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Main features and issues raised Byron Jones Novartis PSI Conference, May 13, 2014 Multiregional Trials.

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Presentation on theme: "Main features and issues raised Byron Jones Novartis PSI Conference, May 13, 2014 Multiregional Trials."— Presentation transcript:

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2 Main features and issues raised Byron Jones Novartis PSI Conference, May 13, 2014 Multiregional Trials

3 Multiregional Clinical Trials | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones3 Definition and Motivation  A single clinical trial that is conducted simultaneously in multiple geographical regions under a common protocol  Increasingly, clinical trials are run using patients from various regions worldwide. More patients needed to demonstrate treatment advantages, as new treatments may have only incremental benefits vs existing therapies. Local health authorities would like to see representation / evidence within their domains. Varied settings may enhance confidence in observed effects. Expanded markets interest trial sponsors.

4 Multiregional Clinical Trials | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones4 Advantages  Advantages (Ando, Y., ICSA/ISBS Conference, 2013) Prevents unnecessary duplication of clinical trials Makes drug development more efficient and cost-effective Enables simultaneous global drug submission and approval Gets effective and safe drugs to patients faster.

5 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones5 679 study centres Example: multiregional trial

6 Example of an MRCT | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones6  7,216 patients were enrolled from 5 geographical regions, 39 countries and 679 study centers.  3,581patients were randomized to the drug group and 3,635 to the placebo group. Region Number of countries N Drug (n) Placebo (n) Treatment difference Standard error P-value Asia <.0001 Europe <.0001 Latin America <.0001 North America <.0001 Other Global <.0001

7 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones7 ICH E5 and ICH E5 Q&A Regulatory Guidance A11. A multi-regional trial... The objectives of such a study would be: (1) to show that the drug is effective in the region and (2) to compare the results of the study between the regions with the intent of establishing that the drug is not sensitive to ethnic factors.

8 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones8 What is a region?

9 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones9 Is it based on geography? What is a region?

10 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones10 Not necessarily defined by location  “... region should not be limited to geographic boundaries but should take into consideration relevant intrinsic genetic and physiological or pathological factors as well as extrinsic factors such as medical practice” ICH E5.

11 Inconsistency in the definition of regions | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones11 Review of 60 FDA Advisory Committee Meetings  90% of submissions were multiregional. “Region was most often defined based on geography, and specifically continent...”  “No trends or consistency was observed in how regions were defined within or across therapeutic areas nor any rationale for the definition of region...”  “We propose that adequate justification of the definition should take into consideration factors such as race or ethnicity, disease epidemiology, medical practice, and geographic proximity, among others.” Tanaka et al. (2011) [The PhRMA MRCT Key Issue Team]

12 PhRMA MRCT KIT Perspective on Region | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones12  “... regions should be predefined in the design stage and properly documented.”  “... Geography alone may not be adequate when defining regions.... Intrinsic and extrinsic factors should be considered.”  “ Country and site selection should be considered at the design stage as part of predefining regions...”  Analytical approach to defining regions (e.g., factor analysis, principal components).  The number of regions should not be large.

13 Analysis models for multiregional clinical trials | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones13 Fixed or Random effects for regions/centres Recall: Multicentre Trials  Fixed-effects Model [centre is a fixed factor] –The centers have been specifically chosen. Conclusions reached here only apply to the centers considered and can not be extended to other centers that are not in the trial  Random-effects Model [centre is a random factor] –The centers are a random sample from a large population of centers. Conclusions reached here can be extended to all the centers in the population

14 For MRCTs: Are regional estimates fixed or random? | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones14  Surely “region” is a fixed-effect – cannot think of a random sample of regions?  Possible model might assume centres are randomly nested within the levels of a fixed regional factor.  However, this are differing opinions in the literature.

15 MTCT: fixed or random effects? | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones15 Random: Chen, Hung and Hsiao (2012)  Chen, Hung and Hsiao (2012) define a random effects model for the true treatment difference that applies to region i, i=1,2,..., M.  They derive the global estimate of the treatment difference by applying well-known results for the random-effects estimator obtained from a meta-analysis, using the DeSimonian and Laird (1986) estimator of the between region variance.  Give sample size formula based on global estimate.

