Download Predicting the Cost and Pace of Pharmacogenomic Advances

1
Predicting the Cost and Pace of Pharmacogenomic
Advances: An Evidence-Based Study
1. Supplementary Methods
1.1. Cost per coauthor
1.2. Size of labor force
1.3. Local and national patterns of prescription drug use
1.4. Determination of adverse outcome profiles
1.5. Correlations among frequency of adverse outcome, variant frequency, and
percent attributable risk
2. Supplementary Figures (with Legends)
2.1. Fig. S1: Frequency Distribution, Number of Papers Published Per Author Per
Year in Pharmacogenomics
2.2. Figure S2: Local and National Patterns of Prescription Drug Use
2.3. Figure S3. Additional representative drug adverse outcome profiles
2.4. Figure S4: Size and Growth of the Labor Force
3. Supplementary Tables
3.1. Table S1: Summary of Supporting Literature and Estimated Costs
3.2. Table S2: Years for Each Step in Process
3.3. Table S3: Data for Number of Years to First step
3.4. Table S4: Incidence of Drug-Related Adverse Outcomes
4. Supplementary References
2
1. Supplementary Methods
1.1. Cost per coauthor. The cost per coauthor can be estimated by dividing the average
cost of a researcher by the productivity of the researcher measured as the number of
publications per year.
We used two independent methods to calculate the average cost of a researcher. First, as
a top-down method, we divided the NIH budget (1) by the number of researchers that
budget supports (2). Second, as a bottom-up method, we used the average researcher
salary from the United States Bureau of Labor Statistics (3) and PayScale.com
(www.payscale.com), a 30% fringe-benefit rate, a 50% indirect cost rate (4), and
$40,000 in research materials, reagents, and related expenses. These methods were
consistent with an estimate of approximately $200,000/year ($184,000-$199,000).
To estimate productivity in pharmacogenomics, we picked nine papers at random from
Table S1, and for each coauthor on each paper, we counted the total number of papers
that that person published in the same calendar year. This represented a sample size of
58 pharmacogenomics researchers. The nine papers, and the number of papers
published by each coauthor, were as follows: PMID 18305455: 4, 8, 4, 2, 9, 3, 3, 3, 1, 4, 3,
10, 4, 4 (14/20 coauthors checked, rest had common names); PMID 16772608: 2, 1, 4, 1,
4, 1, 1, 3, 1; PMID 18256392: 11, 9, 12, 14, 3, 1, 1, 1, 1, 1, 7, 12, 4, 4, 1, 1, 2, 1; PMID
19833260: 2, 9, 10, 1, 2, 3, 7; PMID 19106084: 6, 5, 11, 3, 5, 4, 19, 4, 38, 17 (9/10
coauthors checked; 1 had common name). Figure S1 shows a frequency distribution on
log-log axes. The distribution is well fit (R2=0.87) by a power law (black line). Because
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there is no “peak” to this highly skewed (“long-tailed”) distribution, we used the median
number of papers—4—for our estimate. Note that if one were to ignore this observation
and instead use the average (5.3), the total cost of guideline development would differ by
only ~20%.
Because initial and smaller reports tend to overstate effects (5) we recorded from the
confirmatory report (or a subsequent meta-analysis, where available) each association’s
percent attributable risk for its adverse outcome (Table S1). For each drug we used
product inserts, US Food and Drug Administration safety reports, and Medline to
determine its adverse-outcome profile, defined as the frequencies of treatment failure
and side effects documented to lead to drug non-adherence (Table S4). For tamoxifen we
were not able to find frequencies at which different adverse effects led to non-adherence
and so we excluded its profile from the model. We took 60 percent as the maximum
incidence that genetic guidelines can prevent, although this may be conservative given
that 83 percent of outpatient adverse drug events are considered non-preventable (6)
and possibly genetic and 77 percent of warfarin dose variability is thought to be genetic
(7).
