Integrating Genomics Results & EHR Functionality€¦ · • Define how EHR interacts with genomic...
Transcript of Integrating Genomics Results & EHR Functionality€¦ · • Define how EHR interacts with genomic...
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Integrating Genomics Results & EHR FunctionalitySession #89, February 21, 2017
Kamalakar Gulukota, Director, Bioinformatics
Henry “Mark” Dunnenberger, Program Director, Pharmacogenetics
NorthShore University HealthSystem
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Speaker Introduction
Kamalakar Gulukota, PhD, MBADirector, Bioinformatics
NorthShore University HealthSystem
Over 25 years experience in computational biology
Directed bioinformatics at Wyeth (Pfizer) to discover novel drug targets
Started-up a CRO for GVK Biosciences
Directed Product development at GenomeQuest
Translational research into tumor heterogeneity
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Speaker Introduction
Henry “Mark” Dunnenberger, PharmDProgram Director, Pharmacogenetics
NorthShore University HealthSystem
Board Certified Pharmacotherapy Specialist
Co-authored four Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines
Member of CPIC’s Infomatics working group since its inception
Developed a dedicated multidisciplinary pharmacogenomics clinic
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Conflict of Interest
Kamalakar Gulukota, PhD, MBA
Has no real or apparent conflicts of interest to report.
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Conflict of Interest
Henry “Mark” Dunnenberger, PharmD
Has no real or apparent conflicts of interest to report.
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Agenda
• What is Pharmacogenetics and it’s promise?
• A provider’s perspective
• What is needed to implement pharmacogenetics?
– How we built it at NorthShore
• Some in-house metrics for pharmacogenomics
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Learning Objectives
• Define how EHR interacts with genomic data, even as interpretation of that
data keeps changing
• Analyze build vs. buy decision in genomic medicine, recognizing that some
amount of building is inevitable
• Plan the structure of a team needed for launching genomic medicine
• Assess the need for genomics technology in modern medicine
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Pharmacogenetics helps clinicians choose between therapeutic equals• Safer and more effective drug treatment
• Increased adherence to drug therapy
• Decreased hospitalizations
• Decreased health care costs
Drug therapy
• Safety
• Efficacy
• Compliance
• Admissions
• Expenses
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A triad of revolutions
Agent
HostEnviron
ment
Environment revolution
19th century (2 presidents)
Agent revolution
20th century (2 pandemics)
Host revolution
21st century (2 patients) The Epidemiologic Triad
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Deaths of two presidents
Agent
HostEnviron
ment
1881: Pres. James Garfield
Two-bullet assassination attempt
Died 11 weeks later of (nosocomial?)
wound infection
1893: Pres. Grover ClevelandGrowth on hard palate
Secret surgery for “epithelioma”
Died in 1908 of unrelated causes
1980 diagnosis: verrucous carcinoma
The environment
revolution
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Courses of two epidemics
Agent
HostEnviron
ment
1918: Influenza pandemic (H1N1)~500 million infected
At least 50 million killed
Life expectancy dropped by 12 years
Interim victories: polio, small pox, lead, …
2009: Influenza pandemic (H1N1/09)~10 million infected
At most 0.5 million killed
“WHO has exaggerated…”
The Agent
revolution
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A triad of revolutions
Agent
HostEnviron
ment
Environment revolution
19th century (2 presidents)
Agent revolution
20th century (2 pandemics)
Host revolution – just starting
21st century (2 patients) The Epidemiologic Triad
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Early days of a third revolution
Agent
HostEnviron
ment
13 y/o female
diagnosed with depression and anxiety
5 attempted drugs fail – 2 year struggle
13 y/o female
diagnosed with depression and anxiety
Genetic testing done
“Avoid SSRIs. Try SNRIs”
2nd attempted drug provides relief
The Host
Revolution
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For a “Host revolution” consider …
• How the patient’s genetic makeup impacts
– Predilection (disease risk, counseling)
– Diagnosis (disease type)
– Treatment (pharmacogenetics, disease targets)
– Prognosis (disease severity)
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Poll Question 1
Does your organization use pharmacogenetictesting?
1. Yes, routinely
2. Yes, but only in special cases
3. No, but plans are underway
4. No plans at this time
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A provider’s perspective
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Her CYP2C19 genotype is consistent with an increased risk of
therapeutic failure with citalopram and escitalopram. Please
consider alternative therapies when initiating drug therapy to
treat depression.
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Pharmacogenetic translation
Haplotype
Diplotype
Phenotype
Therapeutic Recommendation
Examples: *1, *2, *3, *17
Examples: *1/*1, *1/*2
Example: poor metabolizer (PM)
Am J Health Syst Pharm. 2016 Dec 1;73(23):1967-1976.
