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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

Please use blank slide if more space is required for charts, graphs, etc.

To remove background graphics, right click on selected slide,

choose “Format Background” and check “Hide background graphics”.

Remember to delete this slide, if not needed.

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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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45

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 (kgulukota@northshore.org)

• Henry “Mark” Dunnenberger (MDunnenberger@northshore.org)

• Please complete online session (#89) evaluation