1.2. Size of labor force. To estimate the number of researchers available to carry out the
required work we counted the number of unique co-authors of all reports cited in
PharmGKB’s database of drug-association relationships (8). As a check and comparison
we also counted the number of unique co-authors of all reports returned from two
pertinent Medline searches: association polymorphism (pharmaceutical OR drug) and
“genome wide association study” OR gwas. To estimate the bandwidth of a single
4
researcher we counted the number of authors per published report, reports per author,
and reports per association for authors of reports (Table S1).
1.3. Local and national patterns of prescription drug use. To produce the estimate
described in the main text requires extrapolating from a small set of drugs to a larger set
of drugs. Specifically, we extrapolated from the well studied set of drugs in Table S1 to
the set of drugs that guidelines will have to cover in order to halve drug-related adverse
outcomes. This requires knowing how many people use each prescription drug. This
information turns out to be difficult to ascertain directly at a national level for the United
States; the closest available surrogate is the number of prescriptions (scripts) used
nationwide for the most-prescribed drugs (9). Therefore, we tool the number of users of
each prescription drug at our own care center (Fig. S2A) and the number of
prescriptions per drug per user as representative of use nationwide. To test this
hypothesis, we first confirmed that the number of prescriptions for the most-prescribed
drugs at our hospital is in broad agreement (R2=0.51) with national data (9) (Fig. S2B).
We then confirmed a close correspondence (R2=0.90) between the number of
prescriptions and the number of users of each of the most commonly used drugs at our
hospital (Fig. S2C). This gave confidence that the shape of the distribution of
prescription drug use at our institution is representative of the nation’s as a whole. Fig.
S2 shows data for 2009, the year of the available national prescribing data (9);
distributions of hospital data for 2010 and 2011 are essentially identical (not shown).
1.4. Determination of adverse outcome profiles. Product inserts, US Food and Drug
Administration safety reports, and Medline were used to determine drug adverse-
5
outcome profiles, defined as the frequencies of treatment failure and side effects
documented to lead to drug non-adherence (see Table S4). Warfarin and the nicotine
replacement patch had too few listed adverse outcomes for curve fit to be meaningful
(bleeding/no response and treatment failure at 12-16 weeks, respectively). For
tamoxifen we were not able to find frequencies at which different adverse effects led to
non-adherence and so we excluded its profile from the model. Note the good exponential
fits (R2 0.87-0.99) across a range of slopes (0.13-1.93), suggesting applicability of the
model across a wide variety of drugs.
1.5. Correlations among frequency of adverse outcome, variant frequency, and
percent attributable risk. Our data showed a negative correlation between, e.g., percent
attributable risk and the frequency of the adverse outcome, as shown in Fig. 2 of the
main text. This relationship can be described as follows: percent attributable risk = 0.24*ln(frequency of outcome). The R2 for this relationship was robust to bootstrapping
(i.e., leaving one datapoint out, replacing it with a duplicate of another datapoint at
random, and repeating the curve fit): 0.76±0.05. This relationship means the higher the
frequency of the adverse outcome, the lower the percent attributable risk from any given
variant. To account for this relationship, our model chose a percent given exactly by the
above equation R2 of the time, and a random one from our data the remaining (1–R2) of
the time.
6
Supplementary Figures and Tables
Figure S1: Frequency Distribution, Number of Papers
Published Per Author Per Year in Pharmacogenomics
100
Count
y = 33.87x-1.11
R² = 0.87
10
1
1
10
100
Number of papers/au/year
Data compiled as described in supplementary methods.
7
Figure S2: Local and National Patterns of Prescription Drug Use
B. Nat'l v. local scripts
PharmTimes scripts (,000)
No. users, hospital
A. Users per drug
10,000
1,000
100
1
100
150
y = 3.57x
R² = 0.51
100
50
0
0
20
40
Local scripts (,000)
Drugs, most-least used
C. Scripts vs. users
Prescriptions (,000)
100
10
1
y = 1.56x
R² = 0.90
0
0
1
10
100
Users (,000)
(A) Frequency distribution of prescription drug use at our hospital. (B) The number of
prescriptions for the most-prescribed drugs at our hospital is in broad agreement with
national data (R2=0.51) (9). (C) Correspondence between the number of prescriptions
and the number of users of each of the most commonly used drugs at our hospital
(R2=0.90).
