Transcript Issues on Recent Drug Development in Japan

Issues on Recent Drug
Development in Japan
Masahiro Takeuchi
Hajime Uno
Fumiaki Takahashi
Outline
Introduction
 Clinical Trial Environment
 Recent R&D Trend
 Statistical Issues and Potential
Approaches
 Safety Issues
 Conclusion
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Introduction
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ICH - General Purpose
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Unification of necessary documentation and
its formats for NDA submission
E5 Guideline:
Extrapolation of foreign clinical data
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Avoidance of unnecessary clinical trials
New GCP Guideline
Quality assurance of clinical trial data
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Simultaneous Global Drug Development
Better drugs in a timely fashion
Regulatory Environment
Review time
 A number of approved drugs by
application of E5 guideline
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New Drug Approval Times in Japan
80
60
40
20
0
Drugs/month
100
By the year of NDA
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Year
Source: Research Paper No.14 (Office of Pharmaceutical Industry Research, JPMA )
Annual list of E5 applied NDA
1998
E5 implementation
1999
2 products approved
2000
3 products approved
2001
5 products approved
2002
11 products approved
2003
9 products approved
Source: ICH presentation by Mori, Nov., 2003
Clinical Trial Environment in Japan
Current Situation in Japan
• Clinical Trial Costs:
Very High
• Numbers of Clinical Trials:
Diminishing
Costs of Clinical Trials in Japan
Relative cost
Average cost per patient per year
Relative cost per patient
6
6
5
5
4
4
3
3
2
2
1
1
0
0
1997
1998
1999
2000
2001
2001 (ex
advertise)
Hong Kong
Korea
Japan
US
Turkey
Argentina
Presentation by Dr. Uden at 3rd Kitasato-Harvard Symposium, 2002
No. of Initial Clinical Trial Notifications
Location of Clinical Trials conducted by Japanese Companies
Even Japanese companies conduct clinical trials in foreign countries
Speed of Clinical Trials in Japan
High cost to
conduct clinical trials
Domestic companies
conduct their clinical trials
outside of Japan
Hollowing out of
Clinical Trials
Slow speed
of clinical trials
Recent R&D Trend
From bridging to global studies
 Importance of basic science
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Concept:
Avoidance of Unnecessary Clinical Trials
Bridging studies
Foreign
data
New
Regions
Simultaneous global studies
US
EU
ASIA
Issues to be shown

Intrinsic factors
Intra variability >> Inter variability
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Extrinsic factors
Conduct of a proposed clinical trial among regions
Difference in Medical Practice
- Different study design
- Different adverse event reporting system
Intrinsic factors
(Influence of Genotype)
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Fukuda et. al.(2000) investigated
whether the disposition of
venlafaxine was affected by the
CYP2D6 genotype.
# subject=36
blue(*10/*10) = 6
red(*1/*10,*2/*10)=13
orange(*1/*1,*1/*2,*2/*2)=16
green(others)=1
may affect efficacy and safety – adjustment of dosage
Mixture of Target Disease Population
 DNA micro array: NEJM,2002
- Target Population: diffuse large-B-cell lymphoma
- Efficacy：anthracycline chemotherapy
-35% - 40%
-mixture of target disease population
-Gene expression:
- grouped target population
- clearly defined target disease population
Mixture of Target Disease Population
DNA micro array: NEJM,2002
Cox regression
Gene-expression signatures: 4 distinct gene-expression signatures
score by the combination of the 4 signatures
Extrinsic factors
Different medical practice

Ex: Depression Trials
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US and EU: Placebo Controlled Trial
Japan: Non-inferiority Trial or
Placebo Controlled Relapse Trial
3 Major Studies
Drug
Source
Indication
Type of Study
Tolterodine
Presentation by Dr.Kong
Gans at the 3rd K-H
Sympo.
Overactive Bladder
Asian Study
(Japan and Korea)
Irresa
Review report by
PMDEC
Non-small Lung Cancer
Global phase II study
(Japanese vs. NonJapanese)
Losartan
NEJM
Renal Disease
Global study
Lessons
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Intrinsic factors: design (phase I and II)
Importance of basic science
Clear definition of a target population
- P450 information: investigate individual variation
w.r.t. efficacy and safety
- pharmacogenomics: possibly identified individual
characteristics
- surrogate markers: quick detection of efficacy
different angles of profile
- PPK analysis: investigation of possible factors
Lessons
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Extrinsic factors

