2008/z800074_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Abstract Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn’s disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer’s disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

Detection of biased agonists for the serotonin 5-HT_2A receptor can guide the discovery of safer and more efficient antipsychotic drugs. However, the rational design of such drugs has been hampered by the difficulty detecting the impact of small structural changes on signaling bias. To overcome these difficulties, we characterized the dynamics of ligand-receptor interactions of known biased and balanced agonists using molecular dynamics simulations. Our analysis revealed that interactions with residues S5.46 and N6.55 discriminate compounds with different functional selectivity. Based on our computational predictions, we selected three derivatives of the natural balanced ligand serotonin and experimentally validated their ability to act as biased agonists. Remarkably, our approach yielded compounds promoting an unprecedented level of signaling bias at the 5-HT_2A receptor, which could help interrogate the importance of particular pathways in conditions like schizophrenia.

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies.For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval.However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs.The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk.The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue.Multiple channel-drug interactions and state-of-the-art human ventricular action potential models (O'Hara, T., et al.PLos Comput.Biol.2011, 7, e1002061) were used in our simulations.Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current (I Ks and I Kr , respectively) and the L-type calcium current (I CaL ) at different levels.The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties.On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC).Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds.Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC 50 based test.

A series of 52 conformationally constrained butyrophenones have been synthesized and pharmacologically tested as antagonists at 5-HT(2A), 5-HT(2B), and 5-HT(2C) serotonin receptors, useful for dissecting the role of each 5-HT(2) subtype in pathophysiology. These compounds were also a consistent set for the identification of structural features relevant to receptor recognition and subtype discrimination. Six compounds were found highly active (pK(i) > 8.76) and selective at the 5-HT(2A) receptor vs 5-HT(2B) and/or 5-HT(2C) receptors. Piperidine fragments confer high affinity at the 5-HT(2A) receptor subtype, with benzofuranone- and thiotetralonepiperidine as the most selective derivatives over 5-HT(2C) and 5-HT(2B) receptors, respectively; K(i) (2A/2C) and/or K(B) (2A/2B) ratios greater than 100 were obtained. Compounds showing a more pronounced selectivity at 5-HT(2A)/5-HT(2C) than at 5-HT(2A)/5-HT(2B) bear 6-fluorobenzisoxazolyl- and p-fluorobenzoylpiperidine moieties containing one methylene bridging the basic piperidine to the alkanone moiety. An ethylene bridge between the alkanone and the amino moieties led to ligands with higher affinities for the 5-HT(2B) receptor. Significant selectivity at the 5-HT(2B) receptor vs 5-HT(2C) was observed with 1-1[(1-oxo-1,2,3,4-tetrahydro-3-naphthyl)methyl]-4-[3-(p-fluorobenzoyl)propyl]piperazine (more than 100-fold higher). Although piperidine fragments also confer higher affinity at 5-HT(2C) receptors, only piperazine-containing ligands were selective over 5-HT(2A). Moderate selectivity was observed at 5-HT(2C) vs 5-HT(2B) (10-fold) with some compounds bearing a 4-[3-(6-fluorobenzisoxazolyl)]piperidine moiety in its structure. Molecular determinants for antagonists acting at 5-HT(2A) receptors were identified by 3D-QSAR (GRID-GOLPE) studies. Docking simulations at 5-HT(2A) and 5-HT(2C) receptors suggest a binding site for the studied type of antagonists (between transmembrane helices 2, 3, and 7) different to that of the natural agonist serotonin (between 3, 5, and 6).

Single Nucleotide Polymorphisms, among other type of sequence variants, constitute key elements in genetic epidemiology and pharmacogenomics. While sequence data about genetic variation is found at databases such as dbSNP, clues about the functional and phenotypic consequences of the variations are generally found in biomedical literature. The identification of the relevant documents and the extraction of the information from them are hampered by the large size of literature databases and the lack of widely accepted standard notation for biomedical entities. Thus, automatic systems for the identification of citations of allelic variants of genes in biomedical texts are required.Our group has previously reported the development of OSIRIS, a system aimed at the retrieval of literature about allelic variants of genes http://ibi.imim.es/osirisform.html. Here we describe the development of a new version of OSIRIS (OSIRISv1.2, http://ibi.imim.es/OSIRISv1.2.html) which incorporates a new entity recognition module and is built on top of a local mirror of the MEDLINE collection and HgenetInfoDB: a database that collects data on human gene sequence variations. The new entity recognition module is based on a pattern-based search algorithm for the identification of variation terms in the texts and their mapping to dbSNP identifiers. The performance of OSIRISv1.2 was evaluated on a manually annotated corpus, resulting in 99% precision, 82% recall, and an F-score of 0.89. As an example, the application of the system for collecting literature citations for the allelic variants of genes related to the diseases intracranial aneurysm and breast cancer is presented.OSIRISv1.2 can be used to link literature references to dbSNP database entries with high accuracy, and therefore is suitable for collecting current knowledge on gene sequence variations and supporting the functional annotation of variation databases. The application of OSIRISv1.2 in combination with controlled vocabularies like MeSH provides a way to identify associations of biomedical interest, such as those that relate SNPs with diseases.

