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Prospective Genotyping and Pharmacogenomics in the Electronic Health Record
Josh Denny, MD MS
October 18, 2012
Case: A 57yo female with chest pain
First admission
for angina,
receives stent
Jan 2010 Dec 2010
9th admission, 5th
intervention, 9th stent placed
Recath, stent
“Plavix x 1 year minimum.
ASA life long.”
April
In-stent
thrombosis,
restent
In-stent
thrombosis,
restent
Angina, Cath, more stents
clopidogrel started
A perception of genomic medicine - pharmacogenomics
"Here's my sequence...”
New Yorker, 2000 Francis Collins, 9/16/2009
Discovery and Application using the EHR
VanderbiltBioVU
De-identified laboratory for
genomics and
pharmacogenomics
discovery In-house developed, web-
based EHR with CPOE,
patient portal, messaging,
etc.
BioVU: an Opt-Out DNA Biobank
Extracting DNA from left over blood samples
Jo
hn
Do
e
On
e w
ay h
ash
A7CCF99DE65732….
Jo
hn
Do
e
1.7 million records
The Synthetic Derivative:
updated regularly from EMR
eligible Jo
hn
Do
e
On
e w
ay h
ash
A7CCF99DE5732….
A7CCF99DE65732….
Extract
DNA
A7CCF99DE65732….
Jo
hn
Do
e
1.7 million records
The Synthetic Derivative:
updated regularly from EMR
151,000 DNA samples
19,000 pediatric
0.5 5
Replicating known associations in BioVU
0.5 50.5 5.0 1.0
Odds Ratio
rs2200733 Chr. 4q25
rs10033464 Chr. 4q25
rs11805303 IL23R
rs17234657 Chr. 5
rs1000113 Chr. 5
rs17221417 NOD2
rs2542151 PTPN22
rs3135388 DRB1*1501
rs2104286 IL2RA
rs6897932 IL7RA
rs6457617 Chr. 6
rs6679677 RSBN1
rs2476601 PTPN22
rs4506565 TCF7L2
rs12255372 TCF7L2
rs12243326 TCF7L2
rs10811661 CDKN2B
rs8050136 FTO
rs5219 KCNJ11
rs5215 KCNJ11
rs4402960 IGF2BP2
Atrial fibrillation
Crohn's disease
Multiple sclerosis
Rheumatoid arthritis
Type 2 diabetes
disease gene /
region marker
2.0
Ritchie et al., AJHG 2010
observed published
Finding accurate cases • Billing codes alone only 50-80% accurate
– “Wrong” codes result from screening, misdiagnosis, or because “a certain code pays for a test/treatment”
• Negation terms
– “I don’t think this is MS”
• Context:
– “FAMILY MEDICAL HISTORY: positive for rheumatoid arthritis.”
– “Multiple Sclerosis Clinic Note: This is a 46yo seen for peripheral neuropathy…”
True
cases
Notes
(NLP or text
searches)
Billing
codes
Medications
& Labs
11
Coordinating
center
Vanderbilt
Marshfield Northwestern Mayo Group Health/UW
Mount Sinai
Geisinger
• Started in 2007 – 5 sites; now – 9 sites
• Each has ≥3000 GWAS EMR patients
• Goal: to perform GWAS for ~40
phenotypes with existing samples
• Translate to clinical practice
CHOP
Cincinnati
Partners
eMERGE GWAS to date
Site Primary phenotype Secondary Phenotypes
Group Health Dementia white blood cell counts
Marshfield Cataracts diabetic retinopathy
Mayo Clinic Peripheral Arterial
Disease
red blood cell counts
ESR levels
Platelet levels
Northwestern Type 2 Diabetes lipids and height
Vanderbilt PR Duration
QRS Duration
Phenome-wide association studies
(PheWAS)
Network Phenotypes
Autoimmune Hypothyroidism
Resistant hypertension =first description or
new findings
bold=GWAS completed
eMERGE-PGx: perform clinical sequencing to guide drug choice
• “Demonstration project” for pharmacogenomics:
– Clopidogrel and CYP2C19
– Warfarin and VKORC1 and CYP2C9
• Step 2 (Ongoing) - Discover/validate new associations
VESPA Vanderbilt Electronic Systems for Pharmacogenomic Assessment
Clopidogrel label revision March 2010
clopidogrel 2-oxoclopidogrel CYP2C19
16
Finding clopidogrel failures in the EMR
Survival analysis of all who
were on clopidogrel for at
least a year.
