The drug effect is the quantifiable change in disease processes that result from the pharmacological or physical properties of an active treatment. To figure out the effectiveness of the drug,
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The drug effect is the quantifiable change in disease processes that result from the pharmacological or physical properties of an active treatment. To figure out the effectiveness of the drug,
To check the toxicity of the drug
and to investigate if symptoms of a serious side effect is present evaluating a drug effect is essential.
A workshop presentation for Medical Affairs Strategic Summit West held in San Diego on September 23, 2019. The workshop covered the following learning objectives:
* Understand the factors involved in selecting and prioritizing indications
* Understand the importance of strategic market segmentation
* Understand how Medical Affairs can be involved in the process of new product planning
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2. Topics
Introduction of pharmaceutical industry
Drug development process and clinical trials.
Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
Other topics
STATISTICS IN DRUG DEVELOPMENT 2
3. Drug
Drug definition:
according to the Food, Drug, and Cosmetic Act (FD&C Act):
(1): a substance recognized in an official pharmacopoeia or formulary
(2): a substance intended for use in the diagnosis, cure, mitigation,
treatment, or prevention of disease
(3): a substance other than food intended to affect the structure or function
of the body
(4): a substance intended for use as a component of a medicine but not a
device or a component, part, or accessory of a device Novartis (60.9B),
Simple version: A Chemical Substance that Interacts with a Living System
and Produces a Biological Response
STATISTICS IN DRUG DEVELOPMENT 3
4. Drug
“New Drug” vs “Generic Drug”
New Drug patents expire 20 years from the date of filing. Many other
factors can affect the duration of a patent.
Since the company applies for a patent long before the clinical trial to assess a drug’s
safety and efficacy has commenced, the effective patent period after the drug has finally
received approval is often around seven to twelve years.
New drugs (brand names) and generic drugs have the same
pharmacological effects (dosage, intended use, effects, side effects,
route of administration, risks, safety, etc.) and pharmacokinetics.
For example: Glucophage vs. metformin.
“Prescription Only” vs “Over the Counter”
Other drugs (orphan drugs), medical devices, vaccine
STATISTICS IN DRUG DEVELOPMENT 4
5. Pharmaceutical Industry
List of 2013 top biotech and pharmaceutical companies (Revenue):
Johnson & Jonson (71.3B),
Novartis (60.9B),
Roche (52.4B),
Pfizer (51.6B),
Sanofi, Merck, GlaxoSmithKline, AstraZeneca, Eli Lilly, Abbott Lab.
List of 2013 top biotech and pharmaceutical companies (Net income):
Pfizer (US, 22B),
Johnson & Jonson (13.8B),
Roche (Switzerland, 12.B),
Novartis (Switzerland, 9.5B), GlaxoSmithKline (UK, 9B), Sanofi (France,
5.1B), Amgen (US, 5.1B), Eli Lilly, Novo Nordisk, Merck.
STATISTICS IN DRUG DEVELOPMENT 5
6. Pharmaceutical Industry: Drug
Example of Blockbuster drugs from AstraZeneca:
AstraZeneca's Nexium (heartburn and acid reflux medication)
Sale: 5B (2009), 5B (2010), 4.4B (2011), 3.9B (2012),
6.14B (2013, the top 2nd)
Patent expired May 2014. AstraZeneca will not release an OTC drug.
AstraZeneca's Crestor (indication: cholesterol)
Sale: 5.3B (2013, the top 4th). Patent is going to expire in 2016.
Example of Blockbuster drugs from Pfizer:
Lipitor (Pfizer, indication: cholesterol) had sale Q3/2012 as $0.186B. The
patent expired in November, 2011.
Lipitor sales in 2010: $10.7B for the year.
STATISTICS IN DRUG DEVELOPMENT 6
7. Pharmaceutical Industry: R&D
Research and Development Cost (Forbes, 08/2013)
The median cost per new drug is $4 ~ 5 billion dollars.
STATISTICS IN DRUG DEVELOPMENT 7
Company Number of new drugs 10 year R&D spending (B$)
Abbott 1 13.2
Sanofi 6 60.8
AstraZeneca 4 38.2
Roche 8 70.9
Pfizer 10 77.8
Eli Lilly 4 26.7
GSK 11 57.6
J&J 13 67.6
8. Pharmaceutical Industry: CRO
Contract Research Organization:
A contract research organization (CRO) is an organization that provides
support to the pharmaceutical, biotechnology, and medical device
industries in the form of research services outsourced on a contract basis.
CROs that specialize in clinical-trials services can offer their clients the
expertise of moving a new drug or device from its conception to FDA/EMA
marketing approval, without the drug sponsor having to maintain a staff for
these services.
Top CROs:
Quintiles, PAREXEL, Covance, PPD, ICON
The outsourcing market is about $20-30 billions.
Quintiles (Durham NC) has 30,000+ employees in 60 countries. Year 2011
revenue is about $3.00 billion, and Year 2013 revenue is $3.8 billion.
STATISTICS IN DRUG DEVELOPMENT 8
9. Agenda
√ Introduction of pharmaceutical industry
Drug development process and clinical trials.
Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
Other topics
STATISTICS IN DRUG DEVELOPMENT 9
12. Clinical Trial Phases
Phase 1:
Studies that are usually conducted with healthy volunteers and that
emphasize safety. The goal is to find out what the drug's most frequent and
serious adverse events are and, often, how the drug is metabolized and
excreted (pharmacokinetics).
ADME (Absorption, Distribution, Metabolism, and Excretion) will be studies.
Phase 1 clinical trials include:
FIM (first-in-man) and/or single ascending dose cohorts study
Multiple ascending dose cohorts study
Food effect study
Drug-drug interaction study
Bioavailability and bioequivalence
PK study based on special populations
Thorough QT study
STATISTICS IN DRUG DEVELOPMENT 12
13. Clinical Trial Phases
Phase 2:
Studies that gather preliminary data on effectiveness (whether the drug
works in people who have a certain disease or condition). Safety continues
to be evaluated, and short-term adverse events are studied.
Phase 3:
Studies that gather more information about safety and effectiveness by
studying different populations and different dosages and by using the drug
in comb
Phase 4:
Studies are done after the drug or treatment has been marketed to gather
information on the drug's effect in various populations and any side effects
associated with long-term use.
STATISTICS IN DRUG DEVELOPMENT 13
15. Agenda
√ Introduction of pharmaceutical industry
√ Drug development process and clinical trials.
Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
Other topics
STATISTICS IN DRUG DEVELOPMENT 15
16. Biostatistics: Responsibilities
Design the study and develop the protocol
Design the randomization algorithm if needed
Draft the statistical analysis plan (SAP)
Programming the study data based on the SAP: tables/
figures/ listings (TFLs).
Present the results and write the interpretation of study
results in clinical study report (CSR).
STATISTICS IN DRUG DEVELOPMENT 16
17. Biostatistics (1): Study Classification
Phase I PK/PD study
Thorough QT study
Superiority study
Active control and equivalence/non-inferiority study
Dose-response study
Safety follow-up study
STATISTICS IN DRUG DEVELOPMENT 17
18. Biostatistics (2): Study Design
Conventional study designs:
Parallel Group Design
Crossover Design
Other study designs:
Titration Design
Cluster Randomized Designs
Group Sequential Design and other adaptive designs
Oncology studies: up-and-down design, continual
reassessment method, etc.
STATISTICS IN DRUG DEVELOPMENT 18
21. Things to Consider
Selection of control:
Placebo control
Positive control
Randomization
Is randomization necessary?
Cluster randomization?
Is randomization practical at certain level (such as site)?
Randomization algorithm: single site vs. multiple site, permuted-
block method vs. dynamic method with minimization algorithm
Blinding
Sample size calculation
STATISTICS IN DRUG DEVELOPMENT 21
22. Biostatistics (3): Clinical Data
Continuous data
Lab measures & vital signs: blood pressure, blood glucose, etc.
Instruments: questionnaire scores, etc.
Calculated parameters: AUC, etc.
Categorical data
Binary outcome: response (Yes or no), fatal, etc.
Multiple level outcome: event severity, number of events, etc.
Censored data (time-to-event)
Time to disease progress
Time to heal
Other data format, such as imaging data, questionnaires, ROC
STATISTICS IN DRUG DEVELOPMENT 22
23. Biostatistics (4): Case Studies
Case Study 1: Diabetes
Case Study 2: Stroke Response
Case Study 3: Cardiovascular Endpoint Study
Case Study 4: Pharmacokinetics Study
STATISTICS IN DRUG DEVELOPMENT 23
24. Case Study 1: Diabetes
Thirty six weeks open-label study for diabetes patients.
Objective: To evaluate the effects of insulin glargine with
metformin over NPH insulin with metformin on glycated
hemoglobin (HbA1C).
STATISTICS IN DRUG DEVELOPMENT 24
25. The primary efficacy variable is the change in HbA1c value
from baseline (0 weeks) to last visit (36 weeks).
The primary analysis will be performed using an analysis of
covariance (ANCOVA) model with HbA1c change from
baseline as a response variable, treatment group and
center as fixed effects, and the baseline value as a
covariate.
STATISTICS IN DRUG DEVELOPMENT 25
Case Study 1
26. How to define the endpoint:
End of study (EOS) measures (Week 36): what if missing, what if off drug for
more than two weeks? What if…
Change from baseline to EOS
Percent change from baseline to EOS
Two data points vs. one to reduce variability?
Log transformation or other transformations: still make senses?
Statistical modeling:
Modeling diagnosis
Need to add more factors/predictors or remove some: country, sex, race, etc.?
Non-parametric approach?
STATISTICS IN DRUG DEVELOPMENT 26
Case Study 1
27. Case Study 2: Stroke
This is a Phase 2, randomized, double-blind, placebo-controlled,
multicenter study. The total trial duration is 12 months.
Approximately 200 subjects who experienced stroke will be
randomized (100:100) into the trial.
The modified Rankin Scale (mRS) score will be assessed at Day 90
and used as efficacy. The mRS is a commonly used scale for
measuring the degree of disability or dependence in the daily
activities of people who have suffered a stroke or other causes of
neurological disability, and it has become the most widely used
clinical outcome measure for stroke clinical trials.