16 Shrinkage estimates of regional treatment effects | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones16 Qui et al. (2013). Statistics in Medicine  Recommend : fixed-effects model to estimate global effect and estimates of individual region treatment differences using an empirical shrinkage estimator based on a random effects model Individual region estimates borrow strength from other regions’ estimates.

17 Consistency | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones17 Are individual region estimates similar to the global estimate?  It is (or should be) a basic premise of an MRCT that that there is no, or at most only a small amount of, regional variation Regional variation can be reduced by good design and by inclusion of region-specific covariates in models for the response.  Should testing for such consistency be part of the analysis plan? Sponsor more interested in global estimate Regulator more interested in local estimate for their region Ideally, global estimate of treatment difference is significantly different from zero and all regional estimates are significantly different from zero. Sample size implications are different for the two situations.

18 Two well-known examples of possible inconsistency | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones18  PLATO trial  MERIT trial

19 PLATO Trial | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones19 PLATlet inhibition and patient Outcomes trial (Wallentin, et. al., 2009)  Compare ticagrelor (novel) vs clopidogrel (standard)  Patients with ACS (acute coronary syndomes)  Primary endpoint: CV death, MI, stroke  patients, followed for a year.  Very strong evidence that ticagrelor is superior.  BUT... EndpointticagrelorcopidogrelHRP-value Primary9.8%11.7%0.84<0.001 Death4.5%5.9%0.78<0.001

20 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones20 “Ticagrelor works, except if you’re an American” – Stuart Pocock (LSHTM) PLATO trial

21 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones21 “Ticagrelor works, except if your an American” – Stuart Pocock (LSHTM) PLATO trial

22 PLATO: A chance result? | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones22  Given 31 subgroup analyses were done, can this significant interaction be due to chance alone?  The chance of a “reversal” in sign of estimated treatment difference is not negligible.  But “region” is a special subgroup and will be of interest to US regulators (FDA).  Can this “chance” finding be explained?  Is it caused by the Aspirin (ASA) loading dose and long- term maintenance dose that patients received on day of randomization to treatment?

23 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones23 Does Aspirin use explain the interaction (?)

24 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones24 Metoprolol Controlled –Release Randomised InterventionTrial in Heart Failure MERIT-HF trial

25 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones25 Overall results MERIT-HF trial in heart failure Endpoint Death MetoprololPlaceboHRP-value95% CL Lower limit 95% CL Upper limit Sample size Total deaths

26 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones26 Make USA a subgroup MERIT-HF trial in heart failure Endpoint Death MetoprololPlaceboHRP-value95% CL Lower limit 95% CL Upper limit Sample size Total deaths USA Other countries Interaction test: P = 0.003

27 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones27 Break out deaths by country and treatment countrymetoprololplacebo Hungary1629 Germany1931 Netherlands1425 Belgium313 Czech Republic917 Sweden29 Norway611 UK49 Finland02 Switzerland01 Iceland22 Poland88 Denmark11 USA5149 Why concentrate Interaction test on USA?

28 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones28 Break out deaths by country and treatment countrymetoprololplacebo Hungary1629 Germany1931 Netherlands1425 Belgium313 Czech Republic917 Sweden29 Norway611 UK49 Finland02 Switzerland01 Iceland22 Poland88 Denmark11 USA5149 Why focus on USA? Unlike the PLATO Trial, there seem no reason to believe Interaction is real

29 Are the other/better methods to test for consistency? | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones29  Quan et al. (2010b) proposed 5 alternative methods.  These are of two types: Methods that tend to conclude consistency until there is sufficient evidence to the contrary, e.g., interaction tests Methods requiring a certain strength of signal of similarity in order to conclude consistency, e.g., Japanese MHLW proposals  Which type is appropriate for a given situation?  Where should the burden of evidence lie?

30 Five methods to test for consistency | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones30 Quan et al. (2005) 1. Each region should achieve a proportion, π, of the observed overall effect. 2. Each region should achieve a common pre-specified constant value (b ≥ 0). 3. Demonstrate through hypothesis testing that each region achieves a proportion, π, of the overall effect. 4. A test for treatment-by-region interaction must not yield a significant result. 5. Tests for individual regions having effects lower than the overall effect must all not yield significant results.

