Additive Dose Response Models: Defining Synergy

Abstract

In synergy studies, one focuses on compound combinations that promise a synergistic or antagonistic effect. With the help of high-throughput techniques, a huge amount of compound combinations can be screened and filtered for suitable candidates for a more detailed analysis. Those promising candidates are chosen based on the deviance between a measured response and an expected non-interactive response. A non-interactive response is based on a principle of no interaction, such as Loewe Additivity [Loewe, 1928] or Bliss Independence [Bliss, 1939]. In Lederer et al. [2018a], an explicit formulation of the hitherto implicitly defined Loewe Additivity has been introduced, the so-called Explicit Mean Equation. In the current study we show that this Explicit Mean Equation outperforms the original implicit formulation of Loewe Additivity and Bliss Independence when measuring synergy in terms of the deviance between measured and expected response. Further, we show that a deviance based computation of synergy outper-forms a parametric approach. We show this on two datasets of compound combinations that are categorized into synergistic, non-interactive and antagonistic [Yadav et al., 2015, Cokol et al., 2011].