Guided screen for synergistic three-drug combinations

Abstract

Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

“…To facilitate the prioritization of drug combinations, informatics tools that enable formal assessment of the degree of synergy as well as sensitivity are highly needed. We provide a major update to the commonly used SynergyFinder R package that allows the modelling of drug interactions for any higher-order combinations 15 . Higher-order drug combinations have been recently explored in, for example, lung cancer 35 , colorectal cancer 36 and ovarian cancer 37 .…”

“…We provide a major update to the commonly used SynergyFinder R package that allows the modelling of drug interactions for any higher-order combinations 15 . Higher-order drug combinations have been recently explored in, for example, lung cancer 35 , colorectal cancer 36 and ovarian cancer 37 .…”

“…However, only the HSA model is compatible with its lower-order scenarios in its implementation. Furthermore, the visualization of the high-order drug combinations becomes nontrivial, as none of the existing visualization methods can deal with combinations of three or more drugs 15 . More importantly, there is a lack of implementation tools that can harmonize the multiple synergy models to derive a consensus about the degree of interactions 16 .…”

Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design

“…However, a major challenge with identifying higher order interactions is the mounting evidence suggesting that the null models predicted by Loewe and Bliss poorly fit experimental data [ 7 , 19 ]. In response, some authors have proposed data-driven predictive models trained only on pairs of drugs and then evaluated on three or more drugs, such as the dose model [ 7 , 20 ], pairs model [ 8 , 21 ], and the static λ score [ 18 , 22 , 23 ]. Given that they have no access to data from higher order combinations, predictive models effectively become null models for interactions between more than two drugs.…”

“…The diagonal sampling method has been proposed as a way to feasibly sample in higher dimensions and has been justified with the claim that the diagonal “provides the most information about the shape of the contour [phenotype isobole]” [ 18 ]. While the validity of diagonal sampling for Loewe synergy has received experimental support in some studies [ 22 , 23 ], to date no work has rigorously justified its use or provided any guarantee about what kinds of synergies a diagonal design may or may not uncover. Absent such a rigorous justification, any study that fails to find synergy leaves open the possibility that synergy may still exist.…”