Hidden suppressive interactions are common in higher-order drug combinations

Abstract

Summary The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

“…Like previous studies, we noticed that the direction of ExE interaction is sometimes specific to a multidrug environment (33, 59). For example, most of the models we tried tend to overpredict fitness in the high rad + low flu environment (HRLF).…”

“…Like previous studies, we noticed that the direction of ExE interaction is sometimes specific to a multidrug environment (Lozano-Huntelman et al, 2021;Yeh et al, 2006).…”

“…These trends are important because identifying synergistic drug combinations (those that are more detrimental than expected) could be helpful in treating viral (Jin et al, 2021), bacterial (Roemer & Boone, 2013), and fungal infections (Kane & Carter, 2022), and cancers (Menden et al, 2019). Identifying drug pairs that interact antagonistically could be helpful as well by suggesting functional relationships between drug targets and strategies for restraining the evolution of drug resistance (Lozano-Huntelman et al, 2021; Yeh et al, 2006, 2009).…”

“…Other studies describe the biggest challenge in detecting synergy as there being more possible drug combinations than one can study (Madani Tonekaboni et al, 2018; Menden et al, 2019; Pan et al, 2023), but this ignores that studying every drug combination in every genetic background would be orders of magnitude more difficult. Despite the combinatorics challenge, heroic efforts have been made to measure massive numbers of drug interactions (Menden et al, 2019), including higher-order interactions (Lozano-Huntelman et al, 2021), which have fueled sophisticated multidrug treatment strategies and evolutionary models (Baym et al, 2016). But these treatments and models could fail if mutations change the drug interactions on which they are based.…”

The evolution of resistance to synergistic multi‐drug combinations is more complex than evolving resistance to each individual drug component

“…Here, highly conserved point mutations in genes encoding mobile genetic elements and transposases were found Historically, net interactions (DA) have been the focus of evolutionary biologists (Chen et al, 2008;Cokol et al, 2017;Katzir et al, 2019;Zimmer et al, 2016) and ecologists (McCoy et al, 2012;Sitvarin & Rypstra, 2014;Sokol-Hessner & Schmitz, 2002) because of the difficulties in determining and describing higher-order emergent interactions accurately. Within the past 15 years, there has been substantial progress in determining and analyzing higher-order interactions (Beppler et al, 2016(Beppler et al, , 2017Cokol et al, 2017;Lozano-Huntelman et al, 2021;Palmer et al, 2015;Tekin et al, 2016;Tekin, White, et al, 2018;Yeh et al, 2009;Zimmer et al, 2016). In both net and emergent interactions, the effects of an interaction are not primarily due to the chemical interactions between the compounds but rather due to how each individual component affects the physiology of the cell and how those effects interact (Bollenbach, 2015;Bollenbach et al, 2009;Mason et al, 2017).…”