Measuring higher-order drug interactions: A review of recent approaches

Tekin, Elif; Savage, Van M.; Yeh, Pamela J.

doi:10.1016/j.coisb.2017.05.015

Cited by 33 publications

(41 citation statements)

References 33 publications

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“…53, 89 Identification and classification of higher-order interactions is an exciting current area of research due to the important distinction between net (overall) and emergent interactions, 89 the multiple classification systems already existing at the pairwise level, 27 and considerable debate about whether higher-order interactions need to be considered at all or whether pairwise interactions are sufficient to predict the overall behavior of complex systems. 90, 91 …”

Section: Suppression In Non-antibacterial Contextsmentioning

confidence: 99%

Suppressive drug combinations and their potential to combat antibiotic resistance

Singh

Yeh

2017

J Antibiot

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Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously, and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called “suppressive interactions” in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance, and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine their applicability in the clinic.

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Section: Suppression In Non-antibacterial Contextsmentioning

confidence: 99%

Suppressive drug combinations and their potential to combat antibiotic resistance

Singh

Yeh

2017

J Antibiot

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“…hence, they are valued and used clinically while antagonistic combinations have been traditionally avoided 11 . Our study reveals for the first time, to our knowledge, that for most higher-order drug combinations, net synergy does not imply emergent synergy.…”

Section: Discussionmentioning

confidence: 99%

“…However, when there are more than two drugs in the environment, interactions among different lower-order subsets (2-way versus 3-way versus 4-way or the combination of 2-ways, etc.) of drugs can change the dynamics of N-way interactions, and those effects need to be accounted for by characterizing the emergent interactions 11 . Here, the contribution to the effect that comes solely from a lower-order combination corresponds to the total interaction when only that specific lower-order interaction is present.…”

Section: Emergent N-way ( ) Interactionsmentioning

confidence: 99%

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Prevalence and patterns of higher-order interactions

Tekin

White

Kang

et al. 2017

Preprint

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Interactions and emergent processes are essential for research on complex systems involving many components. Most studies focus solely on pairwise interactions and ignore higher-order interactions among three or more components. To gain deeper insights into higher-order interactions and complex environments, we study antibiotic combinations applied to pathogenic Escherichia coli and obtain unprecedented amounts of detailed data (251 two-drug combinations, 1512 three-drug combinations, 5670 four-drug combinations, and 13608 five-drug combinations). Directly opposite to previous assumptions and reports, we find higher-order interactions increase in frequency with the number of drugs in the bacteria's environment.Furthermore, we observe a shift towards net synergy (effect greater than expected based on independent individual effects) and towards emergent antagonism (effect less than expected based on lower-order interaction effects). These findings have implications for the potential efficacy of drug combinations and are crucial for better navigating problems associated with the combinatorial complexity of multi-component systems.

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“…However, generalizing 2-way (pairwise) interaction measures to three or more component systems is often challenging, often done incorrectly or incompletely, and requires a heuristic process of learning the complex calculations or directly implementing software packages with implicit choices and computations. Consequently, measures for higher-order interactions are often not known or not well-defined (Tekin et al, 2017). All in all, a simplified yet rigorous approach for studying pairwise and higher-order interactions is vitally important for creating a basis to explore the patterns and consequences of emergent phenomena in a wide range of systems.…”

Section: Introductionmentioning

confidence: 99%

General Form for Interaction Measures and Framework for Deriving Higher-Order Emergent Effects

2018

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Interactions are ubiquitous and have been extensively studied in many ecological, evolutionary, and physiological systems. A variety of measures-ANOVA, covariance, epistatic additivity, mutual information, joint cumulants, Bliss independence-exist that compute interactions across fields. However, these are not discussed and derived within a single, general framework. This missing framework likely contributes to the confusion about proper formulations and interpretations of higher-order interactions. Intriguingly, despite higher-order interactions having received little attention, they have been recently discovered to be highly prevalent and to likely impact the dynamics of complex biological systems. Here, we introduce a single, explicit mathematical framework that simultaneously encompasses all of these measures of pairwise interactions. The generality and simplicity of this framework allows us to establish a rigorous method for deriving higher-order interaction measures based on any of the pairwise interactions listed above. These generalized higher-order interaction measures enable the exploration of emergent phenomena across systems such as multiple predator effects, gene epistasis, and environmental stressors. These results provide a mechanistic basis to better account for how interactions affect biological systems. Our theoretical advance provides a foundation for understanding multi-component interactions in complex systems such as evolving populations within ecosystems or communities.

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Measuring higher-order drug interactions: A review of recent approaches

Cited by 33 publications

References 33 publications

Suppressive drug combinations and their potential to combat antibiotic resistance

Suppressive drug combinations and their potential to combat antibiotic resistance

Prevalence and patterns of higher-order interactions

General Form for Interaction Measures and Framework for Deriving Higher-Order Emergent Effects

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