issues on recent drug development in japan masahiro takeuchi hajime uno fumiaki takahashi
TRANSCRIPT
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
020
4060
8010
0
Dru
gs/
mo
nth
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Source: Research Paper No.14 (Office of Pharmaceutical Industry Research, JPMA )Year
New Drug Approval Times in Japan
By the year of NDA
Annual list of E5 applied NDA
9 products approved2003
11 products approved2002
5 products approved2001
3 products approved2000
2 products approved1999
E5 implementation1998
Source: ICH presentation by Mori, Nov., 2003
Clinical Trial Environment in Japan
• Clinical Trial Costs:
• Numbers of Clinical Trials:
Current Situation in Japan
Very HighVery High
DiminishingDiminishing
Costs of Clinical Trials in Japan
0
1
2
3
4
5
6
1997 1998 1999 2000 2001 2001 (exadvertise)
Rel
ativ
e co
st
Average cost per patient per year Relative cost per patient
Presentation by Dr. Uden at 3rd Kitasato-Harvard Symposium, 2002
0
1
2
3
4
5
6
Hong Kong Korea Japan US Turkey Argentina
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
Hollowing out ofClinical Trials
High cost toconduct clinical trials
Domestic companiesconduct their clinical trials
outside of JapanSlow speed
of clinical trials
Recent R&D Trend
From bridging to global studies Importance of basic science
Concept:Avoidance of Unnecessary Clinical Trials
US
ASIAEU
Foreigndata
NewRegions
Bridging studiesBridging studies
Simultaneous global studiesSimultaneous global studies
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)
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
DNA micro array: NEJM,2002
Cox regression
Gene-expression signatures: 4 distinct gene-expression signatures score by the combination of the 4 signatures
Mixture of Target Disease Population
Extrinsic factors
US and EU: Placebo Controlled Trial Japan: Non-inferiority Trial or Placebo Controlled Relapse Trial
Different medical practice
Ex: Depression Trials
3 Major Studies
Drug Source Indication Type of Study
TolterodinePresentation by Dr.Kong Gans at the 3rd K-H Sym
po.Overactive Bladder
Asian Study
(Japan and Korea)
IrresaReview report by
PMDECNon-small Lung Cancer
Global phase II study
(Japanese vs. Non-Japanese)
Losartan NEJM Renal Disease 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 factorsClearly 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, efficacyRegulatory science
- Placebo response: how to estimateDifferent 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
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
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?
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)?
→ One of the challenging statistical issues
Analysis model for local inference
Zthth k exp)()( Zthth kk exp)()(
Fit the Cox model to each country
One extremePooled Analysis
(borrowing directly)
another extremeBy-country Analysis(borrowing NO info)
Compromised approaches in between
(borrowing information)
Suppose Cox-modelFit 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.
Zthth kk exp)()(
),(ˆ VN kk ～
k
: treatment difference
: covariate 1=treatment group 0=control group
Z
)(thk : baseline hazard function for k-th country
k : treatment difference for k-th country
Get CI for Get CI for k
AMN ,～
1
),( 111 VNY ～
1y
2
2y
K
Ky
IndividualSampling Density
Distribution of random parameter of interest
),( 222 VNY ～ ),( KKK VNY ～
A normal-normal hierarchical model
True treatment Difference in each country
AMN ,～
1
),(ˆ111 VN ～
1̂
2
2̂
K
K̂
IndividualSampling Density
),(ˆ222 VN ～ ),(ˆ
KKK VN ～
A normal-normal hierarchical model
Normal Approx. of MLE
True treatment differenceIn each country
Distribution of random parameter of interest
AMN ,～
1
),(ˆ111 VN ～
1̂
2
2̂
K
K̂
IndividualSampling Density
),(ˆ222 VN ～ ),(ˆ
KKK VN ～
A normal-normal hierarchical model
Normal Approx. of MLE
Empirical Bayes:Estimating UNKOWNhyper parameter using observed data
True treatment differenceIn each country
Distribution of random parameter of interest
“Naive” EBCI is constructed from the posterior distribution of with plugging-in the estimates to unknown
A reason why we picked a N-N model on EBThere is a well-known issue on EBCI: “Naive” EBCI fails to attain their nominal coverage probability.
AM ˆ,ˆ
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.
AM ,
)ˆ,ˆ,|(96.1)ˆ,ˆ,|( :EBCI Naive AMdataVarAMdataE kk k
However, since are random, the posterior variance should be
)]ˆ,ˆ,|([)]ˆ,ˆ,|([)|( ˆ,ˆˆ,ˆ AMdataEVarAMdataVarEdataVar kAMkAMk
AM ˆ,ˆ
The term under the square root is just an approximation of the first term of RHS in above equation.
Approximated likelihood / Posterior distribution
Pooled Analysis By-Country AnalysisEmpirical Bayes
)( AA ExponetialT ～
),( VMN～
CTCTX AAA 1,,min,
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.
, the coverage probability of 95% CI is calculated
Simulation scheme:
Parameter of interest (treatment difference):
Survival time of group A:
)( elExponentiaT AB～Survival time of group B:
)(lExponentiaC ～Censoring time of both groups:
Simulation studies
Thus, generated data for group A:
generated data for group B: CTCTX BBB 1,,min,
1.0,1,3.0 Fixed AM
2.0. and 5.0under V
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` backgro
und for signal detection, pharmacoepidemiology
Overall Picture
Medical Facility 2 Medical Facility N
Medical Facility 3Medical Facility 5
Medical Facility 4
Medical Facility 1
Data Center
Step 1
Step 2
Step 1: Within a MF
Unification of Medical Recordsper Patient regarding-Patient`s background- Dosage and duration-Efficacy -Safety
Connect Necessary Medical Records per Patient
Step 2: Among MFs
Medical Facility 2 Medical Facility N
Medical Facility 1
Data Center
Step 2
(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