Repurposed existing drugs and updated global health policy and clinical guidelines will be essential for limiting the social and economic devastation caused by this virus. So, we are leading a three-phase multinational Network Medicine clinical study (MNM COVID-19 study). The study will apply Network Medicine methodologies to repurpose existing drugs for SARS-CoV-2 infected patients and update global health policy and clinical guidelines.

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
epetrillo@bwh.harvard.edu
MNM COVID-19 STUDY
Paolo Parini, MD, PhD
Karolinska Institutet,
Karolinska University Hospital
paolo.parini@ki.se

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 epetrillo@bwh.harvard.edu or paolo.parini@ki.se
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