Lecture given for the biotechnology program at the Kellogg Graduate School of Business (NWU)

1. Diagnos(cs
and
Personalized
Medicine
Dynamics
in
the
Biotechnology
and
Life
Science
Industry
Tuesday,
February
6,
2007

2. Objec(ves
• Challenge
common
wisdom:
help
you
think
• Prepare
you
for
pitches
• Provide
basic
background
©
2013
Winton
Gibbons
2

3. Topics
to
Cover
• Overview
of
diagnos(c
market
• Nomenclature
• Marker
mining
and
valida(on
• Personalized
medicine
• Miscellaneous
and
Q
&
A
– Recent,
major
acquisi(ons
– Point-­‐of-­‐care
©
2013
Winton
Gibbons
3

4. Overall
Market
Size
and
Structure
Source:
BBC,
Amersham
and
WG
analysis
In-vitro
81%
In-vivo
19%
100%=$41.6
billion
©
2013
Winton
Gibbons
4

5. IVD
Market
Size
and
Structure
$7,997
$6,582
$6,034
$1,861
$1,827
$1,740
$1,295
$1,217
$1,228
$4,088
Diabetes
Infectious Disease
Clinical Chemistry
Hematology
Immunology
Endocrinology
Coagulation
Cancer
Cardiac
Other
Source:
BBC
and
WG
analysis
©
2013
Winton
Gibbons
5

6. IVD
Market
Size
and
Structure
Source:
BBC
and
WG
analysis
US
37%
Europe
35%
Japan
10%
ROW
18%
©
2013
Winton
Gibbons
6

7. IVD
Market
Size
and
Structure
Source:
BBC
and
WG
analysis
Lab
75%
PST
18%
Ambulatory
7%
©
2013
Winton
Gibbons
7

8. Roche
Dominates
the
IVDs,
Especially
A`er
GE’s
Move
Roche
21%
Abbott (pre GE)
12%
J&J
10%
Bayer (Siemens)
9%
Beckman
7%
Dade
6%
Other
35%
Source:
BBC
and
WG
analysis
©
2013
Winton
Gibbons
8

9. Other
Thoughts
on
Industry
Structure
• Top
4
Diagnos(cs
players
part
of
Larger
Medical
Product
Firm
(Roche,
GE,
J&J
and
Siemens)
– Compe((ve
Informa(on
Spoey
• Overlap
with
Life
Science
firms
– Diagnos(cs
uses
much
of
the
same
technology
as
Life
Sciences,
so
a
number
of
companies
straddle
both
(Beckman,
BioRad,
Cepheid,
Celera
and
even
Roche).
– However,
Diagnos(cs
is
different
due
to
regulatory,
medical
prac(ce,
reimbursement,
razor
/
razor
blade
and
larger,
diversified
players.
• In-­‐vitro
Diagnos(cs
is
a
large
($34
billion),
but
generally
grows
about
the
same
rate
as
nominal
GDP;
however,
there
are
a
few
fast-­‐growing
sub-­‐sectors
and
some
niche
opportuni(es
– Molecular
diagnos(cs
(e.g.,
DNA)
– Blood
Glucose
– Novel
protein
markers
(e.g.,
BNP
and
others)
©
2013
Winton
Gibbons
9

10. M.D.s
Rx firms
Device firms
Dx
Hospitals
Pharmacies
Distribution
Stronger
Poli(cal
Power
for
IVD
Firms
Typically
is
not
Strong
©
2013
Winton
Gibbons
10

11. Dimension RxL Max Chemistry/Immunochemistry Analyzer
GeneXpert
Triage
Diagnos(c
Instruments
Vary
in
Size
and
Complexity
©
2013
Winton
Gibbons
11

