Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
Sci Rep. 2020 Mar 20;10(1):5144. doi: 10.1038/s41598-020-61923-1.
Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Previous work suggests that common methods of combination analysis are too susceptible to noise to support robust scientific conclusions. In this paper, we use simulated and real-world combination datasets to demonstrate that traditional index methods are unstable and biased by pharmacological and experimental conditions, whereas response-surface approaches such as the BRAID method are more consistent and unbiased. Using a publicly-available data set, we show that BRAID more accurately captures variations in compound mechanism of action, and is therefore better able to discriminate between synergistic, antagonistic, and additive interactions. Finally, we applied BRAID analysis to identify a clear pattern of consistently enhanced AKT sensitivity in a subset of cancer cell lines, and a far richer array of PARP inhibitor combination therapies for BRCA1-deficient cancers than would be identified by traditional synergy analysis.
联合治疗在现代医学中越来越重要。然而，可靠的联合研究分析仍然是一个未解决的挑战。之前的研究表明，联合分析的常用方法容易受到噪声的影响，无法得出可靠的科学结论。在本文中，我们使用模拟和真实世界的联合数据集证明，传统的指数方法不稳定，并且受到药理学和实验条件的影响，而像 BRAID 方法这样的曲面响应方法则更一致且无偏倚。使用一个公开可用的数据集，我们表明 BRAID 更准确地捕捉了化合物作用机制的变化，因此能够更好地区分协同、拮抗和相加相互作用。最后，我们应用 BRAID 分析来确定在一部分癌细胞系中 AKT 敏感性持续增强的明显模式，并确定了比传统协同分析更多的 PARP 抑制剂联合治疗 BRCA1 缺陷型癌症的方案。