Realization of conductivity of a planned trial
Regulatory aspects:
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New GCP implementation
regulatory science practice – depends on structure of a review system
Design aspects:
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study design: different medical practice
independent data monitoring committee
• Simulation studies probably play an important role for future prediction
Statistical Issues and Potential Approaches
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How can statistics play a role in
extrapolation of foreign clinical data?
Statistical Issues
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Intrinsic factors
Clearly defined target population
intra-variability >> inter-variability
Randomization Scheme
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Statistical Issues:
-
Definition of similarity
- Statistical test vs point estimation
- Variability within a region
- Required sample size?
Practical Issues
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Extrinsic factors
Conductivity of a proposed clinical trial
- Regulatory agencies
- Different medical practice
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Statistical Issues:
-
What should be shown?
- Similarity: dose response, efficacy
Regulatory science
- Placebo response: how to estimate
Different medical practice
Kitasato-Harvard-Pfizer-Hitachi project
Under various settings, using real data sets and
simulation techniques, we are trying to figure out
how to deal with the important issues concerning
design and analysis of global clinical trials.
Project team member
[Kitasato] M. Takeuchi, X. M. Fang, F. Takahashi, H. Uno
[Harvard] LJ Wei
[Pfizer] C. Balagtas, Y. Ii, M. Beltangady, I. Marschner
[Hitachi] J. Mehegan
The 6th Kitasato-Harvard Symposium, Oct 24-25, 2005, Tokyo, Japan
Global/Multi-national Trials
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Global trials involve many regions/countries.
Global trials provide us information about
investigational drug worldwide simultaneously.
As to getting new drug approval, there is the fact
that each region/country has its own regulatory
policy.
A lot of statistical issues for DESIGN, ANALYSIS
and MONITORING of global trials still remain.
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we are trying to figure out how to deal with these
issues, using real data sets.
Today’s talk is concerning with the analysis issues
regarding local inference.
Questions
Although a single summary of the treatment difference across countries
is important, but local inference is also desirable.
What can we say about the treatment difference in one country, for
example, in Japan (with ONLY 14 subjects)?
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Can we think of the treatment difference
derived from “pooled analysis” as that in
Japan?
Should we believe the results derived from
“by-country analysis” ?
Can we borrow the information from other
countries? How to borrow information?
→ One of the challenging statistical issues
Analysis model for local inference
One extreme
Pooled Analysis
(borrowing directly)
another extreme
By-country Analysis
(borrowing NO info)
Compromised
approaches in between
(borrowing information)
Suppose Cox-model
An empirical Bayes approach
Fit the stratified Cox
- Fit Cox model to each country
model (strata=country)
h(t )  hk (t ) exp Z 
Get CI for

-

: treatment difference
Z : covariate
1=treatment group
0=control group
hk (t ) : baseline hazard
function for k-th
country
-
h(t )  h (t ) exp  Z 
k
k
Normal approximation
of MLE
for the treatment difference
ˆk ～ N ( k ,V )
Fit a Normal-Normal
hierarchical model (next page)
Get the posterior distribution of
 and Confidence Set.
Fit the Cox model to
each country
h(t )  hk (t ) exp  k Z 
Get CI for  k
k
 k : treatment difference
for k-th country
A normal-normal hierarchical model
 ～ N M , A
1
Y1 ～ N (1 ,V1 )
2
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Distribution of random
parameter of interest
True treatment
 K Difference
in each country
YK ～ N ( K ,VK )
Y2 ～ N ( 2 ,V2 )

y1
y2

yK
Individual
Sampling
Density
A normal-normal hierarchical model
 ～ N M , A
1
Normal Approx.
of MLE ˆ
1 ～ N (1 ,V1 )
2

Distribution of random
parameter of interest
True treatment
 K difference
In each country
ˆK ～ N ( K ,VK )
ˆ2 ～ N ( 2 ,V2 )

ˆ1
ˆ2

ˆK
Individual
Sampling
Density
A normal-normal hierarchical model
Empirical Bayes:
Estimating UNKOWN
hyper parameter
using observed data
1
Normal Approx.
of MLE ˆ
1 ～ N (1 ,V1 )
 ～ N M , A
2

Distribution of random
parameter of interest
True treatment
 K difference
In each country
ˆK ～ N ( K ,VK )
ˆ2 ～ N ( 2 ,V2 )

