European Patients’ Academy on Therapeutic Innovation Principles of New Trial Designs

European Patients’ Academy on Therapeutic Innovation 2 Steps in clinical development programs Phase I: Studies on dosing Phase III: Assess therapeutic effect and benefit-risk in patients of candidate vs. standard of care Confirmatory trials ‘confirm safety and efficacy’ Exploratory trials ‘learn’ Medicine approved Clinical trials (humans) Animal and lab tests Phase 0: Evaluate pharmacodynamic and pharmacokinetic properties of candidate compounds (usually at low dose) Phase II: Assess efficacy and safety, best dose and identification of sub-groups Phase IV: Post-marketing evaluation

European Patients’ Academy on Therapeutic Innovation Standard treatment (control) Standard treatment (control) Treatment A Treatment B Treatment C Treatment D Treatment E R R Phase II Phase III 3 Traditional paradigm of clinical development over time Treatment A R R Standard treatment (control) Standard treatment (control) Treatment C Standard treatment (control) Standard treatment (control) Treatment E R R = Randomisation R R

European Patients’ Academy on Therapeutic Innovation  Inefficient use of financial resources  Many patients need to be involved in each trial  Slow and inflexible:  Does not allow for real-time learning during the course of a trial  Modifications during the course of the trial to make the approach more applicable (to reality) are not allowed. 4 Challenges to traditional approach Adaptive design streamlines and optimises the traditional medicines development process.

European Patients’ Academy on Therapeutic Innovation 5 What is an adaptive design? European Medicines Agency (2007). CHMP/EWP/2459/02 Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. Retrieved 4 September, 2015 from ‘… A study design is called ‘adaptive’ if statistical methodology allows the modification of a design element (e.g. sample-size, randomisation ratio, number of treatment arms) at an interim analysis...‘

European Patients’ Academy on Therapeutic Innovation  To control the operating characteristics  To control for bias due to the adaptation  Statistical  Operational  To guarantee that the results can be explained and interpreted 6 Challenges of adaptive design

European Patients’ Academy on Therapeutic Innovation  Modifications of  study eligibility criteria  study sample size or study objective to maintain overall power  treatment arm  analysis plan  Early stopping rules for futility or efficacy (group sequential designs)  Drop treatment arm(s) / ‘pick the winner’ designs  Enrichment designs  Adaptive randomisation 7 Several possible approaches Well understood Less well understood

European Patients’ Academy on Therapeutic Innovation 8 Most adaptive designs come down to… One trial Change null hypothesis Change design parameters Confirm Learn

European Patients’ Academy on Therapeutic Innovation Blinded vs. Unblinded 9 Blinded Based on interim non- comparative analyses Study endpoint data in control arm Discontinuation rates Baseline characteristics Unblinded Based on interim comparative analyses Analyses of study endpoints Outcomes potentially correlated with these endpoints Bias

European Patients’ Academy on Therapeutic Innovation 10 Group sequential design

European Patients’ Academy on Therapeutic Innovation Multi-arm multi-stage (MAMS) design 11

European Patients’ Academy on Therapeutic Innovation 12 Example 2 – Pros and cons of multi- arm, multi-stage trial design Pros Fewer patients Less overall time for medicine discovery Fewer applications and approvals required Flexible: Can add or drop arms Reduced cost Cons Complex operating characteristics Required number of patients Trial duration Continued recruitment to control arm No comparison between trial arms

European Patients’ Academy on Therapeutic Innovation 13 Seamless Phase II/III design

European Patients’ Academy on Therapeutic Innovation 14 Pros and cons of Seamless II/III Design Pros Both steps are conducted independently and combined in one test result Shortens time and patient exposure Relatively flexible Efficient use of patient resources Cons Complex statistics design Gap in recruitment between Phase II and Phase III Logistically challenging Difficult in studies with long- term endpoints Shortens patient exposure (not always good) Can “signals” (good or bad) be lost in the combination?

