Report

Multiregional Trials Main features and issues raised Byron Jones Novartis PSI Conference, May 13, 2014 Multiregional Clinical Trials 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. 3 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Multiregional Clinical Trials 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. 4 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Example: multiregional trial 679 study centres 5 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Example of an MRCT 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. Number of countries N Drug (n) Placebo (n) Treatment difference Standard error P-value Asia 5 441 214 227 -6.97 1.675 <.0001 Europe 20 3819 1889 1930 -5.43 0.531 <.0001 Latin America 9 1229 630 599 -3.96 0.991 <.0001 North America 2 1525 750 775 -4.93 0.829 <.0001 Other 3 202 98 104 -3.18 2.258 0.16 Global 39 7216 3581 3635 -5.10 0.391 <.0001 Region 6 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Regulatory Guidance ICH E5 and ICH E5 Q&A 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. 7 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones What is a region? 8 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones What is a region? Is it based on geography? 9 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones What is a region? 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. 10 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Inconsistency in the definition of regions Review of 60 FDA Advisory Committee Meetings 2008-2010 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] 11 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones PhRMA MRCT KIT Perspective on Region “... 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. 12 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Analysis models for multiregional clinical trials 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 13 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones For MRCTs: Are regional estimates fixed or random? 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. 14 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones MTCT: fixed or random effects? 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. 15 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Shrinkage estimates of regional treatment effects 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. 16 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Consistency 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. 17 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Two well-known examples of possible inconsistency PLATO trial MERIT trial 18 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones PLATO Trial 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 18624 patients, followed for a year. Endpoint ticagrelor copidogrel HR P-value Primary 9.8% 11.7% 0.84 <0.001 Death 4.5% 5.9% 0.78 <0.001 Very strong evidence that ticagrelor is superior. BUT... 19 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones PLATO trial “Ticagrelor works, except if you’re an American” – Stuart Pocock (LSHTM) 20 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones PLATO trial “Ticagrelor works, except if your an American” – Stuart Pocock (LSHTM) 21 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones PLATO: A chance result? 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 longterm maintenance dose that patients received on day of randomization to treatment? 22 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Does Aspirin use explain the interaction (?) 23 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones MERIT-HF trial Metoprolol Controlled –Release Randomised InterventionTrial in Heart Failure 24 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones MERIT-HF trial in heart failure Overall results Endpoint Death Metoprolol Placebo Sample size 1990 2001 Total deaths 145 217 25 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones HR P-value 0.66 0.00009 95% CL Lower limit 95% CL Upper limit MERIT-HF trial in heart failure Make USA a subgroup Endpoint Death Metoprolol Placebo Sample size 1990 2001 Total deaths 145 217 USA 51 49 Other countries 94 168 Interaction test: P = 0.003 26 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones HR P-value 95% CL Lower limit 95% CL Upper limit 1.05 0.71 1.56 0.55 0.43 0.70 0.00009 Break out deaths by country and treatment country metoprolol placebo Hungary 16 29 Germany 19 31 Netherlands 14 25 Belgium 3 13 Czech Republic 9 17 Sweden 2 9 Norway 6 11 UK 4 9 Finland 0 2 Switzerland 0 1 Iceland 2 2 Poland 8 8 Denmark 11 11 USA 51 49 27 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Why concentrate Interaction test on USA? Break out deaths by country and treatment country metoprolol placebo Hungary 16 29 Germany 19 31 Netherlands 14 25 Belgium 3 13 Czech Republic 9 17 Sweden 2 9 Norway 6 11 UK 4 9 Finland 0 2 Switzerland 0 1 Iceland 2 2 Poland 8 8 Denmark 11 11 USA 51 49 28 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Why focus on USA? Unlike the PLATO Trial, there seem no reason to believe Interaction is real Are the other/better methods to test for consistency? 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? 29 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Five methods to test for consistency 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. 