A series of new benzolactam derivatives was synthesized and the derivatives were evaluated for their affinities at the dopamine D(1), D(2), and D(3) receptors. Some of these compounds showed high D(2) and/or D(3) affinity and selectivity over the D(1) receptor. The SAR study of these compounds revealed structural characteristics that decisively influenced their D(2) and D(3) affinities. Structural models of the complexes between some of the most representative compounds of this series and the D(2) and D(3) receptors were obtained with the aim of rationalizing the observed experimental results. Moreover, selected compounds showed moderate binding affinity on 5-HT(2A) which could contribute to reducing the occurrence of extrapyramidal side effects as potential antipsychotics.

Despite vast amount of money and research being channeled toward biomedical research, relatively little impact has been made on routine clinical practice. At the heart of this failure is the information and communication technology “chasm” that exists between research and healthcare. A new focus on “knowledge engineering for health” is needed to facilitate knowledge transmission across the research–healthcare gap. This discipline is required to engineer the bidirectional flow of data: processing research data and knowledge to identify clinically relevant advances and delivering these into healthcare use; conversely, making outcomes from the practice of medicine suitably available for use by the research community. This system will be able to self-optimize in that outcomes for patients treated by decisions that were based on the latest research knowledge will be fed back to the research world. A series of meetings, culminating in the “I-Health 2011” workshop, have brought together interdisciplinary experts to map the challenges and requirements for such a system. Here, we describe the main conclusions from these meetings. An “I4Health” interdisciplinary network of experts now exists to promote the key aims and objectives, namely “integrating and interpreting information for individualized healthcare,” by developing the “knowledge engineering for health” domain. Hum Mutat 33:797–802, 2012. © 2012 Wiley Periodicals, Inc.

Abstract Proteinprotein docking methodology is frequently used to model complexes of transmembrane proteins, in particular oligomers of G protein‐coupled receptors (GPCRs), even if its applicability for these systems has never been fully validated. The aim of this work is to perform a systematic study on the suitability of some widely‐used proteinprotein docking software for modeling complexes of transmembrane proteins. In this study we tested the programs ZDOCK, ClusPro, HEX, GRAMM‐X, PatchDock, SymmDock, and HADDOCK, using a set of membrane protein oligomers for which the 3D structure has been obtained experimentally, including opsin dimer, the recently published chemokine CXCR4 and kappa opioid receptor dimers. The results show that the docking success depends on the applied docking algorithm and scoring functions, but also on inherent structural features of the transmembrane proteins. Thus, proteins with large interface surfaces, rich in surface cavities, high‐order symmetry, and small conformational change upon complex formation are well predicted more often than proteins without these features. The results of this systematic analysis provide guidelines that can be used for obtaining reliable models of transmembrane proteins, including GPCRs. Therefore they can be useful for the application of structure‐based methods in drug discovery projects involving these targets.

Functional selectivity is a property of G protein-coupled receptors that allows them to preferentially couple to particular signaling partners upon binding of biased agonists. Publication of the X-ray crystal structure of serotonergic 5-HT1B and 5-HT2B receptors in complex with ergotamine, a drug capable of activating G protein coupling and β-arrestin signaling at the 5-HT1B receptor but clearly favoring β-arrestin over G protein coupling at the 5-HT2B subtype, has recently provided structural insight into this phenomenon. In particular, these structures highlight the importance of specific residues, also called micro-switches, for differential receptor activation. In our work, we apply classical molecular dynamics simulations and enhanced sampling approaches to analyze the behavior of these micro-switches and their impact on the stabilization of particular receptor conformational states. Our analysis shows that differences in the conformational freedom of helix 6 between both receptors could explain their different G protein-coupling capacity. In particular, as compared to the 5-HT1B receptor, helix 6 movement in the 5-HT2B receptor can be constrained by two different mechanisms. On the one hand, an anchoring effect of ergotamine, which shows an increased capacity to interact with the extracellular part of helices 5 and 6 and stabilize them, hinders activation of a hydrophobic connector region at the center of the receptor. On the other hand, this connector region in an inactive conformation is further stabilized by unconserved contacts extending to the intracellular part of the 5-HT2B receptor, which hamper opening of the G protein binding site. This work highlights the importance of considering receptor capacity to adopt different conformational states from a dynamic perspective in order to underpin the structural basis of functional selectivity.

