MNM COVID-19 Study
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The Multinational Network Medicine COVID-19 Study aims to leverage network medicine methodologies to update clinical guidelines, repurpose existing drugs, and improve policies to combat COVID-19. Led by renowned researchers from top universities, the study will utilize a federated analysis data system to gather and analyze disparate clinical data without compromising patient privacy. The study is designed in three phases, aiming to collect data, analyze it, and update health policies, with an overarching goal to mitigate the pandemic's impact on global health.
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MNM COVID-19 Study
- 1. June 2020 Applying Network Medicine methodologies to update policy, clinical guidelines, and repurpose existing drugs for SARS-CoV-2 Multinational Network Medicine COVID-19 Study (MNM COVID-19 Study)
- 2. COVID-19 (the disease) presents a unique challenge. SARS-CoV-2 (virus causing the disease) is complex, spreads rapidly, and the varied outcomes of COVID-19 cases challenge what current clinical research is equipped to handle. There is a long road ahead to update policy and clinical guidelines, approve novel treatments, develop effective vaccines, and manufacture enough to distribute around the world.
- 3. Repurposed existing drugs in addition to updated global health policy and clinical guidelines will be essential for limiting the social and economic devastation caused by this virus. We need a multifaceted approach to bring this crisis to an end. POLICY AND PREVENTION CLINICAL GUIDELINES AND TESTING TREATMENTS NEAR-TERM 0-2 years MEDIUM TERM 2-5 years SOCIAL DISTANCING TARGETED TESTING AND CONTACT TRACING REPURPOSE EXISTING DRUGS and novel treatments (w limited availability) VACCINES WIDESPREAD TESTING AND SURVEILLANCE
- 4. OVERVIEW WHO ARE WE? WHAT IS NETWORK MEDICINE? The Network Medicine Alliance and Institute represent 28 leading universities and academic medical centers around the world committed to improving global health and advancing the field of Network Medicine. Network Medicine combines principles and approaches from network sciences, systems biology, and human dynamics to understand the causes of human diseases and develop new treatments. HOW CAN NETWORK MEDICINE IMPACT COVID-19? We are raising $20M to launch a three-phase clinical study applying Network Medicine methodologies to update policy, clinical guidelines, and repurpose existing drugs for SARS-CoV-2.
- 5. MULTINATIONAL NETWORK MEDICINE COVID-19 STUDY (MNM COVID-19 STUDY) The MNM COVID-19 study is spearheaded by researchers at Brigham and Women's Hospital, one of Harvard Medical School’s flagship teaching hospitals. $700M+ The Brigham oversees the second largest hospital-based research program in the world. Brigham Annual Research Budget The Channing Division of Network Medicine at the Brigham is renowned as the site of the Nurses' Health Study. Now in its third generation, the studies count more than 280K participants and have influenced health policy and clinical guidelines around the world.
- 6. MULTINATIONAL NETWORK MEDICINE COVID-19 STUDY (MNM COVID-19 STUDY) The Brigham’s Channing Division of Network Medicine (CDNM) CDNM uses an integrated network-based, systems biology-driven approach to determine the causes of complex diseases, reclassify them, and develop new treatments and preventive strategies. CDNM applies novel network science and artificial intelligence capabilities to its vast clinical databases and research studies with genetic, clinical, and epidemiological information on hundreds of thousands of research subjects. CDNM has more than 80 Harvard Medical School (HMS) faculty and 42 fellows in addition to 160 non-faculty Brigham and Women’s Hospital (BWH) employees, who are primarily in research and administration. In fiscal year 2019, CDNM investigators received 54 new funding awards resulting in 173 active grants. CDNM’s FY18 total research expenditures represented 25% of the Department of Medicine annual budget.
