Adaptive Licensing: Taking the Next Step in the Evolution of Drug Approval
Adaptive Licensing: Taking the Next Step in the Evolution of Drug Approval
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DOI：
10.1038/clpt.2011.345
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发表时间：
2012-03-01
影响因子：
6.7
通讯作者：
Hirsch, G.
中科院分区：
文献类型：
--
作者：
Eichler, H-G;Oye, K.;Hirsch, G.
传统的药物许可方法是基于二元决策的。在获得许可的那一刻，一种实验性疗法就被推定为一种经过全面审查、安全有效的疗法。相比之下，自适应许可（AL）方法基于在公认的不确定性条件下的逐步学习，具有数据收集和监管评估的迭代阶段。这种方法使审批更紧密地符合患者及时获得新技术和为医疗决策提供信息的数据的需求。人工智能的概念包含了一系列的观点。有些人认为人工智能是一个进化的步骤，扩展了现有的元素。其他人则设想一个变革性框架，可能需要在实施之前采取立法行动。本文总结了近年来人工智能的建议；讨论建议如何转化为实践，并在不同的治疗领域的插图；识别未解决的问题，为人工智能的设计和实施决策提供信息。
Traditional drug licensing approaches are based on binary decisions. At the moment of licensing, an experimental therapy is presumptively transformed into a fully vetted, safe, efficacious therapy. By contrast, adaptive licensing (AL) approaches are based on stepwise learning under conditions of acknowledged uncertainty, with iterative phases of data gathering and regulatory evaluation. This approach allows approval to align more closely with patient needs for timely access to new technologies and for data to inform medical decisions. The concept of AL embraces a range of perspectives. Some see AL as an evolutionary step, extending elements that are now in place. Others envision a transformative framework that may require legislative action before implementation. This article summarizes recent AL proposals; discusses how proposals might be translated into practice, with illustrations in different therapeutic areas; and identifies unresolved issues to inform decisions on the design and implementation of AL.