12. Large
System
Purchases
Typically
Don’t
Depend
on
Single
Markers
• 5- to 6-year repurchase cycle • Labor savings (2/3 of cost)
– Laboratory automation
• 12- to 24-month selling cycle – Ease of use
– Easy maintenance / reliability
•Important analytes on the menu:
Troponin I, HbA1c, BNP/NT-proBNP • Menu should cover 90%+ of volume
high-sensitivity TSH and HCG
Source: William Blair & Company, L.L.C. analysis
Purchasing Behavior for Mainframe Immunodiagnostic Analyzers
©
2013
Winton
Gibbons
12

13. Some
Myths
in
Diagnos(cs
• Best
test
– Standardiza(on
/
installed
based—VHS
versus
Betamax
(e.g.,
Troponin
I
versus
T;
BNP
versus
NT-­‐proBNP?)
– Plaoorm
migra(on
(NA
to
IA
to
CC)
– Trial
and
error
(e.g.,
sta(ns)
• POC
– Cost
center
versus
total
cost
– Lab
Director
power
– MD
office
• Work
flow
• Profit
(Stark
II—July
26)
• Pharmacogenomics
– Metabolizing
enzymes
(CYP450s)
• Yes
• Drug-­‐drug
interac(ons
– Individualized
medicine
• Not
always
• Except
certain
cancers
or
orphans
• Drugs
to
target
big
markets,
just
using
new
biology
©
2013
Winton
Gibbons
13

14. Nomenclature
• Sensi(vity
– Percent
with
disease
who
test
posi(ve
• Specificity
– Percent
of
without
disease
who
test
nega(ve
• Posi(ve
Predic(ve
Value
– Prevalence*Sensi(vity/(Prevalence*Sensi(vity+(1-­‐Prevalence)*(1-­‐Specificity))
• Nega(ve
Predic(ve
Value
– (1-­‐
Prevalence)*Specificity/((1-­‐
Prevalence)*Specificity+Prevalence*(1-­‐Sensi(vity)
• Odds
Ra(o
– Odds/Odds
– Odds=p/(1-­‐p)
• ROC
Curve
– True
Posi(ve
Frac(on
versus
False
Posi(ve
Disease Present Disease Absent
Positive Test A B A+B
Negative Test C D C+D
A+C B+D
Sensitivity A/A+C
Specificity D/B+D
©
2013
Winton
Gibbons
14

15. Reading
List
• Believability
of
rela(ve
risks
and
odds
ra(os
in
abstracts:
cross
sec(onal
study.
– BMJ,
Jul
2006;
333:
231
-­‐
234
• Evidence
of
bias
and
varia(on
in
diagnos(c
accuracy
studies
– Can.
Med.
Assoc.
J.,
Feb
2006;
174:
469
-­‐
476.
• Tips
for
learners
of
evidence-­‐based
medicine:
5.
The
effect
of
spectrum
of
disease
on
the
performance
of
diagnos(c
tests
– Can.
Med.
Assoc.
J.,
Aug
2005;
173:
385
-­‐
390
• Predic(on
of
cancer
outcome
with
microarrays:
a
mul(ple
random
valida(on
strategy.
– Lancet.
2005;365:488-­‐92.
• Can
Genentech
Double
Its
NHL
Franchise?
Focus
on
Fc
Receptors
– William
Blair
&
Company
Research
Note.
December
2,
2004
• Limita(ons
of
the
Odds
Ra(o
in
Gauging
the
Performance
of
a
Diagnos(c,
Prognos(c,
or
Screening
Marker
– Am.
J.
Epidemiol.,
May
2004;
159:
882
-­‐
890.
• When
can
a
risk
factor
be
used
as
a
worthwhile
screening
test?
– BMJ,
Dec
1999;
319:
1562.
• Drug
Metabolism
and
Variability
among
Pa(ents
in
Drug
Response
– N.
Engl.
J.
Med.,
May
2005;
352:
2211
-­‐
2221.
• Codeine
Intoxica(on
Associated
with
Ultrarapid
CYP2D6
Metabolism
– N.
Engl.
J.
Med.,
Dec
2004;
351:
2827
-­‐
2831.
• Developmental
Pharmacology
—
Drug
Disposi(on,
Ac(on,
and
Therapy
in
Infants
and
Children
– N.
Engl.
J.
Med.,
Sep
2003;
349:
1157
-­‐
1167.
©
2013
Winton
Gibbons
15

