Accelerated evolution of resistance in multidrug environments

Hegreness, Matthew; Shoresh, Noam; Damian, Doris; Hartl, Daniel L.; Kishony, Roy

doi:10.1073/pnas.0805965105

Cited by 279 publications

(363 citation statements)

References 29 publications

Supporting

Mentioning

345

Contrasting

Unclassified

Order By: Relevance

“…This importance and clinical relevance is because highly synergistic triple-drug combinations are of utmost importance due to the high efficacy of the treatment compared with pairwise interactions [1,[36][37][38][39][40]. On the other hand, identifying suppressive interactions may be especially valuable because it has been shown that, counterintuitively, these interactions may slow and thus suppress the evolution of antibiotic resistance [8,15,17,[41][42][43]. That is, there may be a trade-off between killing efficiency and the evolution of resistance.…”

Section: Discussionmentioning

confidence: 99%

Enhanced identification of synergistic and antagonistic emergent interactions among three or more drugs

Tekin

Beppler

White

et al. 2016

J. R. Soc. Interface.

100

View full text Add to dashboard Cite

Interactions among drugs play a critical role in the killing efficacy of multidrug treatments. Recent advances in theory and experiment for three-drug interactions enable the search for emergent interactions-ones not predictable from pairwise interactions. Previous work has shown it is easier to detect synergies and antagonisms among pairwise interactions when a rescaling method is applied to the interaction metric. However, no study has carefully examined whether new types of normalization might be needed for emergence. Here, we propose several rescaling methods for enhancing the classification of the higher order drug interactions based on our conceptual framework. To choose the rescaling that best separates synergism, antagonism and additivity, we conducted bacterial growth experiments in the presence of single, pairwise and triple-drug combinations among 14 antibiotics. We found one of our rescaling methods is far better at distinguishing synergistic and antagonistic emergent interactions than any of the other methods. Using our new method, we find around 50% of emergent interactions are additive, much less than previous reports of greater than 90% additivity. We conclude that higher order emergent interactions are much more common than previously believed, and we argue these findings for drugs suggest that appropriate rescaling is crucial to infer higher order interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Enhanced identification of synergistic and antagonistic emergent interactions among three or more drugs

Tekin

Beppler

White

et al. 2016

J. R. Soc. Interface.

100

View full text Add to dashboard Cite

show abstract

“…Based on these findings, Torella et al (2010) developed a mathematical model for the evolution of multidrug resistance under synergistic and antagonistic drug interactions (implementing no form of recombination). The model shows that resistance evolves less easily under antagonistic interactions but again only if competition among cells is high (for experiments, see Hegreness et al 2008). Our results suggest that even without competition, antagonistic drug interactions (with a relatively fit wild type but unfit single mutants) can strongly hamper the evolution of resistance for infections with pathogens that readily recombine in vivo, such as HIV.…”

mentioning

confidence: 86%

The Role of Recombination in Evolutionary Rescue

Uecker

Hermisson

2015

Genetics

View full text Add to dashboard Cite

How likely is it that a population escapes extinction through adaptive evolution? The answer to this question is of great relevance in conservation biology, where we aim at species' rescue and the maintenance of biodiversity, and in agriculture and medicine, where we seek to hamper the emergence of pesticide or drug resistance. By reshuffling the genome, recombination has two antagonistic effects on the probability of evolutionary rescue: it generates and it breaks up favorable gene combinations. Which of the two effects prevails depends on the fitness effects of mutations and on the impact of stochasticity on the allele frequencies.In this article, we analyze a mathematical model for rescue after a sudden environmental change when adaptation is contingent on mutations at two loci. The analysis reveals a complex nonlinear dependence of population survival on recombination. We moreover find that, counterintuitively, a fast eradication of the wild type can promote rescue in the presence of recombination. The model also shows that two-step rescue is not unlikely to happen and can even be more likely than single-step rescue (where adaptation relies on a single mutation), depending on the circumstances.KEYWORDS rapid adaptation; epistasis; drift; population dynamics; ecology P OPULATIONS facing severe environmental change need to adapt rapidly to the new conditions, or they will go extinct. The most prominent examples for evolutionary rescue in natural populations are provided by failed eradication of pathogens or pests that develop resistance against drugs or pesticides. Understanding which factors drive the evolution of resistance has been a concern since the application of drugs and pesticides. In recent years, the topic of evolutionary rescue has attracted increasing interest of evolutionary biologists at a broader front. Both theoretical models and laboratory experiments have been used to investigate the influence of many genetic or environmental factors on the survival probability of an endangered population, e.g., the importance of standing genetic variation, sexual reproduction, the history of stress, the severity and speed of environmental deterioration, or population structure Unckless 2008, 2014; Gonzalez 2009, 2011;Agashe et al. 2011;Lachapelle and Bell 2012;Gonzalez and Bell 2013;; see also the reviews by Alexander et al. 2014 andCarlson et al. 2014). Despite significant progress, a largely open area in research on rescue concerns the influence of recombination on the probability of population survival.Recombination has two fundamental effects on adaptation that work against each other: it brings favorable gene combinations together but it also breaks them up. Recombination hence has the potential both to promote rescue and to impede it. In classical population genetics (assuming a constant population size), the interplay of the two opposing effects of recombination has been an intensively studied problem for decades (e.g., reviewed in Barton and Charlesworth 1998;Otto 2009;Hartfield and Keigh...

