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“Big Data, Big Opportunities:
It’s time to Roll up our sleeves
and get to work!”
Brian D. Athey, Ph.D.
Professor and Chair,
Department of Computational Medicine
and Bioinformatics
Professor of Psychiatry and of Internal Medicine
Director, Academic Informatics
University of Michigan Medical School
• I am founding chair of the S&TAB of Appistry, Inc.; St. Louis, MO
• I am a founding member of the SAB, Accentia Biosciences; Tampa, FL
• I am joining the SAB or AssureRx Health; Mason, OH
• I am chair of the Technical Advisory Board; 1Mind4Research, Washington, DC
• I serve (or have served) on numerous CTSA Steering Committees and Boards at Academic Health Centers and Clinics (e.g Marshfield Clinic)
• I serve on the ad hoc sub-committee of overseeing caBIG and the NCI Informatics Strategy, reporting to the NCI Director and the NCI Board of Scientific Advisors
• I am no longer a consultant to the NIH CIO (ended March, 2011)
4/23/2012 3 4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics
Outline
• Our new data intensive world/issues
• The science of Biomedical Informatics
• Genomics and Medicine: Toward Personalized
Medicine
• Gearing up the Academic Health Center
Enterprise data infrastructure
• Regional and national projects: Promise and
Complexity
• Issues to think about to enhance future success
4/23/2012 4 4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics
• Personal Computing Social Networks
• “Mobile”
• “Cloud”
• “Big Data”
• Media Driven
-----------------------------
Net = New way of life for us and
for Academic Health Centers
See also The Economist, Oct. 8th – 14th, 2011
Lee Hood, IOM February 27, 2012
Lee Hood, IOM February 27, 2012
4/23/2012 7 4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics
New York Times, January 4, 2012
"IBM's chairman, Samuel Palmisano, said in a
speech last September. "But there are also upward
of a trillion interconnected and intelligent objects and
organisms - what some call the Interconnected and
intelligent objects and organisms - what some call
the Internet of Things. all of this is generating vast
stores of information. It is estimated that there will
be 44 times as much data and content coming over
the next decade...reaching 35 zettabytes in 2020. A
zettabyte is a 1 followed by 21 zeros. And thanks to
advanced computation and analytics, we can now
make sense of that data in something like real time”.
“Data volumes are growing exponentially”
• There are many reasons for this growth:
– the creation of nearly all data today in digital
form
– a proliferation of sensors (e.g. Next-Generation
Sequencing)
– new data sources such as high-resolution
imagery and video.
• The collection, management, and analysis of data
is a fast-growing concern of NIT research.
• Automated analysis techniques such as data
mining and machine learning facilitate.
• Transformation of data into knowledge, and of
knowledge into action.
“Every federal agency needs to have a ‘big data’
strategy”
4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics
“In this data-rich world, your competitive advantage is your ability to transport, collect, store, organize, mine, visualize, and machine learn against data. This ‘computational knowledge extraction’ lies at the heart of 21st century discovery.”