31 Difficulties in implementation of methods: Method 1 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones31 Quan et al. (2010a)

32 Conclusions | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones32  Statistical methodology for MRCTs is still evolving  Experience over time will determine acceptable methods  Issues relate to conflict in the desire to estimate a global effect versus a local (single region) effect.  Regulatory agency involvement can focus attention on a single region with unwanted consequences (e.g., for Type I error rate control, effect reversal, etc. ) familiar to users of subgroup analysis.  Definition of “a region” needs to be clarified

33 References | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones33  Ministry of Health, Labour and Welfare of Japan (2007). Basic Concepts for Joint Clinical Trials.  Chen, C-T, Hung, H.M.J and Hsiao, C-F, (2012). Design and evaluation of multiregional trials with heterogeneous treatment effects across regions. Journal of Biopharmaceutical Statistics, 22,  Chen, J., et al. (2011). Consistency of treatment effects across regions in multiregional clinical trials, Part 1: design considerations. Drug Information Journal, 45,  Gallo, P., et al. (2011). Consistency of treatment effects across regions in multiregional clinical trials, Part 2: monitoring, reporting and interpretation. Drug Information Journal, 45,  Kawai,N., et al. (2008). An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Information Journal, 42,

34 References | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones34  Quan, H., et al. (2010a). Sample size considerations for Japanese patients in a multiregional trial based on MHLW guidance. Pharmaceutical Statistics, 9,  Quan, H., et al. (2010b). Assessment of consistency of treatment effects in multiregional clinical trials. Drug Information Journal, 44,  Tanaka, Y. (2011). Points to consider in defining a region for a multiregional clinical trial: defining region workstream in PhRMA MRCT Key Issue Team. Drug Information Journal, 45,  Wendel, H., et al. (2001). Challenges of subgroup analyses in multinational clinical trials: experiences from MERIT-HF trial. American Heart Journal, 142,

35 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones35 END OF PRESENTATION

36 Models for MRCT data | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones36 Extensions of models for multicentre clinical trials (MCTs)

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38 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones38

39 Framework for Random Effects Models for MCTs | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones39 Fedorov and Jones (2005)

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41 Heterogeneity in multiregional studies and a new proposal for exact tests on interaction Joachim Röhmel University of Bremen

42 Reasons for regional differences can be manyfold Genetic sensitivity Culture Dose regimen Application scheme Disease epidemiology Disease definition Economic standing Health care system Medical practice Regulatory environment Quality of trial conduct Availability of concomitant medicines Evaluation of outcomes (in particular in composite endpoints) Insufficient standardisation and validation of scores (East Europe) Patient compliance

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44 Estimated treatment effects by geographic region for the primary endpoint (CV death, MI, or stroke) of the PLATO trial (hazard ratios with 95% CIs, interaction P-value <0.05). From Pocock et al. PLATO TRIAL 2011

45 Conclusions of the FDA statistical review (Sep 2010) From the additional analyses, we continue to be troubled by the qualitative interaction between the region (US versus non- US) and treatment. In our view, neither play of chance nor concurrent use of ASA provides a satisfactory explanation for the US versus non-US disparity observed in this trial. Even though multiple factors have been screened for potential causes, the question remains unsolved.

46 Conclusions of the FDA statistical review (Sep 2010) The disparity can still be caused by the difference in standard medical practice between US and the rest of the world, which is hard to quantify and has not been quantified. We ought to seek further data to either confirm or dismiss this disturbing finding. Without the data, we would recommend that this drug not be approved. Another study should be required if this drug is to be approved for use in US.

47 Pockock‘s conclusions In the PLATO trial, the between-region comparison was one of 32 pre-planned subgroup analyses, and hence purely by chance one could expect one or two such analyses to have interaction P  Furthermore, post hoc emphasis on the most striking subgroup finding (geography, in this case) means that even if the finding is not entirely due to chance, the observed data are prone to exaggerate any true disparities (between regions). Alternatively, one can assess all 43 countries separately, and the global interaction test for heterogeneity among the 43 hazard ratios yields P =0.95.