16. One
Week’s
Worth
of
Gene(c
Biomarker
Discovery
• “Gene(c
fingerprints
iden(fy
brain
tumors'
origins”
(Feb
1)
• “Mayo
Clinic
Research
Shows
35
Percent
Of
49
Young
People
Who
Died
Suddenly
And
Inexplicably
Had
Gene(c
Heart
Defects”
(Jan
31)
• “UCLA
Researchers
Discover
Genes
Linked
To
Lymphoma,
Opening
Way
For
New
Targeted
Drugs”
(Jan
31)
• “Study
finds
genes
that
predict
transplant
rejec(on”
(Jan
30)
• “A
Form
Of
The
Alcohol
Dehydrogenase
Gene
May
Protect
Afro-­‐Trinidadians
From
Developing
Alcoholism”
(Jan
30)
• “Autoimmune
Disease
Breakthrough
Gained
By
Iden(fica(on
Of
30
Errant
Genes”
(Jan
29)
• “Gene
'could
predict
ADHD
drug
reac(on'”
(Jan
29)
• “50%
of
Americans
have
gene
that
affects
how
body
burns
sugar”
(Jan
28)
• “Scien(sts
link
paternal
gene,
au(sm”
(Jan
26)
• “Gene(c
Risk
Factor
For
Parkinson's
Found”
(Jan
25)
• “Calculated
Risk:
Scien(sts
Discover
Gene(c
Risk
Factor
For
Smoking-­‐linked
Head
And
Neck
Cancer”
(Jan
25)
Source: National Office of Public Health Genomics (NOPHG)
©
2013
Winton
Gibbons
16

17. Senator
Barack
Obama
Introduces
the
Genomics
and
Personalized
Medicine
Act
The
Personalized
Medicine
Coali(on
welcomes
the
introduc(on
of
S.3822,
the
Genomics
and
Personalized
Medicine
Act,
and
looks
forward
to
working
with
Senator
Barack
Obama,
the
bill's
author,
and
his
colleagues
in
Congress,
to
hasten
the
introduc(on
of
personalized
medicine.
The
legisla(on,
among
other
things,
aims
to
improve
the
coordina(on
of
public
and
private
efforts
to
facilitate
the
development
of
safer
and
more
effec(ve
drugs,
create
a
biobanking
ini(a(ve,
expand
the
genomics
workforce,
and
improve
the
quality
of
clinical
gene(c
tes(ng.
©
2013
Winton
Gibbons
17

18. The
Genomics
and
Personalized
Medicine
Act
of
2006
• Sponsoring
Research.
The
bill
sets
aside
$150
million
to
sponsor
research
on
genomics.
It
enables
a
na(onal
biobanking
ini(a(ve
and
sets
up
a
system
to
pool
and
synthesize
genomic
data
from
local
sources.
This
act
establishes
an
interagency
task
force
to
accelerate
the
transla(on
of
research
into
medical
prac(ce.
Finally,
the
legisla(on
invests
in
the
next
genera(on
genomics
workforce
by
encouraging
the
recruitment
and
reten(on
of
genomic
professionals,
and
promotes
the
integra(on
of
genomics
across
all
clinical
and
public
health
disciplines.
• Encouraging
InnovaAon.
The
legisla(on
provides
a
100
percent
tax
credit
for
the
development
of
companion
diagnos(c
tests
that
can
improve
the
effec(veness
or
safety
of
certain
drugs.
In
addi(on,
the
Na(onal
Academies
will
conduct
a
study
to
determine
what
addi(onal
incen(ves
are
needed,
and
how
they
should
be
structured.
• Modernizing
the
FDA
and
CMS.
The
bill
requires
that
FDA
and
CMS
study
and
update
regulatory
processes
to
assure
the
quality
of
genomic
tests
through
improved
oversight
and
regula(on.
• ProtecAng
Consumers.
The
legisla(on
protects
consumers
by
reaffirming
Congress
commitment
to
stopping
gene(c
discrimina(on
and
protec(ng
gene(c
privacy.
In
addi(on,
direct-­‐to-­‐consumer
marke(ng
of
gene(c
tests
would
receive
greater
scru(ny
and
regula(on.
©
2013
Winton
Gibbons
18