This CI idea pervades all fields of modern research
--Ed Lazowska
Bill & Melinda Gates Professor at the University of Washington
Co-Chair, PCAST NITRD Subcommittee
E-mail responding to Athey-Glotzer U-Michigan CI Report
• Associate the avalanche of genomic and high-throughput molecular information with disease risk
• Powerful computational methods and integrated cyberinfrastructure to enable sophisticated hierarchal systems modeling and analysis
• Effective linkages with better environmental, dietary, and behavioral datasets for eco-genetic analyses
• Credible privacy and confidentiality protections in research and clinical care
• Breakthrough tests, vaccines, drugs, behaviors, and regulatory actions to reduce health risks and cost-effectively treat patients globally. Omenn and Athey, 2010
Population(s)
Athey and Omenn, 2010
QSP White Paper October, 2011 Page 14
Contemporary systems biology: four complementary approaches
Systems biology is advancing in four distinct but complementary directions, all of which are relevant to
pharmacology. The first involves largescale measurement and network inference. This approach aims
to discover interactions among hundreds or even thousands of genes and proteins using systematic,
highthroughput measurements (e.g. mRNA profiling, twohybrid screening, mass spectrometrybased
proteomics and metabolomics). The resulting data, which typically derive from highthroughput
genomic, proteomic or other –omic approaches are assembled into complex networks whose properties are studied using graphbased methods derived from computer science. Networks of this type have
been used to characterize drug targets in a systematic manner [3032] and are increasingly important in developing disease classifiers based on sequence or transcription data (sometimes called “systems
medicine” [28, 33]). The second direction involves attempts to elucidate the principles of biological design or function based on analogies with engineering or physics. Properties elucidated for one
biological network may be generalized into concepts such as “feed forward control”, “robustness”,
“adaptation”, etc. A notable success of these efforts has been the recognition that noise plays an
important role in limiting the accuracy of biochemical circuits and in creating celltocell variability;
conversely, the ability of some regulatory motifs (positive feedback for example) to increase precision in the face of this variability has attracted interest in it as a design feature [34]. The third thrust in
systems biology involves combining mathematical modeling of regulatory and signaling pathways with multiplex and singlecell experimental data as a means to understand the precise biochemistry,
dynamics and functions of the networks that control normal cellular physiology and cause disease. This approach is a natural complement to molecular, structural and cellular biology [35, 36]. At the
moment, this type of analysis is often limited to pathways of 20100 components, but the size of
networks that can be analyzed is expected to increase rapidly in the future. Because systems
pharmacology is necessarily multiscale, all three of these systems biology approaches are expected to
be important in the future development of the field. The fourth approach, which may have a large
impact in the long term, is “synthetic biology”. The synthetic strand in systems biology aims to create
fundamentally new biological devices based on discoveries from other areas of systems biology and new approaches to genetic engineering. Synthetic biology adds the fields of biochemical engineering
and industrial process optimization to systems biology and also adds problems outside the purview of conventional biomedicine, such as bioenergy and bioremediation.
Figure 4. Horizontal and vertical integration in systems biology
and pharmacology. One representation of horizontal and
vertical integration emphasizing
changes in physiological
complexity, which tends to parallel
changes in time scales (from
seconds and minutes to years and lifespans). The goal for QSP is to bring networklevel understanding
of drugs to the complex physiology
of patient responses. The arrows
denote trend lines.
Achieving horizontal and vertical integration through multiplex measurement and modeling
The 2008 white paper on quantitative and systems pharmacology (summarized in Appendix 1) carefully
considered the complementary strengths of “horizontal” (Appendix 2) and “vertical” (Appendix 3)
integration in pharmacology (Figure 4). Many practical and conceptual challenges remain in achieving
effective horizontal and vertical integration of biological knowledge, and the difficulties are magnified by
the tendency of practitioners to focus on a single type of data (proteomics or genomics, for example)
and of funding agencies and academic organizations to value specialists over integrators. Cultural
Emergence of Quantitative and Systems Pharmacology:
An NIGMS White Paper (Sorger et al., 2011)
Lee Hood IOM February 27, 2012
The Science in the Middle: Linking Core Facilities to
Models and Driving Research Problems
4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics
The Scope of Biomedical Informatics and
Cyberinfratstructure: Classical View
Ted Shortliffe, 2005
Eric E. Schadt “Molecular networks as sensors and
drivers of common human diseases”. (2009). Nature
461, 218-223. doi:10.1038/nature08454
General Models we Must Consider
Bill Stead, IOM 2007
PCAST NITRD recommends development of:
• Electronic Health Records (EHR), Personal Health Records (PHR) and Health Information Exchange
• Universal Exchange Mechanism for Health IT Data
• Dynamic ‘OMIC’ Analytics and Data Management Infrastructure for Longitudinal Patient-Centric EHR/PHR
• Pharmacogenetic Informatics
• Link Integrated Medication Systems to Basic Research Systems such as High-Through-put Sequencing
• Development of an Informatics-based Surgery Network
Red indicates touch points to Genomics
• “It is recommended that a Dynamic ‘Omics Analytics and Data Management Infrastructure for enhanced analysis and standardized interoperability with a Longitudinal Patient-Centric Electronic Health Record (EHR)/Personal Health Record (PHR) be created. This will enable Integration between ‘multi-omics’ data at Patient/Research Participant level in EHR:
• Genomics; Epigenomics; Proteomics; Metabolomics
• Pharmacogenomics; Toxicogenomics
• Imaging; Cognitive and Behavioral measures; Environmental measures
• Secure links to Patient Data in EHR/PHR
• Socio-economic measures”
Transcriptome Digital Gene
Expression Methylation Small RNA ChIP-Seq
Gene Fusion
Discovery
Mutation discovery
Alternative Splice
(AS) Variants
Gene expression
Discovery
miRNA profiling Binding site
detection Enumerate gene
expression
The ‘Generic’ Genomics Culprit
Stein LD: The case for cloud computing in genome informatics.
