Biostatistics in Pharmaceutical Research Statistics and Information Sciences at Eli Lilly

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Biostatistics in Pharmaceutical Research
Statistics and Information Sciences at Eli
Lilly CompanySenior StatisticianYun-Fei
Chen, Ph.D.
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My Background

• I was always interested in mathematics
• B.A.and M.S., National Taiwan Normal University
• Major Math
• Wanted to teach high school math
• Ph.D., Univ. of Wisconsin-Madison
• Major Statistics
• Wanted to be statistics professor in Taiwan
• Why became a statistician in pharmaceutical
  research? The variety of statistical challenges

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What Applied Statisticians Do

• Learn about their application area
• Pharmaceutical research - Biostatisticians
• Manufacturing / Quality control
• Government statistics / Surveys
• Technology
• Banking / Investment
• Social sciences
• Business / Economics
• Design experiments / Analyze data
• Research new methods for analyzing data
• Teach

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Eli Lilly and Company

• Headquarters Indianapolis, Indiana
• Over 35,000 employees worldwide
• 6,900 (19) in RD
• 11,000 in Indianapolis
• Products sold in 159 countries
• Net sales 11 Billion (2002)
• 6.7 Billion from 6 products
• 19 of sales invested in RD
• 30 compounds expected to reach clinical testing
• Named one of the 100 Best Companies to Work for
  in America by Fortune magazine (2002)

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Statistics and Information Sciences
300 Global Statisticians in 10 Countries
(U.S., Canada, Belgium, England, France,
Austria, Germany, Japan, Singapore,
Australia) Supporting Clinical Health
Economics Toxicology Discovery Clinical
Pharmacology Animal Health Product
Development Manufacturing Clients Clinical
Physicians, Analytical Chemists, Biologists,
Management, Process Engineers, Worldwide
Regulatory Authorities, Industry Groups,
Formulation Scientists, Physical Chemists,
Packaging Engineers, Medical Writers ...
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Three Most Fundamental Stages in Drug Development
Discovery
Clinical Trials
MD, Statistician
MD, Chemist Biologist
Toxicology/ADME
VM, Pathologist, Toxicologist, Pharmacist
10,000?1000 ? 100 ? 1
Researchers test thousands of molecules to
isolate one molecule (a lead candidate) that has
the potential to treat a specific disease or
condition
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Developing a New Drug
Hypothesis generation
Candidate development
Commercialization
Target identificationandvalidation Assay
development Lead generation
Globaloptimization
Globallaunch
PhaseIII
Submit
PhaseIB/II
PhaseIA
FHDPreparation
Leadoptimization
Product phase
Program phase
Project phase
Project must meet critical success factors to
move to next phase
Average Cost gt800 Million Dollars per
Drug Average Time 10-15 Years from Discovery to
Commercial Availability
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The Role of Clinical Physician
Discovery
Clinical Trials
MD
MD

• Provide clinical perspective to early drug
  development
• Translate the pre-clinical results to the
  clinical setting
• Work with statisticians to design clinical
  trials
• Oversee the execution of clinical trials,
  especially with regards to toxicity of a
  compound
• Work with investigators and regulatory
  scientists on the appropriate study design so
  that it can be approved and accepted by the
  health care community
•  

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The Roles of Chemist and Biologist
Discovery

• Chemist Synthesize molecules and SAR
• Play essential role for smaller molecules
• Does the change that I made to this molecule
  make
• it significantly more soluble, potent and less
  toxic?

Chemist, Biologist

• In vitro BiologistDevelopment assay and address
  biology of therapeutic candidate and targets
• Is the assay optimized/ reproducible?
• Is the biology addressed properly?

• In vivo Biologist Conduct animal study
• Is the animal model valid?
• Does the effect of this drug in animals really
  increase
• with dose?

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The Roles of Toxicology/ADME
Discovery
Toxicology/ADME
Clinical Trials
VM, Pathologist, Toxicologist, Pharmacist
Absorption
Distribution
Absorption phase
Excretion (kidneys)
Metabolism (liver)
Elimination phase
concentration
Elimination
Time (hours)
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Phase I/II Statistics

• Development Phase
• Traditional Topics
• Safety
• Pharmacokinetics (blood levels of drug and
  metabolites over time)
• Optimal dosing
• Efficacy
• All using small sample studies
• Area Undergoing Major Transformation
• High attrition rates (80 candidate drugs fail at
  this point)
• New emphasis on biomarkers
• Predict safety/efficacy outcomes (surrogate
  markers)
• Target patient profiles

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What I do A Typical Day?

• Answer questions from scientists
• How should these data be analyzed?
• Should I use SD or SE when I plot my data?
  Whats the difference?
• Im developing a new assay. Can you design an
  experiment to help me optimize the assay
  conditions?
• Design experiment /Analyze data
• work with others / teams to understand their
  studies
• Determining the safety and effectiveness of
  compounds before going into clinical testing.
  Also, determining the validity of the data and
  analyses that support these decisions.
• Present my analysis results to the team
• In/Formal presentation to 5-50 people

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What I do A Typical Day?