19. Cytochrome
p450
Enzymes
• The
superfamily
has
undergone
divergent
evolu(on,
and
the
ancestral
gene
is
likely
2
to
3
1/2
billion
years
old.
• The
recent
'burst'
in
new
P450
genes,
par(cularly
in
the
II
family
during
the
past
800
million
years,
appears
to
be
the
result
of
'animal-­‐plant
warfare'.
• Due
to
the
presence
or
absence
of
a
par(cular
P450
gene
in
one
species
but
not
the
other,
it
may
not
be
correct
to
extrapolate
toxicity
or
cancer
data
from
rodent
to
human.
• Increases
in
the
P450
gene
product
(enzyme
induc(on)
almost
always
reflect
an
elevated
rate
in
gene
transcrip(on,
although
there
are
several
excep(ons.
©
2013
Winton
Gibbons
19

20. Posted
on:
Monday,
22
January
2007,
21:00
CST
GENETIC
MEDICINE
;
Some
Heart
PaAents
Get
DNA
Tests
to
Determine
Correct
Drug
Dose
By
Linda
A.
Johnson
Personalized
medicine,
the
tailored
treatments
that
a
few
pa(ents
now
get
based
on
their
own
DNA,
is
finally
headed
for
the
masses:
the
many
heart
pa(ents
at
risk
of
deadly
blood
clots.
At
least
2
million
Americans
with
an
abnormal,
clot-­‐triggering
heart
rhythm
take
the
pill
warfarin,
also
sold
as
Coumadin.
Gewng
too
liele
can
lead
to
a
stroke,
and
too
much
can
cause
life-­‐threatening
bleeding.
To
find
the
right
dose
for
each
pa(ent,
doctors
use
trial
and
error
-­‐-­‐
and
the
errors
lead
to
tens
of
thousands
of
hospitaliza(ons
and
deaths
every
year.
Star(ng
this
month,
about
1,000
pa(ents
who
have
a
condi(on
known
as
atrial
fibrilla(on
will
take
part
in
a
project
that
will
match
their
Coumadin
dose
to
their
specific
gene(c
needs.
This
gene(c
fingerprin(ng
should
single
out
the
many
people
whose
bodies
break
down
warfarin
faster
or
slower
than
normal,
and
their
doctors
can
immediately
adjust
their
dosage
to
prevent
dangerous
complica8ons.
"Twenty
percent
to
30
percent
of
people
are
either
very
fast
or
very
slow"
to
metabolize
many
drugs
but
don't
know
it,
said
Dr.
Robert
Epstein,
chief
medical
officer
at
prescrip(on
benefit
manager
Medco
Health
Solu(ons
of
Franklin
Lakes,
N.J.,
which
is
collabora(ng
in
the
effort
with
the
Mayo
Clinic,
based
in
Rochester,
Minn.
©
2013
Winton
Gibbons
20