- 7. MNM COVID-19 STUDY And other distinguished Network Medicine Alliance members and affiliates will contribute additional scale, capabilities, and an international presence to the MNM COVID-19 Study: UCLouvain in Belgium Autonomous University of Barcelona Vall d'Hebron in Spain Maastricht University UMC+ in the Netherlands Technical University of Munich in Germany Sapienza Università di Roma in Italy Semmelweis University in Hungary Our co-partner, Karolinska Institutet and Karolinska University Hospital in Sweden, whose Nobel Assembly decides the Nobel prize in Medicine and Physiology, is committed to lead the European branch of the study.
- 8. 1) Which contributing biological and environmental factors are protective or detrimental to an individual's response to SARS-CoV-2 infection? and how do combinations of such factors explain the varied clinical outcomes observed which range from asymptomatic to severely ill requiring hospitalization? MNM COVID-19 STUDY KEY QUESTIONS 2) How can updated policies and clinical guidelines lessen the socioeconomic burden on society? 3) Which currently available drugs or combinations would be most effective when repurposed to treat specific patient groups?
- 9. THE MNM COVID-19 STUDY HAS THREE WORK STREAMS Collect Data Analyze Data Update Policy and Clinical Guidelines Establish multinational patient registry with data in format conducive to analysis using state-of-the-art Network Science and Artificial Intelligence techniques Analyze data to answer three fundamental questions: 1) Which contributing biological and environmental factors are protective or detrimental to an individual's response to SARS-CoV-2 infection? 2) How can updated policies and clinical guidelines lessen the socioeconomic burden on society? 3) Which existing drugs can be repurposed to treat specific patient groups defined by the analysis? Update global health policy and clinical guidelines including the drug repurposing program Expand access to the patient registry and analysis capabilities through research collaborations with public and private institutions
- 10. THE STUDY WILL LAUNCH IN THREE PHASES Collect Data Analyze Data Update Policy and Clinical Guidelines PHASE ONE PHASE TWO PHASE THREE Launch patient registry in eight countries to validate the common data model and standardized protocols Analyze data and identify preliminary drug repurposing candidates Launch global access and incorporate additional data sources to patient registry Refine global health policy, clinical guidelines, and drug repurposing analysis based on expanded patient registry Launch clinical trials to validate findings of the drug repurposing analysis Collaborate with public and private institutions to update global health policy and clinical guidelines Proof of Concept Global Access Policy Updates and Clinical Trials This is an iterative process. Data will be collected and analyzed, and policy and clinical guidelines updated in all phases of the study.
- 11. $20M FUNDRAISING TARGET FOR PHASES ONE AND TWO PHASE ONE PHASE TWO PHASE THREE $8M to launch study and publish preliminary results $12M to provide global access to patient registry and update policy and clinical guidelines $100M - $500M+ to support: 1) Ongoing epidemiological research and global health policy updates 2) Clinical trials to validate published drug repurposing research 3) Universal access to patient registry and advanced analytic capabilities 4) Continued research to better understand this complex disease Proof of Concept Global Access Policy Updates and Clinical Trials $1M $4M $3M Setup infrastructure and operations center Establish international patient registry and onboard first cohort of hospitals and outpatient clinics Analyze data and publish preliminary results Setup infrastructure and operations center Establish patient registry and onboard launch partners Analyze data and publish preliminary results $5M $7M Setup infrastructure and operations center Establish international patient registry and onboard first cohort of hospitals and outpatient clinics Expand analysis to update policy, clinical guidelines, and drug repurposing opportunities Expand international patient registry and analytic capabilities
- 12. Biomedical data is distributed over countless small and large repositories around the world. So regulatory and technical barriers prevent collaborative use of advanced techniques such as artificial intelligence to analyze these complex data sets comprised of clinical data, -omics data, and other metadata. To understand the varied disease manifestations of COVID-19, we need a way to overcome these barriers. HOW DO YOU ANALYZE DISTRIBUTED CLINICAL DATA WITHOUT COMPROMISING PATIENT PRIVACY? THE CHALLENGE Our Federated Analysis Data System (FADS) is built on a decentralized cloud computing infrastructure that does not require raw data to be transferred to a central site for analysis. THE SOLUTION
- 13. WHAT IS FADS? FADS uses federated learning to analyze medical data. Federated learning is an artificial intelligence (AI) technique that can analyze data stored at multiple locations, such as hospitals and outpatient clinics caring for patients with COVID-19, without the data ever leaving those locations. This means FADS enables researchers to analyze medical data at some of the best hospitals and outpatient clinics in the US, Europe, and the rest of the world in order to better understand the varied clinical outcomes observed in COVID-19 patients.
