Optimizing adaptive design for Phase 2 dose-finding trials incorporating long-term success and financial considerations: A case study for neuropathic pain.

In this paper, we propose an adaptive randomization design for Phase 2 dose-finding trials to optimize Net Present Value (NPV) for an experimental drug. We replace the traditional fixed sample size design (Patel, et al., 2012) by this new design to see if NPV from the original paper can be improved. Comparison of the proposed design to the previous design is made via simulations using a hypothetical example based on a Diabetic Neuropathic Pain Study.

Methodological Extensions of Phase 2 Trial Designs Based on Program-Level Considerations: Further Development of a Case Study in Neuropathic Pain.

BACKGROUND: Traditionally, sample size considerations for Phase 2 (Ph2) trials are based on the desired properties of the design and response information from the trials. METHODS: This work extends that of Patel et al (2012) to design Ph2 trials based on program-level optimization (ie, optimizing the entire Ph2/3 trial design strategy). It describes a framework to evaluate the impact that several Ph2 design features have on the probability of Phase 3 (Ph3) success and the expected net present value (eNPV) of the product. These factors include the Ph2 sample size, decision rules to select Ph3 dose(s) and sample sizes, as well as number of Ph3 trials. Using neuropathic pain as an example, simulations illustrate the framework and show the benefit of including these factors in the overall decision process. Patel et al considered one dose of test drug in each of exactly two Ph3 trials. This work extends that to consider 1 or 2 doses in each of 2 Ph3 trials and, if needed, 1 or 2 additional Ph3 trials to substantiate the usefulness of the second dose. RESULTS: We found that employing a quantitative algorithmic strategy to choose 1 or 2 doses for Ph3 based on trial results does not substantially alter the eNPV compared to a strategy of always taking 2 doses to Ph3, if appropriate. Similar to the findings by Patel et al, for 1 Ph3 dose, we found that Ph2 sample size can be optimized at small to modest sizes when allowing for the possibility of taking 2 doses to Ph3. We found that choice of number of Ph2 doses depends on the magnitudes and shapes of the true underlying efficacy and safety dose-response curves. CONCLUSION: Simulating a sequence of clinical trials can inform trial design and drug development strategy.

Early Clinical Development Planning via Biomarkers, Clinical Endpoints, and Simulation: A Case Study to Optimize for Phase 3 Dose Selection.

BACKGROUND: This study investigated a framework that leverages the relationship between biomarkers and a target clinical endpoint to optimize an early development plan. METHODS: Different biomarker designs were assessed for proof of concept (PoC) and dose finding (DF) to improve phase 2b (Ph2b) design as well as phase 3 (Ph3) dose choice. A case study using a Bayesian trivariate normal distribution model for 2 biomarkers and a clinically relevant endpoint was utilized with simulation to assess performance characteristics. RESULTS: We found the following: (1) at typical sample sizes for early development trials, biomarkers appear useful for PoC but not for clinical endpoint DF; and (2) even with large amounts of prior information and near perfect correlation between biomarkers and clinical endpoints, Ph2b variability is only overcome by increased Ph2b sample sizes to improve Ph3 dose choice. CONCLUSIONS: For highly variable clinical endpoints, the fastest path should be to demonstrate PoC by biomarkers and then go directly to Ph2b to measure the target clinical endpoint.