48 FDA APPLICATION NUMBER:022560Orig1s000 The study center effect was statistically significant in the main effect ANCOVA model. This indicates potential heterogeneity of efficacy responses across the 6 centers. … The mean percent change from baseline BMD in lumbar spine was ranging from –2.5% in the US/Canada ( 139 subjects), –3.1% in Hungary ( 90 subjects), –3.2% in Argentina (222 subjects), –3.2% in France and Belgium ( 64 subjects), –3.8% in Poland (147 subjects), and –3.9% in Estonia ( 140 subjects). Results of subgroups analyses are not powered to draw any meaningful statistical conclusion, mainly due to small number of subjects in subgroups.

49 R.T.O‘Neill, May 28, 2009

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53 Social Court of the Berlin-Brandenburg Reference number: L 1 KR 140/11 KL Dec 6, 2011 Company complains against Escitalopram being merged with all other (generic) SSRIs, which means low reimbursement –Company wins first stage battle in court Health Insurance replies (actually based on IQWiG arguments) : –The results of the Yevtushenko study (2007) (conducted solely in Russia) lie extraordinarily above the estimates of the other studies. Comparability is therefore critical. –Furthermore, the applicability of study results may not be given in the context of German patient care. Generally, it is necessary to take stronger regard to cultural aspects in depression.

54 How do we define region? How do we define consistency? K. J. Caroll, AstraZeneca, 2011

55 What constitutes a region? America –North –Latin –South Europe –North –East –South Asia –China –India –Japan –South-East By Country? Significance of Interaction often disappears when 3 or more regions are included (Caroll, 2011)

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57 Common criteria (Quan et al. DIJ, 2010) 1.Achieving in each region a proportion of the observed overall effect 2.Observing in each region an effect above a certain threshold 3.Tighten 1. by subsituting the lower limit of CIs instead of the observed values 4.Absense of statistical significance in interaction tests, usually at significance levels >> Lack of clinically significant differences from the overall

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59 Strong Interaction

60 Strong Interaction may look less impressive when splitting one category into two

61 Digging fast and deep is possible

62 For exploratory subgroup analyses including regional subgroups Baysian methods may be useful

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64 (Penello, BASS Conference 2013)

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66 An alternative conditional (permutation) approach to interaction

67 full population decomposes into k 2x2 tables stratum/subgroup1 stratum/subgroup k … Treat RTreat TTotals Events a R1 a T1 s ~Events a R0 a T0 f Totals nRnR nTnT n Treat RTreat TTotals Events a 1R1 a 1T1 s1s1 ~Events a 1R0 a 1T0 f1f1 Totals n 1R n 1T n1n1 Treat RTreat TTotals Events a kR1 a kT1 sksk ~Events a kR0 a kT0 fkfk Totals n kR N kT nknk

68 F10F10 S11S11 n 1R  n 1T     a 1R1 a 1R0 a 2T1 b 1T0 a 2R0 a 2T0 a 2R1 F20F20 S21S21 n 2R  n 2T  F10F10 Sk1Sk1 n kR  n kT  a 1T1 a kR0 a kT0 a kT1 a kR1 VR0VR0 VT0VT0 V  T1 VR1VR1 ZELEN: Search for all configuations such that summation over Columns (successes, failures), Rows (numbers randomised to R or T) and strata (e.g. regions) gives identical results.

69 n 1R  n 1T     a 1R1 a 1R0 a 2T1 a 1T0 a 2R0 a 2T0 a 2R1 n 2R  n 2T  n kR  n kT  a 1T1 a kR0 a kT0 a kT1 a kR1 VR0VR0 VT0VT0 V  T1 VR1VR1 New proposal: Search for all configuations such that summation over Columns (successes, failures), Rows (numbers randomised to R or T) per each Stratum (e.g. regions) and the total numbers of successes (and failures) gives identical results

70 “Zelen/BreslowDay test (Z/BD)“ versus “less restricted permutations (LRP)“ Column totals per each stratum constant  Total no of Events in Treat R (sum over all strata) constant  Total no of Events in Treat T (sum over all strata) constant  Row totals per stratum constant  --- Z/BD | LRP

71 Zelen‘s exact conditional approach all margins fixed }

72 Breslow/Day conditional extention (StatXact 10) Zelen‘s set  of configurations  „Independently ordering the sample space“ is the principle that allows developing more exact tests for discrepancy based on other effect measures such as RR or  or … !!IMPORTANT!!