G-protein coupled receptors (GPCRs) are the most important class of current pharmacological targets. However, it is now widely acknowledged that their regulation is more complex than previously thought: the evidence that GPCRs can couple to several effector pathways, and the existence of biased agonists able to activate them differentially, has introduced a new level of complexity in GPCR drug research. Considering bias represents a challenge for the research of new GPCR modulators, because it demands a detailed characterization of compound properties for several effector pathways. Still, biased ligands could provide an opportunity to modulate GPCR function in a finer way and to separate therapeutic from side effects. Nowadays, a variety of agonists for GPCRs have been described, which differ in their ability to promote receptor coupling to different Gprotein families or even subunits, recruit signal transducers such as arrestins, activate a variety of downstream molecular pathways and induce certain phosphorylation signatures or gene expression patterns. In this review, we will cover some of the experimental techniques currently used to understand and characterize biased agonism and discuss their strengths and limitations. Additionally, we will comment on the computational efforts that are being devoted to study ligand-induced bias and on the potential they hold for rationalizing its structural determinants. Finally, we will discuss which of these strategies could be used for the rational design of biased ligands and give some examples of the potential therapeutic value of this class of compounds.

The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for knowledge discovery in the Life Sciences. The manual curation of facts from published scientific papers is slow and inefficient, and therefore new approaches are needed that can enable the automatic, scalable and reliable extraction of assertions. While the publication of scientific assertions and datasets on the Semantic Web is gaining traction, it also creates new challenges such as the proper representation of provenance and versioning. Here, we address these issues and describe our efforts to represent the DisGeNET database of human gene-disease associations as permanent, immutable, and provenance rich digital objects called nanopublications. Our nanopublications are the first instance of a Linked Data model that ensures stable interlinking of the assertion and its metadata by Trusty URIs. As DisGeNET integrates manually curated as well as text-mined data of different origins, the semantic description of the evidence for each assertion is important to provide trust and allow evidence-based hypothesis generation. Here, we describe our steps to ensure high quality and demonstrate the utility of linking our data to other datasets on the emerging Semantic Web.

Healthcare workers (HCW) are at high risk for suicide, yet little is known about the onset of suicidal thoughts and behaviors (STB) in this important segment of the population in conjunction with the COVID-19 pandemic. We conducted a multicenter, prospective cohort study of Spanish HCW active during the COVID-9 pandemic. A total of n = 4809 HCW participated at baseline (May-September 2020; i.e., just after the first wave of the pandemic) and at a four-month follow-up assessment (October-December 2020) using web-based surveys. Logistic regression assessed the individual- and population-level associations of separate proximal (pandemic) risk factors with four-month STB incidence (i.e., 30-day STB among HCW negative for 30-day STB at baseline), each time adjusting for distal (pre-pandemic) factors. STB incidence was estimated at 4.2% (SE = 0.5; n = 1 suicide attempt). Adjusted for distal factors, proximal risk factors most strongly associated with STB incidence were various sources of interpersonal stress (scaled 0-4; odds ratio [OR] range = 1.23-1.57) followed by personal health-related stress and stress related to the health of loved ones (scaled 0-4; OR range 1.30-1.32), and the perceived lack of healthcare center preparedness (scaled 0-4; OR = 1.34). Population-attributable risk proportions for these proximal risk factors were in the range 45.3-57.6%. Other significant risk factors were financial stressors (OR range 1.26-1.81), isolation/quarantine due to COVID-19 (OR = 1.53) and having changed to a specific COVID-19 related work location (OR = 1.72). Among other interventions, our findings call for healthcare systems to implement adequate conflict communication and resolution strategies and to improve family-work balance embedded in organizational justice strategies.

The inhibitory effect of 44 quinolone antibacterials and derivatives (common structure, 4-oxoquinoline-3-carboxylic acid) on cytochrome P450 isoform CYP1A2 activity was tested using human liver microsomes and caffeine 3-demethylation as a specific test system for this enzyme. By direct comparison of molecules differing structurally in only one position, the following structure-activity relationships were found. 3'-Oxo derivatives had a reduced or similar activity and M1 metabolites (cleavage of piperazinyl substituent) had a greater inhibitory activity, compared with the parent molecule. Alkylation of the 7-piperazinyl substituent resulted in a reduced inhibitory potency. Naphthyridines with an unsubstituted piperazinyl group at position 7 displayed a greater inhibitory potency than did corresponding quinoline derivatives. Derivatives with a fluorine substitution at position 8 had only a minor effect. Molecular modeling studies with inhibitors and caffeine showed that it is possible to explain the potency of the quinolones to inhibit CYP1A2 on a molecular level. The keto group, the carboxylate group, and the core nitrogen at position 1 are likely to be the most important groups for binding to the active site of CYP1A2, because the molecular electrostatic potential of all inhibitors is very similar to that of caffeine in these regions. The presence of a piperazinyl substituent, however, seems to be no prerequisite for inhibitory potency. Finally, an equation to estimate the potency to inhibit CYP1A2 was developed by quantitative structure-activity relationship analysis.