Genome Biology 2010, 11:207.
Colliding worlds of data production and storage
George Poste, IOM Feb. 28, 2012
George Poste,
IOM Feb. 28, 2012
Spanning Discovery, Translation, and Patient Care
•“Clinomics”
•Epigenomics
•Pharmacogenomics
•Microbiome
•Biomarkers
--Courtesy Gianrico Farrugia, M.D.; Mayo Clinic Rochester
Westfall, J. M. et al. JAMA 2007;297:403-406.
Every step of the translational research pathway requires
Integration with HIT
T4 Outcomes
• Interoperability with Institutional EHR Systems
• Clinical transaction systems
• Clinical Data Repository (CDR)
• De-identification/Honest Brokering
• Tools to Facilitate Extracting/Downloading Data Software tools • CTSI Portals • Clinical Trial/Study Databases • Genomic, Proteomic, and Metabolomic High-Throughput Data
Repositories and Analysis Tools • Clinical Imaging Data Repositories and Analysis Tools • An Institutional Specimen Tracking System • A CTSA Core Lab LIMS (Laboratory Information Management System) • Population/Public Health Databases & Informatics Needs • Standards to promote interoperation within and between CTSA sites • Informatics Teaching & Training (Interface with CTSA Education
Program) • Biomedical Informatics Research in Support of C&T Research • Faculty, Staff, and Administrative Structure for Biomedical Informatics
CTSA Informatics Consortium Operations Committee
Bill Hersh (OSHU) and Brian Athey (UMICH), co-chairs.
2007
We need more than IT
Bioinformatics Clinical Informatics
How to utilize basic science data to attain knowledge and make it useful
How to organize, structure and manage clinical data to make it content rich
Data Strategy , Architecture and Translation Functional output for:
“Science” + “Practice” = Research Education Patient Care Administration
Computation
Computer Science Information Technology
Science and research behind computing and data management capabilities: e.g. storage, speed, cost etc.
Hardware + Software – Where and how to capture, store, process and communicate data
William S. Dalton; Moffitt Cancer Center; IOM Feb. 27, 2012
Gender
Ethnicity
Age
Weight
Diagnosis
Medical History
Literature
Databases
Terminologies
Ontologies
Lab Tests
Genes
Proteins
Biological Models
Technologies
Algorithms
Research
Essence of what we need out of the Data Factory
Lawrence Shulman, Dana-Farber Cancer Institute IOM Feb 27, 2012
This perspective highlights the importance of investing in foundational IT and architecture, and interfaces between “silos” to enable secure data flow across patient care, business operations, research, and education.
Operational Management (Historical. e.g. quality, billing, reporting etc.)
Biomedical Research & Education
Trials
Quality Reports
Clinical Data Warehouse 1. CAD 2. QMP 3. ‘HSDW’ 4. Clarity 5. Others……..
Comparative Effectiveness
Research
Population Research
‘Omics Repository Administration Systems
Patient Care (Electronic Health Record)
Multiple Clinical Systems
Research Warehouse Clinical Data Repository
External Organizations
External Organizations
• Honest Broker • De-Identification • Anonymization • Consents • Identity Management • Vocabulary Mapping
---------------Enterprise Data Warehouse------------
Financial Reports
34
Data Warehouses must become Data Factories
William S. Dalton; Moffitt Cancer Center; IOM
Feb. 27, 2012
Biomedical Engineering
Historical Data
Registries
OpenClinica
Velos
BioDBX
RedCAP
Research Data Warehouse
Messaging Bus, ETL & External Collaboration Services (SOA, caGRID, SHRINE, ...)
Vocabulary & Terminology Mapping Services (ICD-9/10 SNOMED, IMO, caDSR, ...)