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Variant Lookup TableGene dbSNP RS ID
cDNA
Nucleotide
change
Genome position Change
Hap
loty
pe
Refe
ren
ce
Va
ria
nt
*1 *2 *3A
*3B
*3C
*3D
*4 *8 *24
TPMT rs1800462 238G>C Ch6:18143955 A80P Y G C C
TPMT rs72552739 292G>T Ch6:18143955 E98X Y G T T
TPMT rs1800460 460G>A Ch6:18143955 A154T Y G A A A A
TPMT rs6921269 537G>T Ch6:18143955 Q179H Y G T T
TPMT rs1800584 626-1G>A Ch6:18143955Splice
DefectY G A A
TPMT rs56161402 644G>A Ch6:18143955 R215H Y G A A
TPMT rs1142345 719A>G Ch6:18143955 Y240C Y A G G G G
TPMT rs2842934 474C>T Ch6:18143955 I158I N C T
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Drug MetabolizersFinal Term Functional Definition Genetic Definition
Example
diplotypes/alleles
Ultra-rapid
Metabolizer
Increased enzyme activity compared to
rapid metabolizers
Two increased function alleles, or >2
normal function alleles
CYP2C19*17/*17
CYP2D6*1/*1XN
Rapid
Metabolizer
Increased enzyme activity compared to
normal metabolizers but less than ultra-
rapid metabolizers
Combinations of normal function and
increased function allelesCYP2C19*1/*17
Normal
MetabolizerFully functional enzyme activity
Combinations of normal function and
decreased function allelesCYP2C19*1/*1
Intermediate
Metabolizer
Decreased enzyme activity (activity
between normal and poor metabolizer)
Combinations of normal function,
decreased function, and/or no function
alleles
CYP2C19*1/*2
Poor Metabolizer Little to no enzyme activityCombination of no function alleles and/or
decreased function allelesCYP2C19*2/*2
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CYP2D6 and Codeine Recommendations
Phenotype Implications for codeine metabolism Recommendations for codeine therapy
Ultrarapid
metabolizer
Increased formation of morphine following
codeine administration, leading to higher risk
of toxicity
Avoid codeine use due to potential for toxicity.
Extensive
metabolizerNormal morphine formation Use label-recommended age or weight-specific dosing.
Intermediate
metabolizerNormal morphine formation Use label-recommended age or weight-specific dosing.
Poor metabolizer
Greatly reduced morphine formation following
codeine administration, leading to insufficient
pain relief
Avoid codeine use due to lack of efficacy
Clin Pharmacol Ther. 2014 Apr;95(4):376-82.
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2.3%
8.0%
23.1%
33.1%
21.8%
8.0%
3.3%0.5%
0%
5%
10%
15%
20%
25%
30%
35%
0 1 2 3 4 5 6 7
Perc
en
t o
f p
ati
en
ts
Number of high-risk variants
97% of patients have at least one clinically high-risk variant
N=399
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Established Drug/Gene Pairs
TPMT –
thiopurines
HLA-B
allopurinol
CYP2C19 –
voriconazole
CYP2D6 –
ondansetron
CYP2C19 –
clopidogrel
CYP2D6/CYP2C19
TCAs
G6PD –
rasburicase
CYP2C19/CYP2D6
SSRIs
CYP2C9/VKORC1
warfarin
HLA-B
carbamazepine
CYP2C9/HLA-B
phenytoin
CYP2D6 –
ADHD drugs
CYP2D6 –
codeineDPYD – fluoropyrimidine
CYP3A5 –
tacrolimus
CFTR
ivacaftor
HLA-B –
abacavir
IFNL3 –
interferon
CYP2C19 –
Proton Pump inhibitors
UGT1A1 –
irinotecan
SLCO1B1 –
simvastatin
http://www.pharmgkb.org/page/cpicGeneDrugPairs
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One Drug/One Gene CDS Decision Tree
Physician orders codeine
System looks for CYP2D6 test results
System looks for CYP2D6
high-risk result
Post-test CYP2D6 alert
fires
No alert firesPre-test
CYP2D6 alert fires
Yes
Yes
No
No
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Physician orders
amitriptyline
System looks for CYP2D6 test result
System looks for a
CYP2D6 high-risk
result
System looks for CYP2C19
test result
Pre-test CYP2C19 alert fires and Post-test CYP2D6
alert fires
System looks for a CYP2C19
high-risk result
Post-test combination CYP2D6 and
CYP2C19 alert fires
Post-test combination CYP2D6 and
CYP2C19 (EM) alert fires
System looks for CYP2C19
test result
Pre-test CYP2C19 alert
fires
System looks for a CYP2C19
high-risk result
Post-test combination
CYP2D6 (EM) and CYP2C19 alert fires
No alert firesSystem looks for CYP2C19
test result
Pre-test CYP2D6 and
CYP2C19 alert fires
System looks for a CYP2C19 high-risk result
Pre-test CYP2D6 alert fires and Post-test CYP2C19
alert fires
Pre-test CYP2D6 alert
fires
YES
NO
YES
NO
YES
NO
One Drug/Two GeneCDS Decision Tree
YES
NO
YES
NO
YES
NO
YES
NO
NO
YES
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Interruptive Alerts
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PGx Profile
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Poll Question 2
Biggest challenge in implementing pharmacogenetics is:
1. Getting buy-in from providers
2. Reimbursement for the test
3. Complicated Informatics
4. Other
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How to bring about the Host Revolution?
