Pairwise interactions and the battle against combinatorics in multidrug therapies

Wood, Kevin B.

doi:10.1073/pnas.1612365113

Cited by 23 publications

(28 citation statements)

References 19 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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“…16–18 There are both conceptual and practical reasons that have led to this view. On the conceptual side, it has been commonly argued that lower-order effects are likely to counteract each other in higher-order combinations such that they essentially cancel out and result in zero or negligible net effect.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and patterns of higher-order drug interactions in Escherichia coli

et al. 2018

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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. Specifically, as more drugs are added, we observe an elevated frequency of net synergy (effect greater than expected based on independent individual effects) and also increased instances of 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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“…without interaction in the sense of Bliss independence [16], is given by the sum E i + E j [17,18]. We assume that higher order combination measurements are not available, so all modeling needs to rely on the measured effects of single drugs and pairs of drugs [19,20]. To this end, we define the therapeutic effect of a combination c = (c i ) i∈D from a set D of N possible drugs according to what is known as a regression model [21,22]:…”

Section: Joint Modeling Of Therapeutic and Nonselective Effectsmentioning

confidence: 99%

Multiobjective Optimization Identifies Cancer-Selective Combination Therapies

Pulkkinen

Gautam

Mustonen

et al. 2020

Preprint

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Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity.

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Pairwise interactions and the battle against combinatorics in multidrug therapies

Cited by 23 publications

References 19 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 drug interactions in Escherichia coli

Multiobjective Optimization Identifies Cancer-Selective Combination Therapies

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