73 Extention allows for a variety for measuring effects in the full population Difference  = (a 1  m 1 ) - (a 2  m 2 ) Relative riskRR = (a 1  m 1 )  (a 2  m 2 ) Odds ratioOR = (a 1  ( m 1 -a 1 ))  (a 2  (m 2 -a 2 ))

74 and further measures of discrepency DifferenceDifference of Differences – CI for interaction (Newcombe 1998) – k=2:  discepency =  subgroup 1 -  subgroup 2 – k>2:  discepency =  ij (  subgroup i -  subgroup j ) 2 Relative riskRatio of relative risks – RR discepency =log [RR subgroup 1 /RR subgroup 2 ] – RR discepency =  ij (log [RR subgroup i /RR subgroup j ]) 2 Odds ratioZelen‘s test for homogeneity of odds ratios – Breslow-Day test

75 Situation investigated – typical for Biomarker + /- Binomial trials with two strata Success probs Treatm1Treatm2 Biomarker - p1 Biomarker + p1p2

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78 More details for three or more regions    ( I propose to study this in the next lesson) Thank you very much for your attention

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80 Tomokazu Inomata Novartis, Japan PSI, May 13 th, 2014 Multi-Regional Clinical Trials from the Japanese viewpoint - Current practice and future challenges - “Disclaimer : The views and opinions expressed in the following slides are those of individual presenter and should not be attributed to Novartis”

81 Motivation |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only81  MRCT has become most popular approach for efficient drug development in Japan  Simultaneous global drug development / submission is our (=Novartis Japan) standard approach  The Japanese regulatory guidance, “Basic Principles on Global Clinical Trials (GCTs)” was issued in 2007 (Note : GCT = MRCT)  Many discussions/conferences regarding the guidance (Japanese sample size, consistency,..) were held ・・・  A good opportunity to share our experiences of MRCTs -Statistical approach vs. Practical / Feasible approach

82 Outline |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only82  Trend in Japan drug development Relevant guidance for MRCTs Characteristics of MRCT  Current practice An example Possible considerations and future challenges  Concluding remarks

83 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only83 Relevant Guidance for MRCTs Trend in Japan drug development  1998: ICH-E5 “ETHNIC FACTORS IN THE ACCEPTABILITY OF FOREIGN CLINICAL DATA” “Bridging study” was accepted for “resolving Drug Lag” Intended to extrapolate foreign data (PIII) to the Japanese population and is generally conducted as a dose-finding study in Japanese subjects Many discussions on how to evaluate “Similarity” of dose-finding profile between Japan population and Non-Japan population.  2003: ICH-E5 Q&A’s 2006: ICH-E5 Q&A’s (R1) - Points to Consider on MRCT was added –  2007: Basic Principles on Global Clinical Trials “MRCT” was encouraged for “efficient and rapid drug development” Many discussions on how to evaluate “Consistency” between Japan population and Non-Japan population.  2012: Basic Principles on GCTs (Ref. Cases / 17 Q&A’s)  2013: Guideline on Data Monitoring Committees Japan Local guidance

84 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only84 In 2010, more than 20% of the total clinical trials were MRCTs (Source: Ando,Y., et al, 2012) 1998 ICH-E ICH-E5 Q&A(R1) 2007 Basic Principles on Global Clinical Trials 2012 “Basic Principles on Global Clinical Trials (Reference Cases) Number of Approvals in Japan - Bridging studies vs. MRCTs -

85 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only “Basic Principles on Global Clinical Trials” Basic principles for design and conduct of GCTs are provided in the 12 Q&As Q 1: Basic requirements Q 2: Timing for Japan to participate Q 3: Phase I trial or PK information Q 4: Dose-finding study in Japanese Q 5: Basic points to consider in designing Q 6: Sample size determination Q 7: Primary endpoint / evaluation index Q 8: Position of separate domestic study Q 9: Control arm Q10: Concomitant medication / therapy Q11: Recommended disease area Q12: Decision chart for joining MRCTs 85

86 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only86 - Some of Key features - Basic Principles on Global Clinical Trials  “Region” No clear definition in the Guidance, -But consider Japan as one region Need a reasonable definition of “region” in each study considering medical practice, guideline or drugs for the disease, etc.  “Consistency” between Japan and the entire study population No clear definition in the Guidance Need a justification as to why the entire population can be deemed as one population.  “Sample Size & Proportion of Japanese subjects” No established recommendable method But two specific methods for calculating sample size are introduced