- 14. FADS IN A COVID-19 SETTING Step One: Define Common Data Model Representative Data in the COVID-19 CDM Patient Demographics Age, sex, race, socioeconomic, etc. Outpatient and Inpatient Medical Record Past and current medical conditions, interventions, medications, vaccinations, lab and imaging results, etc. COVID-19 History Exposure history, timing, location, and circumstances Progression of patient symptoms, clinical observations, lab and imaging results Medication history, dosage and timing of medications Therapeutic interventions needed for treatment Observed results of the medications and therapeutic interventions Outpatient outcomes and if transitioned to inpatient setting Evidence of any long-term complications The Common Data Model (CDM) enables researchers to analyze disparate observational databases. The concept behind this approach is to transform data contained within those databases into a common format with common terminologies, vocabularies, and coding schemes.
- 15. FADS IN A COVID-19 SETTING Step Two: Analyze the Data and Deploy Predictive Models Researchers develop a master set of predictive models based on historical COVID-19 patient data to predict clinical outcomes and identify drugs that can be repurposed to treat specific patient groups defined by the analysis. Federated learning techniques enable researchers to deploy copies of the models at each hospital and outpatient clinic participating in the study while maintaining master predictive models that don’t require access to underlying patient data. Master Set of Predictive Models Copies of Master Predictive Models Deployed Copies of Predictive Models at Hospitals and Outpatient Clinics
- 16. FADS IN A COVID-19 SETTING Step Three: Master Set of Predictive Models Improves Over Time The master set of predictive models improves each time a deployed model returns an updated representation of clinical data. This is an iterative process that accelerates as more hospitals and outpatient clinics join the study. Deployed Models Learn from Patient Data at Hospitals and Outpatient Clinics Deployed Models are Sent Back Periodically to Update Master Set of Predictive Models More Deployed Models = Better Predictions from Master Set of Models
- 17. MNM COVID-19 STUDY EXECUTIVE COMMITTEE Joseph Loscalzo, MD, PhD Chair Board of Directors and Scientific Committee, Network Medicine Alliance and Institute, Hersey Professor of the Theory and Practice of Medicine, Soma Weiss, MD, Distinguished Chair in Medicine: Harvard Medical School. Chairman, Department of Medicine and Physician-in-Chief: Brigham and Women’s Hospital, USA Jean-Luc Balligand, MD, PhD President of Experimental and Clinical Research UC Louvain, Professor of cardiovascular physiology and pharmacology, Physician at Cliniques Universitaire Saint-luc, Universite` Catholique de Louvain (UC Louvain), Belgium Albert-Laszlo Barabasi, PhD Robert Gray Dodge Professor of Network Science, Distinguished University Professor, Director of Center for Complex Network Research, Dept. Of Physics, Northeastern University, Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, USA, Director Network and Data Science, Central European University, Hungary Jan Baumbach, PhD Professor and Chair of Experimental Bioinformatic, Director of Information Technology (ITW), TUM School of Life Sciences (WZW), Technical University of Munich (TUM), Germany Sebastiano Filetti, MD Professor of Medicine, Delegate for "International Relations Health Care”, Sapienza Università di Roma, Italy Eugenio Gaudio, MD Rettore of the Università, Professor of Medicine, Sapienza Università di Roma, Italy Paolo Parini, MD, PhD Professor, Senior Consultant, Director of Research & Development, Education and Innovation, Head of Inflammation and