21. Effect
of
CYP450
Muta(ons
• Rapid
metabolizers
– Carry
mul(ple
copies
(3-­‐13)
of
func(onal
alleles
and
produce
excess
enzyma(c
ac(vity
• Normal
metabolizers
– Possess
normal
func(onal
alleles
• Intermediate
metabolizers
– Possess
one
reduced
ac(vity
allele
or
one
null
allele
• Poor
metabolizers
– Carry
two
mutant
alleles
which
result
in
complete
loss
of
enzyme
ac(vity
• 2D6
gene
duplica(ons
– Ethiopians
16.0%
– Saudi
Arabians
10.4%
– Spaniards
10%
– Italians
8.3%
– Zimbabweans
2%
– Germans
1.8%
– Chinese
1.3%
• 2D6
Intermediate
and
poor
metabolizers
– Caucasians
7-­‐8%
– Japanese
~1%
– Chinese
~1%
– African
Americans~6%
Source:
Roche
©
2013
Winton
Gibbons
21

22. CYP450
Substrates
(drugs)
1A2 2B6 2C8 2C19 2C9
amitriptyline bupropion paclitaxel Proton Pump Inhibitors: NSAIDs:
caffeine cyclophosphamide torsemide lansoprazole diclofenac
clomipramine efavirenz amodiaquine omeprazole ibuprofen
clozapine ifosfamide cerivastatin pantoprazole lornoxicam
cyclobenzaprine methadone repaglinide rabeprazole meloxicam
estradiol E-3810 S-naproxen=>Nor
fluvoxamine piroxicam
haloperidol Anti-epileptics: diazepam=>Nor suprofen
imipramine N-DeMe phenytoin(O)
mexilletine S-mephenytoin Oral Hypoglycemic Agents:
naproxen phenobarbitone tolbutamide
olanzapine glipizide
ondansetron amitriptyline
phenacetin=> carisoprodol Angiotensin II Blockers:
acetaminophen=>NAPQI citalopram losartan
propranolol clomipramine irbesartan
riluzole cyclophosphamide
ropivacaine hexobarbital Sulfonylureas:
tacrine imipramine N-DeME glyburide/
theophylline indomethacin glibenclamide
tizanidine R-mephobarbital glipizide
verapamil moclobemide glimepiride
(R)warfarin nelfinavir tolbutamide
zileuton nilutamide
zolmitriptan primidone amitriptyline
progesterone celecoxib
proguanil fluoxetine
propranolol fluvastatin glyburide
teniposide nateglinide
R-warfarin=>8-OH phenytoin=>4-OH
rosiglitazone
tamoxifen
torsemide
S-warfarin
Source:
CYTOCHROME
P450
DRUG-­‐INTERACTION
TABLE-­‐-­‐Last
Updated:
Tue
Oct
17
2006-­‐-­‐Indiana University
Department of Medicine, Division of Clinical Pharmacology
©
2013
Winton
Gibbons
22

