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Increasingly, tumors are defined on a molecular basis rather than only on histology, and targeted agents, which address these molecular subtypes, are being approved. This profusion of molecular subtypes creates "rare" diseases as subsets of common cancers, leading to difficulties in enrolling sufficiently large cohorts for confirmatory trials. However, if the molecular subtype is shared across various histologies, these may be pooled into a basket trial. To date, basket trials have been primarily for exploratory early development. In this perspective, we consider qualitative designs for confirmatory basket trials. These confirmatory basket designs will provide patients in niche indications with enhanced access to novel therapies, facilitate development and full approval for niche indications, allow accelerated approval for indications within a basket based on a surrogate endpoint, reduce development cost by combining trials, and enhance the ability of regulatory authorities to evaluate risk and benefit in niche indications.

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We describe a value-driven approach to optimizing pharmaceutical portfolios. Our approach incorporates inputs from research and development and commercial functions by simultaneously addressing internal and external factors. This approach differentiates itself from current practices in that it recognizes the impact of study design parameters, sample size in particular, on the portfolio value. We develop an integer programming (IP) model as the basis for Bayesian decision analysis to optimize phase 3 development portfolios using expected net present value as the criterion. We show how this framework can be used to determine optimal sample sizes and trial schedules to maximize the value of a portfolio under budget constraints. We then illustrate the remarkable flexibility of the IP model to answer a variety of 'what-if' questions that reflect situations that arise in practice. We extend the IP model to a stochastic IP model to incorporate uncertainty in the availability of drugs from earlier development phases for phase 3 development in the future. We show how to use stochastic IP to re-optimize the portfolio development strategy over time as new information accumulates and budget changes occur.