Common Identifier Services (Patient, Provider, Research, Specimens, External Mappings)
HIPAA/IRB Services (Honest Broker, DE-ID Consent Management, …)
Epic Clarity
HIM/ Documentation
Radiology
Pathology
Pharmacy
CareLink/ Eclipsys
Others…
Scheduling
Revenue Cycle
Emergency Med.
Ambulatory
International Data Sharing
with External
Collaborators
CTSAs caBIG
TCGA
ULAM
Tissue Biorepositories
Metabolomics
Proteomics
Bioinformatics
Next-Gen Sequencing
Collexis
eThority (billing)
Click Commerce
(IRB)
Research Pre, Post- Award
Industry:
Pharma/
Biotech I2b2/
SHRINE
Others …
Demographics
Diseases
Individuals
Populations
Po
rtals
/ Pro
vid
ers
, Payo
rs, P
. Health
Data
bases / H
IEs / N
HIN
IT S
ER
CU
IRT
Y
Cam
pu
s S
yste
ms
IT S
ecurity
HSDW
i2b2
High
Performance
Cloud
Computing &
Data Storage
IT S
ecu
rity
• Reporting
• Visualization
• Analysis &
• Data Mining
IT S
ecu
rity
Brian Athey
& ECRIT
1/11/11
Health
Sciences
Library
Resources
NIH-Specific &
External Data
Resources
(PubMed, GenBank,
KEGG, GO, etc.)
SPORES
Others
Clinical Analysis
Database (CAD)
UMHS Data Architecture Unifying the Three Missions:
Education, Research, & Patient Care
Visiting Student Application Service(VSAS)
M-Pathways
Ctools/Saki 3
Curriculum Eval. System
Clinical Scheduling & Grading System
Comprehensive Clinical Assessment Exam
Admissions
CAD
CDR Education Knowledge Repository
Research Administration
Data Warehouse
Research
Administration
Systems
Research
Core
Facilities/
‘Omics’
Research
Data
Management
Systems
Research &
Quality
Metrics
Data Marts
Quality
Metrics
Reporting
&
Peer
Review
Patient Care Systems
Legacy+/Epic EHR
Enterprise Federated Data Warehouse
Service-Oriented Information Bus
Education
Bioinformatics and Systems
Biology Workbenches
Biomedical Engineering
Historical Data
Research
Data
Management
Systems
Registries
OpenClinica
Velos
BioDBX
RedCAP
Research Data Warehouse
Messaging Bus, ETL & External Collaboration Services (SOA, caGRID, SHRINE, ...)
Vocabulary & Terminology Mapping Services (ICD-9/10 SNOMED, IMO, caDSR, ...)
Research
Administratio
n
Systems
Common Identifier Services (Patient, Provider, Research, Specimens, External Mappings)
HIPAA/IRB Services (Honest Broker, De-ID Consent Management, …)
Epic Clarity
Patient Care Systems
Centricity Documentation
Radiology
Pathology
Pharmacy
CareLink/ Eclipsys
Others…
Scheduling
Revenue Cycle
Emergency Med.
Ambulatory
Research Core
Facilities/‘Omics’
International Data Sharing
with External
Collaborators
CTSAs caBIG
TCGA
Epic EHR Legacy +
ULAM
Tissue Biorepositories
Metabolomics
Proteomics
Bioinformatics
Next-Gen Sequencing
Bioinformatics and Systems
Biology Workbenches
Collexis
eThority (billing)
Click Commerce
(IRB)
Research Pre, Post- Award
Industry:
Pharma/
Biotech I2b2/
SHRINE
Others …
Demographics
Diseases
Individuals
Populations
Po
rtals
/ Pro
vid
ers
, Payo
rs, P
. Health
Data
bases / H
IEs / N
HIN
IT S
ER
CU
IRT
Y
Cam
pu
s S
yste
ms
IT S
ecurity
HSDW
i2b2
High
Performance
Cloud
Computing &
Data Storage
IT S
ecu
rity
• Reporting
• Visualization
• Analysis &
• Data Mining
IT S
ecu
rity
Research &
Quality Metrics
Data Marts
Brian Athey
& ECRIT
1/11/11
Health
Sciences
Library
Resources
NIH-Specific &
External Data
Resources
(PubMed, GenBank,
KEGG, GO, etc.)
