Download www.pharmgkb.org - Stanford Medicine

Pharmacogenetics & Pharmcogenomics
Russ B. Altman, MD, PhD
Departments of Bioengineering, Genetics, Medicine,
Biomedical Data Science & CS (courtesy)
PharmGKB -- http://www.pharmgkb.org/
Pharmacogenetics is Defined
“The role of genetics in
drug responses.”
F. Vogel, 1959
PharmGKB -- http://www.pharmgkb.org/
CP1077369-1
January 15, 2001
PharmGKB -- http://www.pharmgkb.org/
Genotype <-> Phenotype associations
Relate genetic information (genotype):
1. ATCGCCGGATACCTAGAGAC…
2. ATCGCCGGAGACCTAGAGAC…
to observable traits (phenotypes), e.g.
1. Responds well to cholesterol medication
2. Develops hepatotoxicity
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/
Purine analogs
• 6-mercaptopurine, 6-thioguanine, azathioprine
• Used to treat lymphoblastic leukemia,
autoimmune disease, inflammatory bowel
disease, after transplant
• Interferes with nucleic acid synthesis
• Therapeutic index limited by myelosuppression
PharmGKB -- http://www.pharmgkb.org/
Metabolism of 6-MP
Weinshilboum (Mayo Clinic) 2001
PharmGKB -- http://www.pharmgkb.org/
Levels of TPMT can drastically
affect levels of thioguanines
• More TPMT = less thioguanines
• Associated with risk of severe marrow toxicity
• Shows considerable variability in population
PharmGKB -- http://www.pharmgkb.org/
Variation in TPMT Activity
Weinshilboum (Mayo Clinic) 2001
PharmGKB -- http://www.pharmgkb.org/
6-MP and TPMT Story Summary
• Observation of clinical variability (toxicity)
• Observation of cellular variability (TPMT
activity, TGN concentrations)
• Observation of genetic variability
(genome variations in TPMT gene)
PharmGKB -- http://www.pharmgkb.org/
The logic of pharmacogenetics
1.
Identify variation in drug response
2.
Associate it with genetic variation
3.
Evaluate clinical significance
4.
Develop screening tests
5.
Individualize drug therapy
PharmGKB -- http://www.pharmgkb.org/
What is the clinical promise?
• Focused treatment by pre-identifying
genetic backgrounds likely to respond
• Reduce adverse events by predicting who
is at risk
• A way to save drugs in the pipeline that are
very effective only in subpopulations
• Better understanding of drug interactions
PharmGKB -- http://www.pharmgkb.org/
Defining P-etics vs. P-omics
• Pharmacogenetics = study of individual
gene-drug interactions, usually the gene
that has the dominant effect on a drug
response. (SIMPLE relationship)
• Pharmacogenomics = study of the full set of
PK/PD genes, often using high-throughput
data (sequencing, expression, proteomics)
(COMPLEX interactions)
PharmGKB -- http://www.pharmgkb.org/
Example: Codeine & CYP2D6
• Codeine is a commonly used opioid
– must be metabolized into morphine for activity
• CYP2D6 is the protein that performs this
metabolism
• 7% of caucasians have a variant version of
CYP2D6 with no activity -> codeine does not work
PharmGKB -- http://www.pharmgkb.org/
Candidate Genes Involved in Metabolism of
Codeine and Morphine
PharmGKB -- http://www.pharmgkb.org/
The O-dealkylation of Codeine by CYP2D6
CYP2D6
codeine
morphine
PharmGKB -- http://www.pharmgkb.org/
Cytochrome P450 2D6
• Absent in 7% of Caucasians
• Hyperactive in up to 30% of East Africans
• Catalyzes the primary metabolism of
• propafenone
• Codeine
• -blockers
• tricyclic antidepressants
• Inhibited by
• fluoxteine
• haloperidol
• paroxetine
• quinidine
PharmGKB -- http://www.pharmgkb.org/
CYP2D6 Alleles
• 125 alleles as of October 2008
• 10 alleles where haplotype has not been
determined
• 38 alleles have no activity
• 10 alleles have decreased activity
• The *2 variant can have 1, 2, 3, 4, 5 or 13
copies resulting in increased activity
http://www.cypalleles.ki.se/cyp2d6.htm
PharmGKB -- http://www.pharmgkb.org/
Allelic Frequencies of CYP2D6
PharmGKB -- http://www.pharmgkb.org/
CYP2D6 and Simvastatin
• Simvastatin = HMG CoA reductase, used to
decrease LDL, increase HDL cholesterol.
