SC28.04 Adaptive Clinical Trial Designs

Abstract

Interest in adaptive design study methods stems from the principle that these methods hold promise for improving drug development compared to conventional study design (i.e., non-adaptive) methods. The theoretical advantages of adaptive designs are that (1) they provide similar information more efficiently by reducing sample size and total cost, (2) increase the likelihood of success on the study objective, treating more patients with more effective treatments or (3) lead to better improved appreciation of the effects of therapy such as dose-response relationship or subgroup effects, for example identifying efficacious drugs for specific subgroups of patients based on their biomarker profiles, which may also lead to more impactful subsequent studies). Adaptive designs use accumulating data to modify the ongoing trial without undermining the integrity and validity of the trial. They also hold the potential for shortening the time for drug development. Several aspects of these trials including the dose-finding scheme, interim analysis, adaptive randomization, biomarker-guided randomization, and seamless designs will be discussed. Many, but not all adaptive designs are devised under the Bayesian framework incorporating principles such as (I) obtaining the prior distribution; (II) collecting data to calculate the data likelihood; and then (III) computing the posterior distribution. The Bayesian framework provides an ideal statistical framework for adaptive trial designs.1,2 Examples of trials conducted with adaptive designs include the BATTLE and BATTLE-2 trials and ISPY-2. The basic principle is that patients enrolling earlier in a trial are used to inform how subsequent patients are treated, thus improving the efficiency of the study; this means that fewer patients are required to achieve the same answers regarding safe dosing and/or efficacy. The BATTLE and BATTLE-2 trials are prime examples of this approach. Both trials have implemented adaptive randomization schemes to assign patients to the more efficacious treatments based on their biomarker-guided profiles, and use interim analyses to monitor the efficacy outcomes during the trial. The BATTLE trial3,4 enrolled patients with stage IV recurrent non-small cell lung cancer, employing a primary endpoint of eight-week disease control rate, as a binary outcome. Four targeted therapies, erlotinib, vandetanib, erlotinib plus bexarotene, and sorafenib, were evaluated, with one therapy targeting each one of four biomarker profiles and it used an adaptive randomization scheme to allocate patients to the different treatments; hence, patients had higher probabilities of being assigned to better treatments based on their biomarker profiles. The trial showed that adaptive design could work in a complex trial that assessed multiple drugs and biomarkers and required tissue collection and biomarker analysis. Based on the findings of the BATTLE trial, a follow-up BATTLE-2 trial5 was started, that evaluated four treatment regimens, erlotinib, sorafenib, erlotinib + MK2206, and MK2206 + AZD6244, in a two-stage design with adaptive randomization. The first stage was completed with 200 patients. Biomarker selection was planned in 3 steps: training, testing and validation. In the training step, 10–15 potential prognostic and predictive markers were selected from the previous BATTLE experience, cell line data, and relevant literature information. In the testing step, the selected markers are tested using the data acquired from stage 1 of the BATTLE-2 trial. In the validation step, the markers selected in the first stage of the BATTLE-2 trial are used for adaptive randomization in the second stage of BATTLE-2. In BATTLE-2, we pre-specified an extremely limited set of markers and our intent was to use the first half of the study (200 patients) to conduct prospective testing of biomarkers/gene signatures. Predictive markers were to be used to guide patient assignments in the second half of the study. Although the design theoretically provided advantages, since clear predictive markers did not exist for any of the treatment Arms, activity was modest yielding no new predictive markers and not warranting further exploration. The ISPY-2 trial6 is a multicenter phase II trial in the neoadjuvant setting for patients with breast cancer. The primary end point is pathologic complete response (PCR) at the time of surgery. The patient population is partitioned into ten subgroups depending on hormone-receptor (HR) status, HER2 status and Mamma Print signature. Experimental drugs are added to neoadjuvant therapy with the overall goal to prospectively learn as efficiently as possible which patients respond to each experimental treatment based on their biomarker profiles. Adaptive randomization with interim analysis is used within each biomarker subgroup, with the treatments that are performing better within a subgroup being assigned with greater probability to patients belonging to that subgroup. The phase II drug-screening stage is followed by a phase III confirmatory stage. The ISPY-2 trial has recently shown that two promising drugs improve response rates in specific biomarker subsets and has graduated these two drugs veliparib and neratinib for further development.7 The pharmaceutical industry and regulatory agencies are therefore very interested in adaptive designs because of their potential advantages and because they reflect medical practice in the real world. To recapitulate, incorporation of adaptive designs in carefully designed and executed trials can enhance drug development, provide greater benefit to the enrolled patients, and effectively address many research questions of interest. These designs require deep understanding of theoretical statistical methodology, extensive modeling with simulations, specialized software and robust databases. Continued implementation in trials with guidance from regulatory agencies and innovative methods will contribute towards progress in therapies. 1. Berry DA. Bayesian clinical trials. Nat Rev Drug Discov. 2006;5:27–36. 2. Lee JJ, Chu CT. Bayesian clinical trials in action. Stat Med. 2012;31:2955–2972. 3. Zhou X, Liu S, Kim ES, et al. Bayesian adaptive design for targeted therapy development in lung cancer-a step toward personalized medicine. Clin Trials. 2008;5:181–193. 4. Kim ES, Herbst RS, Wistuba II, et al. The BATTLE Trial: Personalizing therapy for lung cancer. Cancer Discov. 2011;1:44–53. 5. Papadimitrakopoulou V, Lee JJ, Wistuba II et al. The BATTLE-2 Study: A Biomarker-integrated targeted therapy study in previously treated patients with advanced non-small cell lung cancer. J Clin Oncol Aug 1,2016 Epub ahead of print. 6. Barker AD, Sigman CC, Kelloff GJ, et al. I-SPY 2: An adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009;86:97–100. 7. Quantum Leap. I-SPY 2 Trial graduates 2 new drugs. 2013 Available online:http://www.quantumleaphealth.org/spy-2-trial-graduates-2-new-drugs-press-release/ adaptive designs, clinical trials