Thas Olivier, Tourny Annelies, Verbist Bie, Hawinkel Stijn, Nazarov Maxim, Mutambanengwe Kathy, Bijnens Luc
Data Science Institute, I-Biostat, Hasselt University, Hasselt, Belgium.
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Pharm Stat. 2022 Mar;21(2):345-360. doi: 10.1002/pst.2173. Epub 2021 Oct 4.
Combination therapies are increasingly adopted as the standard of care for various diseases to improve treatment response, minimise the development of resistance and/or minimise adverse events. Therefore, synergistic combinations are screened early in the drug discovery process, in which their potential is evaluated by comparing the observed combination effect to that expected under a null model. Such methodology is implemented in the BIGL R-package which allows for a quick screening of drug combinations. We extend the meanR and maxR tests from this package by allowing non-constant variance of the responses and by extending the list of null models (Loewe, Loewe2, HSA, Bliss). These new tests are evaluated in a comprehensive simulation study under various models for additivity and synergy, various monotherapeutic dose-response models (complete, partial and incomplete responders) and various types of deviation from the constant variance assumption. In addition, the BIGL package is extended with bootstrap confidence intervals for the individual off-axis points and for the overall synergy strength, which were demonstrated to have reliable coverage and can complement the existing tests. We conclude that the differences in performance between the different null models are small and depend on the simulation scenario. As a result, the choice of null model should be driven by expert knowledge on the particular problem. Finally, we demonstrate the new features of the BIGL package and the difference between the synergy models on a real dataset from drug discovery. The BIGL package is available at CRAN (https://CRAN.R-project.org/package=BIGL) and as a Shiny app (https://synergy.openanalytics.eu/app).
联合疗法越来越多地被用作各种疾病的标准治疗方法，以改善治疗反应、最大限度地减少耐药性的产生和/或最大限度地减少不良事件。因此，在药物发现过程的早期就会筛选协同联合疗法，通过将观察到的联合效应与零模型下预期的效应进行比较来评估其潜力。这种方法在BIGL R包中得以实现，该包允许快速筛选药物联合。我们扩展了此包中的meanR和maxR检验，允许响应具有非恒定方差，并扩展了零模型列表（Loewe、Loewe2、HSA、Bliss）。在各种加性和协同性模型、各种单药剂量反应模型（完全、部分和不完全反应者）以及各种偏离恒定方差假设的类型下，通过全面的模拟研究对这些新检验进行了评估。此外，BIGL包还扩展了针对各个离轴点和整体协同强度的自助置信区间，结果表明这些区间具有可靠的覆盖率，并且可以补充现有的检验。我们得出结论，不同零模型之间的性能差异很小，并且取决于模拟场景。因此，零模型的选择应由对特定问题的专业知识驱动。最后，我们在一个来自药物发现的真实数据集上展示了BIGL包的新特性以及协同模型之间的差异。BIGL包可在CRAN（https://CRAN.R-project.org/package=BIGL）上获取，也可作为一个Shiny应用程序（https://synergy.openanalytics.eu/app）使用。