• Dose of simvastatin required to get
cholesterol-lowering effect is related to 2D6
mutations and duplications.
Clin Pharmacol Ther. 2001 Dec;70(6):546-51.
• Another report demonstrates that “statins”
are metabolized differently.
Biopharm Drug Dispos. 2000 Dec;21(9):353-64.
PharmGKB -- http://www.pharmgkb.org/
Copy number polymorphisms =
CNPs
• Increasing evidence for variation in the number of
copies of a gene in humans
• Won’t necessarily be picked up with normal
genotyping technology (e.g. sequencing)
• Associated with cancers, genetic diseases, and
now with drug response variation
• Methods for quantifying transcript level, to detect
CNPs are coming down in costs
PharmGKB -- http://www.pharmgkb.org/
American Heart Association November 4, 2012
Clinical Implementation of Pharmacogenomics:
A Focus on Guidelines
Guidelines
“We encourage reviewing the primary
literature and using one’s clinical
judgment rather than relying solely on
recommendations.”
Archives Int Med Jan 2011
For the 99.9% of clinicians who
don’t adequately review primary
literature, a peer-reviewed group of
experts’ review and
recommendations would be better.
Survey: Challenges to implementing
pharmacogenetics in the clinic
What do you think is the most challenging aspect of the
implementation of pharmacogenetics into the clinic?
A. Translation of genetic information into clinical action
B. Test cost, test reimbursement or other economic
issues
C. Availability of high quality genotyping test (CLIA
approved)
D. Electronic medical record use, such as the application
of CDS
E. Clinician and patient resistance and/or ethical concerns
Clin Pharmacol Ther. 2011 Mar;89(3):464-7.
Survey: top 3 Challenges to implementing
pharmacogenetics in the clinic
• 95% of respondents selected: “process
required to translate genetic information into
clinical actions”
• Next 2 responses
– Genotype test interpretation (e.g. using
genotype information to impute phenotype)
– Providing recommendations for selecting the
drug/gene pairs to implement
Clin Pharmacol Ther. 2011 Mar;89(3):464-7.
Clin Pharmacol Ther. 2011 Mar;89(3):464-7.
Key Points about a CPIC guideline
• Based on assumption that the test results are
in hand and NOT to discuss the merits of
doing the test
• Standardized formats
• Grading of evidence and of recommendations
• Peer reviewed
• Freely available
• Updated
• Authorship with COI policy
• Closely follow IOM practices
CPIC
• CPIC guidelines are designed to help clinicians
understand HOW available genetic test results
should be used to optimize drug therapy.
– Not WHETHER tests should be ordered.
• Key Assumption:
– Clinical high-throughput and pre-emptive
genotyping will become more widespread.
– Clinicians will be faced with having patients’
genotypes available even if they did not order test
with drug in mind.