All patients had stent or MI
as first event
Cases =
MI/stroke/revascularization/
Death and compliant with
clopidogrel
Controls = None of the
above
Delaney et al. CPT. 2012
Use of EMR data to predict drug response
Normal metabolizer
Poor or intermediate
metabolizers
clopidogrel failure=MI, stroke, revascularization, death following MI or PCI
n=225 cases, 468 controls
Delaney et al. Curr Pharm Ther. 2012
Warfarin Pharmacogenetics Using genetics to predict effective dose
SNP (Gene) Beta P
rs1057910 (CYP2C9*3) 0.83 2.70x10-26
rs9934438 (VKORC1) 0.87 4.48x10-61
Ramirez et al. Pharmacogenomics. 2012
FDA’s role • FDA began including pharmacogenomic (PGx)
effects in labels in 2007
• Now lists 83 medications with germline variants
– >100 germline and somatic
Gene Drug
Other Germline Variants
VKORC1 warfarin
HLA-B*1501 carbamazepine
HLA-B*5701 abacavir
CCR5 maraviroc
Familial
hypercholesterolemia
atorvastatin
G6PD deficiency rasburicase, primaquine
Protein C deficiency warfarin
urea cycle disorder valproate
Gene Drug Drug Metabolism Pathways
TPMT azathioprine
UGT1A1 irinotecan, nilotinib
CYP2D6 atomoxetine, fluoxetine
CYP2C19 clopidogrel, proton pump
inhibitors
CYP2C9 celecoxib, warfarin
N-acetyl transferase rifampin, isoniazid,
pyrazinamide
DPD capecitabine
http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm
Traditional PGx testing
• test ordered at the point of care when initiating a treatment
regimen
• one-genotype-at-a-time
• dose/med adjusted retrospectively after results returned
• Prospectively collect genotypes and embed in the electronic
medical record (EMR)
• Couple with decision support
• Genotypes available at the time of prescribing
Alternative vision – our vision
How should we implement genetic testing?
Medication initiation: warfarin1
Why Prospective? Risk of Side Effects highest at drug start
1 month 3 months 6 months 9 months 12 months
Medication initiation: simvastatin2
Medication initiation: azathioprine3
Medication initiation: tacrolimus4
Medication initiation: abacavir5
1 month 3 months 6 months 9 months 12 months
1 month 3 months 6 months 9 months 12 months
1 month 3 months 6 months 9 months 12 months
1 month 3 months 6 months 9 months 12 months
1. Ferder et al, Journal of Thrombosis and Haemostasis, 2010
2. The SEARCH Collaborative Group, NEJM 2008
3. Higgs et al, Pharmacogenomics 2010
4. Hesselink et al, 2008; Zhang et al, 2010
5. Mallal et al, NEGM 2008
Finding drug exposures in the EMR
• Found ~53,000 patients who met a “medical home”
definition
• Medications extraction from EMR data
– All inpatient meds (structured)
– ePrescribing (~30-50% outpatients scripts)
– Natural language processing of clinical documentation
using MedEx
MedEx
Clinical Text
“warfarin 5mg tablets,
1.5 tabs daily”
Structured Output
DrugName: warfarin
Dose: 1.5 tablets
Strength: 5 mg
Route: po
Frequency: daily
Xu et al. JAMIA 2010; 17:19-24
Doan et al. JAMIA 2010; 17: 528-31
5-year Medication Exposures in 52,942 “Medical Home” patients
Schildcrout et al. Curr Pharm Ther. 2012
1786
930
1454
2067
2870
3883
5244
6833
8247
9525
0 10000
10+
9
8
7
6
5
4
3
2
1
Number of Patients
Nu
mb
er
of
PG
x M
ed
s
65% received ≥1
med within 5 years
Genotyping may prevent ADEs
Medication
Adverse Event
Gene Nmed Overall Event
Probability
Number of events
prevented
abacavir Reaction within first
6 weeks of treatment
HLA-B*5701 87 -- 3 (3, 3)
azathioprine Myelosuppression TPMT activity 878 0.098 17 (13, 21)
clopidogrel MI, stroke, or death CYP2C19*2 or *3
or *4 or *5 6361 0.089 79 (19, 143)
simvastatin Myopathy SLCO1B1
rs4149056 17631 0.003 18 (5, 32)
tamoxifen breast cancer
recurrence
CYP2D6 540 0.186 15 (1, 29)
warfarin Any bleeding events carrier of
CYP2C9*2 or *3 6651 0.13 251 (91, 408)
Total 383 (212, 552)
Schildcrout et al. Curr Pharm Ther. 2012
Event Cost
Warfarin-related subdural hematoma $35,904
Myocardial infarction $72,446
That first event is just a start…
drug eluting stent
statin
warfarin
time
Cumulative number of
times genetic data are used
1
2
3
4
5
6
0
Prozac
Imuran, tamoxifen….