ˆ1
ˆ2

ˆK
Individual
Sampling
Density
A reason why we picked a N-N model on EB
There is a well-known issue on EBCI:
“Naive” EBCI fails to attain their nominal coverage probability.
“Naive” EBCI is constructed from the posterior distribution of
 k with plugging-in the estimates Mˆ , Aˆ  to unknown M , A
Naive EBCI : E ( k | data, Mˆ , Aˆ )  1.96 Var ( k | data, Mˆ , Aˆ )
However, since Mˆ , Aˆ  are random, the posterior variance
should be
Var ( k | data)  EMˆ , Aˆ [Var ( k | data, Mˆ , Aˆ )]  VarMˆ , Aˆ [ E ( k | data, Mˆ , Aˆ )]
The term under the square root is just an approximation
of the first term of RHS in above equation.
There are a lot of literature concerning EB for a N-N model. Some theories
are available to correct “Naive” EBCI especially for a N-N model. (Morris
(1983), Laird & Louis (1987), Carlin & Gelfand (1990), Datta et al (2002),
etc.)  We applied the Morris’ correction in the following analysis.
Approximated likelihood / Posterior distribution
Pooled Analysis
Empirical Bayes
By-Country Analysis
Simulation studies
A small simulation study was conducted to evaluate the performance of this
approach under the Cox model.
The number of countries and the sample size in each country were fixed,
evaluated the coverage probability and average length of confidence interval
were evaluated based on 10,000 iterations.
Simulation scheme:
Parameter of interest (treatment difference):  ～ N (M ,V )
Survival time of group A: TA ～ Exponetial(A )
Survival time of group B: TB ～ Exponential (Ae )
Censoring time of both groups: C ～ Exponential ( )


generated data for group B:  X ,    min T , C ,1T  C 
B
B
B
Thus, generated data for group A:  X ,  A  min TA , C ,1 TA  C
Fixed M  0.3, A  1,   0.1 , the coverage probability of 95% CI is calculated
under V  0.5 and 2.0.
Conclusion
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This empirical Bayes approach (Normal-Normal hierarchical
model coupled with normal approximation of the estimator of the
treatment difference) can be used in a wide variety of situations.
From a simulation study, the performance of this approach was
not bad in terms of both coverage probability and length of CIs.
As to RALES data, this analysis provides shorter CIs and
suggests that the treatment differences among each country are
toward the same direction.
In global clinical trials, performing this kind of intermediate
analysis can be encouraged as a planned sensitivity analysis in
addition to the pooled analysis and by-country analysis for better
understanding of the treatment difference in a specific country.
References
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Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis,
2nd ed. New York: Springer-Verlag.
Carlin, B. & Gelfand, A. (1990). Approaches for empirical Bayes
confidence intervals. JASA 85, 105-114.
Carlin, B. & Louis, T. (2000). Bayes and Empirical Bayes Methods for
Data Analysis, 2nd ed. London: Chapman & Hall/CRC.
Datta, G et al (2002). On an asymptotic theory of conditional and
unconditional coverage probabilities on empirical Bayes confidence
intervals. Scand. J. Statist 29, 139-152.
Laird, N. & Louis, T. (1987). Empirical Bayes confidence intervals based
on bootstrap samples. JASA 82, 739—750.
Morris, C. (1983a). Parametric empirical Bayes inference: theory and
applications. JASA 78, 47--55.
Morris, C. (1983b). Parametric empirical Bayes confidence intervals. In
Scientific inference, data analysis, and robustness, 25—50, New York:
Academic Press.
Pitt, B et al. (1999) The effect of spironolactone on morbidity and
mortality in patients with severe heart failure. NEJM 341, 709—717.
Safety Issues
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Intrinsic/Extrinsic factors
How can we ensure the safety of the drug if a
drug is approved based on a small clinical
data in a region?
Need a type of a phase IV study after a
approval, i.e., electronic data capturing
system, and how can we analyze the data
and what is a appropriate interpretation.
Safety Issues
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Network system among Hospitals
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Research Grant from MHLW
•
•
•
Network system among hospitals by
EDC to monitor patients
Detection of unexpected AEs
Build data base regarding pats`
background for signal detection,
pharmacoepidemiology
Overall Picture
Medical Facility 1
Step 1
Medical Facility 2
Step 2
Medical Facility N
Data Center
Medical Facility 3
Medical Facility 5
Medical Facility 4
Step 1: Within a MF
Connect Necessary Medical Records per Patient
Unification of Medical Records
per Patient regarding
-Patient`s background
- Dosage and duration
-Efficacy
-Safety
Step 2: Among MFs
Medical Facility 1
Medical Facility 2
Step 2
Medical Facility N
Data Center
(i) Unification of Data base from different MFs and
Establishment of Patients` data base at Data Center
(ii) Detect unexpected AEs and analyze safety profile
according to actual dosage and duration
Conclusion
Asian and Global Studies are a future
direction
 Design and Statistical Issues must
cope with basic science
 Phase IV studies based on EDC are
necessary for assurance of safety
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