30 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Difficulties in implementation of methods: Method 1 Quan et al. (2010a) Trial planned to have 90% power, to detect a one-sided difference between two treatments with significance level 0.025. Consider a single region (e.g., Japan) out of the set of regions. Let be the estimated treatment effect in Japan and be the estimated effect over all regions D Require Pr( > 0.5) ≥ 0.8 If all treatment effects truly equal in all regions • Sample size fraction for Japan = 22.4% • Too high for a country with only 2% of the world population 31 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Conclusions 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 32 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones References 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, 1037-1050 Chen, J., et al. (2011). Consistency of treatment effects across regions in multiregional clinical trials, Part 1: design considerations. Drug Information Journal, 45, 595-602. 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, 603-608. Kawai,N., et al. (2008). An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Information Journal, 42, 139-147. 33 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones References Quan, H., et al. (2010a). Sample size considerations for Japanese patients in a multiregional trial based on MHLW guidance. Pharmaceutical Statistics, 9, 100112. Quan, H., et al. (2010b). Assessment of consistency of treatment effects in multiregional clinical trials. Drug Information Journal, 44, 617-632. 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, 575-585. Wendel, H., et al. (2001). Challenges of subgroup analyses in multinational clinical trials: experiences from MERIT-HF trial. American Heart Journal, 142, 502-511. 34 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones END OF PRESENTATION 35 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Models for MRCT data Extensions of models for multicentre clinical trials (MCTs) Suppose we have two treatment groups, j = 1 denotes the active treatment arm, and j = 2 denotes the placebo arm. Let denotes the response on patient k in treatment and center . Where j = 1, 2; = 1,2, ⋯ , and = 1,2, ⋯ , The full model is = + + + + – is the effect of center – is the effect of treatment – is the interaction effect of center and treatment – ~(0, 2 ) 36 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Model II: = + + + Assumption: With center effect, no treatment-by-center interaction Overall treatment effect: = (1 − 2 ) Estimator (weighted): ∆ = = (( ))−1 (( ))−1 =1 ∆ = , ∆ = 2 [ =1 1 1 1 2 where = ( + )−1 1 ( =1 1 + 1 ( =1 1 1 + )−1 2 1 −1 −1 ) ] 2 Weights for each center are chosen according to the precision of the within-center estimate 37 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Model III: = + + + + Assumption: With center effect and treatment-by-center interaction Overall treatment effect: = (1 −2 ) + 1 Estimator (equally weighted): ∆ = ∆ = , ∆ = 2 2 1 ( =1 1 + (1 −2 ) =1 1 ) 2 All centers are considered having the same importance thus receive the same weights Model III is to be used when the treatment differences vary substantially from center to center 38 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Framework for Random Effects Models for MCTs Fedorov and Jones (2005) Suppose we have two treatment groups, j = 1 denotes the active treatment arm, and j = 2 denotes the placebo arm. Let denotes the response on patient k in treatment and center . Where j = 1, 2. = 1,2, ⋯ , and = 1,2, ⋯ , The full model is = + where – are random and for = [1 , 2 ] : = [1 , 2 ] , = 2 Ʌ – ~(0, 2 ) Overall treatment effect = 1 − 2 39 | PSI Conference 2014 | Multiregional Clinical Trials | Byron Jones Heterogeneity in multiregional studies and a new proposal for exact tests on interaction Joachim Röhmel University of Bremen 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 From Pocock et al. PLATO TRIAL 2011 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). 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 nonUS) 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. 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. 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 P0.05. • 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. 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. R.T.O‘Neill, May 28, 2009 R.T.O‘Neill, May 28, 2009 R.T.O‘Neill, May 28, 2009 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. How do we define region? How do we define consistency? K. J. Caroll, AstraZeneca, 2011 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) 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 >>0.05 5. Lack of clinically significant differences from the overall Strong Interaction Strong Interaction may look less impressive when splitting one category into two For exploratory subgroup analyses including regional subgroups Baysian methods may be useful (Penello , BASS Conference 2013) (Penello , BASS Conference 2013) An alternative conditional (permutation) approach to interaction full population decomposes into k 2x2 tables Events ~Events Totals Treat R Treat T Totals aR1 aR0 aT1 aT0 s f nR nT n stratum/subgroup k stratum/subgroup1 Events ~Events Totals Treat R Treat T Totals a1R1 a1R0 n1R a1T1 a1T0 n1T s1 f1 n1 … Treat R Treat T Totals Events akR1 akT1 sk ~Events akR0 nkR akT0 NkT fk nk Totals S11 n1R F10 n1T a1R1 a1R0 a1T1 b1T0 ZELEN: S21 n 2R F20 a2R1 n 2T a2R0 a2T1 a2T0 F10 Sk1 nkR akR1 n kT akR0 akT1 akT0 VR1 VR0 VT1 VT0 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. n1R n1T a1R1 a1R0 a1T1 New proposal: a1T0 a2R0 n2R a2R1 n 2T a2T1 a2T0 nkR akR1 akR0 nkT akT1 akT0 VR1 VR0 VT1 