Download What is Precision Medicine

The Future of Precision
Medicine
March 22, 2017
Physician Community
Webinar Series
#DrHIT @HIMSS
Welcome to the Physician Community
Webinar Series Sponsored by the HIMSS
Physician Community
• A complimentary virtual event.
• Covers a wide range of topics on Medical Informatics,
HIEs (Health Information Exchange), Standards and
Interoperability, eMeasures and Quality Initiatives, and
how it affects, impacts and involves physicians.
• For more information, visit www.himss.org/physician or
contact Yvonne Patrick at [email protected].
#DrHIT @HIMSS
Welcome to the Physician Community
Webinar Series Sponsored by the HIMSS
Physician Committee
• Please insert all questions in the Q & A box
located on the bottom right of your screen.
• A copy of the recording and slide set will be
available for download within 5 business days on
the Physician Community Webinar Series Archive
Page www.himss.org/physician
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Moderator:
Stuart Rabinowitz, MBA, MSHI, Director of Federal Health Data and
Informatics programs within QuintilesIMS’s IMS Government Services
organizations.
Stuart holds an undergraduate degree from Temple University, an MBA from
Lehigh University, and a Master’s of Science in Health Informatics from the
University of Illinois at Chicago
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Speakers:
John Rigg PhD, Head of Predictive Analytics, Global Real-World Insights, QuintilesIMS
John heads-up the Predictive Analytics practice in IMS Health’s Global Real-World
Insights. He develops innovative solutions to solve challenging healthcare problems
using large-scale, real-world patient-level data based on a variety of advanced
statistical and machine learning methods. This encompasses applications such as
clinical decision-support tools, risk stratification calculators, rare disease detection
algorithms and physician targeting alerts. John has over twenty years developing
predictive analytics solutions in life sciences, financial services and academia. He is
frequently invited to give thought leadership presentations in industry and academia.
John received his PhD from Cambridge University and has held post-doctoral
research positions at the University of Essex and the London School of Economics.
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Speakers:
Ronald Miller, PhD, Real World Insights, QuintilesIMS
Dr. Miller has over 9 years of research lab experience in functional genetics,
transcriptome profiling, and embryonic stem cell research. Over the past 5
years at QuintilesIMS, he has provided scientific consulting support to help
commercial and government clients integrate clinical and genomic data to
address precision medicine issues. Dr. Miller is currently working to develop
a Genomic Real World Data platform that can be applied to support research
and clinical applications within government and industry. Dr. Miller holds a
PhD in Human Genetics from Johns Hopkins School of Medicine and is a
member of the American Society of Human Genetics.
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Speakers:
Ana Maria Rodríguez, PhD, MSc, PT Senior Epidemiologist, Real-World
Evidence Solutions
Dr. Rodriguez designs, conducts, and interprets real-world studies,
specializing in direct-to-patient studies and pragmatic trials. She brings 12
years of experience including physical activity and rehabilitation in chronic
care, the translation of research findings to routine clinical care, and in
patient advocacy and engagement. Trained as both an epidemiologist and a
psychometrician, she specializes in the use of patient-reported outcomes in
research. Dr. Rodriguez earned a PhD in Clinical Epidemiology, and a MSc
in Public Health/Rehabilitation Sciences at McGill University, and completed
a postdoctoral fellowship in Oncology Epidemiology at University of Toronto.
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Speakers:
Maria Murray, PhD. Senior Consultant, IMS Government Solutions
Maria Murray, PhD is a Senior Consultant in IMS Government Solutions, part
of QuintilesIMS. She received her PhD from the University of Pennsylvania
in Bioengineering. Previously, she worked as an AIMBE Policy Scholar at the
Center for Devices and Radiological Health at the US Food and Drug
Administration and Deloitte Consulting.
