Euro Alliance 2 Letters
Euro Alliance 2 Letters – Representing 33 leading universities and institutions worldwide, the network is committed to improving health and advancing the field of network medicine
Network medicine combines principles and methods from network science, systems biology, and human dynamics to understand the causes of human disease and develop new treatments.
Euro Alliance 2 Letters
Network medicine emerged from network science research in the early days of the Internet and advances in systems biology since the Human Genome Project. It is now an established way of studying, reclassifying and developing treatments for complex diseases.
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1999 Barbasi introduced the concept of scale-freenetworks and proposed the Barbasi–Albert model to explain their widespread emergence in natural, technological and social systems. Barbasi’s paper on scale-freenetworks has become the most cited paper in the physical sciences in the journal Science.
2003 The Human Genome Project was declared complete in April 2003. The project determined that there are about 22,300 protein-coding genes in humans. Loscalzo and Barabasi began mapping human disease biology that explains how proteins expressed in the human genome interact to cause specific diseases.
Loscalzo, Kohane, and Barabási published a classification of human disease in the postgenomic era in the journal Molecular Systems Biology that establishes network medicine as a complex systems approach to human pathobiology.
Barabási, Gulbahce, and Loscalzo publish Network Medicine: A Network-Based Approach to Human Disease in Nature in which they present an overview of the principles of organization that govern cellular networks and the implications of these principles for understanding disease.
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2012 The Channing Division of Network Medicine at Brigham and Women’s Hospital was created to study, reclassify, and develop treatments for complex diseases using network science and systems biology.
2016 Joseph Loscalzo and Enrico Petrillo form the Network Medicine Alliance representing 31 leading universities and institutions from around the world.
2018 Joseph Loscalzo and Albert-László Barabási published a network-based approach for in silico drug prediction and population-based validation that demonstrated a unique integration of protein-protein interaction network proximities and complements large-scale patient-level longitudinal data. In vitro studies can facilitate drug repurposing.
The first international conference on The Transformation of Medicine, Network Medicine and Big Data was held in Rome, Italy. The ultimate goal of the meeting was to design a strategy by which this interdisciplinary field could truly transform medicine.
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The Channing Division of Network Medicine (CDNM) continues to expand with a staff of 160 non-faculty Brigham and Women’s Hospital (BWH) employees in addition to more than 80 Harvard Medical School faculty and 42 fellows. BWH oversees the second largest hospital-based research program in the world. CDNM’s annual research expenditure represents 25% of the BWH Department of Medicine’s annual budget. In fiscal year 2019, CDNM investigators received 54 new funding awards resulting in 173 active grants.
In response to the 2020 COVID-19 pandemic, the Network Medicine Institute launched the COVID-19 Global Drug Repurposing Study (GDR Study). The GDR study is based on a multifaceted drug repurposing strategy that includes a global patient registry and an advanced network medicine framework to identify promising therapies among thousands of already FDA-approved drugs. The strategy facilitates evidence-based updates to health policies and clinical guidelines and tailors them to specific patient populations.
REPO4EU was funded by a highly competitive 5-year twenty-five million euro grant from the European Union Governments Horizons Research Programme.
It is building a comprehensive European and global platform for appropriate medicine repurposing to repurpose existing medicines for diseases with significant unmet needs through innovative treatment protocols. At the core of REPO4EU is a team of world-leading scientists using advanced bioinformatics and artificial intelligence (AI) to redefine diseases in a process-based approach on real-world big data. This revolutionary new era of medicine will allow unprecedented effectiveness and cost-effectiveness in treating diseases worldwide. Within 5 years, REPO4EU will establish a first-class coherent and innovative web-based platform for all European researchers and SMEs with a unique open science concept to redevelop safe and efficient medicines for all types of high unmet medical need indications, ensuring worldwide. Medical effects.
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Genome-wide association studies (GWAS) have identified numerous susceptibility genes for Alzheimer’s disease (AD). However, using GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide the development of new therapeutics for patients with AD has not been successful before.
Endophenotype-based in silico network medicine discovery with insurance record data mining identifies sildenafil as candidate drug for Alzheimer’s disease
We developed an endophenotype disease module-based approach for Alzheimer’s disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on a retrospective case-control pharmacoepidemiologic analysis of insurance claims data for 7.23 million individuals, we found that sildenafil use was significantly associated with a 69% reduced risk of AD (hazard ratio 0.31, 95% confidence interval 0.25, 0.25-0.
< 1.0 × 10–8). Propensity score-level analyzes confirmed that sildenafil was significantly associated with reduced risk of AD in the four drug cohorts tested (diltiazem, glimepiride, losartan, and metformin) after adjustment for age, sex, race, and comorbidity. We also found that sildenafil increased neurite outgrowth and reduced phospho-tau expression in induced pluripotent stem cell-derived neuron models from AD patients, mechanistically supporting its potential beneficial effects in AD. The association between sildenafil use and reduced incidence of AD does not establish causality, which would require a randomized controlled trial.
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Traditional drug discovery faces a serious efficacy crisis. Repurposing registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data required for the identification of disease modules, such as pathways and sub-networks describing the mechanisms of complex diseases that contain potential drug targets, are scattered in independent databases. Furthermore, existing studies are limited to predicting specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptive tools to allow biomedical researchers to employ network-based drug reengineering methods in their personal use cases. We close this gap with NeDRex, an integrated and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotation and their relationships. NeDRex allows the construction of heterogeneous biological networks, mining for disease modules, prioritization of drugs targeting disease processes, and statistical validation. We demonstrate the usefulness of NeDRex in five specific use cases.
Cardiovascular disease is a leading cause of death in the general population and the second leading cause of mortality and morbidity among cancer survivors in the United States after recurrent malignancy. Growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; Nevertheless, there is limited knowledge about how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records.
Cognitive impairment such as dementia is an increasingly reported complication of SARS-CoV-2 infection. However, the underlying mechanisms responsible for this complication remain unclear. A better understanding of the causal mechanisms by which COVID-19 may lead to cognitive impairment is essential for the development of preventive and therapeutic interventions.
The COVID-19 pandemic has highlighted the need to prioritize rapidly and reliably clinically approved compounds for potential efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we deployed algorithms based on artificial intelligence, network diffusion, and network proximity, each tasked with ranking 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the prediction, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as lists of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable results across all datasets and metrics. This result motivated us to develop a multimodal technology that fuses the predictions of all algorithms, discovering that a consensus among different predictive methods consistently outperforms the best individual pipelines. We screened the top drug in human cells, achieving a 62% success rate, versus a 0.8% hit rate for nonguided screening. Four of the six drugs that reduce viral infection can be directly repurposed to treat COVID-19, offering novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection did not bind to proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that can be detected using docking-based techniques. can’t be done These advances provide a systematic pathway to identify future pathogens and potential therapeutics for diseases overlooked by the expense and extended timeframe of de novo drug development.
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Polyphenols, natural products present in plant-based foods, play a protective role against various complex diseases through their antioxidant activity and through various molecular mechanisms. Here we develop a network medicine framework to uncover mechanisms for the effects of polyphenols on health by considering molecular interactions between polyphenol protein targets and disease-associated proteins. We find that the protein targets of the polyphenol cluster are in specific neighborhoods of the human interactome, whose proximity to disease-causing proteins predicts known therapeutic effects of molecules. The method reproduces known associations, such as the effects of epigallocatechin-3
Gallate on type 2 diabetes, and predicts that rosmarinic acid has a direct effect on platelet function, presenting a novel mechanism through which it may affect cardiovascular health. We confirm this experimentally