CPIC: Clinical Pharmacogenetics
Implementation Consortium
• As of October 2014:
– >130 Members
• Clinicians and scientists
• 62 institutions
• 14 countries
– 12 Observers (NIH and FDA)
– CPIC Informatics
• 17 members from 11 organizations
CPIC guideline genes (n=13) and drugs, September 2014
• TPMT
– MP, TG, azathioprine
• CYP2D6
– Codeine, tramadol,
hydrocodone, oxycodone,
TCAs
• CYP2C19
– TCAs, clopidogrel,
voriconazole
• VKORC1
– warfarin
• CYP2C9
– Warfarin, phenytoin
• HLA-B
– Allopurinol, CBZ, abacavir,
phenytoin
• CFTR
– ivacaftor
• DPYD
– 5FU, capecitabine, tegafur
• G6PD
– rasburicase
• UGT1A1
– irinotecan
• SLCO1B1
– simvastatin
• IFNL3 (IL28B)
– interferon
• CYP3A5
– tacrolimus
https://www.pharmgkb.org/page/cpicG
eneDrugPairs
Initial Prioritization Considerations for New Gene/Drug Groups
New genes/drugs
CPIC Level Clinical Context
Level of evidence
Strength of
Recommendation
A
Genetic information should be used to
change prescribing of affected drug
Preponderance of
evidence is high or
moderate in favor of
changing prescribing
At least one moderate or
strong action (change in
prescribing)
recommended.
B
Genetic information could be used to
Preponderance of
change prescribing of the affected drug evidence is weak with
because alternative therapies/dosing
little conflicting data
are extremely likely to be as effective
and as safe as non-genetically based
dosing
At least one optional
action (change in
prescribing) is
recommended.
C
There are published studies at varying Evidence levels can vary
levels of evidence, some with
mechanistic rationale, but no
prescribing actions are recommended
because (a) dosing based on genetics
convincingly makes no difference or (b)
alternatives are unclear, possibly less
effective, more toxic, or otherwise
impractical. Most important for genes
that are subject of other CPIC
guidelines or genes that are commonly
included in clinical or DTC tests.
No prescribing actions are
recommended.
D
There are few published studies,
Evidence levels can vary
clinical actions are unclear, little
mechanistic basis, mostly weak
evidence, or substantial conflicting
data. If the genes are not widely tested
for clinically, evaluations are not
needed.
No prescribing actions are
recommended.
Level Definitions for CPIC Genes/Drugs
16 genes, 86 drugs with pharmacogeneticallybased prescribing
Number of current and planned
CPIC genes, drugs and
anticipated guidelines.
Genes Drugs
Anticipated
number of
unique
guidelines
Strong or Moderate prescribing
action-CPIC level A
13
36
19 (14
published)
Optional prescribing actions-CPIC
level B
7a
50
10
No prescribing actions-CPIC level
C
16b
47
20
aCurrently
this is 3 unique genes (four are already subjects of CPIC
level A guidelines). bCurrently this is 13 unique genes (three are also
subject to CPIC level A or B guidelines for other drugs).
Oct 14
Clin Pharmacol Ther. 2013 Feb;93(2):153-8
Clin Pharmacol Ther. 2013 May;93(5):402-8.
Clin Pharmacol Ther. 2013 Sep;94(3):324-8.
Clin Pharmacol Ther. 2013 Apr;93(4):324-5.
Clin Pharmacol Ther. 2013 Sep;94(3):317-23
Clin Pharmacol Ther. 2013 Aug 29. Epub
Clin Pharmacol Ther. 2014 Feb;95(2):141-6.