23. CYP450
Substrates
(drugs)-­‐-­‐con(nued
2E1
Beta Blockers: alprenolol Anesthetics: Macrolide antibiotics: Steroid 6beta-OH:
carvedilol amphetamine enflurane clarithromycin estradiol
S-metoprolol aripiprazole halothane erythromycin (not 3A5) hydrocortisone
propafenone atomoxetine isoflurane NOT azithromycin progesterone
timolol bufuralol methoxyflurane telithromycin testosterone
chlorpheniramine sevoflurane
Antidepressants: chlorpromazine Anti-arrhythmics: alfentanyl
amitriptyline codeine (=>O-desMe) acetaminophen quinidine=>3-OH (not 3A5) aprepitant
clomipramine debrisoquine =>NAPQI aripiprazole
desipramine dexfenfluramine aniline Benzodiazepines: buspirone
imipramine dextromethorphan benzene alprazolam cafergot
paroxetine duloxetine chlorzoxazone diazepam=>3OH caffeine=>TMU
encainide ethanol midazolam cilostazol
Antipsychotics: flecainide N,N-dimethyl formamide triazolam cocaine
haloperidol fluoxetine theophylline codeine- N-demethylation
perphenazine fluvoxamine =>8-OH Immune Modulators: dapsone
risperidone=>9OH lidocaine cyclosporine dexamethasone
thioridazine metoclopramide tacrolimus (FK506) dextromethorphan
zuclopenthixol methoxyamphetamine docetaxel
mexilletine HIV Antivirals: domperidone
minaprine indinavir eplerenone
nebivolol nelfinavir fentanyl
nortriptyline ritonavir finasteride
ondansetron saquinavir gleevec
oxycodone haloperidol
perhexiline Prokinetic: irinotecan
phenacetin cisapride LAAM
phenformin lidocaine
promethazine Antihistamines: methadone
propranolol astemizole nateglinide
sparteine chlorpheniramine odanestron
tamoxifen terfenidine pimozide
tramadol propranolol
venlafaxine Calcium Channel Blockers: quetiapine
amlodipine quinine
diltiazem risperidone
felodipine NOT rosuvastatin
lercanidipine salmeterol
nifedipine sildenafil
nisoldipine sirolimus
nitrendipine tamoxifen
verapamil taxol
terfenadine
HMG CoA Reductase Inhibitors: trazodone
atorvastatin vincristine
cerivastatin zaleplon
lovastatin ziprasidone
NOT pravastatin zolpidem
simvastatin
2D6 3A4,5,7
Source:
CYTOCHROME
P450
DRUG-­‐INTERACTION
TABLE-­‐-­‐Last
Updated:
Tue
Oct
17
2006-­‐-­‐Indiana University Department of Medicine,
Division of Clinical Pharmacology
©
2013
Winton
Gibbons
23

24. CYP450
Inhibitors
Source:
CYTOCHROME
P450
DRUG-­‐INTERACTION
TABLE-­‐-­‐Last
Updated:
Tue
Oct
17
2006-­‐-­‐Indiana University
Department of Medicine, Division of Clinical
Pharmacology
1A2 2C19 2C9 2D6 3A4,5,7
amiodarone chloramphenicol amiodarone amiodarone HIV Antivirals:
cimetidine cimetidine fenofibrate bupropion delaviridine
ciprofloxacin felbamate fluconazole celecoxib indinavir
fluoroquinolones fluoxetine fluvastatin chlorpheniramine nelfinavir
fluvoxamine fluvoxamine fluvoxamine chlorpheniramine ritonavir
furafylline indomethacin isoniazid chlorpromazine
interferon ketoconazole lovastatin cimetidine amiodarone
methoxsalen lansoprazole phenylbutazone citalopram aprepitant
mibefradil modafinil omeprazole probenicid clemastine NOT azithromycin
oxcarbazepine sertraline clomipramine chloramphenicol
2B6 probenicid sulfamethoxazole cocaine cimetidine
thiotepa ticlopidine sulfaphenazole diphenhydramine clarithromycin
ticlopidine topiramate teniposide doxepin diethyl- dithiocarbamate
voriconazole doxorubicin diltiazem
2C8 2E1 zafirlukast duloxetine erythromycin
trimethoprim diethyl- dithiocarbamate escitalopram fluoxetine fluconazole
quercetin disulfiram halofantrine fluvoxamine
glitazones histamine H1 receptor antagonists gestodene
gemfibrozil hydroxyzine grapefruit juice
montelukast levomepromazine imatinib
trimethoprim methadone itraconazole
metoclopramide ketoconazole
mibefradil mifepristone
midodrine nefazodone
moclobemide norfloxacin
paroxetine norfluoxetine
perphenazine mibefradil
quinidine star fruit
ranitidine verapamil
red-haloperidol voriconazole
ritonavir
sertraline
terbinafine
ticlopidine
tripelennamine
©
2013
Winton
Gibbons
24