Software has been developed to locate and determine accurately all the Molecular Electrostatic Potential (MEP) minima of a molecule and to plot the MEP maps corresponding to planes containing any three desired points (minima, atoms or dummy points). The minima description can be used as parameters in QSAR. Some of these techniques are used in the analysis of substances which inhibit caffeine metabolism and other similar, but inactive, compounds. The goal is to find electrostatic requirements associated with this inhibitory activity.

Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks.First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented.In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.

Conocer la prevalencia del uso de sellos de confianza en páginas web sobre vacunas y analizar las diferencias entre las webs con estos sellos y las que no los ostentan, con el fin de proponerlos como indicador de calidad. Estudio observacional transversal. Internet. Páginas web sobre vacunas. Utilizando las palabras clave «vacunas OR vaccines» se analizaron las características, calidad y presencia de sellos de confianza en 40 páginas web sugeridas preferentemente por los motores de búsqueda Google y Yahoo! El número medio de criterios de calidad cumplidos fue de 7 (intervalo de confianza [IC] del 95%: de 3,96 a 10,04) sobre un máximo de 9 en el caso de las páginas web obtenidas con Yahoo! y de 7,3 (IC del 95%: de 3,86 a 10,74) en las obtenidas con Google. Entre las webs obtenidas con Yahoo!, 3 presentaban información inadecuada sobre vacunas, mientras que en Google aparecieron 4 de estas características. La presencia de sellos de confianza en las webs médicas fue de entre un 20% y un 30% y su influencia sobre la puntuación de calidad alcanzó la significación estadística (p=0,033). Hay gran variabilidad en las webs obtenidas por distintos buscadores, incluyendo un elevado número de webs sin utilidad informativa. Aunque la mayoría de las webs analizadas pueden considerarse de buena calidad, entre un 15 y un 20% de ellas presentan información inadecuada sobre vacunas. Las webs con sellos de confianza poseen mayor calidad que aquellas que no los ostentan y ninguna de ellas se encuentra entre las que presentan contenidos inadecuados. To find out the prevalence of quality trust marks present in websites and to analyse the quality of these websites displaying trust marks compared with those that do not display them, in order to put forward these trust marks as a quality indicator. Cross-sectional study. Internet. Websites on vaccines. Using "vacunas OR vaccines" as key words, the features of 40 web pages were analysed. These web pages were selected from the page results of two search engines, Google and Yahoo! Based on a total of 9 criteria, the average score of criteria fulfilled was 7 (95% CI 3.96–10.04) points for the web pages offered by Yahoo! and 7.3 (95% CI 3.86–10.74) offered by Google. Amongst web pages offered by Yahoo!, there were three with clearly inaccurate information, while there were four in the pages offered by Google. Trust marks were displayed in 20% and 30% medical web pages, respectively, and their presence reached statistical significance (P=0.033) when fulfilling the quality criteria compared with web pages where trust marks were not displayed. A wide variety of web pages was obtained by search engines and a large number of them with useless information. Although the websites analysed had a good quality, between 15% and 20% showed inaccurate information. Websites where trust marks were displayed had more quality than those that did not display one and none of them were included amongst those where inaccurate information was found.

The clinical efficacy of antipsychotic drugs has been associated with a certain binding profile for a set of G protein-coupled receptors (GPCR)s. In this work, we use the structurally-related clozapine–olanzapine pair to progress in the understanding of the structural properties that determine their divergent binding profiles and, thereby, their differing therapeutic efficacy. First, we present novel site-directed mutagenesis results that confirm our previous hypothesis on the importance of ligand interaction with positions 5.42 and 5.46 in transmembrane helix 5. Then, we use refined models of ligand-receptor complexes, built from recently published GPCR crystal structures, to gain further insight into the molecular mechanisms responsible for the observed experimental outcomes. In particular, we observe that preventing or potentiating hydrogen bonding with position 5.46, could allow obtaining ligands with, respectively, clozapine or olanzapine-like affinities. Results presented in this study could guide the design of antipsychotic candidates with tailored binding profiles.

Abstract Background Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. Results We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. Conclusions The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information.

