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Issues on Late Medication Improvement in Japan

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  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 • 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

  5. Clinical Trial Environment in Japan

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

  7. 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

  8. No. of Initial Clinical Trial Notifications

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

  10. Speed of Clinical Trials in Japan

  11. 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

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

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

  14. 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

  15. 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

  16. 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

  17. 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

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

  19. 3 Major Studies

  20. 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

  21. 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

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

  23. 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?

  24. 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

  25. 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

  26. 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.

  27. 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

  28. 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 country : 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) Compromised 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

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

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

  31. 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

  32. 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.

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

  34. 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

  35. 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.

  36. 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.

  37. 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.

  38. 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

  39. 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

  40. 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

  41. 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

  42. 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