The null additivity of multi-drug combinations

Russ, Dor; Kishony, Roy

doi:10.1101/239517

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…One central question is whether a combination of two different mutations will result in a bigger or smaller effect on the phenotype than expected from each mutation alone. To define the “expected” effect, which is also known as the “neutral” interaction, models of the combined effect of mutations on a phenotype need to be assumed, and several ways to define neutrality between mutations have been proposed (31–35).…”

mentioning

confidence: 99%

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Levin-Reisman

Brauner

Ronin

et al. 2019

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

151

135

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Understanding the evolution of microorganisms under antibiotic treatments is a burning issue. Typically, several resistance mutations can accumulate under antibiotic treatment, and the way in which resistance mutations interact, i.e., epistasis, has been extensively studied. We recently showed that the evolution of antibiotic resistance in Escherichia coli is facilitated by the early appearance of tolerance mutations. In contrast to resistance, which reduces the effectiveness of the drug concentration, tolerance increases resilience to antibiotic treatment duration in a nonspecific way, for example when bacteria transiently arrest their growth. Both result in increased survival under antibiotics, but the interaction between resistance and tolerance mutations has not been studied. Here, we extend our analysis to include the evolution of a different type of tolerance and a different antibiotic class and measure experimentally the epistasis between tolerance and resistance mutations. We derive the expected model for the effect of tolerance and resistance mutations on the dynamics of survival under antibiotic treatment. We find that the interaction between resistance and tolerance mutations is synergistic in strains evolved under intermittent antibiotic treatment. We extend our analysis to mutations that result in antibiotic persistence, i.e., to tolerance that is conferred only on a subpopulation of cells. We show that even when this population heterogeneity is included in our analysis, a synergistic interaction between antibiotic persistence and resistance mutations remains. We expect our general framework for the epistasis in killing conditions to be relevant for other systems as well, such as bacteria exposed to phages or cancer cells under treatment.

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mentioning

confidence: 99%

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Levin-Reisman

Brauner

Ronin

et al. 2019

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

151

135

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“…Bliss independence is equivalent to additivity in log-effect space. Another baseline model for drugs is Loewe (dose additivity) [27], but seems to be less accurate than the Bliss approximation for high-order drug combinations [28,29].…”

Section: Introductionmentioning

confidence: 99%

Noise-precision tradeoff in predicting combinations of mutations and drugs

et al. 2019

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Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.

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“…The latter states that, a drug is not expected to synergize in a combination with itself. The sham combination principle is rather specific to mutually exclusive agents and violated by several null models, including the Highest Single Agent (HSA) model, the Bliss model and the Fisher's dosage orthogonality (Russ & Kishony, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible

Sinzger

Vanhoefer

Loos

et al. 2018

Preprint

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Null models for the effect of combination therapies are widely used to evaluate synergy and antagonism of drugs. Due to the relevance of null models, their suitability is continuously discussed. Here, we contribute to the discussion by investigating the properties of five null models. Our study includes the model proposed by David J. Hand, which we refer to as Hand model. The Hand model has been introduced almost 20 years ago but hardly was used and studied. We show that the Hand model generalizes the principle of dose equivalence compared to the Loewe model and resolves the ambiguity of the Tallarida model. This provides a solution to the persisting conflict about the compatibility of two essential model properties: the sham combination principle and the principle of dose equivalence. By embedding several null models into a common framework, we shed light in their biochemical validity and provide indications that the Hand model is biochemically most plausible. We illustrate the practical implications and differences between null models by examining differences of null models on published data.

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The null additivity of multi-drug combinations

Cited by 5 publications

References 40 publications

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Noise-precision tradeoff in predicting combinations of mutations and drugs

Comparison of null models for combination drug therapy reveals Hand model as biochemically most plausible

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