VT0 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 “Zelen/BreslowDay test (Z/BD)“ versus “less restricted permutations (LRP)“ Z/BD | 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 --- Zelen‘s exact conditional approach Ω { τ τ τ ijl , i 1,..., k ; j R , T ; l 0 ,1; all margins fixed } p value ( τ ) prob ( σ ) σΩ prob ( σ ) prob ( τ ) n iR n iT N i / i 1 b iR 1 b iT 1 S i 1 k prob ( ) n iR n iT N i / i 1 b iR 1 b iT 1 S i 1 k Breslow/Day conditional extention (StatXact 10) Zelen‘s set of configurations p value ( τ ) prob ( σ ) σΩ 2 2 χ BD ( σ ) χ BD ( τ ) !!IMPORTANT!! „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 … Extention allows for a variety for measuring effects in the full population • Difference = (a1 m1) - (a2 m2) • Relative risk RR = (a1 m1) (a2 m2) • Odds ratio OR = (a1 ( m1-a1)) (a2 (m2-a2)) and further measures of discrepency • Difference – – – Difference of Differences CI for interaction (Newcombe 1998) k=2: discepency = subgroup 1 - subgroup 2 k>2: discepency = ij (subgroup i - subgroup j)2 • Relative risk – – Ratio of relative risks RRdiscepency =log [RRsubgroup 1/RRsubgroup 2] RRdiscepency = ij (log [RRsubgroup i /RRsubgroup j])2 • Odds ratio – Zelen‘s test for homogeneity of odds ratios Breslow-Day test Situation investigated – typical for Biomarker + /• Binomial trials with two strata Success probs Treatm1 Treatm2 Biomarker - p1 p1 Biomarker + p1 p2 Power to detect an interaction, when for treatm1 the prob for sucess is 0.1 in both strata while for treatm2 the prob for success in stratum 1 is 0.1 and in stratum 2 is q. sample sizes are n=100 per treatm and per stratum 1 0.9 0.8 Power 0.7 0.6 Rel 0.5 OR Zelen MaxAbs 0.4 Cochran Q 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 probability q of success in stratum 2, treatm 2 0.8 0.9 1 Power to detect an interaction, when for treatm1 the prob for sucess is 0.1 in both strata while for treatm2 the prob for success in stratum 1 is 0.1 and in stratum 2 is q. sample sizes are n=100 per treatm and per stratum 1 0.9 Power 0.8 0.7 Rel 0.6 OR Zelen MaxAbs 0.5 Cochran Q 0.4 REL MAX 0.3 ODDS 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 probability q of success in stratum 2, treatm 2 0.8 0.9 1 More details for three or more regions This is an issue for the next lesson Thank you very much for your attention • ( I propose to study this in the next lesson) • Thank you very much for your attention Multi-Regional Clinical Trials from the Japanese viewpoint - Current practice and future challenges Tomokazu Inomata Novartis, Japan PSI, May 13th, 2014 “Disclaimer : The views and opinions expressed in the following slides are those of individual presenter and should not be attributed to Novartis” Motivation 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 81 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Outline Trend in Japan drug development • Relevant guidance for MRCTs • Characteristics of MRCT Current practice • An example • Possible considerations and future challenges Concluding remarks 82 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Trend in Japan drug development Relevant Guidance for MRCTs 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 – Japan Local guidance 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 83 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Number of Approvals in Japan - Bridging studies vs. MRCTs - 20 2006 ICH-E5 16 Q&A(R1) 18 1998 ICH-E5 2012 “Basic Principles on Global Clinical Trials (Reference Cases) 2007 Basic Principles on Global Clinical Trials 14 12 # of 10 Approval Bridging 8 MRCT 6 4 2 0 2006 2007 2008 2009 Year 2010 2011 2012 In 2010, more than 20% of the total clinical trials were MRCTs 84 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only (Source: Ando,Y., et al, 2012) “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 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Basic Principles on Global Clinical Trials - Some of Key features - “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 86 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Two Methods for calculating sample size As an example a placebo-controlled study using quantitative endpoints Method 1 Method 2 Probability of “DJapan / Dall > 0.5“ is 80% or higher Probability of “Each of Di exceed 0” is 80% or higher • Condition • Condition - D: Difference between the placebo group and test drug group - Dall: Difference in the entire population - Di : Difference between the placebo group and test drug group in each region i - DJapan: Difference in the Japanese population The probability tends to increase if equal Minimizing the Japanese sample size increases the total sample size Minimizing the total sample size increases number of subjects is enrolled from each region. The Japanese sample size can be set without changing the total sample size the |Multi-Regional Japanese sample size.Inomata | May13 2014| Subject | Business Use Only Clinical Trials| Tomokazu 87 Characteristics of MRCTs in Japan Based on 31 approved drugs between 2006 and 2013 (excluding Oncology) Study Phase PIII • PIII: 29 (94%) PII • 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%) Asian Global • Global trials*: 18 (58%) - No harmonized definition of “Asian region” * Global trials include Asian countries Source : PMDA review reports http://www.pmda.go.jp/ 88 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Characteristics of MRCTs in Japan 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% (2 - 78.5 %) - Asian Trials: 48% (20 - 48%), - Global Trials: 