PREDICT: Pharmacogenomic Resource for
Enhanced Decisions In Care and Treatment
• Multiplexed genotyping
with Illumina ADME chip
• Prospective identification
of those at risk to receive
candidate medications
• Coupled with EMR-based
Decision Support
PREDICT: Genotype preemptively
CYP2C19
(rs4244285)
clopidogrel
poor metabolizer
CYP2D6
(many…)
tamoxifen,
antidepressants
poor metabolizer
SLCO1B1
(rs4149056)
statin myopathy
CYP2C19
(rs12248560)
clopidogrel
Rapid metabolizer
CYP2C9
(rs1799853)
warfarin
slow metabolizer
CYP2C9
(rs1057910)
warfarin
ultra slow metabolizer
VKORC1
(rs9923231)
warfarin dose
Selection of PREDICT Drug-Gene Interactions
Review and Approval by P&T
Committees
Implementation including
automated decision support
Guidance: Professional
Societies, FDA
Evidence Review
Replication in Vanderbilt population
(BioVU)
29
PREDICT Pipeline
DRUG: Clopidogrel Simvastatin Warfarin Thiopurines Phase 1: Peds
Heme/Onc
Thiopurines Phase 2: All
Specialities
GENE: CYP2C19 SLCO1B1 CYP2C9, VKORC1
TPMT TPMT
ADVERSE OUTCOMES: stent thrombosis/
MACE* myopathy/
rhabdomyolysis bleeding/
out of range INR myelosuppresion myelosuppresion
EVENT RATE: 1% / 9% <1% 13% / 45% 7% 7%
FDA/POLICY: black box none recommended recommended recommended
SUMMARY STATUS: Live Live Live Est. Go Live
Oct 2012
Est. Go Live
Nov 2012
ST
EP
S IN
TH
E P
RE
DIC
T P
IPE
LIN
E
Clinical impact of negative outcomes significant
High prevalence of drug utilization (≥5% of VUMC population)
Scientific evidence for drug-gene effect
Patient target identifiable before they receive the drug TBD
Alternative therapy available
Internal validation of established association in VUMC pop not feasible
Assessment of genotype data as high quality in CLIA lab
Completion of DGI summary of evidence (bundle)
Rules development complete (clinical + genotype to identify high risk)
CDS language developed for VGR and RxStar
Pediatric evaluation - Sub P&T Peds Committee
Pediatric P&T approval
Sub P&T Committee endorsement and approval
Main P&T Committee approval of decision support rules
Clinician and provider education and outreach
Algorithm technical development complete
Go live
Results available via MHAV patient portal
Surveillance infrastructure established
Rules refinement based on evolving evidence
Evaluation of quality improvement underway
PREDICT Results Appear in Patient Summary
30
Drug Genome Interactions in the Patient Summary
Point-of-care Decision Support
Prospective Genotyping Using the Prognostic Model
• Model identifies patients who are highest risk for starting warfarin, clopidogrel, or simvastatin therapy within the next three years as candidates for preemptive genotyping
• Used medical home population not on a target med previously (N~18000)
• Factors include:
– Age, gender, race, and BMI when height is available (or weight
when BMI is not available)
– History of…Diabetes, coronary disease, atrial fibrillation,
hypertension, atherosclerosis, congestive heart failure, previous
DVT/PE, and end stage renal disease 32
AGE BMI DM HTN CAD CHF Probability of being placed on Simvastatin, Warfarin, or Clopidogrel
>80
>25 1
<25 1
60-80
>30
1 1 1
1 1
1
1 1
1
25-30
1 1
1
1 1
1
18-25
1 1
1
1 1
1
<18 1
40-60
>30
1 1 1
1 1
1
1 1
1 1
1
25-30
1 1
1
1 1
1 1
1
Patient comes in,
selected for
genotyping (cardiac
cath, predictive
algorithm, etc)
Genotype
DB Select variants put into
EMR (currently ~18 variants)
• Validated
• CDS
• P&T review
184 variants
Drop variants that
don’t work well
New research for drug-
genome interaction
discovery
P&T Committee
PREDICT research team
~130 other variants
validated of unknown
significance
PREDICT is Live
Effect Total
normal risk 7040
intermediate risk 2303
indeterminate 217
high risk 185
9745
Clopidogrel DGI by Genotype Simvastatin DGI by Genotype
Effect Total
normal metabolizer 6491
intermediate metabolizer 1847
indeterminate 693
hypermetabolizer 453
hypometabolizer 263
9747
43% of patients have a variant in SLCO1B1 and/or CYP2C19
Our case: What personalizing medicine really means
57yo with
admitted for
angina, receives
stent
Jan 2010 Dec 2010
9th admission, 5th
intervention, 9th stent
PREDICT: CYP2C19*2/*2
Recath, stent
“Plavix x 1 year minimum.
ASA life long.”
April
In-stent
thrombosis,
restent
In-stent
thrombosis,
restent
Cath, more stents
Switched to prasugrel
clopidogrel started
Future PGx: Variable drug response is common
--41 %41 %
Distribution of Changes in LDL
Cholesterol with Simvastatin 40 mg/d
Percent changePercent change
00
2020
4040
6060
8080
100100
--100100 --8080 --6060 --4040 --2020 00 2020
nn
Changes not related to compliance or changes in other lipids
Simon et al., Am J Cardiol 2006Simon et al., Am J Cardiol 2006
LDL cholesterol changes by
simvastatin 40 mg
QT changes with ibutilide
Therapeutic Warfarin dose
Patients
, %
%FEV1, from baseline
0
10
20
30
40
<-20-20 to -10
-10 to 0
0 to 10
10 to 20
20 to 30
30 to 40
>40
study #1
study #2
study #3
Change in FEV1 with inhaled
corticosteroids in asthma
Summary
• EMR-linked DNA biobanks can be used for
genomic and pharmacogenomic discovery
• Clinical, prospective genetic testing is
feasible and may improve patient outcomes
• Informatics infrastructure is key, but many
challenges remain
The Team
…and many more…