IT Future of Medicine: from molecular analysis to clinical diagnosis and improved treatment

The IT Future of Medicine (ITFoM, http://www.itfom.eu/) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in 'virtual patient' models. The establishment of such 'virtual patient' models is now possible due to the enormous progress in analytical techniques, particularly in the '-omics' technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate 'virtual patient' models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs

ISBE – set out for a Systems Biology Infrastructure for Europe

Systems biology requires the availability, co-ordination and simultaneous interaction of a large number of diverse facilities and activities. These cover an entire spectrum, from mathematical modelling, through biological, biomedical and clinical experiments, to dedicated technology development. The systems biology community needs close cooperation with data-generation groups and bioinformaticians to define a strategy for producing life-science data of sufficiently high quality for model generation. For each medical, biological or biotechnological problem addressed, the optimal combination of facilities and activities is likely to be different. The complexity of biological systems, and the diversity and dynamics of their processes, means that a full analysis is far too complex to be handled by a single entity, industry or country – a variety of specialist expertise and facilities are typically necessary to achieve results suitable for modelling. Systems-level approaches for tackling the complexity of life-science data provide a profound conceptual advance compared to reductionist biological research methods of the past. Rather than focusing on individual laboratories, specialising in a limited number of research technologies, the Infrastructure for Systems Biology in Europe (ISBE) will facilitate the synergistic application of a wide range of research techniques and technologies to problems of major medical and biotechnological importance

National registry for patients with inflammatory rheumatic diseases (IRD) infected with SARS-CoV-2 in Germany (ReCoVery): a valuable mean to gain rapid and reliable knowledge of the clinical course of SARS-CoV-2 infections in patients with IRD

Objectives: Patients with inflammatory rheumatic diseases (IRD) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be at risk to develop a severe course of COVID-19. The influence of immunomodulating drugs on the course of COVID-19 is unknown. To gather knowledge about SARS-CoV-2 infections in patients with IRD, we established a registry shortly after the beginning of the pandemic in Germany. Methods Using an online questionnaire (www.COVID19-rheuma.de.), a nationwide database was launched on 30 March 2020, with appropriate ethical and data protection approval to collect data of patients with IRD infected with SARS-CoV-2. In this registry, key clinical and epidemiological parameters-for example, diagnosis of IRD, antirheumatic therapies, comorbidities and course of the infection-are documented. Results Until 25 April 2020, data from 104 patients with IRD infected with SARS-CoV-2 were reported (40 males;63 females;1 diverse). Most of them (45%) were diagnosed with rheumatoid arthritis, 59% had one or more comorbidities and 42% were treated with biological disease-modifying antirheumatic drugs. Hospitalisation was reported in 32% of the patients. Two-thirds of the patients already recovered. Unfortunately, 6 patients had a fatal course. Conclusions: In a short time, a national registry for SARS-CoV2-infected patients with IRD was established. Within 4 weeks, 104 cases were documented. The registry enables to generate data rapidly in this emerging situation and to gain a better understanding of the course of SARS-CoV2-infection in patients with IRD, with a distinct focus on their immunomodulatory therapies. This knowledge is valuable for timely information of physicians and patients with IRD, and shall also serve for the development of guidance for the management of patients with IRD during this pandemic

The need for standardisation in life science research - an approach to excellence and trust

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation

a European registries collaborative project

Funding: Individual registries had entered into agreements with pharmaceutical companies (AbbVie, BMS, Hospira, MSD, Pfizer, Roche, UCB, Samsung and Eli Lilly). The pharmaceutical companies funding these registers were, however, not involved in the planning of the project, the statistical analyses, the interpretation of the results or the decision to publish.BACKGROUND: Lymphomas comprise a heterogeneous group of malignant diseases with highly variable prognosis. Rheumatoid arthritis (RA) is associated with a twofold increased risk of both Hodgkin's lymphoma (HL) and non-Hodgkin's lymphoma (NHL). It is unknown whether treatment with biologic disease-modifying antirheumatic drugs (bDMARDs) affect the risk of specific lymphoma subtypes. METHODS: Patients never exposed to (bionaïve) or ever treated with bDMARDs from 12 European biologic registers were followed prospectively for the occurrence of first ever histologically confirmed lymphoma. Patients were considered exposed to a bDMARD after having received the first dose. Lymphomas were attributed to the most recently received bDMARD. RESULTS: Among 124 997 patients (mean age 59 years; 73.7% female), 533 lymphomas were reported. Of these, 9.5% were HL, 83.8% B-cell NHL and 6.8% T-cell NHL. No cases of hepatosplenic T-cell lymphoma were observed. Diffuse large B-cell lymphoma (DLBCL) was the most frequent B-cell NHL subtype (55.8% of all B-cell NHLs). The subtype distributions were similar between bionaïve patients and those treated with tumour necrosis factor inhibitors (TNFi). For other bDMARDs, the numbers of cases were too small to draw any conclusions. Patients with RA developed more DLBCLs and less chronic lymphocytic leukaemia compared with the general population. CONCLUSION: This large collaborative analysis of European registries has successfully collated subtype information on 533 lymphomas. While the subtype distribution differs between RA and the general population, there was no evidence of any modification of the distribution of lymphoma subtypes in patients with RA treated with TNFi compared with bionaïve patients. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.publishersversionpublishe

Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7) are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network

Strategies for structuring interdisciplinary education in Systems Biology: an European perspective

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development

predictive precision medicine towards the computational challenge

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level