Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Northeastern University（东北大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Scipher Medicine；Sabanci University（萨班哲大学）；Boston University（波士顿大学）；Central European University（中欧大学）
Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert Davey, Joseph Loscalzo, Albert-László Barabási
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to rare types such as the case in repurposing drugs for novel diseases. This motivates the task of few-shot link prediction. Typically, GCNs are ill-equipped in learning such rare link types since the relation embedding is not learned in an inductive fashion. This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime. The…
Drug repurposing has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. In this study, we propose a clinical connectivity map framework for drug repurposing by leveraging laboratory tests to analyze complementarity between drugs and diseases. We establish clinical drug effect vectors (i.e., drug-laboratory test associations) by applying a continuous self-controlled case ser…
The 2019 novel coronavirus (SARS-CoV-2) pandemic has resulted in more than a million deaths, high morbidities, and economic distress worldwide. There is an urgent need to identify medications that would treat and prevent novel diseases like the 2019 coronavirus disease (COVID-19). Drug repurposing is a promising strategy to discover new medical indications of the existing approved drugs due to several advantages in terms of the costs, safety factors, and quick results compared to new drug design and discovery. In this work, we explore computational data-driven methods for drug repurposing and …
School of Computer Science and Engineering, Hunan University（湖南大学计算机科学与工程学院）；AWS Shanghai AI Lab（亚马逊云科技上海人工智能实验室）；Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic（克利夫兰诊所勒纳研究所基因组医学研究所）；New York University Shanghai（上海纽约大学）；AWS AI（亚马逊云科技人工智能部门）；University of Minnesota（明尼苏达大学）；Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University（凯斯西储大学克利夫兰诊所勒纳医学院分子医学系）；Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine（凯斯西储大学医学院凯斯综合癌症中心）
There have been more than 850,000 confirmed cases and over 48,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there are currently no proven effective medications against COVID-19. Drug repurposing offers a promising way for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehe…
Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, …
An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication
Milner Therapeutics Institute, University of Cambridge（剑桥大学米尔纳治疗学研究所）；LifeArc；KAIST（韩国科学技术院）；University College Dublin（都柏林大学学院）；Dataiku；University of London（伦敦大学）；University of Surrey（萨里大学）；Imperial College London（伦敦帝国学院）；Thalesians Ltd（Thalesians有限公司）；The University of Manchester（曼彻斯特大学）
Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory require…
To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction networks integrating the top ranked host factors, drug target proteins, and directed protein-protein interaction data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular …
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with…
Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. It was first identified in Wuhan, China, and has since spread causing a global pandemic. Various studies have been performed to understand the molecular mechanisms of viral infection for predicting drug repurposing candidates. However, such information is spread across many publications and it is very time-consuming to access, integrate, explore, and exploit. We developed CoVex, the first interactive online platform for SARS-CoV-2 and SARS-CoV-1 host interactome exploration and drug (target) identifica…
The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific …
There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply to these subjective adjustments to reflect program-specific information. However, this approach lacks transparency and fails to make full use of dat…
The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling of host cells plays critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells, and drugs targeting these molecular signaling pathways could be p…
Scientific and technological advances in medicine and systems biology have unequivocally shown that health and disease must be viewed in the context of the interplay among multiple molecular and environmental factors. Understanding the effects of cellular interconnection on disease progression may lead to the identification of novel disease genes and pathways, and hence influence precision diagnostics and therapeutics. To accomplish this goal, the emerging field of network medicine applies network science approaches to investigate disease pathogenesis, integrating information from relevant Omi…
Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximise its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network.…
Introduction: Crohn's disease and ulcerative colitis, both under the umbrella of inflammatory bowel diseases (IBD), involve many distinct molecular processes. The difference in their molecular processes is studied by using the different genes involved in each disease, and it is explored further for drug targeting and drug repurposing. Methods: The initial set of genes was obtained by mining published literature and several curated databases. The identified genes were then subject to Systems and Network analysis to reveal their molecular processes and shed some light on their pathogenesis. Such…
We discuss in this survey several network modeling methods and their applicability to precision medicine. We review several network centrality methods (degree centrality, closeness centrality, eccentricity centrality, betweenness centrality, and eigenvector-based prestige) and two systems controllability methods (minimum dominating sets and network structural controllability). We demonstrate their applicability to precision medicine on three multiple myeloma patient disease networks. Each network consists of protein-protein interactions built around a specific patient's mutated genes, around t…
Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks…
Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Technical University München（慕尼黑工业大学）
Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays, libraries of medical images, or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering…
The investigation of topological properties of ovarian cancer network (OCN) and the roles of hubs involved in it by digging the network at various levels of organization are important to understand how OCN is organized to understand disease states. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties of the network and deeply rooted communities. Also, the network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We then showed that $\langle k\rangle$ can be taken as a scalin…
Antimicrobial resistance is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhanc…
Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large of number deaths around the world. Despite many decades' effort, no drugs have been approved by the Food and Drug Administration (FDA) for the treatment of cocaine dependence. Cocaine dependence is neurological and involves many interacting proteins in the interactome. Among them, dopamine transporter (DAT), serotonin transporter (SERT), and norepinephrine transporter (NET) are three major targets. Each of these targets has a large protein-protein interaction (PPI) network which must be consid…