• Discuss/Coach issues with other statisticians
• What is the best way to analyze these data?
• Teach scientists
• Most biologists and chemists are not aware of
  statistical
• experimental design (factorial design)
• With factorial design it is possible to change
  more than
• one variable at a time and still determine the
  effect of
• each one on the outcome
• Statistical packages/webtools
• Statistics Ethics
• Professional activities
• Attend conferences
• Write papers for publication

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Basic Statistics Mean, SD, SE

• Sample Mean
• average of the sample
• estimate the population mean
• the larger the sample size, the more accurate
• Standard Deviation (SD)
• measures how spread out the sample is from its
  mean
• Use when you describe the patient population
• Standard Error (SE)
• measures the variation of the sample mean
• Use when you describe the mean response

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SD and SE
Mean10, SD5, n4, SE2.5
15
12.5
7.5
5
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Basic Statistics Testing and p-value
Test Treatment A and B to prove A better than
B Start by assuming "No Effect" (null
hypothesis) Gather data Calculate the probability
of getting data at least that rare, assuming "no
effect" (i.e. by chance) The lower this
probability (smaller the p-value), the less
believable the null hypothesis
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Example - Blood Pressure
Blood Pressure
Blood Pressure
Placebo Treated
Before Trt After Trt
Design Single group, measure before and
after treatment Analysis paired
t-test Significant? Depends on SD of the paired
differences. Different p 0.01 Not
Different p 0.21 125 - 80 45 125
- 105 20 145 - 105 40 145 - 130
15 160 - 130 30 160 - 80 80

• Design Parallel Groups
• Analysis 2-sample t-test
• Significant? No
• Example data, p 0.10 Placebo
  Treated 125 80
  145 105 160
  130

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Interesting Fact about SE bars
Number of standard errors required so that
non-overlapping error bars are statistically
significant
Blood Pressure
Placebo Treated

• People tend to judge statistical significant by
  whether not the error bars overlap.
• Usually, we plot /- 1 standard error (SE).
• Unfortunately, this is not the correct error bar
  for doing this.

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Biology - Dose-response Curve

• Does the biological effect increase significantly
  with dose?
• Fit a sigmoidal dose-response curve using
  non-linear regression analysis
• Estimate an ED50, the dose required to achieve a
  50 effectED50 3.8 mg/kg95 CI (1.8,
  8.1) mg/kg

0.1 0.3 1.0 3.0 10
30 100 mg/kg
Actual Dose
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Chemistry - Is one compound more potent?

• Is compound 2 more potent than compound 1?
• Measure potency with EC50 (like ED50)
• Compound 1EC50 9.595 CI (6.6, 13.6)
• Compound 2EC50 3.095 CI (2.6, 3.6)
• The compounds have significantly different
  potencies

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Example - Survival Analysis

• Compare survival time (time to death) between
  cancer
• patients on treatment A to treatment B

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Example Kaplan-Meier Survival Curve
1
0.9
0.8
0.7
0.6
of Patients Free of Relapse
0.5
0.4
0.3
0.2
0.1
0
0
25
50
75
100
125
150
175
200
225
250
Time (days)
Olanzapine
Quetiapine
Log-Rank test p 0.45
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Example - Correlation
The correlation coefficient gives a measure of
the linear relationship between two random
variables -1 correlation coefficient 1 Usually
denoted by r
r -0.95
r -0.03
r 0.95
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Example - Correlation
Correlation is only useful for identifying linear
relationships
Example the correlation coefficient between X
and Y estimated from the data is 0.03 (true
correlation is zero)
Yet the strong (nonlinear) relationship is
obvious True relationship Y (X-0.5) 2
noise Estimated from the data as Y 0.0012
0.9843 (X - 0.5009)2
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Example - Correlation

• If X1 and X2 are independent then they are
  uncorrelated
• If X1 and X2 are uncorrelated they may be
  independent
• Common misunderstandings/mistakes
• Low correlation ? no relationship
• consider Y(X-0.5)2 case previously shown
• High correlation ? line is a good fit
• Not Plotting Data

r0.85 r20.73
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Example - Correlation
r 0.7 in all examples
From pages 78-79 of Graphical Methods for Data
Analysis by Chambers, Cleveland, Kleiner, and
Tukey
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Statistics as a Career Why?
Q There are hundreds of careers available to me.
Why should I be a statistician?

• Advancements in technology, genetics, medicine
  and
• manufacturing have created strong demand of
• statisticians
• Statistician was one of the top 10 most
  desirable jobs
• and first in the category of Best Working
  environment
• in the 2002 Jobs Rated Almanac.
• Statisticians play in policy-making roles in
  government,
• development of pharmaceuticals, market
  research,
• quality control, and educational programs.

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Statistics as a Career How?
Q What do I need?

• MS or PhD has a solid foundation in statistics
  and
• mathematics and computer skills
• Having knowledge in another subject matter is a
  plus
• Communication skills, both oral and written
• Teamwork skills
• Professional flexibility

AMSTAT NEWS, September 2002
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Acknowledgement

• Philip Iversen, Research Scientist, Statistics
  and
• Information Sciences
• Brian Eastwood, Senior Research Scientist,
  Statistics and
• Information Sciences
• Michael Lahn, Clinical Research Physician
• Joe Heuer, Research Scientist, BIOTDR in vivo
  group
• Ho Yeong Song, Research Scientist, BIOTDR in
  vitro group
• Thomas Tan, Senior Biologist, IIRGRB