CPIC guidelines are
posted on
PharmGKB
(www.pharmgkb.org
CPIC guidelines linked to “Practice
Guideline” filter on PubMed
CPIC is cited in NIH’s Genetic Test Registry
(GTR) for clinical pharmacogenetic tests
ASHP is endorsing CPIC
guidelines
External interactions with other
groups
• Endorsement by professional societies
– ASCPT, ASHP
• Continue interactions with
www.guidelines.gov, NIH’s GTR, PubMed,
FDA, NHGRI’s Genomic Medicine Working
Group, IOM’s Genomic Medicine
Roundtable, PGRN, AMIA, and eMERGE
• Grow interactions with ClinGen/ClinVar
• Purpose:
– To describe the development process of the CPIC guidelines
– To compare our process to the Institute of Medicine’s Standards for
Developing Trustworthy Clinical Practice Guidelines
Caudle et al, Current Drug Me
Uniform Elements of CPIC Guidelines (Main)
• Introduction
• Focused Literature Review
• Gene:
– Background
– Genetic Test Interpretation
• Table 1. Assignment of likely _____ [gene] phenotypes
based on genotypes
– Available Genetic Test Options
– Incidental findings
– Other considerations
Uniform Elements of CPIC Guidelines (Main)
• Drug (s):
– Background
– linking genetic variability to variability in drug-related
phenotypes
– Dosage Recommendations
• Table 2. Recommended Dosing of ____ [drug/s] by ____ [gene]
phenotype
• Strength of recommendations grading system
– Recommendations for Incidental Findings
– Other considerations
• Potential Benefits and Risks for the Patient
• Caveats: Appropriate Use and/or Potential Misuse of
Genetic Tests
Uniform Elements of CPIC Guidelines
(Supplement)
•
•
•
•
•
•
•
Literature Review details
Genetic Test Interpretation
Available Genetic Test Options
Supplemental Table . Genotypes that constitute the * alleles for
______
Supplemental Table . Association between allelic variants and
_____ [gene function]
Supplemental Table . Frequencies of alleles in major race/ethnic
groups
Supplemental Table . Evidence linking genotype with phenotype
– Levels of Evidence grading system
• Resources to facilitate incorporation of pharmacogenetics into
an electronic health record with clinical decision support (CDS)
(workflow diagrams and example CDS alerts and consults)
Clin Pharmacol Ther. 2011
Linking genotype to phenotype
Clin Pharmacol Ther. 2011 Mar;89(3):387-91.
Dosing recommendations: strength
based on evidence
High: Evidence includes
consistent results from welldesigned, well-conducted
studies.
Moderate: Evidence is
sufficient to determine
effects, but the strength of
the evidence is limited by the
number, quality, or
consistency of the individual
studies; generalizability to
routine practice; or indirect
nature of the evidence.
Weak: Evidence is
insufficient to assess the
effects on health outcomes
because of limited number or
power of studies, important
flaws in their design or
conduct, gaps in the chain of
evidence, or lack of
information
Clin Pharmacol Ther. 2014
Apr;95(4):376-82
Clin Pharmacol Ther. 2014 Apr;95(4):376-82
Clin Pharmacol Ther. 2012 Apr;91(4):734-8.
Clin Pharmacol Ther. 2012 Jul;92(1):112-7.
CPIC Guidelines Updates
• CPIC guidelines are evaluated on an
ongoing bases and updated regularly
• No change: Update reviewed on
PharmGKB
• Update to publications:
– Minor update: Change to supplemental material
only
– Major update: Changes to both main
manuscript and supplement
• All changes posted to PharmGKB website
Variant annotation lists impact of a genomic variant on drug
response phenotype based on individual publications
Clinical annotation is a summary of the clinical impact of a
genomic variant on drug response phenotype.
Strength based on:
• Implementation
• Statistics
• Replications
• Population size
Dosing Guidelines
Provide therapeutic
recommendations based on
an individual’s genotype
Table 1: Assigning likely CYP2C19 phenotypes based on genotypes1
Likely Phenotype
Genotypes
Examples of Diplotypes
Ultrarapid Metabolizer (UM)
Normal or increased activity
(~5-30% of patients)
An individual carrying two
*1/*17, *17/*17
increased activity alleles (*17)
or one functional allele (*1)
plus one increased activity
allele (*17)
Extensive Metabolizer (EM)
An individual carrying two
Homozygous wild-type or normal activity functional (*1) alleles
(~35-50% of patients)
*1/*1
Intermediate Metabolizer (IM)
Heterozygote or intermediate activity
(~18-45% of patients)
An individual carrying one
functional allele (*1) plus one
loss-of-function allele (*2-*8)
*1/*2, *1/*3
Poor Metabolizer (PM)
Homozygous variant, mutant, low, or
deficient activity
(~2-15% of patients)
An individual carrying two
*2/*2, *2/*3, *3/*3
loss-of-function alleles (*2-*8)
1
Some rare genotype combinations have unclear predicted metabolic phenotypes; see Supplemental
Table S3.
http://www.ncbi.nlm.nih.gov/pubmed/
Table 2: Clopidogrel therapy based on CYP2C19 phenotype for ACS/PCI patients initiating
antiplatelet therapy
ClassifiTherapeutic
cation of
Phenotype (genotype)
Implications for clopidogrel
recommendations
recomendations1
Strong
Ultrarapid Metabolizer
Normal (EM) or increased (UM)
Clopidogrel - label
(UM)
platelet inhibition; normal (EM) or
recommended dosage
and administration.