8
Figure S3: Additional representative drug adverse outcome profiles
B. Escitalopram
A. Atorvastatin
10.00%
10%
1.00%
0.10%
y = 0.24e-0.14x
R² = 0.88
y = 1.60e-1.93x
R² = 0.97
0.01%
1%
1
2
3
4
Rank of adverse outcome
1
3
5
7
9 11 13
Rank of adverse outcome
Drug adverse outcome profiles were generally well fit by exponential curves (R2 0.880.99). For example, the most common adverse outcome for clopidogrel (~9%) is about
three times as common (e1.02) as the second-most common (~3%), which is about three
times as common as the third-most common (~1%), and so forth. Table S4 lists the
actual adverse outcomes and their frequencies.
9
Figure S4: Size and Growth of the Labor Force
Number of researchers
10,000
1,000
100
10
1
1980
1990
2000
2010
Year
We counted the number of unique co-authors of all reports cited in PharmGKB’s
database of drug-association relationships by year (circles). As a check and comparison
we also counted the number of unique co-authors of all reports returned from two
pertinent Medline searches: association polymorphism (pharmaceutical OR drug) and
“genome wide association study” OR gwas, also by year (squares). Both showed patterns
of exponential growth (R2=0.96 and 0.95, respectively) at similar rates (0.24 and 0.28
year-1, respectively) as seen on these log-linear axes.
10
Supplementary Tables
Table S1: Summary of Supporting Literature and Estimated Costs
Associat’n
St
g
PMID
Ref.
Paper title
Journal
Year
#au
#pts
Cytochrome P450
2C19 loss-of-function
polymorphism is a
major determinant of
clopidogrel
responsiveness in
healthy subjects.
Cytochrome p-450
polymorphisms and
response to
clopidogrel.
Genetic determinants
of response to
clopidogrel and
cardiovascular events.
Blood
2006
9
29
CYP2C19*2 LOF
allele has different
platelet
aggregability;
could affect
variability
$450,000
N Engl J
Med
2009
11
1,639
Clopidogrel fails in
CYP2C19*2
$550,000
N Engl J
Med
2009
11
2,208
Replication study
$550,000
Taking this into
account works
$750,000
SLOC1B1*5 is
associated with
higher plasma
levels than other
alleles, in 50
people
$250,000
clopidogrel
CYP2C19
1
16772608
Hulot JS et
al. (10)
clopidogrel
CYP2C19
2
19106084
Mega JL et
al. (11)
clopidogrel
CYP2C19
2
19106083
Simon T et
al. (12)
clopidogrel
CYP2C19
3
20708365
Bonello L et
al. (13)
Clopidogrel loading
dose adjustment
according to platelet
reactivity monitoring
in patients carrying
the 2C19*2 loss of
function
polymorphism.
J Am Coll
Cardiol
2010
15
411
atorvastatin
SLC01B1
1
15116054
Mwinyi J et
al. (14)
Evidence for inverse
effects of OATP-C
(SLC21A6) 5 and 1b
haplotypes on
pravastatin kinetics
Clin
Pharmacol
Ther
2004
5
30
Summary
Est. cost
11
atorvastatin
SLC01B1
2
18650507
SEARCH
Collaborati
ve Group et
al. (15)
SLCO1B1 variants and
statin-induced
myopathy--a
genomewide study.
N Engl J
Med
2008
30
20,632
atorvastatin
SLC01B1
2
19833260
Voora D et
al. (16)
The SLCO1B1*5
genetic variant is
associated with statininduced side effects.
J Am Coll
Cardiol
2009
7
509
atorvastatin
SLC01B1
3
20628837
Harper CR
et al. (17)
Evidence-based
management of statin
myopathy.
Curr
Atheroscler
Rep
2010
2
escitalopram
HTR2A
1
16642436
McMahon
FJ et al.
(18)
Variation in the Gene
Encoding the
Serotonin 2A Receptor
Is Associated with
Outcome of
Antidepressant
Treatment
Am J
Human
Genet
2006
13
1,953
escitalopram
HTR2A
2
19365399
Uher R et
al. (19)
Genetic predictors of
response to
antidepressants in the
GENDEP project.