La enfermedad pulmonar obstructiva crónica (EPOC) es una entidad de presentación heterogénea. Por ello, se han intentado perfilar diferentes fenotipos y endotipos, que permitirían un manejo más diferenciado. El objetivo del proyecto Biomarcadores en la EPOC (BIOMEPOC) es identificar biomarcadores sanguíneos útiles para tipificar mejor a los enfermos. Se analizarán datos clínicos y muestras sanguíneas en un grupo de pacientes y controles sanos. El proyecto constará de fases de prospección y de validación. Se realizarán determinaciones analíticas sanguíneas con técnicas convencionales y de diversas ciencias «ómicas» (transcriptómica, proteómica y metabolómica). Las primeras se realizarán orientadas por hipótesis, mientras que con las segundas se realizará una exploración sin dicho condicionante. Finalmente se realizará un análisis multinivel. En el momento actual se han reclutado 269 pacientes y 83 controles, y se está iniciando el procesamiento de muestras. Con los resultados obtenidos se espera identificar nuevos biomarcadores que, en solitario o combinados, permitan una mejor tipificación de los pacientes. Chronic obstructive pulmonary disease (COPD) is an entity with a heterogeneous presentation. For this reason, attempts have been made to characterize different phenotypes and endotypes to enable a more individualized approach. The aim of the Biomarkers in COPD (BIOMEPOC) project is to identify useful biomarkers in blood to improve the characterization of patients. Clinical data and blood samples from a group of patients and healthy controls will be analyzed. The project will consist of an exploration phase and a validation phase. Analytical parameters in blood will be determined using standard techniques and certain 'omics' (transcriptomics, proteomics, and metabolomics). The former will be hypothesis-driven, whereas the latter will be exploratory. Finally, a multilevel analysis will be conducted. Currently, 269 patients and 83 controls have been recruited, and sample processing is beginning. Our hope is to use the results to identify new biomarkers that, alone or combined, will allow a better characterization of patients.

The present study validates the expert system PNEUMON-IA. The aim of PNEUMON-IA is assessing the etiology of community-acquired pneumonias from clinical, radiological, and laboratory data obtained at the onset of the disease. Validation was performed using data from medical records of 76 patients with proven clinical diagnosis of pneumonia. The etiological diagnoses provided by PNEUMON-IA were compared to those established by five specialists unrelated to the development of the expert system. For each etiological possibility, both PNEUMON-IA and the experts provided a causal possibility, expressed as a linguistic label (i.e., "almost impossible"). Linguistic labels were then converted to numeric values. In the majority of cases, an etiological diagnosis was unavailable to be used as a gold standard. To overcome this limitation, distances between arrays of etiological possibilities given by specialists and by PNEUMON-IA were considered as an agreement measure between diagnoses. Cluster analysis based on those distances was used to classify PNEUMON-IA among experts. Results showed the same differences between specialists and PNEUMON-IA as among the specialists themselves. The method used to validate PNEUMON-IA could prove useful to assess the performance of expert systems in fields where no gold standard is available.

Over two hundred 1-, 3-, 8-, and 9-substituted-9-deazaxanthines were prepared and evaluated for their binding affinity at the recombinant human adenosine receptors, in particular at the hA(2B) and hA(2A) subtypes. Several ligands endowed with sub-micromolar to low nanomolar binding affinity at hA(2B) receptors, good selectivity over hA(2A) and hA(3), but a relatively poor selectivity over hA(1) were obtained. Good antagonistic potencies and efficacies, with pA(2) values close to the corresponding pK(i)s, were observed in functional assays in vitro performed on a selected series of compounds. 1,3-Dimethyl-8-phenoxy-(N-p-halogenophenyl)-acetamido-9-deazaxanthine derivatives appeared as the most interesting leads, some of them showing outstanding hA(2B) affinities, high selectivity over hA(2A) and hA(3), but low selectivity over hA(1). Structure-affinity relationships suggested that the binding potency at the hA(2B) receptor was mainly modulated by the steric (lipophilic) properties of the substituents at positions 1 and 3 and by the electronic and lipophilic characteristics of the substituents at position 8. A comparison among affinity and selectivity profiles of 9-deazaxanthines with the corresponding xanthines suggested some possible differences in their binding mode.

Abstract The electron density and the molecular electrostatic potential of the β‐carbolines are studied using ab initio STO ‐3 G wave functions. The analysis was done from the point of view of a previous model built with monoamine oxidase substrates and irreversible inhibitors. The results confirm the usefulness of the model and make it possible to propose new precision to the molecular electrostatic potential patterns needed to have monoamine oxidase inhibitory activity.

Abstract Similarities and differences between mitochondrial monoamine oxidase (MAO) substrates and inhibitors (both A and B types) are considered, studying quantitatively two molecular properties: electron density and molecular electrostatic potential ( MEP ). The following molecules are considered: substrates : PHEA, BZA, tele‐N‐methyl‐histamine, phenylethanolamine, phenylpropylamine, tryptamine, dopamine, phenoxylethylamine, noradrenaline, serotonin, and p ‐nitro‐phenylethanolamine; inhibitors : Deprenil, Clorgyline, and Lilly 51641 (only the moiety involved in the A–B differentiation is considered). The wave functions needed to calculate the analyzed properties are of ab initio quality, and have been calculated in analogous conformations, all near the energetic minima. Electron densities distributions are qualitatively compared by means of a correlation coefficient defined over the whole space. Otherwise, patterns of the possible zones of electrostatic interactions are described by means of the distances and angles between minima, in order to differentiate MAO‐A and MAO‐B substrates. The results reproduce efficiently the experimental classification and enable us to predict the type of enzymatic action of molecules not yet experimentally classified.