(*1/*17, *17/*17)
decreased (UM) residual platelet
aggregation2
and
Extensvie Metabolizer
(EM) (*1/*1)
Moderate
Intermediate Metabolizer
Reduced platelet inhibition; increased Prasugrel or other
(IM)
residual platelet aggregation;
alternative therapy (if
no contraindication)
(*1/*2)
increased risk for adverse
cardiovascular events
Poor Metabolizer (PM)
(*2/*2)
1
Significantly reduced platelet
Prasugrel or other
inhibition; increased residual platelet alternative therapy (if
no contraindication)
aggregation; increased risk for
adverse cardiovascular events
See Supplement, Strength of Therapeutic Recommendations.
2 The CYP2C19*17 allele may be associated with increased bleeding risks (12).
Strong
Drugs for “Patient 0” Genome
PMID: 20435227
PharmGKB Annotation Method
• Evaluate 2500 SNP annotations for direct
drug relevance to patient 0
• Evaluate CNVs in known important genes
(VIP, PK, PD)
• Evaluate novel SNPs in known important
genes (VIP, PK, PD)
Variant annotation highlight
• Patient is heterozygous for a null mutation
•
of CYP2C19 (metabolizing enzyme)
CYP2C19 critical for metabolism of:
• proton pump inhibitors (lansoprazole,
•
•
omeprazole, pantoprazole, rabeprazole)
antiepileptics (diazepam, Norphenytoin,
phenobarbitone)
Amitryptyline, citalopram, chloramphenicol,
clopidogrel, indomethacin, nelfinavir,
propranolol, R-warfarin, imipramine...
Summary of Pharmacogenetic Bad News
Drug
Summary
Level of
Evidence
PMID
Gene
rsID
Clopidogrel &
CYP2C19
substrates
CYP2C19 poor metabolizer,
many drugs may need
adjustment.
High
19106084
CYP2C19
rs4244285
Warfarin
Requires lower dose
High
15888487
VKORC1
rs9923231
Warfarin
Requires lower dose
High
19270263
CYP4F2
rs2108622
Metformin
Less likely to respond
Medium
18544707
CDKN2A/B
rs10811661
Troglitazone
Less likely to respond
Medium
18544707
CDKN2A/B
rs10811661
Cisplatin
Increased risk of nephrotoxicity
Low
19625999
SLC22A2
rs316019
Citalopram
May increase risk of suicidal
ideation during therapy
Low
17898344
GRIA3
rs4825476
Escitalopram;
Nortriptyline
Depression may not respond as
well
Low
19365399
NR3C1
rs10482633
Morphine
May require higher dose for pain Low
relief
17156920
COMT
rs4680
Paclitaxel
Cancer may respond less well
Low
18836089
ABCB1
rs1045642
Pravastatin
May require higher dose
Low
15116054
SLCO1B1
rs2306283
Talinolol
May require higher dose
Low
18334920
ABCC2
rs2273697
Sildenafil
May not respond as well
Low
12576843
GNB3
rs5443
Summary of Pharmacogenetic Good News
Drug
Summary
Level of
Evidence
PMID
Gene
rsID
HMG CoA
No increased risk of myopathy
Reductase Inhibitors
(statins)
High
18650507
SLCO1B1
rs4149056
Statins
No increased risk of myopathy
High
12811365
SLCO1B1
rs4149056
Desipramine;
Fluoxetine
Depression may improve more than average Medium
19414708
BDNF
rs61888800
Fluvastatin
Good response
Medium
18781850
SLCO1B1
rs11045819
Metoprolol and other Normal CYP2D6 metabolizer.
