Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles
Beijing Institute of Pharmacology and Toxicology（北京药物研究所）；Beihang University（北京航空航天大学）；PLA General Hospital（中国人民解放军总医院）；Chengdu Jianshu Technology Co. Ltd（成都建数科技有限公司）
Lu Han, G. C. Shan, B. F. Chu, H. Y. Wang, Z. J. Wang, S. Q. Gao, W. X. Zhou
The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development. However, there are still many difficulties in identifying drug applications based on cell picture data. This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program. Because the multi-dimensional features of the image are affected by non-experimental factors, the characteristics of similar drugs vary greatly, and the current sample number is not enough to use deep learning and other methods are used for learning optimization. As a consequence, this study is based on the supervised ITML algorithm to convert the characteristics of drugs. The results show that the characteristics of ITML conversion are more conducive to the recognition of drug functions. The analysis of feature conversion shows that different features play important roles in identifying different drug functions. For the current COVID-19, Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting endocytosis, etc., and were classified to the same community. And Clomiphene in the same community inibited the entry of Ebola Virus, indicated a similar MoAs that could be reflected by cell image.
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 …
Towards a more inductive world for drug repurposing approaches
CIMA University of Navarra（纳瓦拉大学CIMA）；IdiSNA（纳瓦拉卫生研究所（IdiSNA））；TECNUN, University of Navarra（纳瓦拉大学TECNUN）；Stanford Center for Biomedical Informatics Research, Stanford University（斯坦福大学生物医学信息学研究中心）；Ghent University（根特大学）；Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra（纳瓦拉大学数据科学与人工智能研究所（DATAI））
Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there are abundantly of opportunities to discover drugs by drug repurposing in an efficient manner. The recent pandemic of SARS-COV-2, that caused the death of 6,245,750 human beings to date, has tremendously increase the exceptional usage of bioinformatics tools in interpreting the mole…
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；University of Helsinki（赫尔辛基大学）
Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge gra…
Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple d…
The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover the underlying associations between viral proteins and antiviral therapeutics that are effective against them by employing neural network models. Using the National Center for Biotechnology Information virus protein database and the DrugVirus database, which provides a comprehensive report of broad-spectrum antiviral agents (BSAAs) and viruses they …
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）
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, …
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）
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 SAR…
The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular 'signature' with minimal side-effects. This principle was defined and popularized by the influential 'connectivity map' study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug 'connectivity score.' Over the past 15 years, several studies have proposed variations in calculating connectivity scores towards improving accuracy and robustness in light of massive growth in reference dr…
Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；Relation Therapeutics（Relation Therapeutics公司）；The Scripps Research Institute（斯克里普斯研究所）；Glyde Bio, Inc（Glyde生物公司）；IRIC, Institute for Research in Immunology and Cancer, Université de Montréal（蒙特利尔大学免疫学与癌症研究所（IRIC））；University of Oxford（牛津大学）；Twitter（推特）
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state of the art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased towards synergistic agents and these results do not necessarily generalise out of distribution. We employ a sequential model optimization search utilising a deep learning model to quickly discover synergistic drug combinations active against a cancer …
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 …
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…
Université de Paris（巴黎大学）；Inserm（法国国家健康与医学研究院）；Université Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；Université Sorbonne Paris Nord（巴黎第十三大学）；Assistance Publique des Hôpitaux de Paris（巴黎公共医疗援助医院）
Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m $\ge$ 1 arms with largest means in a linear bandit model, in the fixed-confidence setting. These algorithms belong to the generic family of Gap-Index Focused Algorithms (GIFA) that we introduce for Top-m identification in linear bandits. We propose a unified analysis of these algorithms, which shows how the use of features might decrease the sample complexity. We further validate these algorithms empirically on simulated data and on a simple drug repurposing task.
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.…
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development. RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used. Meanwhile, artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, have been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. Thus, we conducted a rapid review of articles from the past 20 years, to provide a…
Relation Therapeutics（Relay Therapeutics（注：原文为Relation Therapeutics，直译：关系治疗公司））；University of Cambridge（剑桥大学）；Juvenescence（Juvenescence公司）；Mila, the Quebec AI Institute（米拉魁北克人工智能研究所）；HEC Montreal（蒙特利尔高等商学院）；The Francis Crick Institute（弗朗西斯·克里克研究所）；Imperial College London（帝国理工学院）；Twitter（推特）
Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of sma…
A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. Drug repositioning (or drug repurposing) is a fundamental problem for the identification of new opportunities for the use of already approved or failed drugs. In this paper, we present a method based on a multi-relation unsupervised graph embedding model that learns latent representations for drugs and diseases so that the distance between these representations reveals repositioning opportunities. Once representati…
Boosting drug-disease association prediction for drug repositioning via dual-feature extraction and cross-dual-domain decoding
Institute of Computing Science and Technology, Guangzhou University（广州大学计算科学与技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Electronics and Communication Sciences Unit, Indian Statistical Institute（印度统计研究所电子与通信科学部）
Development of new medications is a very lengthy and costly process. Finding novel indications for existing drugs, or drug repositioning, can serve as a useful strategy to shorten the development cycle. In this study, we present an approach to drug discovery or repositioning by predicting indication for a particular disease based on expression profiles of drugs, with a focus on applications in psychiatry. Drugs that are not originally indicated for the disease but with high predicted probabilities serve as good candidates for repurposing. This framework is widely applicable to any chemicals or…
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）
Drug repurposing can accelerate the identification of effective compounds for clinical use against SARS-CoV-2, with the advantage of pre-existing clinical safety data and an established supply chain. RNA viruses such as SARS-CoV-2 manipulate cellular pathways and induce reorganization of subcellular structures to support their life cycle. These morphological changes can be quantified using bioimaging techniques. In this work, we developed DEEMD: a computational pipeline using deep neural network models within a multiple instance learning framework, to identify putative treatments effective aga…
University College London（伦敦大学学院）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Chicago（芝加哥大学）；RWTH Aachen University（亚琛工业大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Rutgers, the State University of New Jersey（罗格斯大学，新泽西州立大学）；Science Museum（科学博物馆）；Italian Institute of Technology（意大利理工学院）；Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities（巴伐利亚科学与人文学院莱布尼茨超级计算中心）；CERN（欧洲核子研究中心）；Argonne National Laboratory（阿贡国家实验室）；University of Amsterdam（阿姆斯特丹大学）
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here…
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…