87 Two Methods for calculating sample size |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only 87 As an example a placebo-controlled study using quantitative endpoints  Method 1  Method 2 Probability of “Each of D i exceed 0” is 80% or higher Condition -Di : Difference between the placebo group and test drug group in each region i  The probability tends to increase if equal number of subjects is enrolled from each region.  The Japanese sample size can be set without changing the total sample size Probability of “D Japan / D all > 0.5“ is 80% or higher Condition -D: Difference between the placebo group and test drug group -Dall: Difference in the entire population -DJapan: Difference in the Japanese population  Minimizing the Japanese sample size increases the total sample size  Minimizing the total sample size increases the Japanese sample size. However,  Specific sample size setting for individual cases can be discussed with PMDA on the clinical trial consultation  It is necessary that designs and analytical methods for the global clinical trial should be acceptable to Japan “ It is recommended to consult with PMDA reviewers to discuss the study design / Japanese proportion prior to fixing the protocol globally “

88 Characteristics of MRCTs in Japan |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only88 Based on 31 approved drugs between 2006 and 2013 (excluding Oncology)  Study Phase PIII: 29 (94%) PII: 2 (6%) -Not many dose response studies were conducted as MRCTs -But, number of MRCTs in earlier stage has been increased  Region Asian trials: 13 (42%) Global trials*: 18 (58%) -No harmonized definition of “Asian region” * Global trials include Asian countries Source : PMDA review reports

89 Characteristics of MRCTs in Japan |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only89 Based on 31 approved drugs between 2006 and 2013 (excluding Oncology)  Sample Size determination Use Method 1 or/and 2 described in the Japanese guidance No statistical calculation but feasible numbers  Japanese proportion All MRCTs : 32% ( %) -Asian Trials: 48% ( %), -Global Trials: 21% ( %) * Larger Proportion of Japanese population in the Asian Trial

90 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only90 Proportions of the Japanese sample size to the total sample size based on the Clinical Trial Notifications of MRCTs Characteristics of MRCTs in Japan  Many cases the proportion of the Japanese sample size is <0.20  For larger sample size, the proportion of Japanese subjects is <0.10 (Source :Ando and Uyama 2012)

91 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only91 Current practice - An example - Possible considerations and future challenges

92 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only92 Phase III MRCT  Study Design : A 26-week treatment, randomized, double-blind, placebo-controlled, parallel group study to assess the efficacy, safety and tolerability of Seebri in patients with Chronic Obstructive Pulmonary Disease (COPD)  Total Sample Size 755 (Seebri : 512, Placebo: 243) Japanese Sample Size : 92 (62, 30) -12% of the entire population -Calculated based on Method 1 An example - Seebri (COPD medication) – Submitted in Nov 2011 Approved in Sept 2012

93 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only93 Study Design  Region : 13 countries grouped by 6 regions : 1)Japan ⇒ Japan 2)Korea, Singapore, India ⇒ Asia 3)US, Canada ⇒ North America 4)Argentina ⇒ South America 5)Australia, Netherlands, Spain ⇒ European Union 6)Turkey, Romania, Russia ⇒ Eastern Europe -No specific definition of the region documented -Randomization was stratified by region An example - Seebri (COPD medication) – Submitted in Nov 2011 Approved in Sept 2012

94 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only94 Objectives  Primary objective To demonstrate that Seebri vs. placebo significantly increases trough FEV1 (Forced expiratory volume in one second) following 12 weeks of treatment in patients with moderate to severe COPD  Secondary objectives To evaluate the effect of Seebri vs. placebo on the health status by measuring the total score of the St George’s Respiratory Questionnaire (SGRQ) after 26 weeks treatment. To evaluate the effect of Seebri vs. placebo on breathlessness measured using the Transition Dyspnea Index (TDI) after 26 weeks treatment. Many more efficacy variables were evaluated... An example - Seebri (COPD medication) –