The mRS score runs from 0-6, running from perfect health without
symptoms to death. 0 - No symptoms. 1 - No significant disability. 2 -
Slight disability. 3 - Moderate disability. 4 - Moderately severe
disability. 5 - Severe disability. 6 - Dead.
STATISTICS IN DRUG DEVELOPMENT 27
28. The primary efficacy variable is dichotomized outcome at Day
90:
◦ favorable outcome (mRS≤2)
◦ unfavorable outcome (mRS>2)
The proportion of subjects with a favorable outcome will be
compared between the investigational product and the
placebo using the Cochran-Mantel-Haenszel test, controlling
for baseline NIHSS score category.
Any other models to be considered?
STATISTICS IN DRUG DEVELOPMENT 28
Case Study 2
29. A Double-Blind, Randomized, Placebo-Controlled Study of
atorvastatin as Prevention of Cardiovascular Events in
Patients With a Previous Stroke.
STATISTICS IN DRUG DEVELOPMENT 29
Visit S1 Visits T2 to T14
Screening Visit Double-Blind Treatment Period
Placebo
80 mg atorvastatin
Case Study 3: Cardiovascular
30. The primary efficacy variable is the time from randomization
to the first occurrence of a primary clinical endpoint (fatal or
nonfatal stroke)
A Cox proportional hazards regression analysis will be
performed on the time from randomization to the first
occurrence of a primary clinical endpoint. The primary model
will contain the following covariates: treatment, center, and
entry event (stroke). SAS procedure PROC PHREG will be used.
Kaplan–Meier estimator (the product limit estimator) of the
survival function will be plotted.
STATISTICS IN DRUG DEVELOPMENT 30
Case Study 3
31. Data Handling:
Data from all randomized patients will be analyzed.
Patients who experience a primary clinical endpoint are
considered as completer.
Censoring occurs for patients who do not experience a primary
clinical endpoint prior to the completion of the study. The
censoring time will correspond to the study day on which the
patient completed the study or was last contacted during the
study following a withdrawal.
If a patient dies from a cause other than stroke, the survival
time will be censored as if the patient had been lost to follow-
up at that point.
STATISTICS IN DRUG DEVELOPMENT 31
Case Study 3
32. A Phase I, randomized, double-blind, two period, two
sequence crossover BE study to evaluate the effect of
DRUG_A and DRUG_R on DRUG pharmacokinetics in healthy
adult subjects after single dose administration.
STATISTICS IN DRUG DEVELOPMENT 32
Sequence Sample
Size
Period 1:
Days 1-3
Period 2:
Days 15-17
1 32 Treatment DRUG_A Treatment DRUG_R
2 32 Treatment DRUG_R Treatment DRUG_A
Case Study 4: Pharmacokinetics
33. The primary efficacy variable is pharmacokinetic parameters
AUC(0-inf), AUC(0-72hr) and Cmax of DRUG.
STATISTICS IN DRUG DEVELOPMENT 33
Case Study 4
34. The statistical analysis will be performed on the log-
transformed pharmacokinetic parameters.
Analysis of variance will be performed using SAS Mixed Linear
Models procedure. Subject will be fitted as a random effect
and treatment, sequence, period will be fitted as fixed effects
in the model. The ratio and associated 90% CI will be
estimated for the PK parameters.
An example of SAS code is included here.
Proc Mixed;
class subject treatment;
model logPKvar = treatment seq period;
random subject;
lsmeans treatment;
estimate 'test vs ref' treatment -1 1/cl alpha=0.1;
run;
STATISTICS IN DRUG DEVELOPMENT 34
Case Study 4
35. Agenda
√ Introduction of pharmaceutical industry
√ Drug development process and clinical trials.
√ Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
Other topics
STATISTICS IN DRUG DEVELOPMENT 35
36. Oncology Studies
National Cancer Institute at NIH:
The recent breakthroughs in understanding the molecular
biology of cancer have led to an unprecedented number of
new targets in oncology drug development. There are more
cancer drugs in the research pipeline than any other type of
therapy, corresponding directly with the number of oncology
clinical trials.
Different study designs and data analysis methods for
oncology studies than for other drugs.
STATISTICS IN DRUG DEVELOPMENT 36
37. Current Stat Research Topics
Adaptive design
Missing data imputation
Dose finding by the Continual Reassessment Method (CRM)
Randomization algorithm and randomization tests
Risk-based monitoring and Fraud Detection
Design and analysis of non-inferiority studies
STATISTICS IN DRUG DEVELOPMENT 37
38. Medpace Biostatistics
Departments and Groups:
Biostatistician
Statistical analyst
Data manager, data coordinator
Clinical database programmer, SAS programmer
IVRS manager, coordinator
ECG core lab, imaging core lab
Biostatistics Department:
Around 40 biostatisticians and statistical analysts
Two in Scotland, UK, and the rest in Cincinnati office
SAS is the primary software. Currently using Version 9.3
STATISTICS IN DRUG DEVELOPMENT 38