Infection, Karolinska University Hospital, Department of Medicine Karolinska Institutet at Huddinge University Hospital, Sweden Enrico Petrillo, MD Executive Director of the Network Medicine Alliance and Institute, Advisor to Chairman, Department of Medicine and Physician-in-Chief for International Research, Lead Investigator, Division of General Internal Medicine, Brigham and Women’s Hospital, Lecturer, Harvard Medical School, USA Edwin Kepner Silverman, MD, PhD Professor Harvard Medical School, Chief of The Channing Division of Network Medicine, Brigham and Women's Hospital, Physician Pulmonary and Critical Care Medicine, Harvard Medical School, USA
- 18. KEY TAKEAWAYS COVID-19 AND FUTURE PANDEMICS The COVID-19 patient registry and Federated Analysis Data System (FADS) enable unprecedented analytic capabilities in a clinical setting and will lead to publications, public health policies, and updated clinical guidelines to help COVID-19 patients. IT’S TIME TO ACT These new capabilities will have a lasting effect on the medical community. Federated learning can untap the full potential of Network Medicine leaving us better equipped to address global health issues and better prepared for the next pandemic. We would greatly appreciate if you’d consider contributing to help us launch the MNM COVID-19 Study, establish the patient registry, and begin onboarding hospitals and outpatient clinics to FADS so we can do our best to bring an end to this crisis.
- 19. Thank You Please continue to the appendix for additional MNM COVID-19 Study Resources and Network Medicine References. For more information about the Network Medicine Alliance and Institute which is sponsoring the study please visit www.network-medicine.org Enrico Petrillo, MD Brigham and Women's Hospital, Harvard Medical School [email protected] MNM COVID-19 STUDY Paolo Parini, MD, PhD Karolinska Institutet, Karolinska University Hospital [email protected]
- 20. Mission Statement, Network Medicine Applied to Combat COVID-19 This memo from the International Network Medicine Consortium and Foundation expands on why Network Medicine is an essential strategy for the fight against COVID-19. Cover Letter, MNM COVID-19 Study The MNM COVID-19 Study executive committee describes the methodology and goals of the proposed MNM COVID-19 outpatient and inpatient studies. Framework and Outline, MNM COVID-19 Outpatient Study Framework and Outline, MNM COVID-19 Inpatient Study These provide a framework and outline of the three phase MNM COVID-19 outpatient and inpatient studies. Key questions to be addressed by MNM COVID-19 Outpatient Study Key questions to be addressed by MNM COVID-19 Inpatient Study These describe the key questions to be addressed by each branch of the MNM COVID-19 study. Federated Analysis Data System (FADS) The multifaceted disease states of COVID-19, complex regulatory landscape, and advanced Network Medicine techniques require a novel approach to data storage and analysis. This memo describes the proposed Federated Analysis Data System that will manage the international patient registry we create during Phase One of the MNM COVID-19 study. Standardized Protocols and Common Data Model for Federated Analysis Data System (FADS) This describes the Standardized Protocols and Common Data Model required to manage and analyze complex clinical data collected from repositories around the world. Scientific Rationale for the MNM COVID-19 Study This references featured papers in peer reviewed journals and provides a scientific rationale for the proposed MNM COVID-19 study. These MNM COVID-19 Study Resources are available upon request. Please email Rico or Paolo at [email protected] or [email protected] APPENDIX MNM COVID-19 STUDY RESOURCES