SPORES
Others
CIDSS Analytics
& Reporting Tools
Quality
Metrics
Reporting
&
Peer
Review
Education
CAD
CDR Education Knowledge Repository
Research Administration
Data Warehouse HIM
Others…
M-Pathways
CTools/Sakai 3
Curriculum Evaluation System
Clinical Scheduling
Comprehensive Clinical Assessment
Exam
Admissions
UMHS Data Architecture Unifying the Three Missions:
Education, Research, & Patient Care
I2b2/
EMERSE
Biomedical Informatics Layer
Neo
nat
es
UM
ClinicalStu
dies.org
Informed Consent
Process/Forms
Genomic DNA
+ EHR/PHI
Disease Only
Genomic DNA
+ EHR/PHI
No Restrictions
Genomic DNA
+ EHR/PHI
Re-consent
DNA Samples
caTISSUE Database
EHR/PHI Data
Center for Health
Communication
Research Wel
lnes
s
Participant Portal
Asset
Layer
Permission
Layer
Informed
Consent Layer
Vu
lner
abili
ty D
om
ain
s
Ag
ed
Fat
al Il
lnes
s
Research
Data
Warehouse
Ho
nes
t
Bro
ker PI-Driven
Informatics
Analysis
(BIC)
IRB review
& approval
DNA
Sequencing
Core & Data
PI Portal
De-ID
Sequence
Data
Sequence
DNA
Samples
Access DNA
Samples
(De-ID or Re-ID)
Recruitment Enrollment, Biospecimen
Processing & Storage, EHR/PHI Capture Data Organization, Analyses,
Integration & Sharing
Recruitment Layer
MICHR Stewardship
Design
& Enable
Specific
Protocols
(BERD)
INSTITUTIONAL REVIEW BOARD
I2b2/ EMERSE
Research Data
Warehouse
Honest
Broker (ID, De-ID)
PI-Driven Informatics Analysis
IRB review & approval
DNA Sequencing Core & Data
PI Portal
Sequence
Data
Sequence DNA Samples
Access DNA Samples
(ID, De-ID)
Data Organization, Analyses, Integration & Sharing
Design & Enable Specific
Protocols
INSTITUTIONAL REVIEW BOARD
DNA Samples
EHR/PHI Data
Marshfield Clinic
UW-Madison
UW-Milwaukee
Med. College of Wisc.
Wisconsin Genomics Initiative - Expertise/unique resources
- Clinical Data
- Biobank & Genetic Results
- Genotyping Facilities
- Biostatistics
- Machine Learning
• Rarely a “one-stop shop” • Expertise to collect, curate and maintain rich sources of data
• Expertise and resource to process rich sources of data
• Development of shared resources and networks • Move beyond just “big data”
Structured Data
Handwritten Documents (Scanned, Paper Charts)
Images (PACS, Photos)
Electronic Text
Past Present Future Optical Character Recognition (OCR)
Manual Abstraction
Image Analysis Manual Abstraction
Natural Language Processing (NLP)
Data Warehouse (DW) Queries
How To Process It
• The richest source of information isn’t always the easiest to get to
• A single source of information rarely tells the whole picture
• Once processed, data doesn’t always “make sense”
• Multi-disciplinary and iterative approach
• Scientists (what the goal of the data is)
• Data experts (how data is represented)
• Content experts (how the data is collected/created) Peissig P, et al. JAMIA 2012:19 Rasmussen L, et al. JAMIA 2011:ePub Starren J, Personal Communication
Genomics High Performance Computing (HPC)
NIH National Center for Integrated Biomedical
Informatics (NCIBI): Overview
Integrated Tools and that are Integrative
and Analyzable
Athey, B.D., Cavalcoli, J., H.V Jagadish, G.S. Omenn, B. Mirel, M. Kretzler, C. Burant, R. Isopheki, C. DeLisi, the NCIBI faculty, trainees, and
staff. 2011. The NIH National Center for Integrative Biomedical Informatics (NCIBI). J. Am. Med. Inform. Assoc. doi:10.1136/amiajnl-2011-
00552.