95 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only95 Results : Patient background ALLJapan SeebriPbototalSeebriPbototal n Age 【 <65 years 】 n (%) 278 (50.5%)134 (50.2%)412 (50.4%)14 (21.9%)9 (28.1%)23 (24.0%) Age 【 >=65years 】 n (%) 272 (49.5%)133 (49.8%)405 (49.6%)50 (78.1%)23 (71.9%)73 (76.0%) Age 【 Mean ( SD )】 yr 63.8 (9.47)64.0 (8.96)63.9 (9.30)69.1 (8.00)67.4 (9.75)68.5 (8.61) Weight 【 <60 kg 】 n (%) 139 (27.0%)67 (27.6%)206 (27.2%)33 (51.6%)13 (43.3%)46 (48.9%) Weight 【 >=60 kg 】 n (%) 375 (73.0%)176 (72.4%)551 (72.8%)31 (48.4%)17 (56.7%)48 (51.1%) Severity of 【 Moderate 】 n (%) 331 (60.2%)166 (62.2%)497 (60.8%)42 (65.6%)29 (90.6%)71 (74.0%) COPD 【 Severe 】 n (%) 217 (39.5%)99 (37.1%)316 (38.7%)21 (32.8%)3 (9.4%)24 (25.0%) 【very severe 】 n (%) 2 (0.4%)2 (0.7%)4 (0.5%)1 (1.6%)0 (0.0%)1 (1.0%) Duration of COPD 【 Mean ( SD )】 yr 5.87 (5.798)6.49 (6.790)6.07 (6.143)2.83 (3.067)2.95 (3.328)2.87 (3.139) Smoking history 【 Ex- smoker 】 n (%) 370 (67.3%)176 (65.9%)546 (66.8%)47 (73.4%)19 (59.4%)66 (68.8%) Smoking history 【 Current smoker 】 n (%) 180 (32.7%)91 (34.1%)271 (33.2%)17 (26.6%)13 (40.6%)30 (31.3%) An example - Seebri (COPD medication) – Japanese Patients are older & lighter &...

96 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only96 ALLJapan Seebri (n=512) Pbo (n=243) Seebri (n=64) Pbo (n=30) Baseline Trough FEV1 at Week 12 【 LS mean/(SE) 】 (0.0105) (0.0137) (0.0332) (0.0432) Treatment difference 【 LS mean/(SE) 】 [95%CI] (0.0148) (0.0466) [ to ] p<0.001 [ to ] p=0.022 ANCOVA model: Trough FEV1 = treatment + baseline FEV1 + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). region as fixed effects with center nested within region as a random effect. An example - Seebri (COPD medication) – Efficacy-primary endpoint- Trough FEV1 after 12 weeks treatment Consistent treatment difference, but larger std error in Japan due to small sample size Source: CTD

97 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only 97 ALLJapan Seebri (n=502) Pbo (246) Seebri (n=63) Pbo (n=31) Baseline Week 26 LS Mean (SE) (0.813) (0.992) (1.790) (2.502) Treatment difference LS mean/(SE) 】 [95%CI] (0.961) (2.613) [ to ] P=0.004 [ to 2.683] p=0.340 Analysis of St George’s Respiratory Questionnaire (SGRQ) total score at Week 26 (FAS) ANCOVA model: SGRQ total score = treatment + baseline SGRQ score + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). Center is included as a random effect nested within region. Close similarity in treatment difference larger std error in Japan & quite different p-values Source: CTD

98 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only 98 ALLJapan Seebri (n=493) Pbo (240) Seebri (n=62) Pbo (n=31) Baseline Week 26 LS Mean (SE) 1.84 (0.257) 0.80 (0.294) 0.98 (0.558) 1.02 (0.704) Treatment difference LS mean/(SE) 】 [95%CI] 1.04 (0.235) (0.697) [0.583 to 1.504] P<0.001 [ to 1.347] p=0.955 Efficacy-secondary endpoint- Analysis of Transition Dyspnea Index (TDI) focal score at Week 26 (FAS) ANCOVA model: TDI focal score = treatment + BDI + baseline ICS use (Yes/No) + FEV1 reversibility components + baseline smoking status + region + center(region). Center is included as a random effect nested within region. The treatment differences are quite different Inconsistent result ? Source: CTD

99 PMDA questions related to regional difference | Presentation Title | Presenter Name | Date | Subject | Business Use Only99  Explain reason for the different treatment effects between Japanese subpopulation and the entire population Why the TDI score got to worse for Japanese population etc.?  Explain whether any demographic difference caused treatment effect or not