Pharmacogenomics J
2009
26
811
0
(review)
Showed a
correlation
between SLCO1B1
(the gene that
codes for OATP)
and statin induced
myopathy.
Replication study
$1,500,000
Based on
literature review,
algorithm
presented to
assist clinicians in
using genetic data
to create
treatment
guidelines that
may help avoid
myopathy
HTR2A associated
with 18%
reduction in
adverse outcome
of no response
$100,000
HTR2A variants
predicted
response to
escitalopram; one
marker
(rs9316233)
explains 1.1% of
variance
(P=0.0016)
$1,300,000
$350,000
$650,000
12
nicotine COMT
1
17548664
John-stone
EC et al.
(20)
Association of COMT
Val108/158Met
Genotype with
Smoking Cessation in
a Nicotine
Replacement Therapy
Randomized Trial
Cancer
Epidemiol
Biomarkers
Prev
2007
6
1,686
Patch trial. Of
people who
received the
patch, 33% of
Met/Met
responded vs.
22% of nonMet/Met. 85% of
people go back to
smoking within 12
weeks of trying to
quit---75% if on
the patch, 67% if
on the patch and
lack an A/A
variant.
$300,000
nicotine COMT
2
18192898
Munafo MR
et al. (21)
Association of COMT
Val108/158Met
genotype with
smoking cessation
Pharmacog
enet
Genom
2008
5
910
People with AA
take longer to
relapse to
smoking than AG
or GG. Table 2: At
26-week followup,
success rate was
16% for AA vs.
4.1% for GA or
GG. Thus failure
rate reduced from
~92% to 84%
$250,000
nicotine COMT
2
21330274
David SP et
al. (22)
Pharmacogenetics of
Smoking Cessation in
General Practice:
Results From the
Patch II and Patch in
Practice Trials
Nicotine &
Tobacco
Research
2010
6
0
(review)
Presents more
data from Patch II
and PIP studies
(even though a
review)--inclusion of this
paper a judgment
call
$300,000
13
warfarin
CYP2C9
1
10073515
Aithal GP et
al. (23)
Association of
polymorphisms in the
cytochrome P450
CYP2C9 with warfarin
dose requirement and
risk of bleeding
complications
Lancet
1999
4
188
First
documentation of
correlation
showing patients
with low dose
warafin
requirements
having a high
chance of a allelic
variant of CYP2C9
clinical
confirmation of
Aithal et al
$200,000
warfarin
CYP2C9
2
11926893
Higashi MK
et al. (24)
JAMA
2002
7
185
warfarin
CYP2C9
3
15947090
Sconce EA
et al. (25)
Association between
CYP2C9 genetic
variants and
anticoagulationrelated outcomes
during warfarin
therapy.
The impact of CYP2C9
and VKORC1 genetic
polymorphism and
patient characteristics
upon warfarin dose
requirements:
proposal for a new
dosing regimen
Blood
2005
10
335
Study that utlized
pharmocogenetic
information on
CYP2C9 and
VKORC1
genotypes in
order to create an
effective dosing
regimen
$500,000
warfarin
CYP2C9
3
18305455
Gage BF et
al. (26)
Use of
pharmacogenetic and
clinical factors to
predict the therapeutic
dose of warfarin.
Clinical
pharmacology and
therapeutics
2008
20
627
Replication study
utilizing
pharmocogenetic
information in
order to create a
more effective
warfarin dosing
system
warfarin
VKORC1
1
15358623
D'Andrea G
et al. (27)
A polymorphism in the
VKORC1 gene is
associated with an
interindividual
variability in the doseanticoagulant effect of
warfarin.
Blood
2004
8
147
Allelic variations
of VKOR are
associated with
vairaiblity in
warfarin dose
$350,000
$1,000,000
$400,000
14
warfarin
VKORC1
2
15930419
Rieder MJ
et al. (28)
Effect of VKORC1
haplotypes on
transcriptional
regulation and
warfarin dose.
