Download Precision medicine

Objectives for today’s class
• Questions
– What is “precision
medicine”?
– How is precision
medicine used to treat
patients?
– What do we know about
the genetics of race?
• Topics for today
– Obama’s Precision
Medicine Initiative
– Precision medicine:
Where are we now?
– The first “ethnic” drug
Definitions
• Pharmacogenomics: the study of the role
genetics plays in drug response
• Precision medicine: customization of medical
decisions and courses of treatment based on the
individual patient
• Personalized medicine: creation of new
treatments in response to a particular patient’s
need
Obama’s Precision Medicine Initiative
State of the Union, 2015
“Most medical treatments have been designed for the “average patient”. As a
result of this ‘one-size-fits-all-approach,’ treatments can be very successful for
some patients but not for others.”
The promise: $215 million investment split among the NIH, NCI, FDA, & ONC
The objectives:
• More and better treatments for cancer
• Creation of a voluntary national research cohort
• Commitment to protecting privacy
• Regulatory modernization
• Public-private partnerships
https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative
2 trios
179 individuals
4 populations
697 individuals
7 populations
Estimated numbers of potentially
functional variants in genes
The 1000 Genomes Project samples a
variety of human populations
Precision Medicine Initiative - Background
Mission:
To enable a new era of medicine through research,
technology, and polices that empower patients,
researchers, and providers to work together toward
development of individualized treatments.
Information Input:
• 4 Workshops between April and July 2015
–Unique Scientific Opportunities for the National Research
Cohort
– Digital Health Data in a Million-Person Precision Medicine
Initiative
– Participant Engagement and Health Equity
– Mobile and Personal Technologies in Precision Medicine
• Requests for Information
– Building the cohort
– Strategies to address community engagement and health
disparities
PMI Cohort Program (PMI-CP) WG:
Vision and Design for a longitudinal research cohort of ≥1
million volunteers
Key Questions:
1) Combine existing cohorts, establish new, or both?
2) How to capture the U.S. population diversity?
3) What data types should be included?
4) What policies needed for maximal benefit to all
stakeholders?
Slides from www.healthit.gov
• FNIH Survey of public perceptions of precision medicine
cohort
• White House Privacy and Trust Principles
Anticipated FY2016 Appropriations
Agency
NIH
$ Million
200
•
Cancer
•
70
•
Cohort
•
130
FDA
10
Office of the Natl Coord. for
Health IT (ONC)
5
TOTAL
215
Information
Flow In
Information
Flow Out
What can we learn from a massive cohort?
• Discover new biomarkers predictive of future disease risk
• Discover determinants of individual variation in response to
therapeutics
• Determine quantitative risk estimates in the population by integrating
environmental exposures, genetic factors, and gene-environment
interactions
• Integrate mobile health and sensor technologies
• Determine impact of loss-of-function mutations on clinical outcome
• Discover new classifications and relationships among diseases
• Enable targeted clinical trials of subjects with rich clinical data
Desired characteristics of the cohort
• One million or more
volunteers
• Longitudinal cohort with
continuing interactions
• Re-contactable
• Collect
–
–
–
–
electronic health records
biospecimen,
survey, and
complete a baseline exam
Two methods of recruitment
• Direct volunteers Anyone can sign up
• Healthcare provider
organizations
Sources of diversity
•
•
•
•
•
Groups that are underrepresented
All states of health and disease
All areas of the U.S.
All life-stages
Special policy considerations
– enrolling children
– decisionally impaired
– participants who become incarcerated
Initial Core Data Set – Informatics Opportunities
Centrally collected and stored in a Coordinating Center
Align with other data sets when possible
Leverage existing data standards and common data models
Data Source
Data Provided
Self report measures
Diet, substance use, self-report of disease and symptoms
(e.g., cognitive or mood assessment)
Baseline health exam
Vitals (e.g., pulse, blood pressure, height, weight),
medical history, physical exam
Structured clinical data
(EHR)
ICD and CPT codes, medication history, select laboratory
results, vitals, encounter records
Biospecimens
Blood sample
mHealth data
Passively-collected data (e.g., location, movement, social
connections) from smartphones, wearable sensor data
(activity, hours and quality of sleep, time sedentary
Genetics as a therapy tool in cancer
• Physician-ordered multigene assays can provide recurrence predictions
and guide treatment options in node negative or node positive, ERpositive, HER2-negative invasive breast cancer
• Oncotype DX (16 cancer-related genes and 5 reference genes) analyzed for
gene expression (mRNA)
The current state of affairs
• In 2013, 114 genes were selected by a panel of experts
as “medically actionable genetic conditions possibly
undiagnosed in adults”.
• In this study of 1,000 people the frequency of likelyhigh-impact variants in these genes was ~3.4% for
European descent and ~1.2% for African descent.
Am J Hum Genet. 2013 93(4): 631-640
CYP2D6 is responsible for metabolizing
~25% of all drugs
Nature Reviews Drug Discovery 2002 1: 37-44
Nature Reviews Drug Discovery 2004 3: 749-761
http://www.pharmacytimes.com/publications/issue/2008/2008-07/2008-07-8624
Ultra-rapid metabolizing mother yields
more morphine in breast milk
Nature Reviews Drug Discovery 2002 1: 37-44
Nature Reviews Drug Discovery 2004 3: 749-761
Polymorphisms modulate drug disposition
Polymorphisms modulate drug targets
and actions
The treatment of congestive heart
failure (HF)
• ~5.1 million people in the
U.S.
• More common in African
Americans
• About half of people with
HF will die within 5 years
of diagnosis
• Costs the nation ~$32
billion/year
http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/
Heart failure prevalence is
geographically variable
Isosorbide dinitrate /
hydralazine
colriculsu.freewebsite.biz
V-HeFT suggests greater BiDil efficacy
in self-identified African-Americans
http://www.fda.gov/ohrms/dockets/ac/05/briefing/2005-4145B2_02_01-NitroMed-Background.htm
The A-HeFT Trial
• Multicenter, double-blind, placebo-controlled, randomized
(H + ISDN=BiDil) vs. placebo
• Population = self-reported black (or of African descent)
• New York Heart Association Class III-IV HF on standard therapy
• Left ventricular ejection fraction (LVEF) ≤ 35%
QOL = quality of life
Echo = echocardiogram
J Card Fail 2002 8 (3): 128-35.
BiDil: The first approved “ethnic” drug
A-HeFT results:
• 43% reduction in mortality
in the treatment arm
• 39% reduction in first
hospitalization from HF in
the treatment arm
• Improved quality of life
BiDil approved by the FDA in
June 2005.
The genetic underpinnings of BiDil efficacy are still being determined
NEJM 2004 351(20): 2049-2057.
Drug development can take decades
http://www.mdlingo.com/article/fda-approval-process-for-new-drugs