100  Assessment of Consistency Not only the Primary endpoint but also the secondary endpoints would play an important role for helping the assessment of “consistency” Potential effect of the difference need to be evaluated in advance Regions should be pre-defined based on intrinsic / extrinsic factors Explain any influence of the following factors for each country, then explain if there are no big difference in condition of COPD between Japan and Non-Japan patients Cause of COPD, Onset of COPD Standard Treatment, medical practice Environment (climate etc.), Disease definition, diagnostic |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only 100 Key Considerations for MRCT - Opportunities and Challenges -

101  Assessment of Consistency Not only hypothesis testing (D Japan / D All > 0.5, treatment by region, etc.) but also point estimate, CIs, graphical presentation would be helpful Not only comparing between Japan sub population vs. entire study population but also displaying other regional populations could be helpful  Sample Size for Japanese sub-population A single MRCT could provide limited information Important for Japanese patients to enroll in MRCTs from earlier phase and dose response study, PK/PD -But no established method for sample size determination for DF study -Limited number of Japanese sample size for each dose |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only 101 Key Considerations for MRCT - Opportunities and Challenges -

102 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only102  Consistency assessment on Safety data No well established statistical method available More comprehensive approach to analyzing safety data is necessary  Quality of Clinical Trial Need a good Quality Management on Operational aspects -Operational errors in clinical trials detected in the era of complex design clinical trials and global drug development (Ando (2012) at Bios Workshop )  Decision making at Interim analyses Japanese sub-population assessment / Consistency evaluation necessary for early termination?? Speed of the subjects enrollment need to be well managed Other Considerations for MRCT - - Opportunities and Challenges -

103 Guideline on Data Monitoring Committees |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only103 Issued on April 4 th, Setup and implementation of DMC  3.1 Composition of DMC When DMC is established in a large-scale global clinical trial, it is considered appropriate essentially as possible to select a representative for each participating region or some of regions as committees. When Japanese subjects participate in such a global clinical trial, it may be desirable that specialists in Japan participate as DMC committee members considering on medical environment in Japan and existing safety information. In case that it is difficult, it should be considered how to evaluate safety of Japanese subjects beforehand. Of note, when the special safety monitoring for Japanese subjects is required by reason that experience of study treatment and safe information is poor as compared with other regions, it is more significant that Japanese specialists participate as DMC members.

104 Concluding Remarks |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only104  Knowledge and experience of MRCT can be shared among academia, industry and regulatory agencies, Looks similar, Not markedly different Not surprisingly different How much we know/share information prior to the study is the key to success  Improvement of current practice, “operationally and methodologically” is required for successful simultaneous global drug development Japan vs. Non-Japan ?? Statistical approach vs. Practical/Feasible approach ??

105 References |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only  ICH International Conference on Harmonization Tripartite Guidance E5. Ethnic Factor in the Acceptability of Foreign Data, (1998).  ICH International Conference on Harmonization Tripartite Guidance E5. Ethnic Factor in the Acceptability of Foreign Data, Questions & Answers (2006).  Ministry of Health, Labour and Welfare of Japan (2007). Basic Concepts for Joint Clinical Trials  Ministry of Health, Labour and Welfare of Japan (2012). Basic Concepts for Joint Clinical Trials. (Reference Cases)  Ministry of Health, Labour and Welfare of Japan (2013). DMC Guidance  Kawai, N., et al. (2008). An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Information Journal, 42,  Ando, Y. and Uyama, Y. (2012). Multiregional Clinical Trials : Japanese perspective on Drug Development Strategy and Sample Size for Japanese Subjects. Pharmaceutical Statistics, 22,

106 References |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only  Ando, Y. and Hamasaki, T. (2010). Practical issues and lessons learnted from multi-regional clinical trials via case eamples : a Japanese perspective. Pharmaceutical Statistics, 9,  Tsou, H-H., et al. (2010). Proposals of Statistical consideration to evaluation of results for a specific region in multi-regional trials – Asian perspective  Ikeda, K., (2013) Overview of multi-regional trials conducted in Japan. Novartis China Biostatistics Workshop  Ando, Y., (2012) Looking beyond ICH-E9 in the Era of Global Drug Development. Bios Summer Workshop 2012 in Osaka  Pharmaceuticals and Medical Devices Agency. Review Report of Seebri. 106

107 |Multi-Regional Clinical Trials| Tomokazu Inomata | May | Subject | Business Use Only107 End

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