CYP2D6 substrates
Medium
19037197
CYP2D6
rs3892097/rs180
0716
Pravastatin
May have good response
Medium
15199031
HMGCR
rs17238540
Pravastatin,
Simvastatin
No reduced efficacy
Medium
15199031
HMGCR
rs17244841
Caffeine
No increased risk of heart problems with
caffeine
Low
16522833
CYP1A2
rs762551
Calcium channel
blockers
No increased risk of Torsades de Pointe
Low
15522280
KCNH2
rs36210421
Carbamazepine
SNP is part of protective haplotype for
hypersensitivity to carbamazepine
Low
16538175
HSPA1A
rs1043620
Neviraprine
Reduced risk of hepatoxicity
Low
16912957
ABCB1
rs1045642
Efavirenz;
Nevirapine
Reduced risk of hepatoxicity
Low
16912956
ABCB1
rs1045642
Epoetin Alfa
Lower dose of iron and epo required
Low
18025780
HFE
rs1799945
Fexofenadine
Average blood levels expected
Low
11503014
ABCB1
rs1045642
Irbesartan
Irbesartan may work better than beta-blocker Low
15453913
APOB
rs1367117
Lithium
Increased likelihood of response
Low
18408563
CACNG2
rs5750285
Paroxetine
May have improved response
Low
17913323
ABCB1
rs2032582
Novel nonsynonymous damaging SNPs
SNP_loc
Ref
pt0
Coding
PK/P Gene
D?
related drugs
1:33251518
G
CG
H191D
PK
AK2
adefovir dipivoxil; tenofovir;
16:49303700 G
AG
V793M
PD
CARD15
infliximab;
12:54774480 C
CT
H578Y
PD
ERBB3
trastuzumab; erlotinib; gefitinib; lapatinib; PHA-665752; chloroquine;
cisplatin; gemcitabine; cetuximab;
3:124923809 T
AA
I485F
PD
MYLK
mercaptopurine; methotrexate;
13:98176691 T
CT
Y21C
PK
SLC15A1
atorvastatin; fluvastatin; hmg coa reductase inhibitors; lovastatin;
pravastatin; rosuvastatin; simvastatin;
9:86090799
G
AG
S443F
PK
SLC28A3
cladribine; fludarabine; uridine; mercaptopurine; thioguanine;
antineoplastic agents; gemcitabine; azathioprine; folic acid;
20:32342227 G
AG
P246L
PD
AHCY
antimetabolites; mercaptopurine; methotrexate; adenosine;
antineoplastic agents; azathioprine; folic acid; thioguanine;
16:49302615 C
CT
S431L
PD
CARD15
infliximab;
6:32593811
G
TT
T262K
PD
HLA-DRB5
clozapine;
6:31484467
T
CT
I14T
PD
MICA
mercaptopurine; methotrexate;
11:62517376 C
CT
R534Q
PK
SLC22A8
cimetidine; estrone; antiinflammatory and antirheumatic products,
non-steroidals; hmg coa reductase inhibitors;; adefovir dipivoxil;
tenofovir; antineoplastic agents; cyanocobalamin; folic acid;
leucovorin; pyridoxine;
16:31012227 C
CT
G64R
VKORC1
warfarin
A family quartet
PLoS Genetics, 2011
Example: Warfarin dosing
• Warfarin used for anticoagulation widely in
medicine
• Narrow therapeutic index, final dose impossible to
predict -> trial and error
• Three genes known to affect warfarin dose:
CYP2C9, VKORC1 and CYP4F2
• Recent results suggest warfarin dose will be
predicted precisely using genotypes + a few
demographic variables
PharmGKB -- http://www.pharmgkb.org/
Warfarin Dosing (continued)
• Long known: Variation in CYP2C9, but only
accounted for < 15% of variation
• Found in rat-poison-resistant-rats: Vitamin K
epoxide reductase (VKORC1)
• SNP in intron explains 35% of variation!
– VKORC1: -1639 G>A allele
• Clinical trials now underway to predict warfarin
dose required based on genotype
More later on recent developments and
implications of warfarin pharmacogenetics
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/
Observed
vs.