SMART Container
i2b2
PHR
EMR Epic Cerner Vista
CDR
i2b2
ETL
ETL
SHRINE
Cohort DiscoveryFederated Queries
SHRINE
Cohort DiscoveryFederated Queries
i2b2
CTMS
ETL
ETL
SMART Application
Patients
SMART Application
Research
SMART Application
Physicians
Harvard i2b2 – Michigan Partnership: Enterprise SMART i2b2
• EMR View running inside i2b2 web client • Searchable / scrollable list of installed SMART apps
Example – SMART enabled i2b2
The Meducation SMART app processes medication lists from
the patient record and then enables viewing and printing of
simplified medication instructions in any of a dozen languages
(http://smartapp.meducation.com/)
The “SMART Clinical Researc app” allows investigators to
automatically populate clinical research forms (following the
CDISC CDASH content standard) for the “Demograpics” (DM)
and “Prior and Concomitant Medications” (CM) domains for
which the FDA requires to submit data in a standardized way
from the EHR.
Priority Contact™ is an application that enhances the work
process of a clinician by managing contact with patients after
they have left the clinic and new information relevant to their
treatment plan has been obtained (e.g. the results of tests).
Data
Warehouse
Curation
ETL
ETL
Master
Data
ETL
Federation
Mining
Text Corpus (DIP, Conference Abstracts)
Lucene
Index
Internal
Clinical Research and Trials
Clinical Data
Gene Expression
RBM Analysis Results SNP
Curated Content
Non-clinical gene expression
Users
Data
And
Providers
• Clinical data after Database Load; de-identified, anonymized)
• Gene expression data (Affymetrix, Next-Gen Sequencing)
• Protein profiling data (Rules-based Medicine panels)
• Genetic data (candidate SNPs) • Metabolomics data • ELISA assays • Laboratory chemistry data • Proteomics data
Non-Profits Government Committers
Pharma / Biotech eTRIKS Academics
IC London
Subcontractors Core
Developer(s)
tranSMART Foundation
Community
Manager
Project Code and
Data
• Science is global and thrives in the digital dimensions;
• Digital scientific data are national and global assets;
• Not all digital scientific data need to be preserved and
not all preserved data need to be preserved indefinitely;
• Communities of practice are an essential feature of the
digital landscape;
• Preservation of digital scientific data is both a
government and private sector responsibility and benefits
society as a whole;
• Long-term preservation, access, and interoperability
require management of the full data life cycle; and
• Dynamic strategies are required.
CIO CRIO
EHR, ERP, Quality, PACS CTMS, IRB, Bio Banking, Grants Mgt, etc
JCAHO, AHRQ, MU, ACO, HIPAA, OSHA, HIE I2b2, caBIG, VIVO, IRB/HIPAA, clinicaltrials.gov
Customers: Physicians, nurses, CXO, managers Customers: Researchers, students, faculty
HIMSS AMIA
1000s FTEs 10s-100s FTEs
EDW (Clinical, Quality) EDW (Research, Education)
CIO vs. CRIO…
The “new
guy”
IT Analytics
Operations and workflows Decision support
Technologies Knowledge
Security Risk
Predictability, reproducibility Unpredictability, innovation
Engineering, implementation Architecture, research
… and, IT vs. Analytics The “new
buzzword”
Umberto Tachanardi, Ph.D U Wisconsin CTSA Informatics IKFC
An Emerging Need in Academic Health Center IT Organizations
Classic IT
Classic
Academia/NIH
IOM 2011
"If your daily life seems poor, do not blame it; blame yourself
that you are not poet enough to call forth its riches."
--R. M. Rilke
Let’s get to work!
NCIBI Program Officer (PO) – Dr. Karen Skinner, NIDA
NCIBI Lead Science Officer (LSO) – Dr. Jane Ye, NLM Director of Bioinformatics and Computational Biology
Dr. German Cavelier, NIMH; NCIBI Science Officer
Dr. Peter Lyster, NIGMS; Center for Bioinformatics and Computational Biology
Director, Center for Bioinformatics and Computational Biology,
NIGMS; Dr. Karin Remington
Elaine Collier, NCRR
NIGMS/NIDA U54-DA-0215191
UL-1RR024986/NCRR CTSA
tranSMART: Johnson & Johnson Corporation—Garry Neal, Corporate VP Pharma R&D; John Shon, Director, Clinical and Translational Programs