25. CYP450
Inducers
Source:
CYTOCHROME
P450
DRUG-­‐INTERACTION
TABLE-­‐-­‐Last
Updated:
Tue
Oct
17
2006-­‐-­‐Indiana University Department of Medicine, Division
of Clinical Pharmacology
1A2 2C19 3A4,5,7
broccoli carbamazepine HIV Antivirals:
brussel sprouts norethindrone efavirenz
char-grilled meat NOT pentobarbital nevirapine
insulin prednisone
methyl cholanthrene rifampin barbiturates
modafinil carbamazepine
nafcillin 2C9 efavirenz
beta- naphthoflavone rifampin glucocorticoids
omeprazole secobarbital modafinil
tobacco nevirapine
2D6 phenobarbital
2B6 dexamethasone phenytoin
phenobarbital rifampin rifampin
rifampin St. John's wort
2E1 troglitazone
2C8 ethanol oxcarbazepine
rifampin isoniazid pioglitazone
rifabutin
©
2013
Winton
Gibbons
25

26. Personalized
Medicine-­‐Pros
and
Cons
• Desire
for
efficacy
• Desire
for
safety
• Because
we
can…
• Clinical
study
size
and
cost
– Efficacy
– Safety
• COGS
• Marke(ng
expense
• Design
around
– Dosing
(case
studies)
– Ligand
binding
(case
study)
– Alterna(ve
target
or
MOA
©
2013
Winton
Gibbons
26

27. Future Rx Targets Not Likely; Orphan Drugs
Existing Rx Today
Practical with Proper
Data
Only Dosing
Modifications for
Metabolism
One-Size Rationalize Personalize
OneDrug AFew Drugs Many Drugs
Economic Pressure
Scientific Pressure
Outlook
for
Personalized
Medicine
©
2013
Winton
Gibbons
27

28. Cases
• Rituxan
and
NHL
• MRI
for
stroke
(Lancet
2007)
• Gene
signature
for
breast
cancer
(NEJM
2007)
©
2013
Winton
Gibbons
28

29. Thoughts
on
the
Cases
• Read
the
literature
and
do
your
homework
• Design
around
personaliza(on
– Genitope
and
Favrille
s(ll
have
a
chance
• Understand
the
clinical
environment
• Odd
Ra(os
• Comparison
against
normals
versus
mimics
or
common
differen(al
(all
comers)
• Over-­‐fiwng
– Degrees
of
freedom
– Algorithms
– Sta(s(cal
tests
• Valida(on,
valida(on,
valida(on
©
2013
Winton
Gibbons
29

30. Performance
of
Rituxan
Varies
by
Fc
Polymorphism
©
2013
Winton
Gibbons
30

31. An(-­‐idiotype
Vaccines
S(ll
Appear
to
Perform
Best
©
2013
Winton
Gibbons
31

32. High Heaven
Med
Orphan Drugs
Low Hell
Low Med High
(Number and Effect)
Power
of
Target
Degree of Polymorphism
Efficacy
Higher Throughput Screening Needed
Target
Selec(on
Strategy
©
2013
Winton
Gibbons
32

33. Almost
20%
of
ERs
May
Not
Have
Any
Access
to
MRI
Source:
Biosite
Investor
R&D
Day
2006
©
2013
Winton
Gibbons
33

34. Available
MRIs
Take
Long
and
Are
Not
Available
All
Shi`s
Source:
Biosite
Investor
R&D
Day
2006
©
2013
Winton
Gibbons
34

35. Odds
Ra(os
Must
Be
Quite
High
to
Be
Useful
Limitations of the Odds Ratio in
Gauging the Performance of a
Diagnostic, Prognostic, or Screening
Marker. Am. J. Epidemiol., May
2004; 159: 882 - 890.
©
2013
Winton
Gibbons
35

36. Sources
of
Bias
in
DiagnosAcs
©
2013
Winton
Gibbons
36
Evidence of bias and variation in diagnostic accuracy studies.
Can. Med. Assoc. J., Feb 2006; 174: 469 - 476.

37. Q
&
A

38. • LinkedIn
– hep://www.linkedin.com/in/wintongibbons/
• Twieer
– @wingibbons
• Blog
– hep://www.wingibbons.wordpress.com
©
2013
Winton
Gibbons
38