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

Introduction

 ICH - General Purpose  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

Regulatory Environment

 Review time  A number of approved drugs by application of E5 guideline

New Drug Approval Times in Japan

By the year of NDA Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Source: Research Paper No.14 (Office of Pharmaceutical Industry Research, JPMA )

1998 1999 2000 2001 2002 2003

Annual list of E5 applied NDA

E5 implementation 2 products approved 3 products approved 5 products approved 11 products approved 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

Average cost per patient per year

2 1 0 4 3 6 5

1997 1998 1999 2000 2001 2001 (ex advertise) Relative cost per patient

6 5 2 1 4 3 0 Hong Kong Korea Japan US Turkey Argentina

Presentation by Dr. Uden at 3 rd 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 Slow speed of clinical trials Hollowing out of Clinical Trials

Recent R&D Trend

 From bridging to global studies  Importance of basic science

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  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)

  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

 

US and EU

: Placebo Controlled Trial

Japan

: Non-inferiority Trial or Placebo Controlled Relapse Trial

3 Major Studies

Drug

Tolterodine Irresa Losartan

Source Presentation by Dr.Kong Gans at the 3 rd K-H Sympo.

Review report by PMDEC Indication Overactive Bladder Non-small Lung Cancer NEJM Renal Disease Type of Study Asian Study (Japan and Korea) Global phase II study (Japanese vs. Non Japanese) Global study

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

Lessons

 Extrinsic factors  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

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

Statistical Issues

 Intrinsic factors 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?

Practical Issues

 Extrinsic factors 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 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 6 th Kitasato-Harvard Symposium, Oct 24-25, 2005, Tokyo, Japan

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 .

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)?

   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)

Compromised approaches in between

(borrowing information)

another extreme

By-country Analysis (borrowing NO info) Suppose Cox-model

Fit the stratified Cox model (strata=country)

h

(

t

) 

h k

(

t

) exp Get CI for   : treatment difference

Z

: covariate 1=treatment group 0=control group

h k

(

t

) : baseline hazard function for

k

-th country -

An empirical Bayes approach

Fit Cox model to each country

h

(

t

) 

h

(

t

) exp for the treatment difference  ˆ

k

～

N

( 

k

Fit a Normal-Normal ,

V

) hierarchical model (next page) Get the posterior distribution of  and Confidence Set.

k



k

Fit the Cox model to each country

h

(

t

) 

h k

(

t

) exp  

k

Get CI for 

k

: treatment difference for

k

-th country

A normal-normal hierarchical model

 ～

N



M

,

A

 Distribution of random parameter of interest  1  2  

K

True treatment Difference in each country

Y

1 ～

N

(  1 ,

V

1 )

Y

2 ～

N

(  2 ,

V

2 ) 

Y K

～

N

( 

K

,

V K

) Individual Sampling Density

y

1

y

2 

y K

A normal-normal hierarchical model

 ～

N



M

,

A

 Distribution of random parameter of interest  1

Normal Approx. of MLE

 ˆ 1 ～

N

(  1 ,

V

1 )  2  

K

True treatment difference In each country  ˆ 2 ～

N

(  2 ,

V

2 )   ˆ

K

～

N

( 

K

,

V K

) Individual Sampling Density  ˆ 1  ˆ 2   ˆ

K

A normal-normal hierarchical model

Empirical Bayes: Estimating UNKOWN hyper parameter using observed data

 ～

N



M

,

A

 Distribution of random parameter of interest  1

Normal Approx. of MLE

 ˆ 1 ～

N

(  1 ,

V

1 )  2  

K

True treatment difference In each country  ˆ 2 ～

N

(  2 ,

V

2 )   ˆ

K

～

N

( 

K

,

V K

) Individual Sampling Density  ˆ 1  ˆ 2   ˆ

K

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

  

M

,

A

 Naive EBCI :

E

( 

k

|

data

,   , ˆ )  1 .

96

Var

( 

k

|

data

, , ˆ ) should be

Var

( 

k

|

data

) 

E

, [

Var

( 

k

|

data

, , ˆ )] 

Var

, [

E

( 

k

|

data

, , ˆ )] 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: Survival time of group B:

T A T B

～ ～

Exponetial

(

Exponentia l

 ( 

A

)

A e

 ) Censoring time of both groups:

C

Thus, generated data for group A: ～ 

X

generated data for group B: 

X Exponentia l

, ,   

A



B

    min min (    )

T A T B

, ,

C C

 , 1  , 1  

T A T B

 

C

 

C

  Fixed

M

under

V

  0 .

3 , 

A

 1 ,  0 .

5 and 2.0.

 0 .

1 , the coverage probability of 95% CI is calculated

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.

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, 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

 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

  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

Overall Picture

Medical Facility 2 Medical Facility 1

Step 1 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 2 Medical Facility 1

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