New
England
Journal of
Medicine
2005
10
454
Replication study
showing allelic
variatons of VKOR
are associated
with variations in
warfarin dose
requirements
$500,000
warfarin
VKORC1
3
15947090
Sconce EA
et al. (25)
The impact of CYP2C9
and VKORC1 genetic
polymorphism and
patient characteristics
upon warfarin dose
requirements:
proposal for a new
dosing regimen
Blood
2005
10
335
Study that utlized
pharmocogenetic
information on
CYP2C9 and
VKORC1
genotypes in
order to create an
effective dosing
regimen
$500,000
warfarin
VKORC1
3
18305455
Gage BF et
al. (26)
Use of
pharmacogenetic and
clinical factors to
predict the therapeutic
dose of warfarin.
Clinical
pharmacology and
therapeutics
2008
20
627
Replication study
utilizing
pharmocogenetic
information in
order to create a
more effective
warfarin dosing
system
11888582
Mallal S et
al. (29)
Association between
presence of HLAB*5701, HLA-DR7,
and HLA-DQ3 and
hypersensitivity to
HIV-1 reversetranscriptase inhibitor
abacavir
Lancet
2002
12
581
Illustrated genetic
susceptibilty of
abacavir
hypersens. in ppl
with HLA-B-5701
abacavir HLA
1
to
3
$1,000,000
$600,000
15
abacavir HLA
2
11943262
Hetherington S et
al. (30)
Genetic variations in
HLA-B region and
hypersensitivity
reactions to abacavir
Lancet
2002
15
200
abacavir HLA
4
18256392
Mallal S et
al. (31)
HLA-B*5701 screening
for hypersensitivity to
abacavir
N Engl J
Med
2008
18
1,956
Carbamaze-pine
HLA
1
15057820
Chung W-H
et al. (32)
Medical genetics: a
marker for StevensJohnson syndrome.
Nature
2004
8
238
Carbamaze-pine
HLA
2
16538176
Hung S-I et
al. (33)
Genetic susceptibility
to carbamazepineinduced cutaneous
adverse drug reactions
Pharmacogenetics
and
Genomics
2006
19
235
Carbamaze-pine
HLA
3
21428768
Chen P et
al. (34)
Carbamazepineinduced toxic effects
and HLA-B*1502
screening in Taiwan.
N Engl J
Med
2011
34
4877
HLA-B57 was
present in 39
(46%) of 84
patients versus
four (4%) of 113
controls (p<0
small middle
dot0001).
However, because
of low numbers of
women and other
ethnic groups
enrolled, these
findings relate
largely to white
men
HLA-B*5701
screening reduced
the risk of
hypersensitivity
reaction to
abacavir
Patients who get
SJS on
carbamazepine
have HLA-B*1502
Finding holds in a
wider subset of
patients.
Screening for
HLA-B*1502
prevents SJS in
Chinese.
$750,000
$900,000
$400,000
$950,000
$1,700,000
Summary of the literature used for generating the estimate discussed in the main text. Not shown are trials for three
associations—VKORC1 and warfarin-induced bleeding (13), CYP2C9 and warfarin-induced bleeding (13), and CYP2C19 and
16
cardiac events due to clopidogrel resistance—that could validate proposed guidelines (NCT01119300, NCT01006733,
NCT01305148, and NCT00995514; see clinicaltrials.gov). Two of these closed in 2011. For the model we assumed that reports
validating these guidelines will appear in 2012.
17
Table S2: Years for Each Step in Process
Association
clopidogrel
CYP2C19
atorvastatin
SLC01B1
escitalopram
HTR2A
nicotine COMT
warfarin
CYP2C9
warfarin
VKORC1
abacavir HLA
carbamazepine
HLA
Step 1*
Step 1 to 2
Step 2 to 3
Step 3 to 4
1.5
3
1
2
4
4
1.5
4
3
3
1
5
3
4.5
4
8
1
1.5
4
1.5
0
0
6
2
5
Data extracted from Table S1 for ease of reference. *Years to Step 1 was calculated
as the number of years between publication of the Step 1 paper and the median of
the publication dates of the papers cited in its introduction (Table S3). In one case
where the Step 1 paper was not organized into sections (carbamazepine; PMID
15057820), the median of all its references was used.