Predicted
Dose with
PGx
PharmGKB -- http://www.pharmgkb.org/
Example: Herceptin & Breast Cancer
• HER2 = human epidermal growth factor
• HER2 is amplified in 25-35% of breast cancer
patients
• Herceptin = Trastuzumab = antibody
against HER2
• Herceptin is very effective in those cases,
much less effective otherwise
• Testing HER2 levels becomes critical…
• Important “First” (survival benefit, ab, test)
PharmGKB -- http://www.pharmgkb.org/
Example: Bidil
• Combination pill containing two medications for
heart failure (hydralazine & isosorbide dinitrate)
• Clinical trials did not show overall benefit
• Subgroup of African-descent patients showed
benefit
• BiDil approved for use in African-descent patients
PharmGKB -- http://www.pharmgkb.org/
Example: Irinotecan
• Powerful anti-neoplastic used in colon/rectal
cancers
• Use is limited by severe life-threatening diarrhea
side effect
• Side effect related to patient genotype of UGT1A1
(helps metabolize irinotecan)
• Test now marketed for evaluating UGT1A1
genotype prior to initiation of treatment
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/
Most common major drug ADRs
• QT prolongation (heart problems)
• Liver failure
• Severe dermatological rash
• Many other minor ADRs
– Minor rash
– Abdominal discomfort
– Dry mouth
– Drowsiness or activation
– Headache
PharmGKB -- http://www.pharmgkb.org/
Other examples
•
•
•
•
•
•
•
•
Phenylthiourea non-taster phenotype
P-glycoprotein transporter variation
Aldehyde dehydrogenase
CYP2C19 and omeprazole & diazepam
Dihydropyridine dehydrogenase and 5-FU
UDP glucuronyl transferase 1A1 and bilirubin
N-acetylation polymorphism and INH (for TB)
NO-sythetase and vascular tone variation…
PharmGKB -- http://www.pharmgkb.org/
Example: Gefitinib (Iressa)
• Inhibits the growth factor receptor EGFR
• Promising early trials - but only improved survival in
subset of patients, 10-15%
– Female,
– Japanese
– Non-smokers
Example that shows we have to remember about the
host genome as well
• Iressa works better in those with variant EGFR that
makes it more susceptible to the drug
“Activating mutations in the epidermal growth factor receptor
underlying responsiveness of non-small-cell lung cancer to
gefitinib”, NEJM 2004 350:2129-2139
PharmGKB -- http://www.pharmgkb.org/
Hypertension
• Beta-adrenergic receptor polymorphism at amino
acid 389 (Arg -> Gly)
• Less susceptible to beta-blockers (atenolol,
metorprolol, propranolol, etc…)
• 9 mm Hg vs. 2 mm Hg response to atenolol
• May suggest need for testing before giving certain
types of anti-hypertensives
PharmGKB -- http://www.pharmgkb.org/
ADRB1 haplotypes and metoprolol
0
-2
Decrease in BP
-4
Freq of SR/SR:
• Whites - 45%
• Blacks - 10%
-6
-8
-10
Freq of SR/SR or SR/GR:
-12
• Whites – 57%
• Blacks – 22%
p = 0.006 between groups
-14
Change in DBP from
-16
SR/SR
(n=12)
SR/GR
(n=6)
SR/SG
(n=15)
GR/SG
(n=7)
PharmGKB -- http://www.pharmgkb.org/
Apolipoprotein 4 Allele and Statin Benefits
APOE also
implicated in
Parkinson’s
PharmGKB -- http://www.pharmgkb.org/
Arrhythmia
• QT prolongation
and arrhythmia
risk is the single
commonest
cause of drug
withdrawal or
relabeling in the
last decade
Wall Street Journal Oct. 28, 1999
PharmGKB -- http://www.pharmgkb.org/
Gene-Drug FDA examples
• HLA-B*1501 and 1502 alleles
– hypersensitivity responses which can lead to a rash and skin reactions
that may become serious and life-threatening
– abacavir (Ziagen), carbamezepine (Carbatrol, Tegretol)
• CYP2D6*3, *4, *5, *6, *10, *17 and *29 alleles
– confer "poor metabolism”