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Learning Objectives
• Introduction and definition a discussion regarding the
rise of genomic data / trends / future outlook
• Understanding analytical considerations necessary for
robust predictions in PMI
• Describe and demonstrate ways in which PMI would be
applicable to the patient
• Understanding the future of regulation and guidance
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Precision Medicine
What is Precision Medicine:
“An emerging approach for disease treatment and prevention that takes into account
individual variability in genes, environment, and lifestyle for each person.“*
– Enables physicians to tailor medical treatment for each patient
– Supports the development of molecularly targeted drugs based on biologic pathways
– Identifies at-risk populations for targeted prevention prior to disease onset
Key Drivers of Precision Medicine:
Genomic Sequencing Technologies
– Rapid drop in sequencing costs
– First human genome cost $2.7B
– Currently ~$1,000 / genome with the promise of the $100 genome in near future
Genomic Data and Analytic Capabilities
– Creation of large genomic datasets
– Advanced analytics to identify novel disease associations and treatment strategies
*NIH
- https://ghr.nlm.nih.gov/primer/precisionmedicine/definition
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Genomic Sequence Data is Rapidly Growing
Estimated 100m
– 2B genomes
sequenced by
2025
Genomic Data
Currently
~250,000
genomes
sequenced
Stephens et al. 2015
Raw Whole Genome
~3B base pairs
~30x + coverage
~100 gigabytes
Whole Genome
~3B base pairs
~700 megabytes
Whole Exome
~30M base pairs
~7 gigabytes
Variant Call File
~3M Variants
~125 megabytes
This wealth of genomic information can be applied to multiple groups (patients,
providers, payers, and life sciences) to realize the promise of precision medicine
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Applications of Genomics in Precision Medicine
Patients
• Identification of disease risk / susceptibility to
support preventive medical care
• Targeted prescribing to increase adherence,
improve drug response and reduce adverse events
Providers
• Data driven clinical decision support tools based
on individual patient profiles
• Pharmacogenomic-informed prescribing using
genetic profiles and companion diagnostics
Payers
• Effective preventive medical care to address
disease risks before onset of chronic disease
• Targeted and effective treatment plans to improve
patient care while reducing costs
Life Sciences
•
•
•
•
Discovery of novel drug targets
Improved clinical trial recruitment / execution
Drug repurposing / repositioning
Companion diagnostic development
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Understanding Analytical Considerations Necessary For
Robust Predictions For Precision Medicine
The growing volume and complexity of data creates the potential for more accurate
patient-level predictions for treatment response, disease progression, etc.
But to realize this exciting potential requires embracing modern advanced
analytical methods in artificial intelligence(AI) / machine learning
Different analytical approaches are appropriate for different purposes
Type of approach
Traditional/classical statistics
Artificial intelligence /
machine learning
Scientific philosophy
Hypothesis-driven (deductive)
Data-driven (inductive)
Example application
Clinical trials
Precision medicine
Objective
Confirmation of pre-determined
associations
Maximize predictive accuracy based on
large-scale complex data
Example question
Is treatment X associated with lower risk of
heart failure?
Which patients are at greatest risk of heart
failure based on 1,000s of biomarkers?
Strength
Minimize false-positives
Minimize false-negatives
Limitation
Risk of false-negatives
Risk of false-positives
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Traditional And Machine Learning Methods Can Go Handin-hand But Availability Of Data Is Serious Constraint
Predict which patients are at greatest
risk of given event (e.g. heart failure)
based on genomics data
Use independent data or clinical trial to
confirm whether patients predicted to be
at high-risk subsequently experience
higher incidence of event in question
 Analysis based on advanced
artificial intelligence / machine
learning methods
 Analysis based on classical
hypothesis-driven statistical methods
 Hypothesis generation
 Hypothesis confirmation
Combined approach as described above makes best use of all data

Leverages strengths of different analytical methods whilst addressing limitations

However, often difficult to implement due to lack of data

Acquiring genomics data at scale remains extremely costly despite huge cost reductions
#DrHIT @HIMSS
What Sorts Of Techniques Does Machine Learning Use?
