Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

药物组合发现依赖于可靠的协同作用指标，但对于描述组合相互作用的正确协同作用标准尚无共识。该领域的碎片化状态混淆了分析，阻碍了可重复性，并延迟了潜在联合治疗的临床转化。在这里，我们提出了一种基于群众行动的形式主义来量化协同作用。通过这种形式主义，我们阐明了主要药物协同原则之间的关系，并将常用框架映射到统一的协同作用图谱上。由此，我们展示了偏见是如何由于内在假设而出现的，这些假设阻碍了它们的广泛适用性并影响了对发现工作中协同作用的解释。具体来说，我们描述了传统指标如何掩盖相应的协同交互，并且包含依赖于单药的山坡和最大效应的偏差。我们展示了这些偏见如何系统地影响大型组合筛选中的协同分类，可能会误导发现工作。因此，所提出的形式主义可以为药物协同作用提供一致、公正的解释，并加速协同研究的可翻译性。