– tamoxifen (Novaldex); CYP forms appear to predict poorer
prognosis for long term survival
– fluoxetine (Prozac) and atomoxetine (Strattera)
• VKORC1 A/A and A/B haplotypes and CYP2C9*2 and *3 alleles
– interactions at the target site and the removal from the body
– warfarin (Coumadin)
• Patients with combinations of these alleles can experience elevated
INR measures and increased risk of dangerous bleeding events
PharmGKB -- http://www.pharmgkb.org/
Gene-Drugs FDA Examples
• UGT1A1*28 allele
– reduced clearance of the active form SN-38 of the anticancer drug
irinotecan (Camptosar) which can lead to neutropenia and a lifethreatening diarrhea
• TPMT*2, *3A, and *3C alleles
– in homozygous patients leads to decreased clearance
– 6-mercaptopurine (Purinethol) or the pro-drug azothiaprine
(Imuran) used to treat acute lymphoblastic leukemia and autoimmune
diseases leading to rapid bone marrow suppression that is severe and
can become fatal
• SLC01B1 (rs4363657) and OATP1B (rs4149056)
– found in liver can determine the blood levels of a wide range of drugs
– simvastatin (Zocor) and pravastatin (Pravachol) are associated with
development of myopathies
• ADRB2 Arg16 allele of the beta-2 adrenergic receptor
– linked in homozygous patients to a sub-class of asthma and lack of longterm responsiveness to treatment with albuterol (Ventolin)
PharmGKB -- http://www.pharmgkb.org/
PGx info in drug labels approved by FDA
Frueh et al, Pharmacotherapy 2008 28:992-998.
• Reviewed labels of FDA approved drugs
– to identify those that contained PGx biomarker information
– to collect prevalence information on the use of those drugs for
which PGx information was included in drug labelling
• 1200 drug labels reviewed for 1945-2005
– 121 drug labels contain PGx info
• 69 labels refer to human genomic biomarkers
– 62% (43) pertained to cytochrome p450s with CYP2D6
most common
• 52 labels referred to microbial genomic biomarkers
• 24.3% of prescriptions in 2006 (8.8 million out of 36.1 million)
processed by a large pharmacy benefits manager received one or
more drugs with human genomic biomarker information in the drug
label.
PharmGKB -- http://www.pharmgkb.org/
PGx info in drug labels approved by FDA
Frueh et al, Pharmacotherapy 2008 28:992-998.
Pharmacogenomic biomarkers
identified in drug labels with human
genomic information (1945–2005),
and percentage of drug labels
associated with each.
Number of drugs that were approved
With pharmacogenomic information in
their drug labels during
each 10-year period from 1945–2005
PharmGKB -- http://www.pharmgkb.org/
Conclusions from Drug Data 1945-2005
“Nearly one fourth of all outpatients received one
or more drugs that have pharmacogenomic
information in the label for that drug.
The incorporation and appropriate use of
pharmacogenomic information in drug labels
should be tested for its ability to improve drug use
and safety in the United States.”
Frueh et al, Pharmacotherapy 2008 28:992-998
PharmGKB -- http://www.pharmgkb.org/
Table of Valid Genomic Biomarkers
in the Context of Approved Drug Labels
http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm
PharmGKB -- http://www.pharmgkb.org/
Prevalence of Use of Required or Recommended
Pharmacogenomic Test in Drug Labels
PharmGKB -- http://www.pharmgkb.org/
http://medicine.iupui.edu/flockhart/
PharmGKB -- http://www.pharmgkb.org/
P450 Substrates
PharmGKB -- http://www.pharmgkb.org/
P450 Inhibitors
PharmGKB -- http://www.pharmgkb.org/
P450 Inducers and Genetics
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/
PharmGKB -- http://www.pharmgkb.org/