Beckman Robert A, Chen Cong
Departments of Oncology & Biostatistics, Bioinformatics & Biomathematics, Lombardi Comprehensive Cancer Center & Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 4000 Reservoir Road NW, Suite 120 Washington, DC 20007, USA.
Biostatistics & Research Decision Sciences, Merck Research Laboratories, Rahway, NJ, USA.
Biomark Med. 2015;9(9):851-62. doi: 10.2217/bmm.15.56. Epub 2015 Sep 2.
Predictive biomarkers provide essential information to enable personalized medicine, and hold the promise for enhancing the effectiveness and value of cancer therapies. However, they do not always work. This review provides a framework for managing the risk of predictive biomarkers and maximally harvesting their benefit. Methods are provided which permit data-driven, adaptive decision making about the use of predictive biomarkers during clinical development, applying them to the extent they are validated by the clinical data. Techniques for optimizing overall development efficiency, measured as the number of successful drug indications approved per patient utilized, are also presented.
预测性生物标志物为实现个性化医疗提供了关键信息，并有望提高癌症治疗的有效性和价值。然而，它们并非总能发挥作用。本综述提供了一个框架，用于管理预测性生物标志物的风险并最大程度地获取其益处。文中提供了一些方法，可在临床开发过程中基于数据驱动对预测性生物标志物的使用进行适应性决策，并根据临床数据验证的程度来应用它们。还介绍了以每位患者获批的成功药物适应症数量来衡量的优化整体开发效率的技术。
