Uncovering Scaling Laws to Infer Multidrug Response of Resistant Microbes and Cancer Cells

Wood, Kevin B.; Wood, Kris C.; Nishida, Satoshi; Cluzel, Philippe

doi:10.1016/j.celrep.2014.02.007

Cited by 54 publications

(60 citation statements)

References 34 publications

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“…Our goal in this paper is merely to measure the existence of emergent interactions and characterize how prevalent they are and any patterns for their frequency. Therefore, the data produced and patterns identified in the present paper are complementary to these other recent papers 22,31 in trying to identify the patterns and mechanisms that underlie emergent interactions.…”

Section: Introductionmentioning

confidence: 74%

“…Notably, recent papers 22,31 have proposed new formulae to predict three-way interactions based on equations with weighted mixtures of pairwise interactions. This valuable work is useful for trying to mechanistically understand how three-way interactions might arise, with the recipe for the weights of the mixture perhaps corresponding to specific ways in which a third drug might affect the interaction of another pair.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…This raises the question to what extent drug interactions are conserved in mutants and across microbial species. Several studies showed that drug interactions are often conserved in resistant mutants, but they might also change considerably [10,83]. The latter would be a challenge for the optimization of multidrug treatments [14,84].…”

Section: Perspectivesmentioning

confidence: 99%

Antimicrobial interactions: mechanisms and implications for drug discovery and resistance evolution

Bollenbach

2015

Current Opinion in Microbiology

209

149

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Combining antibiotics is a promising strategy for increasing treatment efficacy and for controlling resistance evolution. When drugs are combined, their effects on cells may be amplified or weakened, that is the drugs may show synergistic or antagonistic interactions. Recent work revealed the underlying mechanisms of such drug interactions by elucidating the drugs' joint effects on cell physiology. Moreover, new treatment strategies that use drug combinations to exploit evolutionary tradeoffs were shown to affect the rate of resistance evolution in predictable ways. High throughput studies have further identified drug candidates based on their interactions with established antibiotics and general principles that enable the prediction of drug interactions were suggested. Overall, the conceptual and technical foundation for the rational design of potent drug combinations is rapidly developing.

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“…This interaction is modeled by introducing interaction terms between drug pairs (14). In the model, the effective dose of each drug, D ieff , is its true dose D i times a product of the Michaelis−Menten-like terms for all of the other drugs…”

Section: Significancementioning

confidence: 99%

“…Much work has been devoted to classifying how pairs of drugs interact (10)(11)(12)(13)(14). Across systems, a good first approximation is the Bliss independence model (15,16), in which the pair effect is the product of the individual drug effects: If the effect of the drugs are g 1 and g 2 , the effect of the combination is g 12 = g 1 · g 2 .…”

mentioning

confidence: 99%

Prediction of multidimensional drug dose responses based on measurements of drug pairs

Zimmer

Katzir

Dekel

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

143

173

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Finding potent multidrug combinations against cancer and infections is a pressing therapeutic challenge; however, screening all combinations is difficult because the number of experiments grows exponentially with the number of drugs and doses. To address this, we present a mathematical model that predicts the effects of three or more antibiotics or anticancer drugs at all doses based only on measurements of drug pairs at a few doses, without need for mechanistic information. The model provides accurate predictions on available data for antibiotic combinations, and on experiments presented here on the response matrix of three cancer drugs at eight doses per drug. This approach offers a way to search for effective multidrug combinations using a small number of experiments.drug combinations | drug cocktails | cancer treatment | mechanism-free formula | predictive formula T o kill cancer cells or bacteria, combination therapy can be more effective than individual drugs (1-6). Combination therapy is thought to allow increased efficacy at low doses, thus reducing side effects and toxicity; it is also believed to minimize the chances of resistance (7-9), a pressing problem in treating cancer and infectious diseases.Much work has been devoted to classifying how pairs of drugs interact (10)(11)(12)(13)(14). Across systems, a good first approximation is the Bliss independence model (15,16), in which the pair effect is the product of the individual drug effects: If the effect of the drugs are g 1 and g 2 , the effect of the combination is g 12 = g 1 · g 2 . The Bliss model ignores interactions in which drugs enhance each other effectssynergism-or inhibit each other's effects-antagonism. Some drugs even inhibit each other so much that the combined effect is lower than either drug alone, an effect called hyperantagonism (17)(18)(19)(20)(21).Going beyond drug pairs has been difficult. Experimentally testing high-order combinations beyond pairs in a systematic way is challenging because it requires an exponentially large number of experiments (22-25): For N drugs at D doses, one needs D N experiments. For N = 10 drugs and D = 8 doses, this means ∼10 9 measurements. The combinatorial explosion makes exhaustive testing of drug dose combinations unfeasible. This problem is especially acute in cases where material is scarce, such as testing of patient-derived samples (26)(27)(28)(29). Hence, models for predicting high-order effects are essential.Apart from detailed simulations of particular systems (22,30,31), there has been little study of general mechanism-independent models for multiple drugs. An exception is the elegant study by Wood et al. (32) that showed that combinations of antibiotics can be predicted by an Iserliss-like formula that uses pair effects to predict the effects of higher-order mixtures. For example, the effect of a drug triplet is modeled as g 123 = g 12 g 3 + g 13 g 2 + g 23 g 1 − 2g 1 g 2 g 3 . This formula has not been tested on cancer drug mixtures, to the best of our knowledge.Another line of research u...

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Uncovering Scaling Laws to Infer Multidrug Response of Resistant Microbes and Cancer Cells

Cited by 54 publications

References 34 publications

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

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

Antimicrobial interactions: mechanisms and implications for drug discovery and resistance evolution

Prediction of multidimensional drug dose responses based on measurements of drug pairs

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