1. Issues on Recent Drug Development in Japan
Masahiro Takeuchi
Hajime Uno
Fumiaki Takahashi

2. Outline Introduction Clinical Trial Environment Recent R&D Trend
Statistical Issues and Potential Approaches
Safety Issues
Conclusion

3. Introduction ICH - General Purpose E5 Guideline: New GCP Guideline
Unification of necessary documentation and its formats for NDA submission
E5 Guideline:
Extrapolation of foreign clinical data
Avoidance of unnecessary clinical trials
New GCP Guideline
Quality assurance of clinical trial data
Simultaneous Global Drug Development
Better drugs in a timely fashion

4. Regulatory Environment
Review time
A number of approved drugs by application of E5 guideline

7. Clinical Trial Environment in Japan

8. Numbers of Clinical Trials:
Current Situation in Japan
Clinical Trial Costs:
Very High
Numbers of Clinical Trials:
Diminishing

9. Costs of Clinical Trials in Japan
Average cost per patient per year
Relative cost per patient
Presentation by Dr. Uden at 3rd Kitasato-Harvard Symposium, 2002

10. No. of Initial Clinical Trial Notifications

11. Location of Clinical Trials conducted by Japanese Companies
Even Japanese companies conduct clinical trials in foreign countries

12. Speed of Clinical Trials in Japan

13. Hollowing out of Clinical Trials Domestic companies
conduct their clinical trials
outside of Japan
High cost to
conduct clinical trials
Slow speed
of clinical trials
Hollowing out of
Clinical Trials

14. Recent R&D Trend From bridging to global studies
Importance of basic science

15. Concept: Avoidance of Unnecessary Clinical Trials
Bridging studies
Foreign
data
New
Regions
Simultaneous global studies US EU
ASIA

16. Intra variability >> Inter variability
Issues to be shown
Intrinsic factors
Extrinsic factors
Intra variability >> Inter variability
Conduct of a proposed clinical trial among regions
Difference in Medical Practice
- Different study design
- Different adverse event reporting system

17. Intrinsic factors (Influence of Genotype)
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

18. 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
Instead of application of those sensitive statistical adjustment, NEJM reported the application of DNA microarray information to investigate the existence of mixture of targeted disease population in clinical trials.
The success rate of anthracycline chemotherapy on diffuse large B-cell lymphoma is approx %. The researchers employed information of microarray to define a clear targeted disease population by grouping targeted population.
Gene expression:
- grouped target population
- clearly defined target disease population

19. Mixture of Target Disease Population
DNA micro array: NEJM,2002
Cox regression
Then the researcher applied gene-expression signatures and found 4 distinct gene-expression signatures. The graph shows clear drug effect between the groups.
Gene-expression signatures: 4 distinct gene-expression signatures
score by the combination of the 4 signatures

20. Extrinsic factors Different medical practice Ex: Depression Trials
US and EU: Placebo Controlled Trial
Japan: Non-inferiority Trial or
Placebo Controlled Relapse Trial

21. 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. Non-Japanese)
Losartan
NEJM
Renal Disease
Global study

22. Lessons 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

23. Lessons Extrinsic factors Regulatory aspects:
Realization of conductivity of a planned trial
Regulatory aspects:
New GCP implementation
regulatory science practice – depends on structure of a review system
Design aspects:
study design: different medical practice
independent data monitoring committee
Simulation studies probably play an important role for future prediction

24. Statistical Issues and Potential Approaches
How can statistics play a role in extrapolation of foreign clinical data?

25. Statistical Issues Intrinsic factors Statistical Issues:
Clearly defined target population
intra-variability >> inter-variability
Randomization Scheme
Statistical Issues:
Definition of similarity
Statistical test vs point estimation
Variability within a region
Required sample size?

26. Practical Issues Extrinsic factors Statistical Issues:
Conductivity of a proposed clinical trial
Regulatory agencies
Different medical practice
Statistical Issues:
What should be shown?
Similarity: dose response, efficacy
Regulatory science
Placebo response: how to estimate

27. 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

28. Global/Multi-national Trials
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.
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.

29. Questions → One of the challenging statistical issues
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)?
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

30. 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
Fit the stratified Cox model (strata=country)
An empirical Bayes approach
Fit Cox model to each country
Normal approximation of MLE for the treatment difference
Fit a Normal-Normal hierarchical model (next page)
Get the posterior distribution of and Confidence Set.
Fit the Cox model to each country
Get CI for
Get CI for
: treatment difference
: covariate
1=treatment group
0=control group
: baseline hazard
function for k-th
country
: treatment difference
for k-th country

31. A normal-normal hierarchical model
Distribution of random parameter of interest
True treatment
Difference
in each country
Individual
Sampling Density

32. A normal-normal hierarchical model
Distribution of random parameter of interest
True treatment
difference
In each country
Normal Approx.
of MLE
Individual
Sampling Density

33. A normal-normal hierarchical model
Empirical Bayes:
Estimating UNKOWN
hyper parameter using observed data
Distribution of random parameter of interest
True treatment
difference
In each country
Normal Approx.
of MLE
Individual
Sampling Density

34. 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
with plugging-in the estimates to unknown
However, since are random, the posterior variance should be
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.

35. Approximated likelihood / Posterior distribution
Pooled Analysis
Empirical Bayes
By-Country Analysis

36. 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):
Survival time of group A:
Survival time of group B:
Censoring time of both groups:
Thus, generated data for group A:
generated data for group B:
, the coverage probability of 95% CI is calculated

37. Conclusion
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.

38. References
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,
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,
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,
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.

39. Safety Issues 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.

40. Safety Issues Network system among Hospitals 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

41. Overall Picture Step 1 Step 2 Medical Facility 1 Medical Facility 2
Medical Facility N
Step 2
Data Center
Medical Facility 3
Medical Facility 5
Medical Facility 4

42. 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

43. Step 2: Among MFs (i) Unification of Data base from different MFs and
Medical Facility 1
Medical Facility 2
Medical Facility N
Step 2
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

44. 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