18
Table S3: Data for Number of Years to First Step
Ass’n
PMID
Year
Med. yr, refs
Yrs, refs
clopidogrel
CYP2C19
16772608
2006
2004.5
1992
1994
2001
2005
2001
2005
2004
2004
2003
2005
2006
2003
2005
2005
2005
2005
atorvastatin
SLC0B1
15116054
2004
2000
1997
2001
1999
1999
1998
1998
2000
2001
2002
2002
2002
2000
1999
2001
2003
escitalopram
HTR2A
16642436
2006
2002
2002
2004
1996
2005
2001
2005
2002
1998
1994
1980
2004
nicotine
COMT
17548664
2007
2004
2001
2003
2003
2004
2005
2005
2004
2006
2004
2006
2004
2006
2006
1993
2007
2005
2004
1996
2004
2003
1999
1996
2004
2006
2000
2002
2005
1972
1999
1999
2004
warfarin
CYP2C9
10073515
1999
1994
1994
1996
1996
1992
1993
1991
1995
1986
1997
1996
1994
warfarin
VKORC1
15358623
2005
1997
1993
1993
1992
1993
1996
1996
2001
2000
1992
1996
1994
1997
1996
1997
1999
1999
2000
2000
2000
1995
1998
2002
2002
2004
abacavir
HLA
11888582
2002
2000.5
2001
1999
2001
2001
2001
1998
2000
1983
2001
1991
carbamaz.
HLA*
15057820
2004
1998
1994
1995
1999
2001
2003
1986
2001
1996
1998
1998
1973
19
Table S4: Incidence of Drug-Related Adverse Outcomes
Drug
Adverse outcome leading to
treatment discontinuation
Prevalence
abacavir
abacavir
abacavir
abacavir
atorvastatin
atorvastatin
atorvastatin
atorvastatin
carbamazepine
carbamazepine
carbamazepine
carbamazepine
carbamazepine
carbamazepine
carbamazepine
carbamazepine
carbamazepine
clopidogrel
clopidogrel
clopidogrel
clopidogrel
clopidogrel
clopidogrel
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
hypersensitivity
gastrointestinal symptoms
hepatic disturbances
musculoskeletal disorders
myopathy
myalgias
transaminitis
rhabdomyolysis
no response
skin rash
transaminitis
dizziness
arrhythmia
memory impairment
nausea
neutropenia
alopecia
adverse cardiovascular event
gastrointestinal symptoms
skin symptoms
intracranial hemorrhage
severe thrombocytopenia
severe neutropenia
treatment failure
nausea/vomiting
headache
insomnia
diarrhea
dry mouth
fatigue
somnolence
dizziness
pharyngolaryngeal pain
low libido/impotence
increased sweating
no response
flu-like syndrome
8.5%
3.3%
0.6%
0.3%
16.5%
4.7%
0.7%
0.1%
10.7%
3.6%
2.0%
1.2%
1.2%
0.7%
0.4%
0.3%
0.3%
8.4%
3.2%
1.5%
0.3%
0.2%
0.1%
37%
15.9%
15.7%
8.9%
8.4%
7.2%
7.2%
6.6%
6.3%
5.8%
5.4%
5.2%
5.0%
4.9%
References
(35)
(35)
(35)
(35)
(16, 36)
(37-39)
(37-39)
(37-39)
(40)
(40)
(40)
(40)
(40)
(40)
(40)
(40)
(40)
(41), ALFRED
(42)
(42)
(42)
(42)
(42)
(19)
(43, 44)
(44, 45)
(45)
(45)
(45)
(45)
(43-45)
(43-45)
(45)
(45)
(43-45)
(44, 46)
(45)
20
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
escitalopram
nicotine
replacement patch
back pain
abdominal pain
constipation
excessive yawning
extremity pain
suicidal ideation
jitteriness
hypotension
return to smoking in 12-16 weeks
4.1%
3.8%
3.5%
3.1%
1.4%
0.9%
0.8%
0.7%
85%
(45)
(45)
(44)
(45)
(45)
(45)
(45)
(45)
(47)
warfarin
warfarin
dose out of range
bleeding
40%
10%
(7, 48, 49)
(7, 48, 49)
21
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