The complex etiology of schizophrenia has prompted researchers to develop clozapine-related multitarget strategies to combat its symptoms. Here we describe a series of new 6-aminomethylbenzofuranones in an effort to find new chemical structures with balanced affinities for 5-HT2 and dopamine receptors. Through biological and computational studies of 5-HT2A and D2 receptors, we identified the receptor serine residues S3.36 and S5.46 as the molecular keys to explaining the differences in affinity and selectivity between these new compounds for this group of receptors. Specifically, the ability of these compounds to establish one or two H-bonds with these key residues appears to explain their difference in affinity. In addition, we describe compound 2 (QF1004B) as a tool to elucidate the role of 5-HT2C receptors in mediating antipsychotic effects and metabolic adverse events. The compound 16a (QF1018B) showed moderate to high affinities for D2 and 5-HT2A receptors, and a 5-HT2A/D2 ratio was predictive of an atypical antipsychotic profile.

A level shift procedure is described in the framework of one-operator SCF formalisms. This procedure permits one to obtain in any case unconditional convergence to a stationary energy.

A large series of piperazin-, piperidin- and tetrahydroisoquinolinamides of 4-(1,3-dialkyl-9-deazaxanthin-8-yl)phenoxyacetic acid were prepared through conventional or multiple parallel syntheses and evaluated for their binding affinity at the recombinant human adenosine receptors, chiefly at the hA2B and hA2A receptor subtypes. Several ligands endowed with high binding affinity at hA2B receptors, excellent selectivity over hA2A and hA3 and a significant, but lower, selectivity over hA1 were identified. Among them, piperazinamide derivatives 23 and 52, and piperidinamide derivative 69 proved highly potent at hA2B (Ki = 11, 2 and 5.5 nM, respectively) and selective towards hA2A (hA2A/hA2B SI = 912, 159 and 630, respectively), hA3 (hA3/hA2B SI = > 100, 3090 and >180, respectively) and hA1 (hA1/hA2B SI = > 100, 44 and 120, respectively), SI being the selectivity index. A number of selected ligands tested in functional assays in vitro showed very interesting antagonist activities and efficacies at both A2A and A2B receptor subtypes, with pA2 values close to the corresponding pKis. Structure–affinity and structure–selectivity relationships suggested that the binding potency at the hA2B receptor may be increased by lipophilic substituents at the N4-position of piperazinamides and that an ortho-methoxy substituent at the 8-phenyl ring and alkyl groups at N1 larger than the ones at N3, in the 9-deazaxanthine ring, may strongly enhance the hA2A/hA2B SI.

In essence, the virtual physiological human (VPH) is a multiscale representation of human physiology spanning from the molecular level via cellular processes and multicellular organization of tissues to complex organ function. The different scales of the VPH deal with different entities, relationships and processes, and in consequence the models used to describe and simulate biological functions vary significantly. Here, we describe methods and strategies to generate knowledge environments representing molecular entities that can be used for modelling the molecular scale of the VPH. Our strategy to generate knowledge environments representing molecular entities is based on the combination of information extraction from scientific text and the integration of information from biomolecular databases. We introduce @neuLink, a first prototype of an automatically generated, disease-specific knowledge environment combining biomolecular, chemical, genetic and medical information. Finally, we provide a perspective for the future implementation and use of knowledge environments representing molecular entities for the VPH.

A series of 37 benzolactam derivatives were synthesized, and their respective affinities for the dopamine D(2) and D(3) receptors evaluated. The relationships between structures and binding affinities were investigated using both ligand-based (3D-QSAR) and receptor-based methods. The results revealed the importance of diverse structural features in explaining the differences in the observed affinities, such as the location of the benzolactam carbonyl oxygen, or the overall length of the compounds. The optimal values for such ligand properties are slightly different for the D(2) and D(3) receptors, even though the binding sites present a very high degree of homology. We explain these differences by the presence of a hydrogen bond network in the D(2) receptor which is absent in the D(3) receptor and limits the dimensions of the binding pocket, causing residues in helix 7 to become less accessible. The implications of these results for the design of more potent and selective benzolactam derivatives are presented and discussed.

Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self-contained virtual machine easy to maintain and install. All models produce toxicity-relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D-QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint-based QSAR).

Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction.We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test.When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.