European Patients’ Academy on Therapeutic Innovation  Classical trials for rare diseases are typically powered for large effects.  Adaptive designs provide an appealing alternative because:  they shorten development process without compromising validity or efficacy  ineffective treatments can be identified earlier on  they permit a more efficient use of resources 15 Adaptive designs in rare diseases

European Patients’ Academy on Therapeutic Innovation  New study designs can permit:  Flexible design strategies  More efficient use of resources  Shorter development process  From a regulatory perspective, it is important to maintain validity and integrity in adaptive designs:  They must address the same as questions traditional clinical studies  Operational bias must be controlled  Possible statistically significant errors must be controlled  Results must be reliably interpreted 16 Conclusions (1)

European Patients’ Academy on Therapeutic Innovation 17 Conclusions (2) Kairalla, J.A., Coffey, C.S., Thomann, M.A., & Muller, K.E. (2012) ‘Adaptive trial designs: A review of barriers and open opportunities.’ Trials, 13(145), Retrieved 4 September, 2015, from “Although adaptive designs cannot ‘change the answer’ regarding the effectiveness of a particular treatment, they can increase the efficiency in finding an answer.”

European Patients’ Academy on Therapeutic Innovation  European Medicines Agency (2007). CHMP/EWP/2459/02 Reflection paper on methodological issues in confirmatory clinical trials planned with an adaptive design. Retrieved 4 September, 2015, from e/2009/09/WC pdf  Chow, SC, Chang M (2008). ‘Adaptive design methods in clinical trials – a review.’ Orphanet Journal of Rare Diseases, 3(11), Retrieved 4 September, 2015, from  Judson, I., Verweij, J., Gelderblom, H., et al. (2012). Results of a randomised phase III trial (EORTC 62012) of single agent doxorubicin versus doxorubicin plus ifosfamide as first line chemotherapy for patients with advanced or metastatic soft tissue sarcoma: a survival study by the EORTC Soft Tissue and Bone Sarcoma Group. Retrieved 4 September, 2015, from a/pdf/ a/pdf/ Further reading (1):

European Patients’ Academy on Therapeutic Innovation  Sydes, M.R., Parmar, M.K., James, N.D., et al. (2009). ‘Issues in applying multi-arm multi-stage methodology to a clinical trial in prostate cancer: the MRC STAMPEDE trial.’ Trials, 10(39), Retrieved 4 September, 2015 from  Kairalla, J.A., Coffey, C.S., Thomann, M.A., & Muller, K.E. (2012) ‘Adaptive trial designs: A review of barriers and open opportunities.’ Trials, 13(145), Retrieved 4 September, 2015, from  Mehta, C.R. (2010). Software for adaptive sample size re-estimation of confirmatory time to event trials. Cytel Webinar. Cambridge, M.A.: Cytel. Retrieved 4 September, 2015, from SurvAdapt-Webinar_10.10.pdfhttp:// SurvAdapt-Webinar_10.10.pdf 19 Further reading (2):

20 BACKUPS

European Patients’ Academy on Therapeutic Innovation Source: Cytel Webinar for East®SurvAdapt. October 28, SurvAdapt-Webinar_10.10.pdf 2-sided α = 5% Power = 90% HR = Example 1: Sample-size re-estimation (1)

European Patients’ Academy on Therapeutic Innovation Disadvantages:  May increase the risk of running an enlarged negative trial.  Based on unblinded interim results – bias.  Possibility of ‘second guessing’  A resampling decision can be easily interpreted as ‘the treatment is not as efficient as expected.  Operational bias? Recruitment?  May require extensive (expensive) logistics. Protection of study integrity is essential! 22 Example 1: Sample-size re-estimation (2)

European Patients’ Academy on Therapeutic Innovation Eight-week outcome observed Maximises the chance that the patient receives the most effective treatment 23 Example 2: Adaptive randomisation (1) Randomised using the weights given by prior probability Prior probability of each treatment success given marker Probabilities of treatment success updated based on observed results

European Patients’ Academy on Therapeutic Innovation Challenges:  Requires fast dataflow – logistically demanding, especially in large multi-centre trials.  Does not work for long-term endpoints.  Difficult to interpret results beyond estimation:  Comparisons are difficult  Precision  Recruitment patterns can change during the course of the trial (operational bias). Blinding is essential but may not always be feasible. 24 Example 2: Adaptive randomisation (2)