On the other hand, a sample size of 600 patients was also required to prove that bevacizumab was ineffective in pancreatic DHFR inhibitor cancer despite the use of stopping rules in the trial. In Bayesian designs, uncertainty is measured as a probability. Unknown parameters are given a probability distribution while what is known is taken as a given. However, once the results of the study become more evident, these are no longer probabilities and are taken as a given. Thus these trial designs are inherently adaptive and allow the investigator to modify

trials mid course based on current data. Thus, Bayesian adaptive designs allow for changes to the clinical trial Inhibitors,research,lifescience,medical based on ongoing progress and allow enrichment based on the results. These designs are especially suitable

for the development of biomarker-directed targeted therapy. For instance, the prior distribution of a biomarker profile may not be known with a great deal of certainty; this Inhibitors,research,lifescience,medical can therefore be hypothesized and refined as the trial develops. A pharmaceutical company can tie in the decision rules within the Bayesian trial design to determine the pathway for drug development. Bayesian designs are extensively being utilized at MD Anderson Cancer Center, wherein over a Inhibitors,research,lifescience,medical hundred clinical trials are ongoing using these principles. A detailed review of this trial design is described elsewhere. The disadvantages of this design is that it is computationally intensive, restricted to a limited number of centers with expertise and is not yet widely recognized

Inhibitors,research,lifescience,medical by regulatory agencies as an efficient and economical pathway towards drug development. While these issues appear to be complex, successful implementation is possible and requires a multidisciplinary effort. One such an example is an ongoing study in non-small cell lung cancer at our institution. Battle trial for non small-cell lung cancer The Inhibitors,research,lifescience,medical recently concluded BATTLE 1 (Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination) phase II clinical trial conducted at MD Anderson Cancer Center illustrates the potential of Bayesian adaptive randomization as a study design for evaluating novel targeted therapies in cancer using personalized biomarker profiles to guide treatment much allocations (Fig 1). First, 97 patients with stage IV non small-cell lung cancer who had received at least one prior chemotherapy were each assigned to receive one of four possible drugs (Erlotinib, Vandetanib, Erlotinib + Bexarotene, or Sorafenib) by traditional simple randomization. Core biopsies of the lung were obtained from this initial subset of patients and profiled for four biomarkers (EGFR, KRAS/BRAF, VEGFR-2 and RXR/Cyclin D1). The primary study endpoint was progression-free survival at 8 weeks. Interim analysis was conducted to determine the specific biomarker profiles that predicted a favorable clinical response in each of the four study arms.