Abstract Aim To investigate the occurrence of traumatic stress symptoms (TSS) among healthcare workers active during the COVID-19 pandemic and to obtain insight as to which pandemic-related stressful experiences are associated with onset and persistence of traumatic stress. Methods This is a multicenter prospective cohort study. Spanish healthcare workers ( N = 4,809) participated at an initial assessment (i.e., just after the first wave of the Spain COVID-19 pandemic) and at a 4-month follow-up assessment using web-based surveys. Logistic regression investigated associations of 19 pandemic-related stressful experiences across four domains (infection-related, work-related, health-related and financial) with TSS prevalence, incidence and persistence, including simulations of population attributable risk proportions (PARP). Results Thirty-day TSS prevalence at T1 was 22.1%. Four-month incidence and persistence were 11.6% and 54.2%, respectively. Auxiliary nurses had highest rates of TSS prevalence (35.1%) and incidence (16.1%). All 19 pandemic-related stressful experiences under study were associated with TSS prevalence or incidence, especially experiences from the domains of health-related (PARP range 88.4–95.6%) and work-related stressful experiences (PARP range 76.8–86.5%). Nine stressful experiences were also associated with TSS persistence, of which having patient(s) in care who died from COVID-19 had the strongest association. This association remained significant after adjusting for co-occurring depression and anxiety. Conclusions TSSs among Spanish healthcare workers active during the COVID-19 pandemic are common and associated with various pandemic-related stressful experiences. Future research should investigate if these stressful experiences represent truly traumatic experiences and carry risk for the development of post-traumatic stress disorder.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients’ empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Little is known about healthcare workers' (HCW) use of healthcare services for mental disorders. This study presents data from a 16-month prospective cohort study of Spanish HCW (n=4,809), recruited shortly after the COVID-19 pandemic onset, and assessed at four timepoints using web-based surveys. Use of health services among HCW with mental health conditions (i.e., those having a positive screen for mental disorders and/or suicidal thoughts and behaviours [STB]) was initially low (i.e., 18.2%) but increased to 29.6% at 16-month follow-up. Service use was positively associated with pre-pandemic mental health treatment (OR=1.99), a positive screen for major depressive disorder (OR=1.50), panic attacks (OR=1.74), suicidal thoughts and behaviours (OR=1.22), and experiencing severe role impairment (OR=1.33), and negatively associated with being female (OR = 0.69) and a higher daily number of work hours (OR=0.95). Around 30% of HCW with mental health conditions used anxiolytics and/or hypnotics (benzodiazepines), especially medical doctors. Four out of ten HCW (39.0%) with mental health conditions indicated a need for (additional) help, with most important barriers for service use being too ashamed, long waiting lists, and professional treatment not being available. Our findings delineate a clear mental health treatment gap among Spanish HCW.

Abstract Objectives One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. Materials &amp; methods A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying numerical clinical and imaging features (six in total) is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. A cluster evaluation process is also implemented, by admitting two user-defined parameters (granularity threshold and feature contribution). The model disease chosen is Huntington’s disease (HD), characterized by progressive neurodegeneration. Results From a wide range of examined user-defined parameters, four case examples are highlighted to exemplify the combined effects of feature weights and granularity threshold in the stratification of HD trajectories in homogeneous clusters. For each identified cluster, polynomial fits that describe the temporal behavior of the assessed features are provided for an informative comparison, together with their averaged values. Discussion The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis or future diagnosis, employing user-customized criteria beyond the current clinical practice. Conclusions This work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies.

Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored.PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software.Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTSynthesis and Atypical Antipsychotic Profile of Some 2-(2-Piperidinoethyl)benzocycloalkanones as Analogs of ButyrophenoneJose A. Fontenla, Javier Osuna, Elizabeth Rosa, Ma Elena Castro, Tomas G-Ferreiro, Isabel Loza-Garcia, Jose M. Calleja, Ferran Sanz, Jesus Rodriguez, and Cite this: J. Med. Chem. 1994, 37, 16, 2564–2573Publication Date (Print):August 5, 1994Publication History Published online1 May 2002Published inissue 5 August 1994https://doi.org/10.1021/jm00042a009RIGHTS & PERMISSIONSArticle Views401Altmetric-Citations30LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-Alerts Get e-Alerts