show abstract

“…Moreover, although our model optimized for overall efficacy by targeting all subpopulations, an optimal drug combination would require further understanding of how differential selective pressures imposed by drugs affect the potential for secondary resistance. This issue has been studied particularly in antibiotic resistance, in which strong selection by synergistic drug combinations actually may increase the risk of resistance (33,34). Ultimately, just as multiobjective optimization approaches may be applied to derive small molecule structures with the desired polypharmacological profiles (35), a multiobjective optimization with considerations of these additional properties provides a potentially promising approach to optimizing drug combination in the context of tumor heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Intratumor heterogeneity alters most effective drugs in designed combinations

Zhao

Hemann

Lauffenburger

2014

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

View full text Add to dashboard Cite

The substantial spatial and temporal heterogeneity observed in patient tumors poses considerable challenges for the design of effective drug combinations with predictable outcomes. Currently, the implications of tissue heterogeneity and sampling bias during diagnosis are unclear for selection and subsequent performance of potential combination therapies. Here, we apply a multiobjective computational optimization approach integrated with empirical information on efficacy and toxicity for individual drugs with respect to a spectrum of genetic perturbations, enabling derivation of optimal drug combinations for heterogeneous tumors comprising distributions of subpopulations possessing these perturbations. Analysis across probabilistic samplings from the spectrum of various possible distributions reveals that the most beneficial (considering both efficacy and toxicity) set of drugs changes as the complexity of genetic heterogeneity increases. Importantly, a significant likelihood arises that a drug selected as the most beneficial single agent with respect to the predominant subpopulation in fact does not reside within the most broadly useful drug combinations for heterogeneous tumors. The underlying explanation appears to be that heterogeneity essentially homogenizes the benefit of drug combinations, reducing the special advantage of a particular drug on a specific subpopulation. Thus, this study underscores the importance of considering heterogeneity in choosing drug combinations and offers a principled approach toward designing the most likely beneficial set, even if the subpopulation distribution is not precisely known.systems biology | cancer | combination therapy G enetic intratumor heterogeneity has long been appreciated as present in cancer patients (1). Recent sequencing studies further revealed the extent of this tumor diversity, arising from highly complex clonal evolutionary processes (2). This phenomenon has been observed in many solid (3-5) and hematopoietic cancers (6-8). Moreover, treatments also may have dramatic effects on tumor composition-with a preexisting subclone at diagnosis often becoming dominant at relapse (9-12). To meet this challenge, rational drug combination treatments must be designed so as to account for intratumor heterogeneity to better predict reoccurrence.The history of theoretical studies aimed at drug optimization in cancer therapy is long. Some studies used differential equation models formulated as deterministic optimal control problems (13-16), whereas others used stochastic birth-death process models (17-21), to examine treatment regimens for tumors comprising a sensitive population along with a few resistant subpopulations. However, these studies, many of which dealt only with generic scenarios, focused primarily on drug scheduling, investigating the effects of frequency and dose intensity of drugs on resistance potential. Practical applications of such results have been limited, although some of these strategies recently were combined with experimental validation to examin...

show abstract

Accelerated evolution of resistance in multidrug environments

Cited by 279 publications

References 29 publications

Enhanced identification of synergistic and antagonistic emergent interactions among three or more drugs

Enhanced identification of synergistic and antagonistic emergent interactions among three or more drugs

The Role of Recombination in Evolutionary Rescue

Intratumor heterogeneity alters most effective drugs in designed combinations

Contact Info

Product

Resources

About