21% ( 2 - 53%) * Larger Proportion of Japanese population in the Asian Trial 89 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Characteristics of MRCTs in Japan Proportions of the Japanese sample size to the total sample size based on the Clinical Trial Notifications of MRCTs 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) 90 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Current practice - An example - Possible considerations and future challenges 91 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only An example - Seebri (COPD medication) – Phase III MRCT Submitted in Nov 2011 Approved in Sept 2012 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 92 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only An example - Seebri (COPD medication) – 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 93 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Submitted in Nov 2011 Approved in Sept 2012 An example - Seebri (COPD medication) – 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... 94 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only An example - Seebri (COPD medication) – Results : Patient background ALL Japan Seebri Pbo total Seebri Pbo total 550 267 817 64 32 96 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%) 217 (39.5%) 99 (37.1%) 316 (38.7%) 21 (32.8%) 3 (9.4%) 24 (25.0%) 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【Exsmoker】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%) n COPD【Severe】n (%) 【very severe】n (%) Japanese Patients older13 & lighter30&(31.3%) ... 91 (34.1%) 271 (33.2%) 17are (26.6%) (40.6%) 95 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only An example - Seebri (COPD medication) – Efficacy-primary endpoint- Trough FEV1 after 12 weeks treatment ALL Baseline Trough FEV1 at Week 12 【LS mean/(SE)】 Treatment difference【LS mean/(SE)】 [95%CI] Japan Seebri (n=512) Pbo (n=243) Seebri (n=64) Pbo (n=30) 1.321 1.274 1.253 1.340 1.408 (0.0105) 1.301 (0.0137) 1.404 (0.0332) 1.296 (0.0432) 0.108 (0.0148) 0.108 (0.0466) [0.0785 to 0.1368] p<0.001 [0.0158 to 0.2011] p=0.022 Consistent treatment difference, but larger std error in Japan due to small sample size 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. Source: CTD http://www.info.pmda.go.jp/shinyaku/P201200141/index.html 96 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Analysis of St George’s Respiratory Questionnaire (SGRQ) total score at Week 26 (FAS) ALL Baseline Week 26 LS Mean (SE) Treatment difference LS mean/(SE)】 [95%CI] Japan Seebri (n=502) Pbo (246) Seebri (n=63) Pbo (n=31) 46.11 46.34 38.92 42.28 39.50 (0.813) 42.31 (0.992) 34.70 (1.790) 37.20 (2.502) -2.81 (0.961) -2.31 (2.613) [-4.700 to -0.926] P=0.004 [-7.702 to 2.683] p=0.340 Close similarity in treatment difference larger std error in Japan & quite different p-values 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. Source: CTD http://www.info.pmda.go.jp/shinyaku/P201200141/index.html 97 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Efficacy-secondary endpoint- Analysis of Transition Dyspnea Index (TDI) focal score at Week 26 (FAS) ALL Baseline Week 26 LS Mean (SE) Treatment difference LS mean/(SE)】 [95%CI] Japan Seebri (n=493) Pbo (240) Seebri (n=62) Pbo (n=31) 6.18 6.30 7.27 7.35 1.84 (0.257) 0.80 (0.294) 0.98 (0.558) 1.02 (0.704) 1.04 (0.235) -0.04 (0.697) [0.583 to 1.504] P<0.001 [-1.426 to 1.347] p=0.955 The treatment differences are quite different Inconsistent result ? 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. Source: CTD http://www.info.pmda.go.jp/shinyaku/P201200141/index.html 98 |Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only PMDA questions related to regional difference 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 99 | Presentation Title | Presenter Name | Date | Subject | Business Use Only Key Considerations for MRCT - Opportunities and Challenges - 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 <Seebri’s case > 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 100|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Key Considerations for MRCT - Opportunities and Challenges - Assessment of Consistency • Not only hypothesis testing (DJapan / DAll > 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 101|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Other Considerations for MRCT - - Opportunities and Challenges - 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 102|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Guideline on Data Monitoring Committees Issued on April 4th, 2013 3. 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. 103|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only Concluding Remarks for successful simultaneous global drug development 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 Japan vs. Non-Japan ?? Statistical approach vs. Practical/Feasible approach ?? 104|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only References 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, 139-147 Ando, Y. and Uyama, Y. (2012). Multiregional Clinical Trials : Japanese perspective on Drug Development Strategy and Sample Size for Japanese Subjects. Pharmaceutical Statistics, 22, 977-987. 105|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only References 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, 190-200 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|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only End 107|Multi-Regional Clinical Trials| Tomokazu Inomata | May13 2014| Subject | Business Use Only