Abstract The effective design of catalytic antibodies represents a major conceptual and practical challenge. It is implicitly assumed that a proper transition state analogue (TSA) can elicit a catalytic antibody (CA) that will catalyze the given reaction in a similar way to an enzyme that would evolve (or was evolved) to catalyze this reaction. However, in most cases it was found that the TSA used produced CAs with relatively low rate enhancement as compared to the corresponding enzymes, when these exist. The present work explores the origin of this problem, by developing two approaches that examine the similarity of the TSA and the corresponding transition state (TS). These analyses are used to assess the proficiency of the CA generated by the given TSA. Both approaches focus on electrostatic effects that have been found to play a major role in enzymatic reactions. The first method uses molecular interaction potentials to look for the similarity between the TSA and the TS and, in principle, to help in designing new haptens by using 3D quantitative struture–activity relationships. The second and more quantitative approach generates a grid of Langevin dipoles, which are polarized by the TSA, and then uses the grid to bind the TS. Comparison of the resulting binding energy with the binding energy of the TS to the grid that was polarized by the TS provides an estimate of the proficiency of the given CA. Our methods are used in examining the origin of the difference between the catalytic power of the 1F7 CA and chorismate mutase. It is demonstrated that the relatively small changes in charge and structure between the TS and TSA are sufficient to account for the difference in proficiency between the CA and the enzyme. Apparently the environment that was preorganized to stabilize the TSA charge distribution does not provide a sufficient stabilization to the TS. The general implications of our findings and the difficulties in designing a perfect TSA are discussed. Finally, the possible use of our approach in screening for an optimal TSA is pointed out.

From an analysis of the ab initio molecular electrostatic potential (MEP) maps of some H2-receptor agonists of histamine in their essential trans—trans conformations, for both neutral and cationic species, a good relationship between H2-activity data and the MEP minima located at the Nπ nitrogen atom of the imidazole ring is predicted. From these data it appears easy to define a threshold value according to which the H2-agonists may be classified as being strongly or weakly active. While the MEP values appear to be a good parameter for activity prediction, in this case, the comparison of electron densities does not give any additional useful information. A partir de l'analyse des cartes du potentiel électrostatique moléculaire (PEM) ab initio de quelques agonistes du récepteur-H2 de l'histamine représentés dans leur conformation essentielle trans—trans, aussi bien pour les espèces neutres que pour les cationiques, une bonne relation est prédite entre les données de l'activité-H2 et la valeur des minimums du PEM placés près de l'atome d'azote imidazolique. Il paraît facile, d'après ces données de définir une valeur seuil en fonction de laquelle les agonistes-H2 peuvent être classés comme fortement ou faiblement actifs. Les valeurs du PEM se présentent comme un bon paramètre pour la prédiction de l'activité, toutefois dans ce cas-ci, la comparaison des densités électroniques n'ajoute aucune information utile.

The aim of the present work was to characterize a lead compound displaying relevant multi-target interactions, and with an in vivo behavioral profile predictive of atypical antipsychotic activity. Synthesis, molecular modeling and in vitro and in vivo pharmacological studies were carried out for 2-[4-(6-fluorobenzisoxazol-3-yl)piperidinyl]methyl-1,2,3,4-tetrahydro-carbazol-4-one (QF2004B), a conformationally constrained butyrophenone analogue. This compound showed a multi-receptor profile with affinities similar to those of clozapine for serotonin (5-HT2A, 5-HT1A, and 5-HT2C), dopamine (D1, D2, D3 and D4), alpha-adrenergic (alpha1, alpha2), muscarinic (M1, M2) and histamine H1 receptors. In addition, QF2004B mirrored the antipsychotic activity and atypical profile of clozapine in a broad battery of in vivo tests including locomotor activity (ED50 = 1.19 mg/kg), apomorphine-induced stereotypies (ED50 = 0.75 mg/kg), catalepsy (ED50 = 2.13 mg/kg), apomorphine- and DOI (2,5-dimethoxy-4-iodoamphetamine)-induced prepulse inhibition (PPI) tests. These results point to QF2004B as a new lead compound with a relevant multi-receptor interaction profile for the discovery and development of new antipsychotics.

Sequence variants, in particular single nucleotide polymorphisms (SNPs), are key elements for the identification of genes associated with complex diseases and with particular drug responses. The search for literature about sequence variation is hampered by the large number of allelic variants reported for many genes and by the variability in both gene and sequence variants nomenclatures. We describe OSIRIS, a search tool that integrates different sources of information with the aim to retrieve literature about sequence variation of a gene. In addition, it provides a method to link a dbSNP entry with the articles referring to it.OSIRIS is available for public use at http://ibi.imim.es/

The present study is part of a long-term research project aiming to gain insight into the mechanism of action of atypical antipsychotics. Here we describe a 3D-QSAR study carried out on a series of butyrophenones with affinity for the serotonin-2A receptor, aligned by docking into the binding site of a receptor model. The series studied has two peculiarities: (i) all the compounds have a chiral center and can be represented by two enantiomeric structures, and (ii) many of the structures can bind the receptor in two alternative orientations, posing the problem of how to select a single representative structure for every compound. We have used an original solution consisting of the simultaneous use of multiple structures, representing different configurations, binding conformations, and positions. The final model showed good statistical quality (n = 426, r2 = 0.84, q2LOO = 0.81) and its interpretation provided useful information, not obtainable from the simple inspection of the ligand−receptor complexes.

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.

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended "nanotype" to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.