Decision theory: Decision-trees are created to find the optimal
boundary between uncertain outcomes
Signal processing: Hidden associations are detected as ‘signals’
in noisy data
Artificial neural networks: Associations in the data are simulated
as biological processes
These advanced methods are highly flexible, able to capture
complex patterns in large data
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Some Key Challenges With Application Of Machine
Learning For Precision Medicine
Transparency
Machine learning is purpose-designed to
maximize predictive accuracy
But sometimes difficult to understand which
predictors (e.g. biomarkers) are driving
predictions
Adaptability
Algorithms deployed in a live environment need
to be dynamic to capture changes in:
• Data coverage
• Prescribing behavior
• Requirements / user-experience
But dynamic solutions pose challenges:
A ‘black-box’ algorithm might produce excellent
predictions but backward engineering to identify key
predictors is tricky
Solutions often involve a trade-off between
predictive accuracy and transparency
•
•
Validating updates to algorithms can be costly and
time-consuming,
Undermines ability of the system to be dynamic
How to reconcile need for dynamic solution with
need for validation is complex
Conclusion
Modern advanced methods in artificial intelligence
/ machine learning hold the key to:
 Accurate predictions with complex biological data
 The future for precision medicine
 But requirements for transparency, adaptability and validation must all be addressed in solution
design
#DrHIT @HIMSS
Paradigm Shift in Healthcare Towards Precision Medicine
• Precision medicine is an emerging healthcare approach based on the
customization of disease treatment, prevention, and research, that takes
into account individual variability in environment, lifestyle, and genes for
each person
• Personalized Medicine vs Precision Medicine
• Precision medicine requires understanding of the heterogeneity of
patients and treatments
• As health care becomes more expensive, there is greater interest in
understanding which treatments work for which patients in which
settings
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The 10 Highest-grossing Drugs In The United States Fail To Improve
The Condition Of Between 3 To 24 Persons For Every Person They
Help
Abilify (Schizophrenia
X4
Nexium (Heartburn)
X 24
Humira (Arthritis)
X3
Crestor (High Cholesterol)
X 19
Cymbalta (Depression)
X7
Advair Diskus (Asthma)
Enbrel (Psoriasis)
Remicade (Crohn’s disease)
Copaxone (Multiple Sclerosis)
Neulasta (Neutropenia)
X 19
X3
X3
X 15
X 12
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Schork, Nature 2015, 520 (7549)
Large Scale Precision Medicine Programs
CLINICAL STUDY
MANAGEMENT
DISEASE
MANAGEMENT
CUSTOM
APPLICATIONS
API
QUALITY &
REIMBURSEMENT
Analysis Tools
SELF-
MOLECULAR
IMAGING
SENSORS
CLINICAL
REPORTED
DATA
shared infrastructure (workflows, frameworks,…)
COLLABORATIVE
COLLABORATIVE
DATA
DATA
PUBLIC DATA
PUBLIC DATA
INVESTOR
DATA
#DrHIT @HIMSS
#DrHIT @HIMSS
Understanding The Future Of Regulation And
Guidance
Key Question: Will my new precision medicine innovation be subject to
FDA regulation? What will I have to do to get my innovation approved or
cleared?
• Some software systems fit the legal definition of a
medical device and are subject to regulation
• Example of SaMD under FDA regulation:
– Heartflow creates a personalized 3D model of the
coronary arteries from a standard CT scan.
Received 510(k) clearance in 2014
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Understanding The Future Of Regulation And
Guidance
• The US FDA has laid out a series of frameworks to detail how they foresee
regulating Software as a Medical Device (SaMD)
– Software as a Medical Device (SaMD): Clinical Evaluation Draft
Guidance from IMDRF
– Mobile Medical Applications Final Guidance
State of
Healthcare
Situation or
Condition
Significance of Information Provided by SaMD
to healthcare decision
Treat or
Diagnose
Drive Clinical
Management
Inform Clinical
Management
Critical
IV.i
III.i
II.i
Serious
III.ii
II.ii
I.ii
Non-Serious
II.iii
I.iii
I.i
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Q&A
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Continuing Education Credit
• This program has been designated for 1 hour of
CAHIMS credit.
• This program has been designated for 1 hour of
CPHIMS credit.
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