- 21. Molecular networks in Network Medicine: Development and applications Edwin K. Silverman, Harald H. H. W. Schmidt, Eleni Anastasiadou, Lucia Altucci, Marco Angelini, Lina Badimon, Jean-Luc Balligand ,Giuditta Benincasa, Giovambattista Capasso, Federica Conte, Antonella Di Costanzo, Lorenzo Farina, Giulia Fiscon, Laurent Gatto, Michele Gentili, Joseph Loscalzo, Cinzia Marchese Claudio Napoli, Paola Paci, Manuela Petti, John Quackenbush, Paolo Tieri, Davide Viggiano, Gemma Vilahur, Kimberly Glass, Jan Baumbach Systems Biology and Medicine March 20, 2020 Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study Castaldi PJ, Boueiz A, Yun J, Estepar RSJ, Ross JC, Washko G, Cho MH, Hersh CP, Kinney GL, Young KA, Regan EA, Lynch DA, Criner GJ, Dy JG, Rennard SI, Casaburi R, Make BJ, Crapo J, Silverman EK, Hokanson JE; COPDGene Investigators. Chest. 2019 Dec 28. pii: S0012-3692(19)34456-3. doi: 10.1016/j.chest.2019.11.03 A genome-wide positioning systems network algorithm for in silico drug repurposing Feixiong Cheng, Yuan Hou, Diane E. Handy, Ruisheng Wang, Yuzheng Zhao, Yi Yang, Jin Huang, David E. Hill, Marc Vidal, Charis Eng & Joseph Loscalzo Nature Communications volume 10, Article number: 3476 (2019) Network Medicine, Complex Systems in Human Disease and Therapeutics Joseph Loscalzo, Albert-László Barabási, & Edwin K. Silverman Harvard University Press February 1, 2017 The exposome and health: Where chemistry meets biology Roel Vermeulen, Emma L. Schymanski, Albert-László Barabási, Gary W. Miller Science 24 Jan 2020: 367, 6476, 392-396 The unmapped chemical complexity of our diet Albert-László Barabási, Giulia Menichetti & Joseph Loscalzo Nature Food 1, 33-37 (2019) Network-based prediction of protein interactions István A. Kovács, Katja Luck Yang Wang, Carl Pollis, Sadie Schlabach, Wenting Bian, Dae-Kyum Kim, Nishka Kishore, Tong Hao, Michael A. Calderwood, Marc Vidal & Albert-László Barabási Nature Communications 10, Article number: 1240 (2019) Network-based prediction of drug combinations Feixiong Chen, István A. Kovács & Albert László Barabási Nature Communications 10, Article number: 1197 (2019) Network-based approach to prediction and population-based validation of in silico drug repurposing Feixiong Cheng, Rishi J. Desai, Diane E. Handy, Ruisheng Wang, Sebastian Schneeweiss, Albert-László Barabási & Joseph Loscalzo Nature Communications, volume 9, Article number: 2691 (2018) APPENDIX SELECTED NETWORK MEDICINE REFERENCES
- 22. The exposome and health: Where chemistry meets biology Roel Vermeulen, Emma L. Schymanski, Albert-László Barabási, Gary W. Miller Science 24 Jan 2020: 367, 6476, 392-396 January 24, 2020 The unmapped chemical complexity of our diet Albert-László Barabási, Giulia Menichetti & Joseph Loscalzo Nature Food 1, 33-37 (2019) December 9, 2019 Nature’s reach: narrow work has broad impact Alexander J. Gates, Qing Ke, Onur Varol & Albert-László Barabási Nature 575, 32-34 (2019) November 7, 2019 Network-based prediction of drug combinations Feixiong Chen, István A. Kovács & Albert László Barabási Nature Communications 10, Article number: 1197 (2019) March 13, 2019 Taking Census of Physics Federico Battiston, Federico Musciotto, Dashun Wang, Albert-László Barabási, Michael Szell, and Roberta Sinatra Nature Reviews Physics 1, 89-97 (2019) January 8, 2019 A Structural Transition in Physical Networks Nima Dehmamy, Soodabeh Milanlouei & Albert-László Barabási Nature 563, pages676–680 (2018) The Fundamental Advantages of Temporal Networks A. Li, S. P. Cornelius, Y.-Y. Liu, L. Wang, A.-L. Barabasi Science 358:6366, 1042-1046 (2017). The Elegant Law that Governs Us All A.-L. Barabasi Science 357:6347 (2017) Network Science Albert-László Barabási Philosophical Transactions of The Royal Society 371, 1-3 (2013) APPENDIX SELECTED NETWORK SCIENCES REFERENCES