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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

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Issues on recent drug development in japan

Issues on Recent Drug Development in Japan

Masahiro Takeuchi

Hajime Uno

Fumiaki Takahashi


  • Introduction

  • Clinical Trial Environment

  • Recent R&D Trend

  • Statistical Issues and Potential Approaches

  • Safety Issues

  • Conclusion


  • 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
Regulatory Environment

  • Review time

  • A number of approved drugs by application of E5 guideline


Current Situation in Japan

  • Clinical Trial Costs:

Very High

  • Numbers of Clinical Trials:


Costs of clinical trials in japan
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


Location of Clinical Trials conducted by Japanese Companies

Even Japanese companies conduct clinical trials in foreign countries


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

Recent r d trend
Recent R&D Trend

  • From bridging to global studies

  • Importance of basic science

Concept avoidance of unnecessary clinical trials
Concept:Avoidance of Unnecessary Clinical Trials

Bridging studies





Simultaneous global studies




Issues to be shown
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

Intrinsic factors influence of genotype
Intrinsic factors(Influence of Genotype)

  • Fukuda et. al.(2000) investigated whether the disposition of venlafaxine was affected by the CYP2D6 genotype.

  • # subject=36blue(*10/*10) = 6red(*1/*10,*2/*10)=13orange(*1/*1,*1/*2,*2/*2)=16green(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
Extrinsic factors

Different medical practice

  • Ex: Depression Trials

  • US and EU: Placebo Controlled Trial

  • Japan: Non-inferiority Trial or

    Placebo Controlled Relapse Trial


  • 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


    - surrogate markers: quick detection of efficacy

    different angles of profile

    - PPK analysis: investigation of possible factors


  • 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
Statistical Issues and Potential Approaches

  • How can statistics play a role in extrapolation of foreign clinical data?

Statistical issues
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
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
    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
    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, ANALYSISand 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.


    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

    • 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.

    : treatment difference

    : covariate

    1=treatment group

    0=control group

    : baseline hazard

    function for k-th


    : treatment difference

    for k-th country

    Get CI for

    Analysis model for local inference

    One extreme

    Pooled Analysis

    (borrowing directly)

    another extreme

    By-country Analysis

    (borrowing NO info)


    approaches in between

    (borrowing information)

    Suppose Cox-model

    Fit the stratified Cox model (strata=country)

    Fit the Cox model to each country

    Get CI for


    A normal-normal hierarchical model

    Distribution of random parameter of interest

    True treatment


    in each country


    Sampling Density


    A normal-normal hierarchical model

    Distribution of random parameter of interest

    True treatment


    In each country

    Normal Approx.

    of MLE


    Sampling Density


    A normal-normal hierarchical model

    Empirical Bayes:

    Estimating UNKOWN

    hyper parameter using observed data

    Distribution of random parameter of interest

    True treatment


    In each country

    Normal Approx.

    of MLE


    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

    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.


    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):

    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


    • 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.


    • 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
    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 issues1
    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
    Overall Picture

    Medical Facility 1

    Step 1

    Medical Facility 2

    Medical Facility N

    Step 2

    Data Center

    Medical Facility 3

    Medical Facility 5

    Medical Facility 4

    Step 1 within a mf
    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
    Step 2: Among MFs

    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


    • 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