Cytel Inc., 675 Massachusetts Ave., Cambridge, MA 02139, U.S.A.
Stat Med. 2013 May 10;32(10):1763-77. doi: 10.1002/sim.5731. Epub 2013 Jan 9.
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.
我们描述了一种基于价值的药物研发组合优化方法。该方法通过同时考虑内部和外部因素，整合了研发和商业部门的投入。与当前的实践方法不同，该方法认识到研究设计参数（尤其是样本量）对投资组合价值的影响。我们开发了一个整数规划 (IP) 模型作为贝叶斯决策分析的基础，以期望净现值为标准优化 3 期开发组合。我们展示了如何使用该框架来确定最佳样本量和试验计划，以在预算限制下最大化投资组合的价值。然后，我们说明了 IP 模型的显著灵活性，以回答各种“假设”问题，这些问题反映了实践中出现的情况。我们将 IP 模型扩展为随机 IP 模型，以纳入未来 3 期开发中早期开发阶段药物供应的不确定性。我们展示了如何随着新信息的积累和预算的变化，使用随机 IP 来重新优化投资组合开发策略。