• Lab to generate genomic data
• Bioinformatics pipelines to analyze such data
• Knowledgebases – to integrate with other information
• Most important trick of all:
– Summarize all data into usable information and make available to providers in real time, in the right context
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Informatics for a Host Revolution
Laboratory support
Analysis pipelines
EHR Integration
Clinical decision support
Knowledgebases
Levels of summarization
Interpreter support
Reporting tools
Ingredients
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Laboratory support
• Analysis pipelines
– Fluorescence intensities genotype calls
– Calling diplotypes (translation table)
– Interpreting genotypes and diplotypes (knowledgebases)
– Communicating with vendor partners
– Communicating with EHR
Vendor
Homebrew
Homebrew
Partner
Homebrew
Partner
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EHR Integration• Timely, summarized information display
– Interruptive alerts, where needed
– Overall pharmacogenetics summary
• Population level data
– For quality control
– For investigating newly reported findings
– For research of new hypotheses
Vendor (EHR)
Homebrew
Homebrew
Partner
Partner Homebrew
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Interpreter support• Automate production of calls
– Edit automated calls
– Post to repository, sign out
– Create reports
• Communicate with partner and EHR
– Standardized formats (VCF, HL7, PDF)
Vendor
Homebrew
Homebrew
Homebrew
Homebrew
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Knowledgebases• Public domain
– Genomes, PharmGKB, ClinVar, Cosmic, …
• Vendor / partner supported
– ActX, Mayo, Broad InstituteVendor
Homebrew
Homebrew
Partner
Partner
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Arsenal of available resources• Homebrew software and systems
– Including open source
– Probably the only solution for “plumbing”
• Vendor solutions
– Sequencing / array machines, EHR providers, …
• Partners
– Third party solutions for specific purposes (knowledgebases)
Vendor
Homebrew
Partner
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Build vs buy decision• Many components – decide how much to build in-house
– Open source: not built in-house built but maintained in-house
– Your solution will most likely need some homebrew
• Build a team that can build homebrew software
– Leads to optimal solutions even when you decide to buy
– Important to keep it as small (and agile) as possible
• Make shorter term contracts
– Rapidly evolving field important to keep options open
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Pharmacogenetics status at NorthShore
• In-house solution launched Aug 2016
• Over 400 patients characterized (and in EHR)
• Prescriptions of about 60 commonly used drugs linked to PGX-based decision support. Make progress by:
– obtaining buy-in from providers, specialty-by-specialty
– Focusing on high-risk prescriptions
• 19 drug-metabolizing genes routinely characterized
– On track to expand this to 40 genes in the next year
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Poll Question 3
Who integrates PGX information into prescriptions?
1. Informatics tools for CDS
2. Pharmacist
3. Clinician provider
4. Other
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PM
IMIM
RM
IM
IM
Example data on specific genes
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Potential warnings or cautions per patient
199
96
518 18
920 21
7 1 3 20
50
100
150
200
250
0 1 2 4 5 6 7 8 9 10 12 14
Nu
mb
er
of
Pa
tie
nts
Number of drugs to avoid
High-risk drug profile
10
21
54
85
103
69
45
102
0
20
40
60
80
100
120
0 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39
Nu
mb
er
of
Pa
tie
nts
Number of drugs to use with caution
Medium-risk drug profile
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Genomics impact on medicine
Prognosis: Genetic markers impact disease course
Therapy: genetic markers inform which is a better choice
Diagnosis: A more precise diagnosis includes a genetic descriptor
Disease predilection: Genetic counseling
Population risk: Choosing a screening policy
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Pharmacogenetics helps clinicians choose between therapeutic equals• Safer and more effective drug treatment
• Increased adherence to drug therapy
• Decreased hospitalizations
• Decreased health care costs
Drug therapy
• Safety
• Efficacy
• Compliance
• Admissions
• Expenses
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Questions
• Kamalakar Gulukota ([email protected])
• Henry “Mark” Dunnenberger ([email protected])
• Please complete online session (#89) evaluation